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ISBN 978-91-7485-503-6 ISSN 1651-4238

Address: P.O. Box 883, SE-721 23 Västerås. SwedenAddress: P.O. Box 325, SE-631 05 Eskilstuna. SwedenE-mail: [email protected] Web: www.mdh.se

The arrival of the tipping point of solar photovoltaic technologyYing Yang

Mälardalen University Doctoral Dissertation 334

Mälardalen University Press DissertationsNo. 334

THE ARRIVAL OF THE TIPPING POINT OFSOLAR PHOTOVOLTAIC TECHNOLOGY

Ying Yang

2021

School of Business, Society and Engineering


THE ARRIVAL OF THE TIPPING POINT OFSOLAR PHOTOVOLTAIC TECHNOLOGY

Ying Yang

2021

School of Business, Society and Engineering

1

Copyright © Ying Yang, 2021ISBN 978-91-7485-503-6ISSN 1651-4238Printed by E-Print AB, Stockholm, Sweden

Copyright © Ying Yang, 2021ISBN 978-91-7485-503-6ISSN 1651-4238Printed by E-Print AB, Stockholm, Sweden

2


THE ARRIVAL OF THE TIPPING POINT OF SOLAR PHOTOVOLTAIC TECHNOLOGY

Ying Yang

Akademisk avhandling

som för avläggande av filosofie doktorsexamen i industriell ekonomi och organisation vid Akademin för ekonomi, samhälle och teknik kommer att offentligen försvaras

fredagen den 30 april 2021, 09.00 i R1-343 + (via Zoom), Mälardalens högskola, Västerås.

Fakultetsopponent: Professor Reinhard Madlener, RWTH Aachen University

Akademin för ekonomi, samhälle och teknik


THE ARRIVAL OF THE TIPPING POINT OF SOLAR PHOTOVOLTAIC TECHNOLOGY

Ying Yang

Akademisk avhandling

som för avläggande av filosofie doktorsexamen i industriell ekonomi och organisation vid Akademin för ekonomi, samhälle och teknik kommer att offentligen försvaras

fredagen den 30 april 2021, 09.00 i R1-343 + (via Zoom), Mälardalens högskola, Västerås.

Fakultetsopponent: Professor Reinhard Madlener, RWTH Aachen University

Akademin för ekonomi, samhälle och teknik

3

AbstractSolar photovoltaic (PV) technology has become well-established for addressing both greenhouse gas emission reductions and regional air pollution. Rapid developments within the solar PV sector are still facing various technical barriers, economic impediments, and institutional barriers. The technical system innovations and their uses in society co-evolve with the engagement of multi-actors between scientific communities, users, investors, policymakers, and other stakeholders. Therefore, a holistic and interdisciplinary approach is called to analyze the complexities and solve the issues.

This doctoral thesis takes a social science perspective in conjunction with technical considerations. China is a major producer and market for solar PV. But it is still not clear that how economically competitive solar PV electricity is, compared with the traditional coal-fired power generation without subsidies. Compared with China, the energy transition in Sweden has been progressively proceeding, which enables it to build a low-carbon economy with the lowest share of fossil fuels in the primary energy supply. An interesting part is to explore how solar PV applications, along with smart city transformation, change the electricity market logic with the emerging of new actors. Further, it is vital to assess the potential availability of solar applications for policymaking and grid accommodation.

In this thesis, economic feasibility, grid party capability, and investment values in the market of China are modeled, calculated, and analyzed. The geographical and technical potential of solar PV applications is explored in Sweden. Also, based on a Service-Dominant logic perspective, the new players and their roles in the transformation of smart cities are explored, using the Swedish electricity market as an example. The results of the grid parity analysis show that distributed solar PV projects have reached a tipping point of cost-effectiveness, when solar PV can be guaranteed to be competitive with conventional power sources in the context of a subsidy-free in China. This also implies a gradual replacement of currently operating coal power plants. The investment return examination shows that profitability levels vary from city to city, taking into account local resource endowments and local economic conditions. By highlighting the flexibility issues associated with integrating a higher percentage of solar power, key performance indicators are presented to assess the performance of current individual technology components and combined system modules. Zooming out to the macro level, we show the theoretical explanation of how the Swedish electricity market is being changed by renewable energies and the emergence of new actors.

ISBN 978-91-7485-503-6ISSN 1651-4238

AbstractSolar photovoltaic (PV) technology has become well-established for addressing both greenhouse gas emission reductions and regional air pollution. Rapid developments within the solar PV sector are still facing various technical barriers, economic impediments, and institutional barriers. The technical system innovations and their uses in society co-evolve with the engagement of multi-actors between scientific communities, users, investors, policymakers, and other stakeholders. Therefore, a holistic and interdisciplinary approach is called to analyze the complexities and solve the issues.

This doctoral thesis takes a social science perspective in conjunction with technical considerations. China is a major producer and market for solar PV. But it is still not clear that how economically competitive solar PV electricity is, compared with the traditional coal-fired power generation without subsidies. Compared with China, the energy transition in Sweden has been progressively proceeding, which enables it to build a low-carbon economy with the lowest share of fossil fuels in the primary energy supply. An interesting part is to explore how solar PV applications, along with smart city transformation, change the electricity market logic with the emerging of new actors. Further, it is vital to assess the potential availability of solar applications for policymaking and grid accommodation.

In this thesis, economic feasibility, grid party capability, and investment values in the market of China are modeled, calculated, and analyzed. The geographical and technical potential of solar PV applications is explored in Sweden. Also, based on a Service-Dominant logic perspective, the new players and their roles in the transformation of smart cities are explored, using the Swedish electricity market as an example. The results of the grid parity analysis show that distributed solar PV projects have reached a tipping point of cost-effectiveness, when solar PV can be guaranteed to be competitive with conventional power sources in the context of a subsidy-free in China. This also implies a gradual replacement of currently operating coal power plants. The investment return examination shows that profitability levels vary from city to city, taking into account local resource endowments and local economic conditions. By highlighting the flexibility issues associated with integrating a higher percentage of solar power, key performance indicators are presented to assess the performance of current individual technology components and combined system modules. Zooming out to the macro level, we show the theoretical explanation of how the Swedish electricity market is being changed by renewable energies and the emergence of new actors.

ISBN 978-91-7485-503-6ISSN 1651-4238

4

This thesis is dedicated to my family.

This thesis is dedicated to my family.

5

Acknowledgement

This doctoral thesis was conducted at the School of Business, Society and En-

gineering, Mälardalen University, Västerås, Sweden. I gratefully

acknowledge the financial support from the China Scholarship Council, and

The Swedish (National) Research School of Management and Information

Technology.

I would like to express my profound gratitude toward my supervisors Pro-

fessor Jinyue Yan, Associate Professor Peter Ekman, and Professor Ulf An-

dersson for the continuous support. Thank you for giving me the opportunity

to pursue my Ph.D., and your invaluable guidance, support, and encourage-

ment have been excellent! I am grateful that you fundamentally challenged

me on this journey and made it a truly rewarding experience.

My deep and sincere gratitude goes to my senior colleague and co-author

Associate Professor Pietro Elia Campana for his continuous comments, guid-

ance, and patience in reviewing my papers. Pietro, thank you for taking the

time to help me, give constructive criticism and positive appreciation, and

drive me to higher standards. I would like to pay tribute to Dr. Bengt Stridh

and Dr. Yang Zhang, for their vast knowledge and comments, which led to the

successful completion of my research work. I thank my fellow Ph.D. candi-

dates at the School of Business, Society and Engineering. You have given to

and shared in my time as a Ph.D. student. Special thanks are due to my admin-

istrative colleagues for your seamless administrative support.

I acknowledge the friends from Agape and La Familia, who mean a lot to

me, for accompanying me, feeding me, sharing life with me, and helping me

grow in faith. My friends in China have also been a major source of support.

I could not have made it through the journey on my own without them being

aside. I would like to express my wholehearted gratitude to my friend Pelle.

Your wisdom, warm-heartedness, and care carry a special depth to me.

I would like to give very special thanks to my parents, parents-in-law, and

siblings for supporting me throughout this process and throughout my life in

general. Thank you, my mom and dad, who have always given me the liberty

to pursue what I desire with the selfless love and care. Ultimately, this would

have been impossible without my husband and my best friend Johannes. You

are always around at times with love. You are the one whom I can depend on,

share hardships with, and celebrate each accomplishment with. Most im-

portantly, I thank God for the numerous blessings He has bestowed upon me

throughout my journey.

Acknowledgement

This doctoral thesis was conducted at the School of Business, Society and En-

gineering, Mälardalen University, Västerås, Sweden. I gratefully

acknowledge the financial support from the China Scholarship Council, and

The Swedish (National) Research School of Management and Information

Technology.

I would like to express my profound gratitude toward my supervisors Pro-

fessor Jinyue Yan, Associate Professor Peter Ekman, and Professor Ulf An-

dersson for the continuous support. Thank you for giving me the opportunity

to pursue my Ph.D., and your invaluable guidance, support, and encourage-

ment have been excellent! I am grateful that you fundamentally challenged

me on this journey and made it a truly rewarding experience.

My deep and sincere gratitude goes to my senior colleague and co-author

Associate Professor Pietro Elia Campana for his continuous comments, guid-

ance, and patience in reviewing my papers. Pietro, thank you for taking the

time to help me, give constructive criticism and positive appreciation, and

drive me to higher standards. I would like to pay tribute to Dr. Bengt Stridh

and Dr. Yang Zhang, for their vast knowledge and comments, which led to the

successful completion of my research work. I thank my fellow Ph.D. candi-

dates at the School of Business, Society and Engineering. You have given to

and shared in my time as a Ph.D. student. Special thanks are due to my admin-

istrative colleagues for your seamless administrative support.

I acknowledge the friends from Agape and La Familia, who mean a lot to

me, for accompanying me, feeding me, sharing life with me, and helping me

grow in faith. My friends in China have also been a major source of support.

I could not have made it through the journey on my own without them being

aside. I would like to express my wholehearted gratitude to my friend Pelle.

Your wisdom, warm-heartedness, and care carry a special depth to me.

I would like to give very special thanks to my parents, parents-in-law, and

siblings for supporting me throughout this process and throughout my life in

general. Thank you, my mom and dad, who have always given me the liberty

to pursue what I desire with the selfless love and care. Ultimately, this would

have been impossible without my husband and my best friend Johannes. You

are always around at times with love. You are the one whom I can depend on,

share hardships with, and celebrate each accomplishment with. Most im-

portantly, I thank God for the numerous blessings He has bestowed upon me

throughout my journey.

6

Summary

Solar energy is one of the best options for meeting future energy needs be-

cause of its advantages in terms of availability, accessibility, efficiency, and

cost-effectiveness compared to many other energy sources. Solar photovoltaic

(PV) technology has become well-established for addressing both greenhouse

gas emission reductions and regional air pollution. As one of the main drivers

of the energy transition, an in-depth understanding of the characteristics and

economics of the solar industry is needed in order to integrate with policy

interventions. Rapid developments within the solar technology sector are still

facing various technical barriers, economic impediments, and institutional

barriers. The technical system innovations and their uses in society co-evolve

with the engagement of multi-actors between scientific communities, users,

investors, policymakers, and other stakeholders. Therefore, a holistic and in-

terdisciplinary approach is called to analyze the complexities and solve the

issues.

This doctoral thesis takes a social science perspective in conjunction with

technical considerations. In recent years, there has been growing concern

about solar tariffs in China, not only as a major producer but also as a major

market for solar PV. Advances in technology and declining costs have driven

significant cuts in solar subsidies by the authorities. The cost of solar PV is

influenced by many local factors, so it is a challenge to know whether China

has reached the threshold for grid-connected solar PV systems to supply elec-

tricity to end-users at the same price as grid-supplied electricity or desulfu-

rized coal electricity or even lower. Further raised question is how economi-

cally competitive solar PV electricity is, compared with the traditional coal-

fired power generation.

Compared with China, the energy transition in Sweden has been progres-

sively proceeding, which enables it to build a low-carbon economy with the

lowest share of fossil fuels in the primary energy supply. Sweden's energy

transition has been achieved through information and communication technol-

ogies, policy frameworks, and market instruments that have been able to fun-

damentally change society and the actors within it. Smart cities are therefore

relevant as a context for articulating the market transformation with regard to

revising the understanding of actor roles, resources and business logic that is

relevant for understanding the smart city transition. An interesting part is to

explore how smart city transformation changes energy market logic with the

emerging of new actors in the Swedish electricity market.

Summary

Solar energy is one of the best options for meeting future energy needs be-

cause of its advantages in terms of availability, accessibility, efficiency, and

cost-effectiveness compared to many other energy sources. Solar photovoltaic

(PV) technology has become well-established for addressing both greenhouse

gas emission reductions and regional air pollution. As one of the main drivers

of the energy transition, an in-depth understanding of the characteristics and

economics of the solar industry is needed in order to integrate with policy

interventions. Rapid developments within the solar technology sector are still

facing various technical barriers, economic impediments, and institutional

barriers. The technical system innovations and their uses in society co-evolve

with the engagement of multi-actors between scientific communities, users,

investors, policymakers, and other stakeholders. Therefore, a holistic and in-

terdisciplinary approach is called to analyze the complexities and solve the

issues.

This doctoral thesis takes a social science perspective in conjunction with

technical considerations. In recent years, there has been growing concern

about solar tariffs in China, not only as a major producer but also as a major

market for solar PV. Advances in technology and declining costs have driven

significant cuts in solar subsidies by the authorities. The cost of solar PV is

influenced by many local factors, so it is a challenge to know whether China

has reached the threshold for grid-connected solar PV systems to supply elec-

tricity to end-users at the same price as grid-supplied electricity or desulfu-

rized coal electricity or even lower. Further raised question is how economi-

cally competitive solar PV electricity is, compared with the traditional coal-

fired power generation.

Compared with China, the energy transition in Sweden has been progres-

sively proceeding, which enables it to build a low-carbon economy with the

lowest share of fossil fuels in the primary energy supply. Sweden's energy

transition has been achieved through information and communication technol-

ogies, policy frameworks, and market instruments that have been able to fun-

damentally change society and the actors within it. Smart cities are therefore

relevant as a context for articulating the market transformation with regard to

revising the understanding of actor roles, resources and business logic that is

relevant for understanding the smart city transition. An interesting part is to

explore how smart city transformation changes energy market logic with the

emerging of new actors in the Swedish electricity market.

7

In this thesis, economic feasibility, grid party capability, and investment

values in the market of China are modeled, calculated, and analyzed. The ge-

ographical and technical potential of solar PV applications is explored in Swe-

den. Also, based on a Service-Dominant logic perspective, the new players

and their roles in the transformation of smart cities are explored, using the

Swedish electricity market as an example. The results of the grid parity anal-

ysis show that distributed solar PV projects have reached a tipping point of

cost-effectiveness, when solar PV can be guaranteed to be competitive with

conventional power sources in the context of a subsidy-free in China. This

also implies a gradual replacement of currently operating coal power plants.

The investment return examination shows that profitability levels vary from

city to city, taking into account local resource endowments and local economic

conditions. By highlighting the flexibility issues associated with integrating a

higher percentage of solar power, key performance indicators are presented to

assess the performance of current individual technology components and com-

bined system modules. This is achieved by quantifying the system perfor-

mance in four different categories, i.e. technical, economic, environmental,

and social/policy. Zooming out to the macro level, it shows the theoretical

explanation of how the Swedish electricity market is being changed by renew-

able energies and the emergence of new actors.

In this thesis, economic feasibility, grid party capability, and investment

values in the market of China are modeled, calculated, and analyzed. The ge-

ographical and technical potential of solar PV applications is explored in Swe-

den. Also, based on a Service-Dominant logic perspective, the new players

and their roles in the transformation of smart cities are explored, using the

Swedish electricity market as an example. The results of the grid parity anal-

ysis show that distributed solar PV projects have reached a tipping point of

cost-effectiveness, when solar PV can be guaranteed to be competitive with

conventional power sources in the context of a subsidy-free in China. This

also implies a gradual replacement of currently operating coal power plants.

The investment return examination shows that profitability levels vary from

city to city, taking into account local resource endowments and local economic

conditions. By highlighting the flexibility issues associated with integrating a

higher percentage of solar power, key performance indicators are presented to

assess the performance of current individual technology components and com-

bined system modules. This is achieved by quantifying the system perfor-

mance in four different categories, i.e. technical, economic, environmental,

and social/policy. Zooming out to the macro level, it shows the theoretical

explanation of how the Swedish electricity market is being changed by renew-

able energies and the emergence of new actors.

8

Sammanfattning

Solenergi är ett av de bästa alternativen för att tillgodose framtida energi-

behov på grund av dess fördelar när det gäller tillgänglighet, tillgänglig-

het, effektivitet och kostnadseffektivitet jämfört med många andra ener-

gikällor. Solceller (PV) har blivit väletablerade för att hantera både mins-

kade utsläpp av växthusgaser och regional luftförorening. Som en av de

viktigaste drivkrafterna för energiövergången behövs en djupgående för-

ståelse för solindustrins egenskaper och ekonomi för att integreras med

politiska ingripanden. Den snabba utvecklingen inom soltekniksektorn

står fortfarande inför olika tekniska hinder, ekonomiska hinder och in-

stitutionella hinder. De tekniska systeminnovationerna och deras använd-

ning i samhället utvecklas tillsammans med engagemanget av flera aktö-

rer mellan vetenskapssamhällen, användare, investerare, beslutsfattare

och andra intressenter. Därför kallas ett holistiskt och tvärvetenskapligt

tillvägagångssätt för att analysera komplexiteten och lösa problemen.

Denna doktorsavhandling tar ett samhällsvetenskapligt perspektiv i

samband med tekniska lösningar. Under de senaste åren har det funnits en

växande oro kring ökat oro kring solavgifter i Kina, inte bara som en stor

producent utan också som en stor marknad för solceller. Framsteg inom

teknik och sjunkande kostnader har lett till betydande minskningar av sol-

bidragen från myndigheter. Kostnaden för solceller påverkas av många

lokala faktorer, så det är en utmaning att veta om Kina har nått tröskeln

för nätanslutna solcellssystem att leverera el till slutanvändare till samma

pris som elnätet eller avsvavlat kol el eller ännu lägre. Ytterligare en upp-

dragen fråga är hur ekonomiskt konkurrenskraftig solcellsenergi är jäm-

fört med traditionell koleldad kraftproduktion.

Jämfört med Kina har energiövergången i Sverige successivt fortgått,

vilket gör det möjligt att bygga en koldioxidsnål ekonomi med den lägsta

andelen fossila bränslen i primärenergiförsörjningen. Sveriges energiö-

vergång har uppnåtts genom informations- och kommunikationsteknik,

policyramar och marknadsinstrument som har kunnat förändra samhället

och aktörerna inom det. Smarta städer är därför relevanta som ett sam-

manhang för att formulera marknadsomvandlingen när det gäller att revi-

dera förståelsen för aktörsroller, resurser och affärslogik som är relevant

för att förstå smartstadens övergång. En intressant del är att utforska hur

smart stadsomvandling förändrar energimarknadslogiken med nya aktö-

rer på den svenska elmarknaden.

Sammanfattning

Solenergi är ett av de bästa alternativen för att tillgodose framtida energi-

behov på grund av dess fördelar när det gäller tillgänglighet, tillgänglig-

het, effektivitet och kostnadseffektivitet jämfört med många andra ener-

gikällor. Solceller (PV) har blivit väletablerade för att hantera både mins-

kade utsläpp av växthusgaser och regional luftförorening. Som en av de

viktigaste drivkrafterna för energiövergången behövs en djupgående för-

ståelse för solindustrins egenskaper och ekonomi för att integreras med

politiska ingripanden. Den snabba utvecklingen inom soltekniksektorn

står fortfarande inför olika tekniska hinder, ekonomiska hinder och in-

stitutionella hinder. De tekniska systeminnovationerna och deras använd-

ning i samhället utvecklas tillsammans med engagemanget av flera aktö-

rer mellan vetenskapssamhällen, användare, investerare, beslutsfattare

och andra intressenter. Därför kallas ett holistiskt och tvärvetenskapligt

tillvägagångssätt för att analysera komplexiteten och lösa problemen.

Denna doktorsavhandling tar ett samhällsvetenskapligt perspektiv i

samband med tekniska lösningar. Under de senaste åren har det funnits en

växande oro kring ökat oro kring solavgifter i Kina, inte bara som en stor

producent utan också som en stor marknad för solceller. Framsteg inom

teknik och sjunkande kostnader har lett till betydande minskningar av sol-

bidragen från myndigheter. Kostnaden för solceller påverkas av många

lokala faktorer, så det är en utmaning att veta om Kina har nått tröskeln

för nätanslutna solcellssystem att leverera el till slutanvändare till samma

pris som elnätet eller avsvavlat kol el eller ännu lägre. Ytterligare en upp-

dragen fråga är hur ekonomiskt konkurrenskraftig solcellsenergi är jäm-

fört med traditionell koleldad kraftproduktion.

Jämfört med Kina har energiövergången i Sverige successivt fortgått,

vilket gör det möjligt att bygga en koldioxidsnål ekonomi med den lägsta

andelen fossila bränslen i primärenergiförsörjningen. Sveriges energiö-

vergång har uppnåtts genom informations- och kommunikationsteknik,

policyramar och marknadsinstrument som har kunnat förändra samhället

och aktörerna inom det. Smarta städer är därför relevanta som ett sam-

manhang för att formulera marknadsomvandlingen när det gäller att revi-

dera förståelsen för aktörsroller, resurser och affärslogik som är relevant

för att förstå smartstadens övergång. En intressant del är att utforska hur

smart stadsomvandling förändrar energimarknadslogiken med nya aktö-

rer på den svenska elmarknaden.

9

I denna avhandling modelleras, beräknas och analyseras ekonomisk

genomförbarhet, kapacitet för rutnät och investeringsvärden på Kinas

marknad. Den geografiska och tekniska potentialen för solcelleanlägg-

ningar undersöks i Sverige. Baserat på ett Service-Dominant logikper-

spektiv utforskas också de nya aktörerna och deras roller i omvandlingen

av smarta städer med den svenska elmarknaden som ett exempel. Resul-

taten av nätparitetsanalysen visar att distribuerade solcellsprojekt har nått

en brytpunkt för kostnadseffektivitet när solceller kan garanteras vara

konkurrenskraftiga med konventionella kraftkällor i samband med fria

subventioner Kina. Detta innebär också en gradvis ersättning av nuva-

rande kolkraftverk. Investeringens avkastningsundersökning visar att lön-

samhetsnivåerna varierar från stad till stad med hänsyn till lokala resurser

och lokala ekonomiska förhållanden. Genom att lyfta fram de flexibili-

tetsproblem som är förknippade med att integrera en högre andel solkraft

presenteras viktiga prestandaindikatorer för att bedöma prestandan hos

nuvarande enskilda teknikkomponenter och kombinerade systemmodu-

ler. Detta uppnås genom att kvantifiera systemets prestanda i fyra olika

kategorier, dvs. teknisk, ekonomisk, miljömässig och social/politik. Ge-

nom att zooma ut till makronivå visar det den teoretiska förklaringen till

hur den svenska elmarknaden förändras genom förnybar energi och fram-

växten av nya aktörer.

I denna avhandling modelleras, beräknas och analyseras ekonomisk

genomförbarhet, kapacitet för rutnät och investeringsvärden på Kinas

marknad. Den geografiska och tekniska potentialen för solcelleanlägg-

ningar undersöks i Sverige. Baserat på ett Service-Dominant logikper-

spektiv utforskas också de nya aktörerna och deras roller i omvandlingen

av smarta städer med den svenska elmarknaden som ett exempel. Resul-

taten av nätparitetsanalysen visar att distribuerade solcellsprojekt har nått

en brytpunkt för kostnadseffektivitet när solceller kan garanteras vara

konkurrenskraftiga med konventionella kraftkällor i samband med fria

subventioner Kina. Detta innebär också en gradvis ersättning av nuva-

rande kolkraftverk. Investeringens avkastningsundersökning visar att lön-

samhetsnivåerna varierar från stad till stad med hänsyn till lokala resurser

och lokala ekonomiska förhållanden. Genom att lyfta fram de flexibili-

tetsproblem som är förknippade med att integrera en högre andel solkraft

presenteras viktiga prestandaindikatorer för att bedöma prestandan hos

nuvarande enskilda teknikkomponenter och kombinerade systemmodu-

ler. Detta uppnås genom att kvantifiera systemets prestanda i fyra olika

kategorier, dvs. teknisk, ekonomisk, miljömässig och social/politik. Ge-

nom att zooma ut till makronivå visar det den teoretiska förklaringen till

hur den svenska elmarknaden förändras genom förnybar energi och fram-

växten av nya aktörer.

10

List of Papers

This thesis is based on the following papers, which are referred to in the text

by their Roman numerals.

I Yan J, Yang Y*, Campana PE, He J. City-level analysis of sub-

sidy-free solar photovoltaic electricity price, profits and grid par-

ity in China. Nature Energy. 2019 Aug;4(8):709-17.

II Yang Y*, Campana PE, Yan J. Potential of unsubsidized distrib-

uted solar PV to replace coal-fired power plants, and profits clas-

sification in Chinese cities. Renewable and Sustainable Energy

Reviews. 2020 Oct 1;131:109967.

III Ekman P, Röndell J, Yang Y. Exploring smart cities and market

transformations from a Service-Dominant logic perspective. Sus-

tainable Cities and Society. 2019 Jul 25:101731.

IV Yang Y*, Jurasz J, Li H, Syrri A, Yan J. Key performance indi-

cators on flexibility of a multi-energy system. Applied Energy

Symposium: Low carbon cities and urban energy systems (CUE).

2019 Oct, Xiamen University, Fujian, China.

V Yang Y*, Campana PE, Stridh B, Yan J. Potential analysis of

roof-mounted solar photovoltaics in Sweden. Applied Energy.

2020 Dec 1;279:115786.

List of papers not included in this thesis

VI Yang Y, Zhang Y, Campana PE, Yan J. Peak-shaving and profit-

sharing model by Aggregators in residential buildings with PV:

a case study in Eskilstuna, Sweden. Energy Procedia. 2017 Dec

1;142:3182-93.

VII Zhang Y, Campana PE, Yang Y, Stridh B, Lundblad A, Yan J.

Energy flexibility from the consumer: Integrating local electric-

ity and heat supplies in a building. Applied Energy. 2018 Aug

1;223:430-42.

VIII Zhang Y, Campana PE, Yang Y, Lundblad A, Yan J. Energy

flexibility through the integrated energy supply system in build-

ings: a case study in Sweden. Energy Procedia. 2018 Jul

1;145:564-9.

List of Papers

This thesis is based on the following papers, which are referred to in the text

by their Roman numerals.

I Yan J, Yang Y*, Campana PE, He J. City-level analysis of sub-

sidy-free solar photovoltaic electricity price, profits and grid par-

ity in China. Nature Energy. 2019 Aug;4(8):709-17.

II Yang Y*, Campana PE, Yan J. Potential of unsubsidized distrib-

uted solar PV to replace coal-fired power plants, and profits clas-

sification in Chinese cities. Renewable and Sustainable Energy

Reviews. 2020 Oct 1;131:109967.

III Ekman P, Röndell J, Yang Y. Exploring smart cities and market

transformations from a Service-Dominant logic perspective. Sus-

tainable Cities and Society. 2019 Jul 25:101731.

IV Yang Y*, Jurasz J, Li H, Syrri A, Yan J. Key performance indi-

cators on flexibility of a multi-energy system. Applied Energy

Symposium: Low carbon cities and urban energy systems (CUE).

2019 Oct, Xiamen University, Fujian, China.

V Yang Y*, Campana PE, Stridh B, Yan J. Potential analysis of

roof-mounted solar photovoltaics in Sweden. Applied Energy.

2020 Dec 1;279:115786.

List of papers not included in this thesis

VI Yang Y, Zhang Y, Campana PE, Yan J. Peak-shaving and profit-

sharing model by Aggregators in residential buildings with PV:

a case study in Eskilstuna, Sweden. Energy Procedia. 2017 Dec

1;142:3182-93.

VII Zhang Y, Campana PE, Yang Y, Stridh B, Lundblad A, Yan J.

Energy flexibility from the consumer: Integrating local electric-

ity and heat supplies in a building. Applied Energy. 2018 Aug

1;223:430-42.

VIII Zhang Y, Campana PE, Yang Y, Lundblad A, Yan J. Energy

flexibility through the integrated energy supply system in build-

ings: a case study in Sweden. Energy Procedia. 2018 Jul

1;145:564-9.

11

IX Yang Y, Yan J. Potential roof area for photovoltaics in a Swedish

municipality. Applied Energy Symposium: MIT A+B, 2019 May,

Massachusetts Institute of Technology, Cambridge, USA.

X Syrri ALA, Bindner H, Li H, Jurasz J, Yan J, Yang Y. KPIs and

assessment procedure. European Union’s Horizon 2020 research

and innovation programme under grant agreement No 774309.

Ref. Ares(2019)3992146 - 24/06/2019.

IX Yang Y, Yan J. Potential roof area for photovoltaics in a Swedish

municipality. Applied Energy Symposium: MIT A+B, 2019 May,

Massachusetts Institute of Technology, Cambridge, USA.

X Syrri ALA, Bindner H, Li H, Jurasz J, Yan J, Yang Y. KPIs and

assessment procedure. European Union’s Horizon 2020 research

and innovation programme under grant agreement No 774309.

Ref. Ares(2019)3992146 - 24/06/2019.

12

Contents

Acknowledgement ..........................................................................................ii

Summary ....................................................................................................... iii

Sammanfattning .............................................................................................. v

List of Papers ................................................................................................vii

List of Figures ................................................................................................ xi

List of Tables ................................................................................................xii

Abbreviations .............................................................................................. xiii

Symbols ....................................................................................................... xiv

1 Introduction ........................................................................................... 1 1.1 Background .......................................................................................... 1 1.2 Research gaps and challenges .............................................................. 4 1.3 Scope and objectives ............................................................................ 6 1.4 Thesis contributions ............................................................................. 6 1.5 Thesis structure .................................................................................... 7

2 Previous studies ..................................................................................... 9 2.1 Subsidy and economics in solar PV industry ....................................... 9

2.1.1 Policy and subsidy in Chinese solar PV industry ......................... 9 2.1.2 Subsidy and economic analysis in the literatures ....................... 10

2.2 Flexibility in the electricity system .................................................... 13 2.3 New market actor - aggregator ........................................................... 15 2.4 Service-Dominant logic ...................................................................... 18 2.5 The geographical and technical potential availability ........................ 21

3 Methods ............................................................................................... 24 3.1 Economic viability indices ................................................................. 24

3.1.1 Levelized Cost of Electricity and cost crossover ........................ 24 3.1.2 Levelized Profit of Electricity and other three indices ............... 25 3.1.3 Grid parity indices ...................................................................... 26

3.2 Monte Carlo Analysis ......................................................................... 27 3.3 K-means clustering algorithm ............................................................ 27

Contents

Acknowledgement ..........................................................................................ii

Summary ....................................................................................................... iii

Sammanfattning .............................................................................................. v

List of Papers ................................................................................................vii

List of Figures ................................................................................................ xi

List of Tables ................................................................................................xii

Abbreviations .............................................................................................. xiii

Symbols ....................................................................................................... xiv

1 Introduction ........................................................................................... 1 1.1 Background .......................................................................................... 1 1.2 Research gaps and challenges .............................................................. 4 1.3 Scope and objectives ............................................................................ 6 1.4 Thesis contributions ............................................................................. 6 1.5 Thesis structure .................................................................................... 7

2 Previous studies ..................................................................................... 9 2.1 Subsidy and economics in solar PV industry ....................................... 9

2.1.1 Policy and subsidy in Chinese solar PV industry ......................... 9 2.1.2 Subsidy and economic analysis in the literatures ....................... 10

2.2 Flexibility in the electricity system .................................................... 13 2.3 New market actor - aggregator ........................................................... 15 2.4 Service-Dominant logic ...................................................................... 18 2.5 The geographical and technical potential availability ........................ 21

3 Methods ............................................................................................... 24 3.1 Economic viability indices ................................................................. 24

3.1.1 Levelized Cost of Electricity and cost crossover ........................ 24 3.1.2 Levelized Profit of Electricity and other three indices ............... 25 3.1.3 Grid parity indices ...................................................................... 26

3.2 Monte Carlo Analysis ......................................................................... 27 3.3 K-means clustering algorithm ............................................................ 27

13

3.4 Case study and questionnaire survey .................................................. 30 3.4.1 Case study ................................................................................... 30 3.4.2 Questionnaire survey ................................................................ 31

3.5 Potential availability analysis ............................................................. 32 3.5.1 Usable roof area estimation ........................................................ 33 3.5.2 Configurations of PV module and energy conversion ................ 34

4 Results ................................................................................................. 37 4.1 Grid parity of user-side and plant-side (Paper I) ................................ 37 4.2 Investment profitability (Paper I & II) ............................................... 38 4.3 Electricity market in transformation (Paper III) ................................. 43

4.3.1 The traditional linear value chain market logic ...................... 43 4.3.2 The networked market and an emerging new role - aggregator . 46

4.4 Selected key performance indicators (Paper IV) ................................ 48 4.5 Potential availability analysis (Paper V) ............................................ 50

5 Discussions and policy implications .................................................... 55 5.1 “Soft” cost .......................................................................................... 55 5.2 “Additional cost” ................................................................................ 57 5.3 Self-consumption rate ........................................................................ 58 5.4 Capital subsidy ................................................................................... 59

6 Conclusions ......................................................................................... 62

7 Future work.......................................................................................... 66

References ..................................................................................................... 67

3.4 Case study and questionnaire survey .................................................. 30 3.4.1 Case study ................................................................................... 30 3.4.2 Questionnaire survey ................................................................ 31

3.5 Potential availability analysis ............................................................. 32 3.5.1 Usable roof area estimation ........................................................ 33 3.5.2 Configurations of PV module and energy conversion ................ 34

4 Results ................................................................................................. 37 4.1 Grid parity of user-side and plant-side (Paper I) ................................ 37 4.2 Investment profitability (Paper I & II) ............................................... 38 4.3 Electricity market in transformation (Paper III) ................................. 43

4.3.1 The traditional linear value chain market logic ...................... 43 4.3.2 The networked market and an emerging new role - aggregator . 46

4.4 Selected key performance indicators (Paper IV) ................................ 48 4.5 Potential availability analysis (Paper V) ............................................ 50

5 Discussions and policy implications .................................................... 55 5.1 “Soft” cost .......................................................................................... 55 5.2 “Additional cost” ................................................................................ 57 5.3 Self-consumption rate ........................................................................ 58 5.4 Capital subsidy ................................................................................... 59

6 Conclusions ......................................................................................... 62

7 Future work.......................................................................................... 66

References ..................................................................................................... 67

14

List of Figures

Figure 1. Schematic diagram showing the relationship between the research

topics and the papers. ......................................................................... 6 Figure 2. Historical policies and installed capacity of China's solar PV

industry. ........................................................................................... 10 Figure 3. The narrative and process of S-D logic. ........................................ 19 Figure 4. Categorization of clustering algorithms. ....................................... 28 Figure 5. K-means clustering algorithm procedure in Paper II. .................... 29 Figure 6. Summary of KPI categories, stakeholders and their roles. ............ 31 Figure 7. Schematic diagram of the required distance between rows of solar

panels in scenario A. ........................................................................ 35 Figure 8. East-West orientation configuration with a low tilt angle in

scenario C. ....................................................................................... 36 Figure 9. Grid Parity Indices for distributed PV projects in 344 cities without

subsidies. .......................................................................................... 38 Figure 10. Economic feasibility and profitability of solar PV in 344 cities

without subsidies. ............................................................................. 39 Figure 11. Current running coal-fired power plants cost-risk levels and city

number percent in China and the US. .............................................. 40 Figure 12. The four cost-risk levels of current coal-fired power plants and

their corresponding cities in China. ................................................. 41 Figure 13. Four city clusters of distributed solar PV investment-profits. ..... 42 Figure 14. A simplified illustration of the Swedish energy system’s

traditional linear value chain market logic. ...................................... 45 Figure 15. A future smart and dynamic networked market logic. ................ 47 Figure 16. The percent of electricity losses using different row distances and

tilt angles due to mutual shading. .................................................... 51 Figure 17. The potential area for roof-mounted solar PV systems (a), and the

potential installed capacity on pitched roofs and flat roofs with three

scenarios (b-d). ................................................................................. 53 Figure 18. Different self-consumption rates of 0%, 25%, 50%, 75% and

100% impact economic performances. ............................................ 59 Figure 19. The historical direct subsidy percent and the budgets for solar

industry. ........................................................................................... 60

List of Figures

Figure 1. Schematic diagram showing the relationship between the research

topics and the papers. ......................................................................... 6 Figure 2. Historical policies and installed capacity of China's solar PV

industry. ........................................................................................... 10 Figure 3. The narrative and process of S-D logic. ........................................ 19 Figure 4. Categorization of clustering algorithms. ....................................... 28 Figure 5. K-means clustering algorithm procedure in Paper II. .................... 29 Figure 6. Summary of KPI categories, stakeholders and their roles. ............ 31 Figure 7. Schematic diagram of the required distance between rows of solar

panels in scenario A. ........................................................................ 35 Figure 8. East-West orientation configuration with a low tilt angle in

scenario C. ....................................................................................... 36 Figure 9. Grid Parity Indices for distributed PV projects in 344 cities without

subsidies. .......................................................................................... 38 Figure 10. Economic feasibility and profitability of solar PV in 344 cities

without subsidies. ............................................................................. 39 Figure 11. Current running coal-fired power plants cost-risk levels and city

number percent in China and the US. .............................................. 40 Figure 12. The four cost-risk levels of current coal-fired power plants and

their corresponding cities in China. ................................................. 41 Figure 13. Four city clusters of distributed solar PV investment-profits. ..... 42 Figure 14. A simplified illustration of the Swedish energy system’s

traditional linear value chain market logic. ...................................... 45 Figure 15. A future smart and dynamic networked market logic. ................ 47 Figure 16. The percent of electricity losses using different row distances and

tilt angles due to mutual shading. .................................................... 51 Figure 17. The potential area for roof-mounted solar PV systems (a), and the

potential installed capacity on pitched roofs and flat roofs with three

scenarios (b-d). ................................................................................. 53 Figure 18. Different self-consumption rates of 0%, 25%, 50%, 75% and

100% impact economic performances. ............................................ 59 Figure 19. The historical direct subsidy percent and the budgets for solar

industry. ........................................................................................... 60

15

List of Tables

Table 1. The S-D logic foundational premises. ............................................ 18

Table 2. Summary results of statistical parameters. (T: technology, En:

environmental, Ec: economics, S/P: social/policy) ........................ 48

Table 3. Five most important KPIs without categories. ................................ 49

Table 4. The selected KPIs from the questionnaire analysis in different

categories. ....................................................................................... 49

Table 5. The usable area, potential installed capacity, and electricity

generation for pitched roofs. ........................................................... 50


generation for flat roofs in three scenarios. .................................... 52

List of Tables

Table 1. The S-D logic foundational premises. ............................................ 18

Table 2. Summary results of statistical parameters. (T: technology, En:

environmental, Ec: economics, S/P: social/policy) ........................ 48

Table 3. Five most important KPIs without categories. ................................ 49

Table 4. The selected KPIs from the questionnaire analysis in different

categories. ....................................................................................... 49


generation for pitched roofs. ........................................................... 50


generation for flat roofs in three scenarios. .................................... 52

16

Abbreviations

BiPV Building Integrated with Photovoltaics

CF cash flow

CV coefficient of variation

DCB desulfurized coal benchmark price

DPBP discounted payback period

DSM digital surface models

DSO distribution system operator

DR demand response

EV electric vehicle

FiT feed-in tariff

FP foundational premise

GHG greenhouse gasses

GIS Geographic Information System

GPIp grid parity index of the plant-side

GPIu grid parity index of the user-side

ICT information and communications technology

IRR internal rate of return

I&C industrial and commercial

KPI key performance indicator

LCOE levelized cost of electricity

LiDAR Light Detection and Ranging

LPOE levelized profit of electricity

MCA Monte Carlo Analysis

MP electricity market purchasing price

NDRC National Development and Reform Commission

NEA National Energy Administration

NPV net present value

PF Packing Factor

PV photovoltaic

STD standard deviation

S-D Service-Dominant

TSO transmission system operator

VAT Value-added Tax

ZIP Zoning Improvement Plan

Abbreviations

BiPV Building Integrated with Photovoltaics

CF cash flow

CV coefficient of variation

DCB desulfurized coal benchmark price

DPBP discounted payback period

DSM digital surface models

DSO distribution system operator

DR demand response

EV electric vehicle

FiT feed-in tariff

FP foundational premise

GHG greenhouse gasses

GIS Geographic Information System

GPIp grid parity index of the plant-side

GPIu grid parity index of the user-side

ICT information and communications technology

IRR internal rate of return

I&C industrial and commercial

KPI key performance indicator

LCOE levelized cost of electricity

LiDAR Light Detection and Ranging

LPOE levelized profit of electricity

MCA Monte Carlo Analysis

MP electricity market purchasing price

NDRC National Development and Reform Commission

NEA National Energy Administration

NPV net present value

PF Packing Factor

PV photovoltaic

STD standard deviation

S-D Service-Dominant

TSO transmission system operator

VAT Value-added Tax

ZIP Zoning Improvement Plan

17

Symbols

a , b the 𝑎𝑡ℎ and 𝑏𝑡ℎ iteration (a ≠ b)

A𝑟𝑜𝑜𝑓 surface roof area (𝑘𝑚2)

𝐴𝑏𝑎𝑠𝑒 base area (𝑘𝑚2)

𝐴𝑏𝑎𝑠𝑒𝑚 building base area of the 𝑚𝑡ℎ municipality (𝑚2)

𝐴𝑝𝑣𝑆𝐸 usable area for roof-mounted PV systems in Sweden (𝑘𝑚2)

𝑐𝑘(𝑎)

, 𝑐𝑘(𝑏)

the cluster k at 𝑎𝑡ℎ or 𝑏𝑡ℎ iteration

𝐶𝐹0 initial investment

𝐶𝐹1, 𝐶𝐹2, … 𝐶𝐹𝑡 cash flow of year 1, 2, … , 𝑡

𝐶1(1)

,

𝐶2(1)

, …,𝐶4(1)

the center for initial cluster 1, 2, …, 4

𝑑 distance between the front row and the back row of PV panels

(meter)

d(p, q) the Euclidean Distance

𝑘 the 𝑘𝑡ℎ cluster, k =1,2,...,4

𝑙 width of a solar PV panel (meter)

𝑚 length of the ridge (meter)

m(p, q) the Mahalanobis Distance

mu municipality, mu = 1,2,3 … ,290

𝑁𝑣𝑜𝑡𝑒 normalized numbers of votes

𝑁𝐶𝑉 normalized Coefficient of Variation

𝑝 length of counter beam (meter)

𝑞 length of the hanging beam (meter)

𝑟 discount rate for 𝑡 (%)

𝑟𝑓𝑙𝑎 orientation role for flat roofs

𝑟𝑛_𝑝𝑖𝑡 orientation role for not-applicable orientations

𝑟𝑢_𝑝𝑖𝑡 orientation role for applicable orientations

𝑅𝑓𝑙𝑎 flat roof percentage (%)

𝑅𝑛_𝑝𝑖𝑡 not-applicable orientations for solar PV installations (%)

𝑅𝑢_𝑝𝑖𝑡 applicable orientations for solar PV installations (%)

𝑅𝑛, 𝑅𝑛𝑒, 𝑅𝑛𝑤 orientation of North, North East, and North West, respectively (%)

Symbols

a , b the 𝑎𝑡ℎ and 𝑏𝑡ℎ iteration (a ≠ b)

A𝑟𝑜𝑜𝑓 surface roof area (𝑘𝑚2)

𝐴𝑏𝑎𝑠𝑒 base area (𝑘𝑚2)

𝐴𝑏𝑎𝑠𝑒𝑚 building base area of the 𝑚𝑡ℎ municipality (𝑚2)

𝐴𝑝𝑣𝑆𝐸 usable area for roof-mounted PV systems in Sweden (𝑘𝑚2)

𝑐𝑘(𝑎)

, 𝑐𝑘(𝑏)

the cluster k at 𝑎𝑡ℎ or 𝑏𝑡ℎ iteration

𝐶𝐹0 initial investment

𝐶𝐹1, 𝐶𝐹2, … 𝐶𝐹𝑡 cash flow of year 1, 2, … , 𝑡

𝐶1(1)

,

𝐶2(1)

, …,𝐶4(1)

the center for initial cluster 1, 2, …, 4

𝑑 distance between the front row and the back row of PV panels

(meter)

d(p, q) the Euclidean Distance

𝑘 the 𝑘𝑡ℎ cluster, k =1,2,...,4

𝑙 width of a solar PV panel (meter)

𝑚 length of the ridge (meter)

m(p, q) the Mahalanobis Distance

mu municipality, mu = 1,2,3 … ,290

𝑁𝑣𝑜𝑡𝑒 normalized numbers of votes

𝑁𝐶𝑉 normalized Coefficient of Variation

𝑝 length of counter beam (meter)

𝑞 length of the hanging beam (meter)

𝑟 discount rate for 𝑡 (%)

𝑟𝑓𝑙𝑎 orientation role for flat roofs

𝑟𝑛_𝑝𝑖𝑡 orientation role for not-applicable orientations

𝑟𝑢_𝑝𝑖𝑡 orientation role for applicable orientations

𝑅𝑓𝑙𝑎 flat roof percentage (%)

𝑅𝑛_𝑝𝑖𝑡 not-applicable orientations for solar PV installations (%)

𝑅𝑢_𝑝𝑖𝑡 applicable orientations for solar PV installations (%)

𝑅𝑛, 𝑅𝑛𝑒, 𝑅𝑛𝑤 orientation of North, North East, and North West, respectively (%)

18

𝑅𝑠, 𝑅𝑠𝑤,

𝑅𝑠𝑒, 𝑅𝑤, 𝑅𝑒

orientation of South, South West, South East, West, and East, re-

spectively (%)

𝑡 the year t

𝑇 life of the project (years)

𝑈𝑜𝑟𝑖 utilization factor of orientation

𝑈𝑜𝑟𝑖𝑖𝑛𝑑 utilization factor of orientation for industrial buildings

𝑈𝑜𝑟𝑖𝑛𝑜𝑛_𝑖𝑛𝑑

utilization factor of orientation for non-industrial buildings

𝑈𝑠𝑜 utilization factor due to shadows and obstacles

𝑈𝑠𝑜𝑖𝑛𝑑 utilization factor due to shadows and obstacles for industrial build-

ings

𝑈𝑠𝑜𝑛𝑜_𝑖𝑛𝑑

utilization factor due to shadows and obstacles for non-industrial

buildings

𝑈𝑎𝑏𝑠𝑖𝑛𝑑 percent of absolute reduction on industrial buildings

𝑈𝑎𝑏𝑠𝑛𝑜𝑛_𝑖𝑛𝑑

percent of absolute reduction on non-industrial buildings

𝑥1, 𝑥2, … , 𝑥𝑘 risk factors for Monte Carlo Analysis

𝑥𝑝𝑖, 𝑥𝑞𝑖 p, q are any two points, p, q =1,2,…,344. i is the dimension (i.e.

LPOE, NPV, IRR, and DPBP), i =1,2,…,4

𝑌 financial indicators of investment projects

𝑍 the calculated KPI value

𝛼 solar altitude (or elevation) angle (°)

𝛼𝑟𝑜𝑜𝑓 slope angle of the roof (°)

𝛼𝑓𝑙𝑎𝑡 slope angle of the flat roofs (°)

𝛼𝑝𝑖𝑡 slope angle of the pitched roofs (°)

𝛽 tilt angle of PV module (°)

𝛽𝑜𝑝𝑡 optimal tilt angle of PV module (°) in scenario A

𝜃𝑧 solar zenith angle (°)

𝛾 solar azimuth angle (°)

𝛿 earth’s declination (°)

𝜙 latitude (°)

𝜔 hour angle (°)

𝜎 the standard deviation

𝜇 the mean

𝑅𝑠, 𝑅𝑠𝑤,

𝑅𝑠𝑒, 𝑅𝑤, 𝑅𝑒

orientation of South, South West, South East, West, and East, re-

spectively (%)

𝑡 the year t

𝑇 life of the project (years)

𝑈𝑜𝑟𝑖 utilization factor of orientation

𝑈𝑜𝑟𝑖𝑖𝑛𝑑 utilization factor of orientation for industrial buildings

𝑈𝑜𝑟𝑖𝑛𝑜𝑛_𝑖𝑛𝑑

utilization factor of orientation for non-industrial buildings

𝑈𝑠𝑜 utilization factor due to shadows and obstacles

𝑈𝑠𝑜𝑖𝑛𝑑 utilization factor due to shadows and obstacles for industrial build-

ings

𝑈𝑠𝑜𝑛𝑜_𝑖𝑛𝑑

utilization factor due to shadows and obstacles for non-industrial

buildings

𝑈𝑎𝑏𝑠𝑖𝑛𝑑 percent of absolute reduction on industrial buildings

𝑈𝑎𝑏𝑠𝑛𝑜𝑛_𝑖𝑛𝑑

percent of absolute reduction on non-industrial buildings

𝑥1, 𝑥2, … , 𝑥𝑘 risk factors for Monte Carlo Analysis

𝑥𝑝𝑖, 𝑥𝑞𝑖 p, q are any two points, p, q =1,2,…,344. i is the dimension (i.e.

LPOE, NPV, IRR, and DPBP), i =1,2,…,4

𝑌 financial indicators of investment projects

𝑍 the calculated KPI value

𝛼 solar altitude (or elevation) angle (°)

𝛼𝑟𝑜𝑜𝑓 slope angle of the roof (°)

𝛼𝑓𝑙𝑎𝑡 slope angle of the flat roofs (°)

𝛼𝑝𝑖𝑡 slope angle of the pitched roofs (°)

𝛽 tilt angle of PV module (°)

𝛽𝑜𝑝𝑡 optimal tilt angle of PV module (°) in scenario A

𝜃𝑧 solar zenith angle (°)

𝛾 solar azimuth angle (°)

𝛿 earth’s declination (°)

𝜙 latitude (°)

𝜔 hour angle (°)

𝜎 the standard deviation

𝜇 the mean

19

1

1 Introduction

This chapter presents the background of the study, the research questions for-

mulated based on the research gaps, scope and objectives, contributions, and

the structure of the thesis. The relationship between the research topics and

the papers is also described.

1.1 Background

Human concerns about fossil fuel depletion, energy security, and environmen-

tal degradation have led to an increasing demand for renewable energy

sources. Currently, fossil fuels still account for 87% of the global primary sup-

ply, but the overall share of renewable energy is about to reach 10% (Armaroli

and Balzani, 2016). Solar energy is considered to be a non-polluting, reliable,

and clean energy source. The adoption of solar energy technology has signif-

icantly alleviated and mitigated the problems associated with air pollutants

and greenhouse gas emissions compared to fossil fuel-based power genera-

tion. Solar photovoltaic (PV) technology, as one of the emerging technologies,

has received increasing attention from academia and industry.

The energy transition is a pathway toward the transformation of the global

energy sector which requires urgent action on a global scale. Some countries,

such as China and Sweden, are expected to take the lead on the global efforts

to achieve clean energy and greenhouse gas emissions reduction. China has

made energy transition a long-term strategy and achieved consistent and meas-

urable progress over the past years. Whereas, Sweden is helping lead the way

and is for the third year in a row ranked number one in the global Energy

Transition Index (Bocca, 2020). For China, it is imperative to explore the eco-

nomic viability of unsubsidized solar energy and the profitability of invest-

ments to assess the inclusion of renewables. At the other end of the spectrum,

Sweden is faced with the integration of renewable energy into the current en-

ergy market and the transition to smart cities. In Sweden, a new era of power

systems marked by large-scale renewables is dawning. The energy transition

will be achieved through information and communication technology (ICT),

smart technologies, policy frameworks, and market instruments that can fun-

damentally change society and the players in it. In the regional evaluation,

Sweden scored high in several relevant areas such as innovativeness, infor-

mation and communications technology, and sustainability (Strand, Freeman

1

1 Introduction

This chapter presents the background of the study, the research questions for-

mulated based on the research gaps, scope and objectives, contributions, and

the structure of the thesis. The relationship between the research topics and

the papers is also described.

1.1 Background

Human concerns about fossil fuel depletion, energy security, and environmen-

tal degradation have led to an increasing demand for renewable energy

sources. Currently, fossil fuels still account for 87% of the global primary sup-

ply, but the overall share of renewable energy is about to reach 10% (Armaroli

and Balzani, 2016). Solar energy is considered to be a non-polluting, reliable,

and clean energy source. The adoption of solar energy technology has signif-

icantly alleviated and mitigated the problems associated with air pollutants

and greenhouse gas emissions compared to fossil fuel-based power genera-

tion. Solar photovoltaic (PV) technology, as one of the emerging technologies,

has received increasing attention from academia and industry.

The energy transition is a pathway toward the transformation of the global

energy sector which requires urgent action on a global scale. Some countries,

such as China and Sweden, are expected to take the lead on the global efforts

to achieve clean energy and greenhouse gas emissions reduction. China has

made energy transition a long-term strategy and achieved consistent and meas-

urable progress over the past years. Whereas, Sweden is helping lead the way

and is for the third year in a row ranked number one in the global Energy

Transition Index (Bocca, 2020). For China, it is imperative to explore the eco-

nomic viability of unsubsidized solar energy and the profitability of invest-

ments to assess the inclusion of renewables. At the other end of the spectrum,

Sweden is faced with the integration of renewable energy into the current en-

ergy market and the transition to smart cities. In Sweden, a new era of power

systems marked by large-scale renewables is dawning. The energy transition

will be achieved through information and communication technology (ICT),

smart technologies, policy frameworks, and market instruments that can fun-

damentally change society and the players in it. In the regional evaluation,

Sweden scored high in several relevant areas such as innovativeness, infor-

mation and communications technology, and sustainability (Strand, Freeman

20

2

and Hockerts, 2015), which makes it a suitable environment for understanding

the smart city transition.

China is strongly committed to reducing CO2 emissions from power gen-

eration by building and deploying renewable energy sources such as solar en-

ergy. As part of the Paris Agreement commitment, China has set a target to

increase the share of renewable energy and non-fossil fuel energy consump-

tion to 15% by 2020 and 20% by 2030 (Liu et al., 2018). In regards to the

decarbonization of China’s power system, renewable energy will account for

27% of China's total power generation by 2020 (Gosens, Kåberger and Wang,

2017) (National Development and Reform Commission (NDRC) and National

Energy Administration (NEA), 2016). Remarkable progress has been made in

this by deploying 23%-33% renewable energy in the electricity mix during the

first three quarters of 2020 (International Energy Agency (IEA), 2020). Dur-

ing the same period, the electricity demand increased by 1.3%, and solar PV

power generation increased by 16.9%, regardless of the pandemic lockdown

measures being putting (National Energy Agency (NEA), 2020). The propor-

tion of solar PV electricity is increasing substantially every year, although it

still provides a relatively small share. China is the largest manufacturer of so-

lar PV, and with declining local costs, and potential co-benefits of reduced

pollution and health, domestic use of solar PV may rise even more dramati-

cally in the near future.

While market reforms have addressed some of China's previous problems

in integrating solar energy, the power of administrative measures continues to

actively intervene in the market with various government policies and incen-

tives. Since 2018, the authorities have issued numerous regulations that sig-

nificantly reduced the amount of subsidies on solar PV projects. Previously,

the subsidies were allocated based on the quantity of solar power generation,

rather than quality. The “531 New Policy” subsidy cuts in 2018 were a clear

signal from the government that the solar PV industry needs to reduce its re-

liance on subsidies and shift its focus from scale growth to quality improve-

ment. At the beginning of 2019, however, the government proposed subsidy-

free grid parity for PV electricity. As such, the Chinese solar market is moving

from subsidy-driven towards unsubsidized grid parity. Grid parity is a tipping

point for the diffusion of solar PV technology. In China, the potential for such

grid parity exists due to innovations in solar PV technology and declining

costs. However, there are few uniform conclusions from previous studies as

to whether and when grid parity can be achieved for solar PV. Future research

opportunities exist to explore the required system and financial costs associ-

ated with grid parity. The attractive economics of solar and other renewable

resources present a challenge to coal-fired generation. Cost is often a major

consideration when planning the transition from coal-fired generation to green

power. It is worth comparing the costs of unsubsidized solar PV and coal-fired

generation to meet the goals of the energy transition.

2

and Hockerts, 2015), which makes it a suitable environment for understanding

the smart city transition.

China is strongly committed to reducing CO2 emissions from power gen-

eration by building and deploying renewable energy sources such as solar en-

ergy. As part of the Paris Agreement commitment, China has set a target to

increase the share of renewable energy and non-fossil fuel energy consump-

tion to 15% by 2020 and 20% by 2030 (Liu et al., 2018). In regards to the

decarbonization of China’s power system, renewable energy will account for

27% of China's total power generation by 2020 (Gosens, Kåberger and Wang,

2017) (National Development and Reform Commission (NDRC) and National

Energy Administration (NEA), 2016). Remarkable progress has been made in

this by deploying 23%-33% renewable energy in the electricity mix during the

first three quarters of 2020 (International Energy Agency (IEA), 2020). Dur-

ing the same period, the electricity demand increased by 1.3%, and solar PV

power generation increased by 16.9%, regardless of the pandemic lockdown

measures being putting (National Energy Agency (NEA), 2020). The propor-

tion of solar PV electricity is increasing substantially every year, although it

still provides a relatively small share. China is the largest manufacturer of so-

lar PV, and with declining local costs, and potential co-benefits of reduced

pollution and health, domestic use of solar PV may rise even more dramati-

cally in the near future.

While market reforms have addressed some of China's previous problems

in integrating solar energy, the power of administrative measures continues to

actively intervene in the market with various government policies and incen-

tives. Since 2018, the authorities have issued numerous regulations that sig-

nificantly reduced the amount of subsidies on solar PV projects. Previously,

the subsidies were allocated based on the quantity of solar power generation,

rather than quality. The “531 New Policy” subsidy cuts in 2018 were a clear

signal from the government that the solar PV industry needs to reduce its re-

liance on subsidies and shift its focus from scale growth to quality improve-

ment. At the beginning of 2019, however, the government proposed subsidy-

free grid parity for PV electricity. As such, the Chinese solar market is moving

from subsidy-driven towards unsubsidized grid parity. Grid parity is a tipping

point for the diffusion of solar PV technology. In China, the potential for such

grid parity exists due to innovations in solar PV technology and declining

costs. However, there are few uniform conclusions from previous studies as

to whether and when grid parity can be achieved for solar PV. Future research

opportunities exist to explore the required system and financial costs associ-

ated with grid parity. The attractive economics of solar and other renewable

resources present a challenge to coal-fired generation. Cost is often a major

consideration when planning the transition from coal-fired generation to green

power. It is worth comparing the costs of unsubsidized solar PV and coal-fired

generation to meet the goals of the energy transition.

21

3

The concept of "smart" refers to a forward-looking approach (Chourabi et

al., 2012), where smart cities are composed of a smart economy, people, gov-

ernance, mobility, and environment. It is reasonable to assume that smart cit-

ies may also need to include the concept of "smart markets", which should be

understood beyond traditional neoclassical economics. Sweden has surpassed

the environmental targets set by the EU with an explicit goal of reaching 60%

of renewable energy systems by 2030. The adoption of renewable energies,

such as solar PV power, wind power, hydropower, is closely related to the

initiatives and growth of smart cities. On the other hand, the introduction of

renewable energy brings transitional challenges and new possibilities to the

existing electricity market in Sweden. It alters the roles of traditional market

actors as well as business models. For example, the distributed solar PV ap-

plication transforms customers of different kinds, e.g. households, companies,

and public organizations, from being “pure” consumers to producers. With the

varying availability of solar energy and unstable consumption profiles, the

balance of electricity demand and supply becomes unbalanced. The electricity

prices become more volatile, subject to the changes of supply and demand.

Power system operations, managerial control, and new forming laws and reg-

ulations in regard to distributed renewable electricity require an enhanced un-

derstanding of market logic. In summary, it is of interest to explore how Swe-

den's adoption of solar and other renewable energy sources changes the market

- a change associated with smart cities that incorporate multiple technologies

and services - involving a revised understanding of actors' roles, resources,

and business logic.

A co-evolving set of economic and technical developments have built suf-

ficient momentum for solar PV technology to become accepted as an estab-

lished part of the energy provision system. Extensive efforts are being made

to explore the role of solar PV technologies in the energy transition and smart

city transformation. It is usually associated with the implementation of tech-

nology in order to solve urban problems with a holistic and interdisciplinary

approach. Grubler et al. defined energy transition as "a change in the state of

the energy system" rather than a change in individual energy technologies or

fuel sources (Grubler, Wilson and Nemet, 2016). Similarly, smart city trans-

formation needs to be considered from a broader economic, environmental,

technological, and social perspective to address urban sustainability issues and

improve the quality of urban services (Ekman and Lindh, 2013) (Giourka et

al., 2020). The natural science in complex energy systems draws on wider

insights across different disciplines. In particular, the techno-economic frame-

work illustrates this point clearly. The techno-economic analysis is rooted in

energy systems analysis and various domains of economics, and it focuses on

energy systems and energy deliver services valued by people (Cherp et al.,

2018). In the theories and models of techno-economic analysis, the physical

production and consumption of energy and the trade of services in energy

markets are intertwined.

3

The concept of "smart" refers to a forward-looking approach (Chourabi et

al., 2012), where smart cities are composed of a smart economy, people, gov-

ernance, mobility, and environment. It is reasonable to assume that smart cit-

ies may also need to include the concept of "smart markets", which should be

understood beyond traditional neoclassical economics. Sweden has surpassed

the environmental targets set by the EU with an explicit goal of reaching 60%

of renewable energy systems by 2030. The adoption of renewable energies,

such as solar PV power, wind power, hydropower, is closely related to the

initiatives and growth of smart cities. On the other hand, the introduction of

renewable energy brings transitional challenges and new possibilities to the

existing electricity market in Sweden. It alters the roles of traditional market

actors as well as business models. For example, the distributed solar PV ap-

plication transforms customers of different kinds, e.g. households, companies,

and public organizations, from being “pure” consumers to producers. With the

varying availability of solar energy and unstable consumption profiles, the

balance of electricity demand and supply becomes unbalanced. The electricity

prices become more volatile, subject to the changes of supply and demand.

Power system operations, managerial control, and new forming laws and reg-

ulations in regard to distributed renewable electricity require an enhanced un-

derstanding of market logic. In summary, it is of interest to explore how Swe-

den's adoption of solar and other renewable energy sources changes the market

- a change associated with smart cities that incorporate multiple technologies

and services - involving a revised understanding of actors' roles, resources,

and business logic.

A co-evolving set of economic and technical developments have built suf-

ficient momentum for solar PV technology to become accepted as an estab-

lished part of the energy provision system. Extensive efforts are being made

to explore the role of solar PV technologies in the energy transition and smart

city transformation. It is usually associated with the implementation of tech-

nology in order to solve urban problems with a holistic and interdisciplinary

approach. Grubler et al. defined energy transition as "a change in the state of

the energy system" rather than a change in individual energy technologies or

fuel sources (Grubler, Wilson and Nemet, 2016). Similarly, smart city trans-

formation needs to be considered from a broader economic, environmental,

technological, and social perspective to address urban sustainability issues and

improve the quality of urban services (Ekman and Lindh, 2013) (Giourka et

al., 2020). The natural science in complex energy systems draws on wider

insights across different disciplines. In particular, the techno-economic frame-

work illustrates this point clearly. The techno-economic analysis is rooted in

energy systems analysis and various domains of economics, and it focuses on

energy systems and energy deliver services valued by people (Cherp et al.,

2018). In the theories and models of techno-economic analysis, the physical

production and consumption of energy and the trade of services in energy

markets are intertwined.

22

4

1.2 Research gaps and challenges

The advancement in solar panel technology, driven by the efficiency break-

throughs of cell materials from silicon to perovskites (Honrubia-Escribano et

al., 2018), has facilitated the adoption of solar PV systems at increasingly

competitive costs. Supportive policies from central and local governments -

including the solar PV national Feed-in Tariff (FiT) program, financial fund-

ing and services, demonstration projects, grid access support, and favorable

tax and other measures (Zhao, Wan and Yang, 2015) (Long, Cui and Li, 2017)

- have played an important role in stimulating and attracting investment in the

solar PV industry. However, there is a knowledge gap in large-scale and high-

resolution market analysis, especially in the context of subsidy-free in coun-

tries, for example in China, with considerable geographical differences. Re-

searchers point out that subsidies are an important tool used by governments

to intervene in specific industries, which is one of the main factors contrib-

uting to solar PV growth and overcapacity (Wang, Luo and Guo, 2014) (Zhang

et al., 2016). They also recommend a prudent reform of the subsidy policy for

solar companies. Is it time to withdraw subsidies? A large body of literature

has examined the technological progress and social equity of solar power as

an alternative to coal. However, little effort has been made in terms of eco-

nomic competitiveness and affordability at the large-scale city level in a sub-

sidy-free context. Thus, the first research question is: how do subsidy-free

solar PV projects in China compete economically with conventional coal-

fired generation? (RQ1).

The reduction and the elimination of subsidies in the near future would re-

shuffle China’s solar PV industry, so that only the most technologically ad-

vanced and economically competitive companies can thrive. For solar PV

companies that used to factor subsidies into their revenues, this creates a great

deal of uncertainty because investment and entrepreneurial behavior may

change in response to the policy changes. As a result, this necessitates the

investment evaluation of solar PV projects in China (Ming et al., 2014). Some

researchers have studied the cost-benefit of solar PV projects in China. How-

ever, they focus on the current status of individual solar PV projects/systems

(Wei, Liu and Yang, 2014), or on future scenarios for individual cities (Wang,

Zhou and Huo, 2014). The current study uses government subsidies as the

default parameter to evaluate investments (Wang, Zhou and Huo, 2014). Con-

sequently, their results lack comprehensive analysis of the investment poten-

tial of distributed solar PV projects in various Chinese cities, especially in a

zero-subsidy scenario. Based on this, the second research question is: how do

investment profits of unsubsidized solar PV projects differ from city to

city? (RQ2).

The expansion of small-scale solar applications enables flexible and alter-

native power to the consumers. However, the characters of being spatially dis-

tributed, highly variable, and less predictable thereby impose challenges to the

4

1.2 Research gaps and challenges

The advancement in solar panel technology, driven by the efficiency break-

throughs of cell materials from silicon to perovskites (Honrubia-Escribano et

al., 2018), has facilitated the adoption of solar PV systems at increasingly

competitive costs. Supportive policies from central and local governments -

including the solar PV national Feed-in Tariff (FiT) program, financial fund-

ing and services, demonstration projects, grid access support, and favorable

tax and other measures (Zhao, Wan and Yang, 2015) (Long, Cui and Li, 2017)

- have played an important role in stimulating and attracting investment in the

solar PV industry. However, there is a knowledge gap in large-scale and high-

resolution market analysis, especially in the context of subsidy-free in coun-

tries, for example in China, with considerable geographical differences. Re-

searchers point out that subsidies are an important tool used by governments

to intervene in specific industries, which is one of the main factors contrib-

uting to solar PV growth and overcapacity (Wang, Luo and Guo, 2014) (Zhang

et al., 2016). They also recommend a prudent reform of the subsidy policy for

solar companies. Is it time to withdraw subsidies? A large body of literature

has examined the technological progress and social equity of solar power as

an alternative to coal. However, little effort has been made in terms of eco-

nomic competitiveness and affordability at the large-scale city level in a sub-

sidy-free context. Thus, the first research question is: how do subsidy-free

solar PV projects in China compete economically with conventional coal-

fired generation? (RQ1).

The reduction and the elimination of subsidies in the near future would re-

shuffle China’s solar PV industry, so that only the most technologically ad-

vanced and economically competitive companies can thrive. For solar PV

companies that used to factor subsidies into their revenues, this creates a great

deal of uncertainty because investment and entrepreneurial behavior may

change in response to the policy changes. As a result, this necessitates the

investment evaluation of solar PV projects in China (Ming et al., 2014). Some

researchers have studied the cost-benefit of solar PV projects in China. How-

ever, they focus on the current status of individual solar PV projects/systems

(Wei, Liu and Yang, 2014), or on future scenarios for individual cities (Wang,

Zhou and Huo, 2014). The current study uses government subsidies as the

default parameter to evaluate investments (Wang, Zhou and Huo, 2014). Con-

sequently, their results lack comprehensive analysis of the investment poten-

tial of distributed solar PV projects in various Chinese cities, especially in a

zero-subsidy scenario. Based on this, the second research question is: how do

investment profits of unsubsidized solar PV projects differ from city to

city? (RQ2).

The expansion of small-scale solar applications enables flexible and alter-

native power to the consumers. However, the characters of being spatially dis-

tributed, highly variable, and less predictable thereby impose challenges to the

23

5

current power grid operators, electricity producers, and consumers. The cus-

tomers use the power grid as a means to balance their own generation of solar

electricity and their own demands. So that the power grids become their last

resource (to purchase) when the generation is unavailable. Meanwhile, the ex-

cess electricity is expected to be delivered back to the power grids and be

financially compensated. Due to the intermittent nature of solar electricity

generation, it creates unpredictable supplies and therefore unstable demands

to the electricity producers. To meet those needs, the power grids have to up-

grade the architecture, adopt new technologies, and operate in new ways. Nev-

ertheless, the unbalance between demand and supply makes the electricity

prices volatile. Increasingly, new market actors are needed to address the chal-

lenges including the technical integrations, new regulations, and new business

models. This is incorporated in the smart city transformation in Swedish prac-

tices, where the physical, ICT, business, social, and policy infrastructure are

connected to leverage the collective intelligence of a city. By providing valu-

able power services at scale and breaking regulatory arbitrage, aggregators

can help provide the desired level of customer engagement, value-added ser-

vices, and viable levels of power market unbundling. However, the use of ag-

gregators has mostly been studied as a black box in a single or several energy

systems, and their concepts and practices have not yet been developed or dis-

cussed at a more macro level. Therefore, the following research question is

posed: how do the solar PV applications change the Swedish electricity

market and alter the actor roles in the smart city transformation? (RQ3).

A reasonable assessment of the potential of solar PV energy is essential for

not only policymaking and urban planning, but also the fore-mentioned grid

capacity enhancement. Energy policymakers use this information to determine

whether additional and new types of subsidies are needed. For the power grid

operators and electricity producers, the estimation of potential solar power is

important in forecasting their production, the available capacity of solar power

injected into the grid, accommodation of peak power, and power distribution

& transmission. But the detailed information deficit has become an unfavora-

ble factor to them. Up to date, there is no comprehensive assessment of the

geographical and technical potential analysis regarding solar PV applications

in the Swedish built environment. We also need necessary and sufficient in-

formation, especially scientific and technical information, as a solid basis for

developing evidence-based policies for appropriate investments of public

funds. Therefore, the fourth research question is: what is the potential avail-

ability of solar PV energy in the Swedish built environment? (RQ4).

5

current power grid operators, electricity producers, and consumers. The cus-

tomers use the power grid as a means to balance their own generation of solar

electricity and their own demands. So that the power grids become their last

resource (to purchase) when the generation is unavailable. Meanwhile, the ex-

cess electricity is expected to be delivered back to the power grids and be

financially compensated. Due to the intermittent nature of solar electricity

generation, it creates unpredictable supplies and therefore unstable demands

to the electricity producers. To meet those needs, the power grids have to up-

grade the architecture, adopt new technologies, and operate in new ways. Nev-

ertheless, the unbalance between demand and supply makes the electricity

prices volatile. Increasingly, new market actors are needed to address the chal-

lenges including the technical integrations, new regulations, and new business

models. This is incorporated in the smart city transformation in Swedish prac-

tices, where the physical, ICT, business, social, and policy infrastructure are

connected to leverage the collective intelligence of a city. By providing valu-

able power services at scale and breaking regulatory arbitrage, aggregators

can help provide the desired level of customer engagement, value-added ser-

vices, and viable levels of power market unbundling. However, the use of ag-

gregators has mostly been studied as a black box in a single or several energy

systems, and their concepts and practices have not yet been developed or dis-

cussed at a more macro level. Therefore, the following research question is

posed: how do the solar PV applications change the Swedish electricity

market and alter the actor roles in the smart city transformation? (RQ3).

A reasonable assessment of the potential of solar PV energy is essential for

not only policymaking and urban planning, but also the fore-mentioned grid

capacity enhancement. Energy policymakers use this information to determine

whether additional and new types of subsidies are needed. For the power grid

operators and electricity producers, the estimation of potential solar power is

important in forecasting their production, the available capacity of solar power

injected into the grid, accommodation of peak power, and power distribution

& transmission. But the detailed information deficit has become an unfavora-

ble factor to them. Up to date, there is no comprehensive assessment of the

geographical and technical potential analysis regarding solar PV applications

in the Swedish built environment. We also need necessary and sufficient in-

formation, especially scientific and technical information, as a solid basis for

developing evidence-based policies for appropriate investments of public

funds. Therefore, the fourth research question is: what is the potential avail-

ability of solar PV energy in the Swedish built environment? (RQ4).

24

6

Figure 1. Schematic diagram showing the relationship between the research topics

and the papers.

Figure 1 is a schematic representation of the relationship between the re-

search questions and the papers. Associated with these research questions is

the need for increased commitment to interdisciplinary research with a holistic

perspective that can enable technological and policy interventions.

1.3 Scope and objectives

The overall objective of this thesis is to assess the economic feasibility and

potential of solar PV applications and to explore the new market actor and its

services in the era of energy transition. Corresponding to the raised research

questions, the specific goals include:

• investigating the economical competitivity of unsubsidized solar PV

systems at the city-level;

• estimating the investment profitability of unsubsidized solar PV sys-

tems at the city-level;

• analyzing the new emerging actor and new services brought by solar

energy in smart city transformation;

• evaluating potential geographical and technical availability and sub-

sidy feasibility of solar PV systems.

1.4 Thesis contributions

This thesis is unique in that it takes an interdisciplinary approach that inte-

grates the analysis of technical systems, economic feasibility, and policy in-

terventions. In this way, it goes further than conducting research based on only

6

Figure 1. Schematic diagram showing the relationship between the research topics

and the papers.

Figure 1 is a schematic representation of the relationship between the re-

search questions and the papers. Associated with these research questions is

the need for increased commitment to interdisciplinary research with a holistic

perspective that can enable technological and policy interventions.

1.3 Scope and objectives

The overall objective of this thesis is to assess the economic feasibility and

potential of solar PV applications and to explore the new market actor and its

services in the era of energy transition. Corresponding to the raised research

questions, the specific goals include:

• investigating the economical competitivity of unsubsidized solar PV

systems at the city-level;

• estimating the investment profitability of unsubsidized solar PV sys-

tems at the city-level;

• analyzing the new emerging actor and new services brought by solar

energy in smart city transformation;

• evaluating potential geographical and technical availability and sub-

sidy feasibility of solar PV systems.

1.4 Thesis contributions

This thesis is unique in that it takes an interdisciplinary approach that inte-

grates the analysis of technical systems, economic feasibility, and policy in-

terventions. In this way, it goes further than conducting research based on only

25

7

one theoretical framework and actually contributes to the acquisition of new

knowledge that cannot be achieved using a non-interdisciplinary approach.

Corresponding to the appended papers, the main contributions of this thesis

consist of:

• developing an integrated framework between technical, economic,

and social components of solar PV energy;

• providing a detailed economic analysis with high-resolution data for

Chinese cities, and elaborating of an existing model (LCOE) to a new

model (LPOE) which can be universally used in the other research

works;

• proposing two new indicators and their calculation to measure the grid

parity, which can be universally used in the other research works;

• proposing the concept of a new market actor (aggregator) and its ser-

vices that smart cities need to incorporate;

• offering a new theoretical explanation of how smart city transfor-

mation changes the market logic and thus the prior of resource inte-

gration and actor roles;

• developing a comprehensive approach to assess the technical and ge-

ographical potential of roof-mounted solar applications in Sweden,

and revealing the availabilities to help policymaking, electricity

scheduling, and grid enhancement.

Especially, the Paper I was the first comprehensive study of the feasibility

of unsubsidized solar PV systems at the city-level. It theoretically drove the

solar PV industry to the tipping point, representing a key step in the expansion

of renewable energy sources. This grid parity was a critical milestone made

even more remarkable by the fact that the solar electricity costs estimated did

not include subsidies. The published results in 2019 were reported and shared

by over sixty international media, including CNN, Independent, Daily Mail,

Xinhua News, and other local media in China. Meanwhile, the National De-

velopment and Reform Commission (NDRC) and National Energy Admin-

istration (NEA) approved the first batch of 15 GW subsidy-free grid-parity

solar projects in 2019 and over 33 GW in 2020. Many studies started to em-

ploy the concept of “grid parity” in different scenarios that brought this topic

to a deeper level. The era of grid-parity of solar power has begun since then.

1.5 Thesis structure

This thesis is written based on the appended papers and contains the following

chapters:

7

one theoretical framework and actually contributes to the acquisition of new

knowledge that cannot be achieved using a non-interdisciplinary approach.

Corresponding to the appended papers, the main contributions of this thesis

consist of:

• developing an integrated framework between technical, economic,

and social components of solar PV energy;

• providing a detailed economic analysis with high-resolution data for

Chinese cities, and elaborating of an existing model (LCOE) to a new

model (LPOE) which can be universally used in the other research

works;

• proposing two new indicators and their calculation to measure the grid

parity, which can be universally used in the other research works;

• proposing the concept of a new market actor (aggregator) and its ser-

vices that smart cities need to incorporate;

• offering a new theoretical explanation of how smart city transfor-

mation changes the market logic and thus the prior of resource inte-

gration and actor roles;

• developing a comprehensive approach to assess the technical and ge-

ographical potential of roof-mounted solar applications in Sweden,

and revealing the availabilities to help policymaking, electricity

scheduling, and grid enhancement.

Especially, the Paper I was the first comprehensive study of the feasibility

of unsubsidized solar PV systems at the city-level. It theoretically drove the

solar PV industry to the tipping point, representing a key step in the expansion

of renewable energy sources. This grid parity was a critical milestone made

even more remarkable by the fact that the solar electricity costs estimated did

not include subsidies. The published results in 2019 were reported and shared

by over sixty international media, including CNN, Independent, Daily Mail,

Xinhua News, and other local media in China. Meanwhile, the National De-

velopment and Reform Commission (NDRC) and National Energy Admin-

istration (NEA) approved the first batch of 15 GW subsidy-free grid-parity

solar projects in 2019 and over 33 GW in 2020. Many studies started to em-

ploy the concept of “grid parity” in different scenarios that brought this topic

to a deeper level. The era of grid-parity of solar power has begun since then.

1.5 Thesis structure

This thesis is written based on the appended papers and contains the following

chapters:

26

8

Chapter 1 Introduction

To introduce the thesis background, research gaps and

challenges, scope and objectives, thesis contributions, and

thesis outline.

Chapter 2 Previous studies

To review the situation of the current maturity of solar PV

energy, the flexibility challenge, the Service-Dominant

logic, and the potential availability.

Chapter 3 Methods

To describe the methods, theory and models adopted to

answer the research questions.

Chapter 4 Results

To present the main results achieved in the appended pa-

pers of this thesis.

Chapter 5 Discussions and policy implications

To highlight the main discussion points and policy impli-

cations.

Chapter 6 Conclusions

To draw the main findings of this doctoral thesis.

Chapter 7 Future work

To define the limitations of this doctoral thesis and to in-

troduce the potential future research areas.

8

Chapter 1 Introduction

To introduce the thesis background, research gaps and

challenges, scope and objectives, thesis contributions, and

thesis outline.

Chapter 2 Previous studies

To review the situation of the current maturity of solar PV

energy, the flexibility challenge, the Service-Dominant

logic, and the potential availability.

Chapter 3 Methods

To describe the methods, theory and models adopted to

answer the research questions.

Chapter 4 Results

To present the main results achieved in the appended pa-

pers of this thesis.

Chapter 5 Discussions and policy implications

To highlight the main discussion points and policy impli-

cations.

Chapter 6 Conclusions

To draw the main findings of this doctoral thesis.

Chapter 7 Future work

To define the limitations of this doctoral thesis and to in-

troduce the potential future research areas.

27

9

2 Previous studies

This chapter provides a summary of previous studies conducted on the follow-

ing topics: subsidy and economics in solar PV industry, flexibility in electric-

ity systems, the new actor and new services, Service-Dominant logic, and po-

tential availability analysis.

2.1 Subsidy and economics in solar PV industry

2.1.1 Policy and subsidy in Chinese solar PV industry

Policy support - including grid support, operation specification, and operation

supervision from central and local governments - is an important policy in-

strument available to governments for supporting renewable energy indus-

tries. Policy has played a critical role in China’s stunning achievements in the

fiercely competitive PV markets. There are, in total, over 300 renewable en-

ergy and PV industry policies that have been implemented from 1994 to 2020,

both nationally and regionally. Among them, there have been over 110 pieces

from the central government that specifically target the PV industry (Long,

Cui and Li, 2017).

In 2009, China launched two national solar subsidy programs: the Building

Integrated Photovoltaic (BiPV) (also known as the Solar Rooftop Program)

and the Golden Sun Demonstration Program. These programs were a clear

indication of China's commitment to supporting PV applications. In the first

solar subsidy program, BiPV subsidies covered 30% - 50% of the cost of the

PV system. And in the second program, a 50% investment subsidy was given

to solar power plants and related transmission and distribution systems that

were connected to the grid. In addition, a nationwide solar PV subsidy pro-

gram was first introduced in July 2011 shortly after these two programs, and

has been in place for nearly a decade now. It makes a significant contribution

to stimulating and attracting investment in the solar PV industry. Since then,

policy incentives have continued to be introduced to stimulate the develop-

ment of China's solar PV industry (Figure 2).

9

2 Previous studies

This chapter provides a summary of previous studies conducted on the follow-

ing topics: subsidy and economics in solar PV industry, flexibility in electric-

ity systems, the new actor and new services, Service-Dominant logic, and po-

tential availability analysis.

2.1 Subsidy and economics in solar PV industry

2.1.1 Policy and subsidy in Chinese solar PV industry

Policy support - including grid support, operation specification, and operation

supervision from central and local governments - is an important policy in-

strument available to governments for supporting renewable energy indus-

tries. Policy has played a critical role in China’s stunning achievements in the

fiercely competitive PV markets. There are, in total, over 300 renewable en-

ergy and PV industry policies that have been implemented from 1994 to 2020,

both nationally and regionally. Among them, there have been over 110 pieces

from the central government that specifically target the PV industry (Long,

Cui and Li, 2017).

In 2009, China launched two national solar subsidy programs: the Building

Integrated Photovoltaic (BiPV) (also known as the Solar Rooftop Program)

and the Golden Sun Demonstration Program. These programs were a clear

indication of China's commitment to supporting PV applications. In the first

solar subsidy program, BiPV subsidies covered 30% - 50% of the cost of the

PV system. And in the second program, a 50% investment subsidy was given

to solar power plants and related transmission and distribution systems that

were connected to the grid. In addition, a nationwide solar PV subsidy pro-

gram was first introduced in July 2011 shortly after these two programs, and

has been in place for nearly a decade now. It makes a significant contribution

to stimulating and attracting investment in the solar PV industry. Since then,

policy incentives have continued to be introduced to stimulate the develop-

ment of China's solar PV industry (Figure 2).

28

10

Figure 2. Historical policies and installed capacity of China's solar PV industry.

Solar PV is one of the more rapidly growing industries in China. China’s

13th Five Year Plan for Renewable Energy Development (2016 - 2020) set a

105-GW target for new solar PV capacity by 2020. This target was met almost

three years earlier, by the end of 2017. Cumulative installed capacity experi-

enced a more than 200-fold increase over the 2000 level and China grew into

world’s largest market. Subsidy is a major instrument of government expendi-

ture policy (Dong, Zhou and Li, 2021). It has sometimes been argued that the

concept of a subsidy is just too elusive. In the most general sense, a subsidy

can be defined as any government aid. In this study, we discuss the direct

government payments to PV system owners. In which, the “subsidy” refers to

the government financial support that raises the owners’ incomes beyond

those that would be earned without this intervention (Dong, Zhou and Li,

2021). We exclude other types of subsidies, such as credit subsidies, tax sub-

sidies, equity subsidies, in-kind subsidies, procurement subsidies, regulatory

subsidies, and so on.

2.1.2 Subsidy and economic analysis in the literatures

Illuminating studies were conducted in subsidy analysis in Chinese solar PV

projects with mainly four perspectives: 1) the evolution of subsidies, 2) the

effectiveness of subsidies, 3) the optimal design of subsidies, and 4) the exit-

ing occasion of subsidies.

Some studies used mostly qualitative methods to review the evolution of

subsidies on the PV industry, the type of subsidies, and learning from policy

implementation experiences. Zhang et al. (Zhang, Qin and Wei, 2019) sum-

marized and compared China’s distributed energy policies at national,

10

Figure 2. Historical policies and installed capacity of China's solar PV industry.

Solar PV is one of the more rapidly growing industries in China. China’s

13th Five Year Plan for Renewable Energy Development (2016 - 2020) set a

105-GW target for new solar PV capacity by 2020. This target was met almost

three years earlier, by the end of 2017. Cumulative installed capacity experi-

enced a more than 200-fold increase over the 2000 level and China grew into

world’s largest market. Subsidy is a major instrument of government expendi-

ture policy (Dong, Zhou and Li, 2021). It has sometimes been argued that the

concept of a subsidy is just too elusive. In the most general sense, a subsidy

can be defined as any government aid. In this study, we discuss the direct

government payments to PV system owners. In which, the “subsidy” refers to

the government financial support that raises the owners’ incomes beyond

those that would be earned without this intervention (Dong, Zhou and Li,

2021). We exclude other types of subsidies, such as credit subsidies, tax sub-

sidies, equity subsidies, in-kind subsidies, procurement subsidies, regulatory

subsidies, and so on.

2.1.2 Subsidy and economic analysis in the literatures

Illuminating studies were conducted in subsidy analysis in Chinese solar PV

projects with mainly four perspectives: 1) the evolution of subsidies, 2) the

effectiveness of subsidies, 3) the optimal design of subsidies, and 4) the exit-

ing occasion of subsidies.

Some studies used mostly qualitative methods to review the evolution of

subsidies on the PV industry, the type of subsidies, and learning from policy

implementation experiences. Zhang et al. (Zhang, Qin and Wei, 2019) sum-

marized and compared China’s distributed energy policies at national,

29

11

provincial, and municipal levels, from 1989 to 2016. They pointed out the

particularly abundant numbers and types regarding solar energy. They also

highlighted the challenges and proposed recommendations, accordingly.

Zhang et al. (Zhang, Andrews-Speed and Ji, 2014) conducted a similar study

by examining four phases of Chinese solar PV policy from the mid-1990s to

2013 and proposed a different combination of policy options for each phase.

Zhi et al. (Zhi et al., 2014) also examined the development of China's PV in-

dustry policy system around 1980 to 2013 and compared China's approach

with that of the United States, Germany, and Japan in terms of supply-side and

demand-side policies. The subsidies were integrated as one of the policy in-

struments. Zhou et al. (Zhou, Chong and Wang, 2020) applied a two-dimen-

sional framework (basic policy instrument (x-dimension) and project lifecycle

(y-dimension)) to analyze PV power application policies by using content

analysis. Similar to (Zhi et al., 2014) and (Zhang, Andrews-Speed and Ji,

2014), the subsidies were recognized as the main policy instrument for stim-

ulating solar PV industry; whereas, the subsidy itself was not comprehensively

examined. Zhao et al. (Zhao, Zeng and Zhao, 2015) reviewed and classified

three modes of subsidies in Chinese solar PV industry, historically. They also

evaluated the economic performance of a specific distributed PV system in

five Chinese cities and showed a good level of efficiency of subsidies. These

types of research were featured with a comprehensive historical review of sub-

sidies on Chinese soar PV industry in descriptive ways, covering the longitu-

dinal evolution, the classification, the challenges, and the implications. They

mostly employed a theoretical framework in framing a qualitative study.

Significant number of literatures discussed the effectiveness of subsidies

on solar projects. Torani et al. (Torani, Rausser and Zilberman, 2016) devel-

oped a stochastic dynamic model of solar PV adoption under two sources of

uncertainty. The authors aimed to investigate the effectiveness of four policies

on accelerating PV adoption. The results indicated that subsidies became in-

creasingly ineffective as the rate of technological change increases. Wang et

al. (Wang et al. 2016) investigated the effect of downstream FiT policies using

quarterly data of solar PV listed companies from 2009 to 2015. Their results

showed that FiT policy had a significant positive effect on inventory turnover

of listed PV companies, midstream companies, and private companies. This

paper confirmed the effectiveness of the FiT policy on the sustainable devel-

opment of the Chinese solar PV industry. Such a positive correlation can also

be found in studies, such as Peng & Liu (Peng and Liu, 2018), Lin & Luan

(Lin and Luan, 2020), Chen et al.(Chen, 2019), He et al. (He et al. 2018), Xu

et al. (Xu et al., 2020), and Jia et al. (Jia et al., 2020). Studies of (Wang et al.,

2016), (Peng and Liu, 2018), (Lin and Luan, 2020), and (Chen, 2019) all em-

ployed regression models to examine the government subsidy with the perfor-

mance of the Chinese PV industry (inventory turnover, entrepreneurial com-

panies' growth, innovation, and corporate value, respectively). Studies of (He

et al., 2018), (Xu et al., 2020), and (Jia et al., 2020) used different quantitative

11

provincial, and municipal levels, from 1989 to 2016. They pointed out the

particularly abundant numbers and types regarding solar energy. They also

highlighted the challenges and proposed recommendations, accordingly.

Zhang et al. (Zhang, Andrews-Speed and Ji, 2014) conducted a similar study

by examining four phases of Chinese solar PV policy from the mid-1990s to

2013 and proposed a different combination of policy options for each phase.

Zhi et al. (Zhi et al., 2014) also examined the development of China's PV in-

dustry policy system around 1980 to 2013 and compared China's approach

with that of the United States, Germany, and Japan in terms of supply-side and

demand-side policies. The subsidies were integrated as one of the policy in-

struments. Zhou et al. (Zhou, Chong and Wang, 2020) applied a two-dimen-

sional framework (basic policy instrument (x-dimension) and project lifecycle

(y-dimension)) to analyze PV power application policies by using content

analysis. Similar to (Zhi et al., 2014) and (Zhang, Andrews-Speed and Ji,

2014), the subsidies were recognized as the main policy instrument for stim-

ulating solar PV industry; whereas, the subsidy itself was not comprehensively

examined. Zhao et al. (Zhao, Zeng and Zhao, 2015) reviewed and classified

three modes of subsidies in Chinese solar PV industry, historically. They also

evaluated the economic performance of a specific distributed PV system in

five Chinese cities and showed a good level of efficiency of subsidies. These

types of research were featured with a comprehensive historical review of sub-

sidies on Chinese soar PV industry in descriptive ways, covering the longitu-

dinal evolution, the classification, the challenges, and the implications. They

mostly employed a theoretical framework in framing a qualitative study.

Significant number of literatures discussed the effectiveness of subsidies

on solar projects. Torani et al. (Torani, Rausser and Zilberman, 2016) devel-

oped a stochastic dynamic model of solar PV adoption under two sources of

uncertainty. The authors aimed to investigate the effectiveness of four policies

on accelerating PV adoption. The results indicated that subsidies became in-

creasingly ineffective as the rate of technological change increases. Wang et

al. (Wang et al. 2016) investigated the effect of downstream FiT policies using

quarterly data of solar PV listed companies from 2009 to 2015. Their results

showed that FiT policy had a significant positive effect on inventory turnover

of listed PV companies, midstream companies, and private companies. This

paper confirmed the effectiveness of the FiT policy on the sustainable devel-

opment of the Chinese solar PV industry. Such a positive correlation can also

be found in studies, such as Peng & Liu (Peng and Liu, 2018), Lin & Luan

(Lin and Luan, 2020), Chen et al.(Chen, 2019), He et al. (He et al. 2018), Xu

et al. (Xu et al., 2020), and Jia et al. (Jia et al., 2020). Studies of (Wang et al.,

2016), (Peng and Liu, 2018), (Lin and Luan, 2020), and (Chen, 2019) all em-

ployed regression models to examine the government subsidy with the perfor-

mance of the Chinese PV industry (inventory turnover, entrepreneurial com-

panies' growth, innovation, and corporate value, respectively). Studies of (He

et al., 2018), (Xu et al., 2020), and (Jia et al., 2020) used different quantitative

30

12

methods, such as combination of economic cost & environmental benefit sim-

ulation and regression analysis, dynamic game model, and techno-economic

evaluation. These papers reported different, both positive and negative, im-

pacts in different subsidy scenarios. Some studies, such as Yu et al. (Yu et al.,

2016) reported a significant crowding-out effect of government subsidies on

firms' Research & Development investment behavior. Shen & Luo (Shen and

Luo, 2015) also showed the subsidies may have some negative effect on re-

newable energy of different types. There was no simple answer regarding the

effect of government subsidies. These quantitative analysis methods were of-

ten applied with scenarios.

Some studies attempted to design an optimal scale of subsidies. Research-

ers, such as Jeon et al. (Jeon, Lee and Shin, 2015), Zhang et al. (Zhang et al.,

2016), employed system dynamics with real option models to optimize finan-

cial subsidies in Korean and China. An early study (in 2010) by Rigter & Vid-

ican (Rigter and Vidican, 2010) calculated the theoretically optimal FiT for

Chinese small-scale commercial and residential PV installations. If comparing

with the real FiTs from 2010 to 2019, this study significantly overestimated

the level of FiTs. Some studies revealed that subsidies led an overcapacity of

solar companies, such as Zhang et al. (Zhang et al., 2016). This study revealed

that even that even if subsidies fell into relatively effective intervals, they still

exacerbated the risk of overcapacity. Therefore, the results conducted by

Chong et al. (Chong, Zhou and Wang, 2019) showed that the cancellation of

subsidies would have little impacts on the development of distributed renew-

able energy power generation, such as solar PV power. Similar implications

that the gradual cancellation of solar subsidies were made in (Zhou, Chong

and Wang, 2020), (Zhao, Zeng and Zhao, 2015) and (Xiong and Yang, 2016).

A considerable number of techno-economic and policy studies have been

published. As a result, researchers have developed a well-established toolbox

of techno-economic indicators that guide the decisions of profit-maximizing

firms and have also been widely applied to PV systems (Sommerfeldt and

Madani, 2017). The analysis framework always includes 1) the system com-

ponents, which are the basic physical components of the PV system and the

building (if applicable), 2) the economic components, which include the costs

of the PV system and electricity bill (if applicable), and benefits of exporting

electricity to the power grid. The technical analysis of PV systems includes

some sub-models of the climate and irradiance, PV system, electric loads, and

so on. The economic analysis representing by the calculation of costs and ben-

efits, includes wide range of indicators, such as Levelized Cost of Electricity

(LCOE), Net Present Value (NPV), Internal Rate of Return (IRR), Discounted

Payback Period (DPBP) and so on. The literature reviewed 25 indicators

which deviated slightly (Sommerfeldt and Madani, 2017). Most of the availa-

ble studies covered all costs and benefits in detail, especially regarding subsi-

dies. However, as markets move towards de-subsidization, the real economic

12

methods, such as combination of economic cost & environmental benefit sim-

ulation and regression analysis, dynamic game model, and techno-economic

evaluation. These papers reported different, both positive and negative, im-

pacts in different subsidy scenarios. Some studies, such as Yu et al. (Yu et al.,

2016) reported a significant crowding-out effect of government subsidies on

firms' Research & Development investment behavior. Shen & Luo (Shen and

Luo, 2015) also showed the subsidies may have some negative effect on re-

newable energy of different types. There was no simple answer regarding the

effect of government subsidies. These quantitative analysis methods were of-

ten applied with scenarios.

Some studies attempted to design an optimal scale of subsidies. Research-

ers, such as Jeon et al. (Jeon, Lee and Shin, 2015), Zhang et al. (Zhang et al.,

2016), employed system dynamics with real option models to optimize finan-

cial subsidies in Korean and China. An early study (in 2010) by Rigter & Vid-

ican (Rigter and Vidican, 2010) calculated the theoretically optimal FiT for

Chinese small-scale commercial and residential PV installations. If comparing

with the real FiTs from 2010 to 2019, this study significantly overestimated

the level of FiTs. Some studies revealed that subsidies led an overcapacity of

solar companies, such as Zhang et al. (Zhang et al., 2016). This study revealed

that even that even if subsidies fell into relatively effective intervals, they still

exacerbated the risk of overcapacity. Therefore, the results conducted by

Chong et al. (Chong, Zhou and Wang, 2019) showed that the cancellation of

subsidies would have little impacts on the development of distributed renew-

able energy power generation, such as solar PV power. Similar implications

that the gradual cancellation of solar subsidies were made in (Zhou, Chong

and Wang, 2020), (Zhao, Zeng and Zhao, 2015) and (Xiong and Yang, 2016).

A considerable number of techno-economic and policy studies have been

published. As a result, researchers have developed a well-established toolbox

of techno-economic indicators that guide the decisions of profit-maximizing

firms and have also been widely applied to PV systems (Sommerfeldt and

Madani, 2017). The analysis framework always includes 1) the system com-

ponents, which are the basic physical components of the PV system and the

building (if applicable), 2) the economic components, which include the costs

of the PV system and electricity bill (if applicable), and benefits of exporting

electricity to the power grid. The technical analysis of PV systems includes

some sub-models of the climate and irradiance, PV system, electric loads, and

so on. The economic analysis representing by the calculation of costs and ben-

efits, includes wide range of indicators, such as Levelized Cost of Electricity

(LCOE), Net Present Value (NPV), Internal Rate of Return (IRR), Discounted

Payback Period (DPBP) and so on. The literature reviewed 25 indicators

which deviated slightly (Sommerfeldt and Madani, 2017). Most of the availa-

ble studies covered all costs and benefits in detail, especially regarding subsi-

dies. However, as markets move towards de-subsidization, the real economic

31

13

situation of solar PV projects with the consideration of zero subsidies should

be revealed.

2.2 Flexibility in the electricity system

The transition to renewable energy sources such as solar energy is not an easy

task. Technical problems arise in the aspects of power quality, voltage stabil-

ity, frequency, and congestion (Johansson, Vendel and Nuur, 2020), due to the

increasing levels of solar power generation and its intermittency. The interests

and publications on this topic have been surging. In the face of the technical

complexity, researchers have come to a consensus that the “flexibility” capac-

ity of the power system is able to help integrate the intermittent nature of re-

newable energy resources (Ma et al. 2013) (Emmanuel et al., 2020). In the

literal sense, the term “flexibility” describes the ability to respond to change.

In the earlier and many current studies, the term “flexibility” was used to de-

scribe “the ability of a power system to cope with variability and uncertainty

in both generation and demand, while maintaining a satisfactory level of reli-

ability at a reasonable cost, over different time horizons” (Ma et al., 2013).

Wide-spread deployment of renewable energy sources have led to highly fluc-

tuating and neither perfectly predictable nor fully controllable power market.

In more recent studies, the perspectives of institutional frameworks and mar-

ket designs were integrated (Emmanuel et al., 2020).

A fairly large body of research on providing physical flexibility resources

focused on solving the technical problems on generation-side flexibility re-

sources, demand-side flexibility resources, energy storage flexibility re-

sources, and grid flexibility resources.

The possibility to modify generation injections according to signals (e.g.,

price signals or other types of signals) in required time and duration is a means

to provide generation-side flexibility services. This can be obtained in the

form of dynamically fast responding conventional flexible power plants, such

as gas- or oil-fueled turbines or rather flexible modern coal-fired power plants

(Ulbig and Andersson, 2015), or unit commitment and economic dispatch in

volatile renewable power generations (Wang, Shahidehpour and Li, 2008).

Chen et al. (Chen et al., 2015) proposed a linear centralized dispatch model to

balance power and heat demands in multiple areas in Northern China, at mul-

tiple time periods. This was a representative study in providing generation-

side flexibility resources. In similar studies, stochastic optimization was ex-

tensively used, which explicitly incorporated uncertainty in the decision pro-

cess (Pappala et al., 2009) (Zhang et al., 2011). However, relying on genera-

tors alone to provide flexibility is expensive, as it often involves using more

flexible but less efficient generating units to produce energy, or operating

thermal plants at loads below their maximum efficiency (Carreiro, Jorge and

Antunes, 2017).

13

situation of solar PV projects with the consideration of zero subsidies should

be revealed.

2.2 Flexibility in the electricity system

The transition to renewable energy sources such as solar energy is not an easy

task. Technical problems arise in the aspects of power quality, voltage stabil-

ity, frequency, and congestion (Johansson, Vendel and Nuur, 2020), due to the

increasing levels of solar power generation and its intermittency. The interests

and publications on this topic have been surging. In the face of the technical

complexity, researchers have come to a consensus that the “flexibility” capac-

ity of the power system is able to help integrate the intermittent nature of re-

newable energy resources (Ma et al. 2013) (Emmanuel et al., 2020). In the

literal sense, the term “flexibility” describes the ability to respond to change.

In the earlier and many current studies, the term “flexibility” was used to de-

scribe “the ability of a power system to cope with variability and uncertainty

in both generation and demand, while maintaining a satisfactory level of reli-

ability at a reasonable cost, over different time horizons” (Ma et al., 2013).

Wide-spread deployment of renewable energy sources have led to highly fluc-

tuating and neither perfectly predictable nor fully controllable power market.

In more recent studies, the perspectives of institutional frameworks and mar-

ket designs were integrated (Emmanuel et al., 2020).

A fairly large body of research on providing physical flexibility resources

focused on solving the technical problems on generation-side flexibility re-

sources, demand-side flexibility resources, energy storage flexibility re-

sources, and grid flexibility resources.

The possibility to modify generation injections according to signals (e.g.,

price signals or other types of signals) in required time and duration is a means

to provide generation-side flexibility services. This can be obtained in the

form of dynamically fast responding conventional flexible power plants, such

as gas- or oil-fueled turbines or rather flexible modern coal-fired power plants

(Ulbig and Andersson, 2015), or unit commitment and economic dispatch in

volatile renewable power generations (Wang, Shahidehpour and Li, 2008).

Chen et al. (Chen et al., 2015) proposed a linear centralized dispatch model to

balance power and heat demands in multiple areas in Northern China, at mul-

tiple time periods. This was a representative study in providing generation-

side flexibility resources. In similar studies, stochastic optimization was ex-

tensively used, which explicitly incorporated uncertainty in the decision pro-

cess (Pappala et al., 2009) (Zhang et al., 2011). However, relying on genera-

tors alone to provide flexibility is expensive, as it often involves using more

flexible but less efficient generating units to produce energy, or operating

thermal plants at loads below their maximum efficiency (Carreiro, Jorge and

Antunes, 2017).

32

14

Demand-side flexibility is achieved by allowing the system operators to

control or provide price signals to various sources of electricity demands, in

order to reduce, increase, shift, or shave the portion of demands in the system

within a specific duration. The types of demand include power-to-heat, power-

to-hydrogen, electric-vehicle charging, smart appliances and industrial de-

mand response. Razmara et al. (Razmara et al., 2017) considered two common

building-integrated configuration topologies for demand-side energy storage

systems and renewable energy. The authors designed a real-time optimization

framework to a commercial building in Michigan, US. Their framework,

Model Predictive Control, utilized the building's inherent thermal mass stor-

age and energy storage systems as a means of providing demand-side flexibil-

ity. This framework included different modeling, such as building thermal

model, PV and battery model, and distribution grid model. The results showed

it can significantly reduce the maximum load slope of the grid and prevent the

duck-curve problem, i.e. provide demand-side flexibility for the electric grid.

And it can further decrease the building operational cost. Such research can

also be found in Zhang et al. (Zhang et al. 2018), in which the authors used

the Mixed Integer Linear Programming to optimize the energy system under

given economic assumptions. The heat pump, electrical heater, battery and hot

water storage tank were integrated into the electricity and heat supply system

of a Swedish office building. The result showed such system could provide

great flexibility to the electrical grid. Helistö et al. (Helistö, Kiviluoma and

Holttinen, 2018) presented a potential role for thermal power generation in

future power systems with a high proportion of solar power, while integrating

different sources of demand-side flexibility, such as heat pumps and storage

in district heating, industrial and electric vehicle demand response. These

studies, Razmara et al. (Razmara et al., 2017), Zhang et al. (Zhang et al.,

2018), and Helistö et al. (Helistö, Kiviluoma and Holttinen, 2018) presented

how different technical solutions can be formed at demand-side to provide

power grid flexibility; meanwhile, the energy storage flexibility resources

were integrated. Much more efforts have been made on demand-side flexibil-

ity, such as Golmohamadi et al. (Golmohamadi et al., 2019). As presented

above, such studies tended to investigate the coupling of power systems with

various demand-side technologies, such as energy storage, heat pumps, dis-

trict heating, electric vehicles, hydrogen production, etc.

Lastly, some researchers studied the grid flexibility resources. Chen et al.

(Chen et al. 2018) investigated distribution-level power aggregation strategies

for transmission and distribution interactions. The authors proposed a method

to model and quantify the total power flexibility in an unbalanced distribution

system over time. Heleno et al. (Heleno et al., 2015) used Monte Carlo simu-

lation to estimate the range of active and reactive power flexibility at the

boundary nodes of the transmission and distribution system, considering the

available flexibility at the distribution level (e.g., with on-load tap-changer

transformers).

14

Demand-side flexibility is achieved by allowing the system operators to

control or provide price signals to various sources of electricity demands, in

order to reduce, increase, shift, or shave the portion of demands in the system

within a specific duration. The types of demand include power-to-heat, power-

to-hydrogen, electric-vehicle charging, smart appliances and industrial de-

mand response. Razmara et al. (Razmara et al., 2017) considered two common

building-integrated configuration topologies for demand-side energy storage

systems and renewable energy. The authors designed a real-time optimization

framework to a commercial building in Michigan, US. Their framework,

Model Predictive Control, utilized the building's inherent thermal mass stor-

age and energy storage systems as a means of providing demand-side flexibil-

ity. This framework included different modeling, such as building thermal

model, PV and battery model, and distribution grid model. The results showed

it can significantly reduce the maximum load slope of the grid and prevent the

duck-curve problem, i.e. provide demand-side flexibility for the electric grid.

And it can further decrease the building operational cost. Such research can

also be found in Zhang et al. (Zhang et al. 2018), in which the authors used

the Mixed Integer Linear Programming to optimize the energy system under

given economic assumptions. The heat pump, electrical heater, battery and hot

water storage tank were integrated into the electricity and heat supply system

of a Swedish office building. The result showed such system could provide

great flexibility to the electrical grid. Helistö et al. (Helistö, Kiviluoma and

Holttinen, 2018) presented a potential role for thermal power generation in

future power systems with a high proportion of solar power, while integrating

different sources of demand-side flexibility, such as heat pumps and storage

in district heating, industrial and electric vehicle demand response. These

studies, Razmara et al. (Razmara et al., 2017), Zhang et al. (Zhang et al.,

2018), and Helistö et al. (Helistö, Kiviluoma and Holttinen, 2018) presented

how different technical solutions can be formed at demand-side to provide

power grid flexibility; meanwhile, the energy storage flexibility resources

were integrated. Much more efforts have been made on demand-side flexibil-

ity, such as Golmohamadi et al. (Golmohamadi et al., 2019). As presented

above, such studies tended to investigate the coupling of power systems with

various demand-side technologies, such as energy storage, heat pumps, dis-

trict heating, electric vehicles, hydrogen production, etc.

Lastly, some researchers studied the grid flexibility resources. Chen et al.

(Chen et al. 2018) investigated distribution-level power aggregation strategies

for transmission and distribution interactions. The authors proposed a method

to model and quantify the total power flexibility in an unbalanced distribution

system over time. Heleno et al. (Heleno et al., 2015) used Monte Carlo simu-

lation to estimate the range of active and reactive power flexibility at the

boundary nodes of the transmission and distribution system, considering the

available flexibility at the distribution level (e.g., with on-load tap-changer

transformers).

33

15

More and more studies on power system flexibility have combined the

technical solutions with market designs. Mechanism flexibility resources refer

to the mechanism designs that can make full use of the physical flexibility

resources of the power system, including the design of the power market and

power system regulation mechanisms. In the power market, the mechanism

flexibility resources are transformed as effective price signals and quantity of

electricity determined by the suppliers and demanders (Kiyak and de Vries,

2017). Ma et al. (Ma et al., 2013) provided a systematic approach to evaluate

the flexibility level of both individual generators and the whole system by

proposing an index. They also introduced a new optimization algorithm (Unit

Construction and Commitment) to determine the optimal investment for addi-

tional flexible units. Based on this tool, the authors assessed the impact of the

market design on the profitability of providing flexibility. Fonteijn et al.

(Fonteijn et al., 2020) introduced a generic four-step approach to operational-

ize the flexibility needs of distribution system operator. The proposed method

was implemented on a Dutch’ H2020 InterFlex demonstrator. The implemen-

tation results showed that the proposed steps enabled distribution system op-

erators (DSOs) to predict congestion, assign a monetary value to flexibility,

and use that value to evaluate the flexibility offered through the flexibility

market of the studied case.

Overall, the research on providing physical flexibility resources focused on

solving the technical problems with different system components, using dif-

ferent mathematical models and simulation & optimization algorithms, apply-

ing in different cases and locations. Flexibility is system-specific, which

means that a "one-size-fits-all" approach cannot be applied to all power sys-

tems.

2.3 New market actor - aggregator

The use of ICT, smart devices, and autonomous controllers are integrated into

the entire energy system with advanced data management and two-way com-

munication means, from generation to end-user consumption. The progressive

implementation of these technologies is expected to improve the overall effi-

ciency, reliability, and flexibility of the grid. All stakeholders are increasingly

interested in evaluating and improving the response of their systems to the

new conditions brought about by the rapidly evolving power system. With the

opportunities and challenges presented by PV installations, a new actor has

emerged on the scene. The aggregator, also known as the intermediary or me-

diators/brokers (Gkatzikis, Koutsopoulos and Salonidis, 2013) or middleman

company (Carreiro, Jorge and Antunes, 2017) between users and the utility

operators, are coming into sight.

15

More and more studies on power system flexibility have combined the

technical solutions with market designs. Mechanism flexibility resources refer

to the mechanism designs that can make full use of the physical flexibility

resources of the power system, including the design of the power market and

power system regulation mechanisms. In the power market, the mechanism

flexibility resources are transformed as effective price signals and quantity of

electricity determined by the suppliers and demanders (Kiyak and de Vries,

2017). Ma et al. (Ma et al., 2013) provided a systematic approach to evaluate

the flexibility level of both individual generators and the whole system by

proposing an index. They also introduced a new optimization algorithm (Unit

Construction and Commitment) to determine the optimal investment for addi-

tional flexible units. Based on this tool, the authors assessed the impact of the

market design on the profitability of providing flexibility. Fonteijn et al.

(Fonteijn et al., 2020) introduced a generic four-step approach to operational-

ize the flexibility needs of distribution system operator. The proposed method

was implemented on a Dutch’ H2020 InterFlex demonstrator. The implemen-

tation results showed that the proposed steps enabled distribution system op-

erators (DSOs) to predict congestion, assign a monetary value to flexibility,

and use that value to evaluate the flexibility offered through the flexibility

market of the studied case.

Overall, the research on providing physical flexibility resources focused on

solving the technical problems with different system components, using dif-

ferent mathematical models and simulation & optimization algorithms, apply-

ing in different cases and locations. Flexibility is system-specific, which

means that a "one-size-fits-all" approach cannot be applied to all power sys-

tems.

2.3 New market actor - aggregator

The use of ICT, smart devices, and autonomous controllers are integrated into

the entire energy system with advanced data management and two-way com-

munication means, from generation to end-user consumption. The progressive

implementation of these technologies is expected to improve the overall effi-

ciency, reliability, and flexibility of the grid. All stakeholders are increasingly

interested in evaluating and improving the response of their systems to the

new conditions brought about by the rapidly evolving power system. With the

opportunities and challenges presented by PV installations, a new actor has

emerged on the scene. The aggregator, also known as the intermediary or me-

diators/brokers (Gkatzikis, Koutsopoulos and Salonidis, 2013) or middleman

company (Carreiro, Jorge and Antunes, 2017) between users and the utility

operators, are coming into sight.

34

16

The concept of aggregator has received special attention from the European

Commission. Aggregators can provide flexibility services such as load shed-

ding and better load analysis to different power system entities (i.e., utilities,

DSOs, independent system operators, capacity markets, etc.) (Force,

2015).The aggregators can effectively integrate the distributed energies such

as solar PV energy, into electricity networks using ICT and industry

knowledge (Ponds et al., 2018). Such grid-oriented players as an entity can

provide new services to electricity markets and system operators by aggregat-

ing flexible distributed energy resources, including demand response, genera-

tion resources, and transmission and distribution networks (Di Somma,

Graditi and Siano, 2019).

The concept of aggregator was widely used in demand response (DR). The

aggregators accumulated and managed the electricity demands from end-users,

including residential, commercial, and industrial customers and helped reduce

imbalances by shifting flexible loads (Okur et al., 2019) (Rafique et al., 2019)

(Golmohamadi et al., 2019). Aggregators had the technology to perform DR

and were responsible for installing communication and control devices (i.e.,

smart meters) in end-users’ side. Since each aggregator represented a signifi-

cant amount of total demand in the DR market, it can negotiate more effec-

tively with the operator on behalf of the homeowner. The aggregator's role

was to make monthly payments to contracted end-users (mainly industrial) for

direct control of their equipment. As a result, they can turn off the user's en-

ergy-intensive equipment, such as air conditioners, for a short period of time

in case of emergency during peak demand (Gkatzikis, Koutsopoulos and

Salonidis, 2013).

More specifically, at the top level of the hierarchical DR market model,

utility operators provided monetary incentives for aggregators to provide DR

services. The goal was to minimize its own operating costs. In the middle tier,

aggregators provided demand response services to operators by providing ag-

gregated demand profiles, thereby minimizing the cost to the operator of sup-

porting that demand. Aggregators attempted to achieve this by providing mon-

etary incentives to end-users to change their demand patterns. Each aggrega-

tor's goal was to maximize its own net profit, i.e., the revenue it received from

the operator minus the compensation it provided to end-user subscribers. At a

lower level, the end-user negotiated with the aggregator to receive monetary

compensation to change its consumption pattern. The end-user weighed the

inconvenience of deviating from its preferred or customary usage pattern in

order to lower its electricity bill. Given compensation, the goal of each end-

user was to determine the best balanced consumption pattern to achieve this

tradeoff by maximizing the net benefit function (Gkatzikis, Koutsopoulos and

Salonidis, 2013). By managing the loads and optimally use the available en-

ergy resources for their customers, the aggregators could minimize the cost of

energy consumption and maximize the profit of the market participants

(Rafique et al., 2019) (Golmohamadi et al., 2019). For example, Deng et al.

16

The concept of aggregator has received special attention from the European

Commission. Aggregators can provide flexibility services such as load shed-

ding and better load analysis to different power system entities (i.e., utilities,

DSOs, independent system operators, capacity markets, etc.) (Force,

2015).The aggregators can effectively integrate the distributed energies such

as solar PV energy, into electricity networks using ICT and industry

knowledge (Ponds et al., 2018). Such grid-oriented players as an entity can

provide new services to electricity markets and system operators by aggregat-

ing flexible distributed energy resources, including demand response, genera-

tion resources, and transmission and distribution networks (Di Somma,

Graditi and Siano, 2019).

The concept of aggregator was widely used in demand response (DR). The

aggregators accumulated and managed the electricity demands from end-users,

including residential, commercial, and industrial customers and helped reduce

imbalances by shifting flexible loads (Okur et al., 2019) (Rafique et al., 2019)

(Golmohamadi et al., 2019). Aggregators had the technology to perform DR

and were responsible for installing communication and control devices (i.e.,

smart meters) in end-users’ side. Since each aggregator represented a signifi-

cant amount of total demand in the DR market, it can negotiate more effec-

tively with the operator on behalf of the homeowner. The aggregator's role

was to make monthly payments to contracted end-users (mainly industrial) for

direct control of their equipment. As a result, they can turn off the user's en-

ergy-intensive equipment, such as air conditioners, for a short period of time

in case of emergency during peak demand (Gkatzikis, Koutsopoulos and

Salonidis, 2013).

More specifically, at the top level of the hierarchical DR market model,

utility operators provided monetary incentives for aggregators to provide DR

services. The goal was to minimize its own operating costs. In the middle tier,

aggregators provided demand response services to operators by providing ag-

gregated demand profiles, thereby minimizing the cost to the operator of sup-

porting that demand. Aggregators attempted to achieve this by providing mon-

etary incentives to end-users to change their demand patterns. Each aggrega-

tor's goal was to maximize its own net profit, i.e., the revenue it received from

the operator minus the compensation it provided to end-user subscribers. At a

lower level, the end-user negotiated with the aggregator to receive monetary

compensation to change its consumption pattern. The end-user weighed the

inconvenience of deviating from its preferred or customary usage pattern in

order to lower its electricity bill. Given compensation, the goal of each end-

user was to determine the best balanced consumption pattern to achieve this

tradeoff by maximizing the net benefit function (Gkatzikis, Koutsopoulos and

Salonidis, 2013). By managing the loads and optimally use the available en-

ergy resources for their customers, the aggregators could minimize the cost of

energy consumption and maximize the profit of the market participants

(Rafique et al., 2019) (Golmohamadi et al., 2019). For example, Deng et al.

35

17

(Deng et al., 2020) used the concept of aggregator to manage the power re-

serve between the power system operator and vehicle owners. A dispatching

strategy of electric vehicles aggregator for balancing the load demand and user

trip was proposed. A dispatching strategy of electric vehicles (EV) aggregator

for balancing the load demand and user trip was proposed. The dispatching

guidance was used to dynamically modify the trip chain, the EV’s spatial dis-

tribution and State of Charge were re-determined after the EV’s participation

in the flexibility response.

Some studies combined the role of aggregators with power grid at a trans-

mission and distribution network level. Olivella-Rosell et al. (Olivella-Rosell

et al., 2018) proposed a framework where aggregators acted as supervisors of

local market operators and local energy community flexibility transactions.

Aggregators can also aggregate and optimize the net injection power of sub-

stations in an unbalanced distribution system over time for transmission and

distribution interaction (Chen et al., 2018). In (Lampropoulos et al., 2019), the

authors focused on the interactions between the aggregators and the grid/mar-

ket operators by proposing a hierarchical control framework. In the applica-

tion of their case study, the framework enabled demand-side resources,

through aggregators, to participate in energy transactions in wholesale mar-

kets, as well as the provision of ancillary services to the grid both at the trans-

mission and distribution levels.

According to the current studies, the services of an aggregator included the

management of energy and financial interactions between the market and local

energy systems (Di Somma, Graditi and Siano, 2019), through modeling dis-

tributed energy devices, modeling the behavior of consumption units

(Rahnama et al., 2014), designing optimal bidding strategies, designing opti-

mal operation strategies, designing incentive compatible mechanism (Di

Somma, Graditi and Siano, 2019) and so on. In some cases, the aggregators

could theoretically act in an applicable and profitable way (a case in the Nor-

dic Electricity Market (Golmohamadi et al., 2019)). In some cases, aggrega-

tors had no financial incentive to implement demand response to achieve in-

ternal balancing after assessing the imbalance caused by uncertain solar gen-

eration (a case in the Netherland (Okur et al., 2019)). In some cases, there was

a tradeoff between the size of aggregators and benefits (Di Somma, Graditi

and Siano, 2019).

Johansson et al. (Johansson, Vendel and Nuur, 2020) studied the challenges,

activities and capabilities of Swedish DSOs in managing the integration of

distributed energy resources and found that there were regulatory and organi-

zational barriers to the adoption of distributed energy resources. As such, the

Swedish authorities recommended increasing the level of collaboration and

coordination between DSOs and other actors in the electricity market and re-

ducing barriers to market entry for aggregators (Johansson, Vendel and Nuur,

2020). Swedish DSOs have also been mandated to adopt new ICT-solutions,

for example, diffusing smart meters to the customers.

17

(Deng et al., 2020) used the concept of aggregator to manage the power re-

serve between the power system operator and vehicle owners. A dispatching

strategy of electric vehicles aggregator for balancing the load demand and user

trip was proposed. A dispatching strategy of electric vehicles (EV) aggregator

for balancing the load demand and user trip was proposed. The dispatching

guidance was used to dynamically modify the trip chain, the EV’s spatial dis-

tribution and State of Charge were re-determined after the EV’s participation

in the flexibility response.

Some studies combined the role of aggregators with power grid at a trans-

mission and distribution network level. Olivella-Rosell et al. (Olivella-Rosell

et al., 2018) proposed a framework where aggregators acted as supervisors of

local market operators and local energy community flexibility transactions.

Aggregators can also aggregate and optimize the net injection power of sub-

stations in an unbalanced distribution system over time for transmission and

distribution interaction (Chen et al., 2018). In (Lampropoulos et al., 2019), the

authors focused on the interactions between the aggregators and the grid/mar-

ket operators by proposing a hierarchical control framework. In the applica-

tion of their case study, the framework enabled demand-side resources,

through aggregators, to participate in energy transactions in wholesale mar-

kets, as well as the provision of ancillary services to the grid both at the trans-

mission and distribution levels.

According to the current studies, the services of an aggregator included the

management of energy and financial interactions between the market and local

energy systems (Di Somma, Graditi and Siano, 2019), through modeling dis-

tributed energy devices, modeling the behavior of consumption units

(Rahnama et al., 2014), designing optimal bidding strategies, designing opti-

mal operation strategies, designing incentive compatible mechanism (Di

Somma, Graditi and Siano, 2019) and so on. In some cases, the aggregators

could theoretically act in an applicable and profitable way (a case in the Nor-

dic Electricity Market (Golmohamadi et al., 2019)). In some cases, aggrega-

tors had no financial incentive to implement demand response to achieve in-

ternal balancing after assessing the imbalance caused by uncertain solar gen-

eration (a case in the Netherland (Okur et al., 2019)). In some cases, there was

a tradeoff between the size of aggregators and benefits (Di Somma, Graditi

and Siano, 2019).

Johansson et al. (Johansson, Vendel and Nuur, 2020) studied the challenges,

activities and capabilities of Swedish DSOs in managing the integration of

distributed energy resources and found that there were regulatory and organi-

zational barriers to the adoption of distributed energy resources. As such, the

Swedish authorities recommended increasing the level of collaboration and

coordination between DSOs and other actors in the electricity market and re-

ducing barriers to market entry for aggregators (Johansson, Vendel and Nuur,

2020). Swedish DSOs have also been mandated to adopt new ICT-solutions,

for example, diffusing smart meters to the customers.

36

18

2.4 Service-Dominant logic

In order to improve the understanding of tomorrow’s electricity markets, a

more suitable theoretical perspective, Service-Dominant (S-D) logic, is em-

ployed (Vargo and Lusch, 2016). The S-D logic goes beyond the goods-dom-

inant logic. In good-dominant logic, the exchange of tangible goods and dis-

crete transactions are considered primary (Vargo and Lusch, 2004). S-D logic

provides a more holistic, dynamic, and realistic view on the value creation

through exchange, as well as a more comprehensive configuration of actors

(more than suppliers and customers). In this way, S-D logic offers a new un-

derstanding of the market actors, resources, and service, associated with value

co-creation in the electricity market. The following Table 1 shows the eleven

fundamental premises (FP) and five axioms, which formulate the core con-

cepts of S-D logic (Lusch and Vargo, 2014) (Vargo and Lusch, 2016)

(Vargo and Lusch, 2017).

Table 1. The S-D logic foundational premises.

Axiom Foundational premise (FP)

Description

A1 FP1 Service is the fundamental basis of exchange.

FP2 Indirect exchange masks the fundamental basis of ex-change.

FP3 Goods are a distribution mechanism for service provi-sion.

FP4 Operant resources are the fundamental source of strategic benefit.

FP5 All economies are service economies. A2 FP6 Value is cocreated by multiple actors, always including

the beneficiary. FP7 Actors cannot deliver value but can participate in the cre-

ation and offering of value propositions. FP8 A service-centered view is inherently beneficiary ori-

ented and relational. A3 FP9 All social and economic actors are resource integrators.

A4 FP10 Value is always uniquely and phenomenologically deter-mined by the beneficiary.

A5 FP11 Value cocreation is coordinated through actor-generated institutions and institutional arrangements.

The actor, such as an individual, a group, or an organization, is put at the

center of S-D logic, because the actor is the resources integrating unity. In the

treatment of S-D logic, all market actors are seen as operant and endogenous

resources to the process of value exchange and value creation. All of them can

be value creators and value beneficiaries.

The actors engage in the activities of resource integration, based on their

specialized competences (knowledge or skills, or expectations emanating

from experiences) and the relationship with other market actors. The resources

18

2.4 Service-Dominant logic

In order to improve the understanding of tomorrow’s electricity markets, a

more suitable theoretical perspective, Service-Dominant (S-D) logic, is em-

ployed (Vargo and Lusch, 2016). The S-D logic goes beyond the goods-dom-

inant logic. In good-dominant logic, the exchange of tangible goods and dis-

crete transactions are considered primary (Vargo and Lusch, 2004). S-D logic

provides a more holistic, dynamic, and realistic view on the value creation

through exchange, as well as a more comprehensive configuration of actors

(more than suppliers and customers). In this way, S-D logic offers a new un-

derstanding of the market actors, resources, and service, associated with value

co-creation in the electricity market. The following Table 1 shows the eleven

fundamental premises (FP) and five axioms, which formulate the core con-

cepts of S-D logic (Lusch and Vargo, 2014) (Vargo and Lusch, 2016)

(Vargo and Lusch, 2017).

Table 1. The S-D logic foundational premises.

Axiom Foundational premise (FP)

Description

A1 FP1 Service is the fundamental basis of exchange.

FP2 Indirect exchange masks the fundamental basis of ex-change.

FP3 Goods are a distribution mechanism for service provi-sion.

FP4 Operant resources are the fundamental source of strategic benefit.

FP5 All economies are service economies. A2 FP6 Value is cocreated by multiple actors, always including

the beneficiary. FP7 Actors cannot deliver value but can participate in the cre-

ation and offering of value propositions. FP8 A service-centered view is inherently beneficiary ori-

ented and relational. A3 FP9 All social and economic actors are resource integrators.

A4 FP10 Value is always uniquely and phenomenologically deter-mined by the beneficiary.

A5 FP11 Value cocreation is coordinated through actor-generated institutions and institutional arrangements.

The actor, such as an individual, a group, or an organization, is put at the

center of S-D logic, because the actor is the resources integrating unity. In the

treatment of S-D logic, all market actors are seen as operant and endogenous

resources to the process of value exchange and value creation. All of them can

be value creators and value beneficiaries.

The actors engage in the activities of resource integration, based on their

specialized competences (knowledge or skills, or expectations emanating

from experiences) and the relationship with other market actors. The resources

37

19

that come into play are operand resources, which means the resources that

require activities to become valuable (e.g. raw materials), and operant re-

sources, which are often dynamic and intangible (e.g. knowledge and capaci-

ties). The resources, especially higher-order and core competences resources,

are identified and integrated to obtain competitive advantage and perfor-

mance. Notably, the resources must be developed and coordinated to generate

a “service” (e.g., benefits as solutions to a problem) and the (future) elec-

tricity market hold a multitude of services (Fell, 2017).

The “service” could be viewed as a “process of doing something for some-

one” but not the “units of output” (Lusch and Vargo, 2006). Services are of-

fered directly traditionally, such as a customer is offered a haircut and received

at a certain time. Nevertheless, service could be offered indirectly, such as an

“app” for multimodal travel supported by ICT service (Kramers et al., 2014)

which is designed and then “activated” by the customer when needed.

S-D logic claims that value could be co-created by multiple actors with

the integration and transformation of resources (FP6 and FP9, in Table 1),

which also requires interaction and implies network. S-D logic prescribes

the concept of "value in context", which means that resources are only

transformed according to context and through the combination of resource

integration with other resources. This is analogous to the fact that energy

is neither created nor destroyed, but simply transformed from one form to

another (i.e., the first law of thermodynamics). A value proposition can

originate from any actor (e.g., provider or customer) or have a bilateral

origin (Ekman, Raggio and Thompson, 2016) (Vargo and Lusch, 2016).

In line with the above, the core components of S-D logic are shown in Fig-

ure 3.

Figure 3. The narrative and process of S-D logic.

19

that come into play are operand resources, which means the resources that

require activities to become valuable (e.g. raw materials), and operant re-

sources, which are often dynamic and intangible (e.g. knowledge and capaci-

ties). The resources, especially higher-order and core competences resources,

are identified and integrated to obtain competitive advantage and perfor-

mance. Notably, the resources must be developed and coordinated to generate

a “service” (e.g., benefits as solutions to a problem) and the (future) elec-

tricity market hold a multitude of services (Fell, 2017).

The “service” could be viewed as a “process of doing something for some-

one” but not the “units of output” (Lusch and Vargo, 2006). Services are of-

fered directly traditionally, such as a customer is offered a haircut and received

at a certain time. Nevertheless, service could be offered indirectly, such as an

“app” for multimodal travel supported by ICT service (Kramers et al., 2014)

which is designed and then “activated” by the customer when needed.

S-D logic claims that value could be co-created by multiple actors with

the integration and transformation of resources (FP6 and FP9, in Table 1),

which also requires interaction and implies network. S-D logic prescribes

the concept of "value in context", which means that resources are only

transformed according to context and through the combination of resource

integration with other resources. This is analogous to the fact that energy

is neither created nor destroyed, but simply transformed from one form to

another (i.e., the first law of thermodynamics). A value proposition can

originate from any actor (e.g., provider or customer) or have a bilateral

origin (Ekman, Raggio and Thompson, 2016) (Vargo and Lusch, 2016).

In line with the above, the core components of S-D logic are shown in Fig-

ure 3.

Figure 3. The narrative and process of S-D logic.

38

20

In S-D logic, the conceptualization of actor challenges the limiting view of

a “seller” and “buyer” engaged in market transactions. It avoids separating

market actors into either active or passive roles. It requires acknowledging and

understanding the dynamic role and perceived identity of actors regarding the

potential input to other actors’ value creation activities and functions in a

smart electricity market holds (Chourabi et al., 2012). In the process where

an electricity market strives to become “smart”, new actors can be ex-

pected and old actors might disappear or be integrated, as an effect of dis-

ruptive technologies and new business models. Values are created in a setting

colored by a variety of actors with interdependencies. The actors engage as

providers and beneficiaries of value cocreation simultaneously but might

not be the originators of a value proposition or the final recipient of a core

offering. They might act as an intermediary, which enables the value co-

creation among multi-actors (Nätti et al., 2014). Previous studies showed

that some actors could act as knowledge brokers to influence and imprint

business practices (Truong, Simmons and Palmer, 2012). In a similar way,

the “aggregator” role identified in the energy research (Burger et al., 2016)

(Carreiro, Jorge and Antunes, 2017)(Giordano and Fulli, 2012), i.e.,

offering services to address the challenges of a dynamic electric grid, re-

assembles many of the characteristics of a knowledge broker (Von

Malmborg, 2004). The knowledge broker offers expertise in areas such as

technology, markets, and business processes (Theeke, Polidoro Jr and

Fredrickson, 2018), and they offer support and resources to others in the

service ecosystem. This transition from a linear value-chain system to a

service ecosystem (where activities are bilateral or networked) is well rep-

resented in the smart city transformation, which is used as a backdrop for

the upcoming case study.

By exploring multiple market actors in the market, a smart city can be

depicted as a service ecosystem where the creation of value in various de-

grees involves different actors, activities, and resources (Lusch and

Nambisan, 2015). The term “ecosystem” roots in natural science; whereas,

the “service ecosystem” emphasizes the more general role of institutions.

Service ecosystems could be seen as resource-integration networks to col-

laborate with in the co-creation of service (Vargo and Akaka, 2009). It can

be defined as “relatively self-contained, self-adjusting system[s] of re-

source-integrating actors connected by shared institutional arrangements

and mutual value creation through service exchange” (Lusch and Vargo,

2014). Service ecosystem is similar to a market or business network

(Truong, Simmons and Palmer, 2012). A common aspect is that the system

as a whole has a structure in which power flows dynamically through the

network in relation to the ability to respond to external changes. The tra-

ditional marketing and economics literature perceived the marketing ac-

tivities adhere to a predetermined set of structural and behavioral aspects.

In that sense, the intra-organizational and marketing activities were

20

In S-D logic, the conceptualization of actor challenges the limiting view of

a “seller” and “buyer” engaged in market transactions. It avoids separating

market actors into either active or passive roles. It requires acknowledging and

understanding the dynamic role and perceived identity of actors regarding the

potential input to other actors’ value creation activities and functions in a

smart electricity market holds (Chourabi et al., 2012). In the process where

an electricity market strives to become “smart”, new actors can be ex-

pected and old actors might disappear or be integrated, as an effect of dis-

ruptive technologies and new business models. Values are created in a setting

colored by a variety of actors with interdependencies. The actors engage as

providers and beneficiaries of value cocreation simultaneously but might

not be the originators of a value proposition or the final recipient of a core

offering. They might act as an intermediary, which enables the value co-

creation among multi-actors (Nätti et al., 2014). Previous studies showed

that some actors could act as knowledge brokers to influence and imprint

business practices (Truong, Simmons and Palmer, 2012). In a similar way,

the “aggregator” role identified in the energy research (Burger et al., 2016)

(Carreiro, Jorge and Antunes, 2017)(Giordano and Fulli, 2012), i.e.,

offering services to address the challenges of a dynamic electric grid, re-

assembles many of the characteristics of a knowledge broker (Von

Malmborg, 2004). The knowledge broker offers expertise in areas such as

technology, markets, and business processes (Theeke, Polidoro Jr and

Fredrickson, 2018), and they offer support and resources to others in the

service ecosystem. This transition from a linear value-chain system to a

service ecosystem (where activities are bilateral or networked) is well rep-

resented in the smart city transformation, which is used as a backdrop for

the upcoming case study.

By exploring multiple market actors in the market, a smart city can be

depicted as a service ecosystem where the creation of value in various de-

grees involves different actors, activities, and resources (Lusch and

Nambisan, 2015). The term “ecosystem” roots in natural science; whereas,

the “service ecosystem” emphasizes the more general role of institutions.

Service ecosystems could be seen as resource-integration networks to col-

laborate with in the co-creation of service (Vargo and Akaka, 2009). It can

be defined as “relatively self-contained, self-adjusting system[s] of re-

source-integrating actors connected by shared institutional arrangements

and mutual value creation through service exchange” (Lusch and Vargo,

2014). Service ecosystem is similar to a market or business network

(Truong, Simmons and Palmer, 2012). A common aspect is that the system

as a whole has a structure in which power flows dynamically through the

network in relation to the ability to respond to external changes. The tra-

ditional marketing and economics literature perceived the marketing ac-

tivities adhere to a predetermined set of structural and behavioral aspects.

In that sense, the intra-organizational and marketing activities were

39

21

considered controllable. A service ecosystem includes the dynamics of in-

ter-organizational relationships in which activities need to be related to

those of other actors. This means that service ecosystems cannot be con-

trolled and managed by a single actor; instead, they are dynamic and based

on the relationships of multiple actors. This market perspective coincides

with the development of smart cities, which require smart grids (Kylili and

Fokaides, 2015) and renewable energy systems (Calvillo, Sánchez-

Miralles and Villar, 2016), which induce dynamism in energy systems,

interrupting the linear demand-response logic of the previous producer-

distributor-consumer structure. Thus, the management challenges in ser-

vice ecosystems go beyond the company and include the coordination of

multiple actors in the value constellation. Thus, management in service

ecosystems implies managing interdependencies in terms of resource port-

folios and actor portfolios.

2.5 The geographical and technical potential availability

Spatial information technology, especially Geographical Information Systems

(GIS), has been widely used to evaluate the feasibility of solar power plants

in a given region and to determine the best locations. GIS is a powerful tool

for spatial analysis and integration of geospatial data to provide a comprehen-

sive feasibility assessment of solar energy potential on a regional scale. The

estimation of PV power potential is challenging, but essential for relevant re-

newable energy decisions. The assessment of sufficient available roof surface

is one of the most critical stages in the implementation of rooftop integrated

PV applications (Li and Liu, 2017).

Previous studies have examined the spatial distribution of PV applications

and its determinants. Snape (Snape, 2016) revealed the evolution of the spatial

and temporal distribution of PV applications in the UK. The spatial distribu-

tion highlighted the number of systems installed per spatial unit, the system

capacity, the proportion of households with rooftop PV, and the variation over

time. The apparent distribution pattern was found to coincide with the an-

nouncement of the feed-in tariff policy. Balta-ozkan et al. (Balta-ozkan,

Yildirim and Connor, 2020) presented a spatial econometric approach to study

the regional distribution of PV deployment in the UK and its determinants.

Their study showed that accumulated capital (represented by home owner-

ship) and financial savings - rather than income - were the key factors driving

PV adoption in the U.K. Using Zoning Improvement Program (ZIP) code level

data, Lee Kwan (Lee Kwan, 2012) investigated the impact of local environ-

mental, social, economic, and political variables on the distribution of residen-

tial solar PV arrays in the United States. In terms of spatial performance, the

Southwest and Florida performed poorly in terms of the number of residential

units installed with solar PV. California was considered to have the ideal

21

considered controllable. A service ecosystem includes the dynamics of in-

ter-organizational relationships in which activities need to be related to

those of other actors. This means that service ecosystems cannot be con-

trolled and managed by a single actor; instead, they are dynamic and based

on the relationships of multiple actors. This market perspective coincides

with the development of smart cities, which require smart grids (Kylili and

Fokaides, 2015) and renewable energy systems (Calvillo, Sánchez-

Miralles and Villar, 2016), which induce dynamism in energy systems,

interrupting the linear demand-response logic of the previous producer-

distributor-consumer structure. Thus, the management challenges in ser-

vice ecosystems go beyond the company and include the coordination of

multiple actors in the value constellation. Thus, management in service

ecosystems implies managing interdependencies in terms of resource port-

folios and actor portfolios.

2.5 The geographical and technical potential availability

Spatial information technology, especially Geographical Information Systems

(GIS), has been widely used to evaluate the feasibility of solar power plants

in a given region and to determine the best locations. GIS is a powerful tool

for spatial analysis and integration of geospatial data to provide a comprehen-

sive feasibility assessment of solar energy potential on a regional scale. The

estimation of PV power potential is challenging, but essential for relevant re-

newable energy decisions. The assessment of sufficient available roof surface

is one of the most critical stages in the implementation of rooftop integrated

PV applications (Li and Liu, 2017).

Previous studies have examined the spatial distribution of PV applications

and its determinants. Snape (Snape, 2016) revealed the evolution of the spatial

and temporal distribution of PV applications in the UK. The spatial distribu-

tion highlighted the number of systems installed per spatial unit, the system

capacity, the proportion of households with rooftop PV, and the variation over

time. The apparent distribution pattern was found to coincide with the an-

nouncement of the feed-in tariff policy. Balta-ozkan et al. (Balta-ozkan,

Yildirim and Connor, 2020) presented a spatial econometric approach to study

the regional distribution of PV deployment in the UK and its determinants.

Their study showed that accumulated capital (represented by home owner-

ship) and financial savings - rather than income - were the key factors driving

PV adoption in the U.K. Using Zoning Improvement Program (ZIP) code level

data, Lee Kwan (Lee Kwan, 2012) investigated the impact of local environ-

mental, social, economic, and political variables on the distribution of residen-

tial solar PV arrays in the United States. In terms of spatial performance, the

Southwest and Florida performed poorly in terms of the number of residential

units installed with solar PV. California was considered to have the ideal

40

22

combination of environmental, economic, social, and political characteristics

to promote solar PV. The authors identified solar insolation, cost of electricity,

and available financial incentives as important factors influencing the adop-

tion of residential solar PV systems. Sun et al. (Sun et al., 2013) evaluated the

combined potential of solar PV in Fujian Province, China, using a high-reso-

lution solar radiation grid map with geographic constraints. The study pro-

posed a GIS-based approach to facilitate feasibility analysis of geographic and

technical potential, and based on this, investigated the economic feasibility

under two feed-in tariff scenarios. He and Kammen (He and Kammen, 2016)

developed provincial solar availability curves using hourly solar radiation data

from 200 representative sites from 2001 to 2010. The authors combined GIS

modeling with solar PV simulations. The results found that the potential sta-

tionary solar capacity ranged from 4,700 GWp to 39,300 GWp and distributed

solar of about 200 GWp, reaching 6,900 TWh to 70,100 TWh of annual solar

power. Under the homogeneous approach, Israel (Vardimon, 2011), Slovakia

(Hofierka and Kaňuk, 2009), Andalusia (Spain) (Jadraque et al., 2010),

Bangladesh (Hossain and Islam, 2011), Piedmont Region (Italy) (Bergamasco

and Asinari, 2011), Pennsylvania (USA) (Choi et al., 2011), Germany

(Strzalka et al., 2012) (Mainzer et al., 2014), Greece (Theodoridou, Karteris

and Mallinis, 2012), and Karachi (Pakistan) (Khan and Arsalan, 2016) have

done extensive work.

In addition, several researchers have demonstrated that the use of Light

Detection and Ranging (LiDAR) point cloud data can significantly improve

the identification of roof geometry and thus the estimation of PV power gen-

eration. Nguyen et al. (Nguyen et al., 2012) provided a methodology for ap-

plying LiDAR point cloud data to analyze PV deployment on a regional scale.

Based on the proposed method, the authors identified and quantified the chal-

lenges in solar PV deployment assessment. Szabo et al. (Szabo et al., 2020)

extracted building and roof models from LiDAR data and UAV surveys.

Multi-resolution segmentation and orthophoto overlays were applied to the

digital surface model (DSM) to identify buildings and roof planes. This ap-

proach was then validated and applied to 50 buildings in Debrecen, Hungary.

A similar study was conducted by Song et al. (Song et al., 2018) in Chaoyang

District, Beijing, China. To estimate the solar PV power potential, the authors

developed a method to simulate monthly and annual solar radiation on roof-

tops for hourly time steps. The method combined remotely sensed images and

roof retrieval by DSM. The method was then applied to a region in China to

calculate the number of available roofs, available roof area, and annual PV

generation potential. Similar studies were conducted in Georgetown, Malaysia

(Latif et al., 2012), Arizona, USA (Kucuksari et al., 2014), and Seoul, South

Korea (Byrne et al., 2015) , using LiDAR data to model buildings and energy

systems.

Vector maps, digital cadastral services and national GIS are reference

sources for the evaluation of potential buildings for the installation of PV

22

combination of environmental, economic, social, and political characteristics

to promote solar PV. The authors identified solar insolation, cost of electricity,

and available financial incentives as important factors influencing the adop-

tion of residential solar PV systems. Sun et al. (Sun et al., 2013) evaluated the

combined potential of solar PV in Fujian Province, China, using a high-reso-

lution solar radiation grid map with geographic constraints. The study pro-

posed a GIS-based approach to facilitate feasibility analysis of geographic and

technical potential, and based on this, investigated the economic feasibility

under two feed-in tariff scenarios. He and Kammen (He and Kammen, 2016)

developed provincial solar availability curves using hourly solar radiation data

from 200 representative sites from 2001 to 2010. The authors combined GIS

modeling with solar PV simulations. The results found that the potential sta-

tionary solar capacity ranged from 4,700 GWp to 39,300 GWp and distributed

solar of about 200 GWp, reaching 6,900 TWh to 70,100 TWh of annual solar

power. Under the homogeneous approach, Israel (Vardimon, 2011), Slovakia

(Hofierka and Kaňuk, 2009), Andalusia (Spain) (Jadraque et al., 2010),

Bangladesh (Hossain and Islam, 2011), Piedmont Region (Italy) (Bergamasco

and Asinari, 2011), Pennsylvania (USA) (Choi et al., 2011), Germany

(Strzalka et al., 2012) (Mainzer et al., 2014), Greece (Theodoridou, Karteris

and Mallinis, 2012), and Karachi (Pakistan) (Khan and Arsalan, 2016) have

done extensive work.

In addition, several researchers have demonstrated that the use of Light

Detection and Ranging (LiDAR) point cloud data can significantly improve

the identification of roof geometry and thus the estimation of PV power gen-

eration. Nguyen et al. (Nguyen et al., 2012) provided a methodology for ap-

plying LiDAR point cloud data to analyze PV deployment on a regional scale.

Based on the proposed method, the authors identified and quantified the chal-

lenges in solar PV deployment assessment. Szabo et al. (Szabo et al., 2020)

extracted building and roof models from LiDAR data and UAV surveys.

Multi-resolution segmentation and orthophoto overlays were applied to the

digital surface model (DSM) to identify buildings and roof planes. This ap-

proach was then validated and applied to 50 buildings in Debrecen, Hungary.

A similar study was conducted by Song et al. (Song et al., 2018) in Chaoyang

District, Beijing, China. To estimate the solar PV power potential, the authors

developed a method to simulate monthly and annual solar radiation on roof-

tops for hourly time steps. The method combined remotely sensed images and

roof retrieval by DSM. The method was then applied to a region in China to

calculate the number of available roofs, available roof area, and annual PV

generation potential. Similar studies were conducted in Georgetown, Malaysia

(Latif et al., 2012), Arizona, USA (Kucuksari et al., 2014), and Seoul, South

Korea (Byrne et al., 2015) , using LiDAR data to model buildings and energy

systems.

Vector maps, digital cadastral services and national GIS are reference

sources for the evaluation of potential buildings for the installation of PV

41

23

systems (Bu, Grassi and Raubal, 2018). These sources provide the building

footprint and certain useful data such as height or building type classification

(e.g. residential, commercial or industrial buildings). In some cases, all these

data are used together with a digital surface model of the urban area. Moreo-

ver, aerial imagery is a good complement to examine the objects, which con-

stitute the city model (Szabo et al., 2020). When calculating the solar potential,

the results are influenced not only by the condition of each building, but also

by the size and type of the roof (flat or sloping). In this sense, these data are

sufficient to provide an overview of the study area, provided that appropriate

assumptions are made. Taking into account the general characteristics of the

buildings, it is possible to estimate the number of different types of roofs and

to determine the parameters needed to determine the solar PV capacity poten-

tial. In order to evaluate building roofs at the urban scale, it is fundamental to

have a 3D city model with LiDAR point cloud data (Machete et al., 2018).

However, an important aspect to consider is that the extent of the study area

is sometimes limited by the available data (Bu, Grassi and Raubal, 2018)

(Africani et al., 2013). Researchers such as Lingfors and Widén studied the

Swedish municipalities of Blekinge (Lingfors and Widén, 2014), Skåne

(Lingfors and Widén, 2018b), and Dalarna (Widén and Weiss, 2012). With

these reports they used a consistent methodology to estimate the available area

and potential solar energy conversion in Västmanland County, to which the

municipality of Västerås belongs. The authors presented the order of magni-

tude of the potential (Lingfors and Widén, 2018a).

To our knowledge, a rigorous and comprehensive assessment of the geo-

graphic and technical potential of rooftop PV in our study area has not been

published. Furthermore, it is still not self-explanatory how to evaluate the cur-

rent solar PV policy and how to optimize the use of funds. The problems and

experiences of Sweden are by no means unique. Indeed, necessary and suffi-

cient information, especially scientific and technical, is needed as a solid basis

for developing evidence-based policies on appropriate investments of public

funds.

23

systems (Bu, Grassi and Raubal, 2018). These sources provide the building

footprint and certain useful data such as height or building type classification

(e.g. residential, commercial or industrial buildings). In some cases, all these

data are used together with a digital surface model of the urban area. Moreo-

ver, aerial imagery is a good complement to examine the objects, which con-

stitute the city model (Szabo et al., 2020). When calculating the solar potential,

the results are influenced not only by the condition of each building, but also

by the size and type of the roof (flat or sloping). In this sense, these data are

sufficient to provide an overview of the study area, provided that appropriate

assumptions are made. Taking into account the general characteristics of the

buildings, it is possible to estimate the number of different types of roofs and

to determine the parameters needed to determine the solar PV capacity poten-

tial. In order to evaluate building roofs at the urban scale, it is fundamental to

have a 3D city model with LiDAR point cloud data (Machete et al., 2018).

However, an important aspect to consider is that the extent of the study area

is sometimes limited by the available data (Bu, Grassi and Raubal, 2018)

(Africani et al., 2013). Researchers such as Lingfors and Widén studied the

Swedish municipalities of Blekinge (Lingfors and Widén, 2014), Skåne

(Lingfors and Widén, 2018b), and Dalarna (Widén and Weiss, 2012). With

these reports they used a consistent methodology to estimate the available area

and potential solar energy conversion in Västmanland County, to which the

municipality of Västerås belongs. The authors presented the order of magni-

tude of the potential (Lingfors and Widén, 2018a).

To our knowledge, a rigorous and comprehensive assessment of the geo-

graphic and technical potential of rooftop PV in our study area has not been

published. Furthermore, it is still not self-explanatory how to evaluate the cur-

rent solar PV policy and how to optimize the use of funds. The problems and

experiences of Sweden are by no means unique. Indeed, necessary and suffi-

cient information, especially scientific and technical, is needed as a solid basis

for developing evidence-based policies on appropriate investments of public

funds.

42

24

3 Methods

This chapter illustrates the methods for solving the research questions, includ-

ing models and theories.

A (mono) disciplinary approach, be it a technical or economic one, is too

limited to capture the interaction and mutual shaping between solar PV sys-

tems and society. This requires the joint involvement and collaboration of

mixed methods cross disciplinary borders. A co-evolution of the technical and

the economic takes place both at the micro-level of the practical use of the

technology and at more structural levels of society where investments and pol-

icies are interdependent with technological infrastructures and knowledge

production. Techno-economic analysis intends to inform investors and poli-

cymakers regarding in the decision making process in light of new information

(Sommerfeldt and Madani, 2017).

3.1 Economic viability indices

3.1.1 Levelized Cost of Electricity and cost crossover

Levelized Cost of Electricity (LCOE) is a simple metric commonly used to

compare the cost of electricity from different energy technologies. LCOE is a

measure of the average construction and operating costs of a generation asset

over its lifetime divided by the total amount of electricity generated by the

asset over its lifetime (Lai and McCulloch, 2016).

In Paper I, in order to estimate the LCOE of solar PV systems in different

Chinese cities, it required an integrated knowledge of economics and technol-

ogies. In this study, both the cost analyses and physical design of a solar PV

system were considered. From the point of economic analysis, the calculation

included the costs of land area, module prices, taxes, bank loan and interest,

insurances, and so on. From the point of technical analysis, the mechanical

specifications were considered, including the power capacities, inverter, life-

time, and so on. Cost and electricity production varied based on locations,

capacities for generation, and other factors, which were considered in Paper I.

The solar PV system efficiencies and lifetime were taken as given. From the

point of the lifetime of a solar PV system, the LCOE calculation included the

cost of electricity generation, pre-development costs, construction costs, op-

eration and maintenance costs, as well as the discount rate, fixed asset

24

3 Methods

This chapter illustrates the methods for solving the research questions, includ-

ing models and theories.

A (mono) disciplinary approach, be it a technical or economic one, is too

limited to capture the interaction and mutual shaping between solar PV sys-

tems and society. This requires the joint involvement and collaboration of

mixed methods cross disciplinary borders. A co-evolution of the technical and

the economic takes place both at the micro-level of the practical use of the

technology and at more structural levels of society where investments and pol-

icies are interdependent with technological infrastructures and knowledge

production. Techno-economic analysis intends to inform investors and poli-

cymakers regarding in the decision making process in light of new information

(Sommerfeldt and Madani, 2017).

3.1 Economic viability indices

3.1.1 Levelized Cost of Electricity and cost crossover

Levelized Cost of Electricity (LCOE) is a simple metric commonly used to

compare the cost of electricity from different energy technologies. LCOE is a

measure of the average construction and operating costs of a generation asset

over its lifetime divided by the total amount of electricity generated by the

asset over its lifetime (Lai and McCulloch, 2016).

In Paper I, in order to estimate the LCOE of solar PV systems in different

Chinese cities, it required an integrated knowledge of economics and technol-

ogies. In this study, both the cost analyses and physical design of a solar PV

system were considered. From the point of economic analysis, the calculation

included the costs of land area, module prices, taxes, bank loan and interest,

insurances, and so on. From the point of technical analysis, the mechanical

specifications were considered, including the power capacities, inverter, life-

time, and so on. Cost and electricity production varied based on locations,

capacities for generation, and other factors, which were considered in Paper I.

The solar PV system efficiencies and lifetime were taken as given. From the

point of the lifetime of a solar PV system, the LCOE calculation included the

cost of electricity generation, pre-development costs, construction costs, op-

eration and maintenance costs, as well as the discount rate, fixed asset

43

25

depreciation and/or asset salvage value considered for a fixed period. One of

the criticisms of using the LCOE is that the use of outdated data (Branker,

Pathak and Pearce, 2011) which was overcome in this research with the utili-

zation of the most updated data and of the high-resolution data.

It is understandable that the present value of LCOE multiplies by the elec-

tricity generation in the given period should be equal to the present valued net

costs, as shown in Equation (1), where 𝑡 represents the year and 𝑇 represents

the life of the project.

∑ (𝐿𝐶𝑂𝐸𝑡

(1+𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒)𝑡 ∗ (𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦)𝑡) 𝑇

𝑡=0 = ∑ ((𝑐𝑜𝑠𝑡)𝑡

(1+𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒)𝑡 )𝑇𝑡=0 (1)

Through the rearrangement, the LCOE could be expressed as Equation (2).

𝐿𝐶𝑂𝐸 = ∑ (

(𝑐𝑜𝑠𝑡)𝑡(1+𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒)𝑡 )𝑇

𝑡=0

∑ ((𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦)𝑡

(1+𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒)𝑡) 𝑇𝑡=0

(2)

Coal power has always dominated the electricity supply in China. Similar

to the United States, coal power in China is currently in a "cost crossover"

situation (Yuan et al., 2018). To investigate this issue, Paper II compared the

desulfurized coal benchmark (DCB) prices and LCOEs for distributed solar

PV projects in 344 cities, and after making this comparison, as in the United

States, the study of Paper II identified four levels of cost-risk for operating

coal plants (Gimon et al., 2019):

1) Deemed no cost-risk: if DCB is > 25% lower than LCOE.

2) Potentially at cost-risk: if DCB is 0-25% lower than LCOE.

3) At cost-risk: if DCB is 0-25% higher than LCOE.

4) Substantially at cost-risk: if DCB is > 25% higher than LCOE.

These levels of cost-risk can be reliable means to determine the less expen-

sive option between traditional desulfurized coal generation and distributed

solar PV power generation.

3.1.2 Levelized Profit of Electricity and other three indices

As an analogue and complement to LCOE, the Levelized Profit of Electricity

(LPOE) was proposed, which measured the net revenue per kWh of solar PV

generation without subsidies. It took into account not only the project costs

over the life of the project, but also the various potential revenues, i.e., savings

from selling surplus electricity to the grid and savings from avoiding power

purchases from the grid. In the calculation, the LPOE was the discounted net

profit divided by the electricity generation, where the discounted net profit

included both discounted net revenue and net costs. The LPOE formula in

Paper I (Equation (3)) was developed on the basis of the LCOE.

𝐿𝑃𝑂𝐸 = ∑ (

(𝑝𝑟𝑜𝑓𝑖𝑡)𝑡(1+𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒)𝑡 )𝑇

𝑡=0



(3)

Net Present Value (NPV) is the difference between the present value of all

cash inflows and outflows associated with the investment (Equation (4))

25

depreciation and/or asset salvage value considered for a fixed period. One of

the criticisms of using the LCOE is that the use of outdated data (Branker,

Pathak and Pearce, 2011) which was overcome in this research with the utili-

zation of the most updated data and of the high-resolution data.

It is understandable that the present value of LCOE multiplies by the elec-

tricity generation in the given period should be equal to the present valued net

costs, as shown in Equation (1), where 𝑡 represents the year and 𝑇 represents

the life of the project.

∑ (𝐿𝐶𝑂𝐸𝑡

(1+𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒)𝑡 ∗ (𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦)𝑡) 𝑇

𝑡=0 = ∑ ((𝑐𝑜𝑠𝑡)𝑡

(1+𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒)𝑡 )𝑇𝑡=0 (1)

Through the rearrangement, the LCOE could be expressed as Equation (2).

𝐿𝐶𝑂𝐸 = ∑ (

(𝑐𝑜𝑠𝑡)𝑡(1+𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒)𝑡 )𝑇

𝑡=0



(2)

Coal power has always dominated the electricity supply in China. Similar

to the United States, coal power in China is currently in a "cost crossover"

situation (Yuan et al., 2018). To investigate this issue, Paper II compared the

desulfurized coal benchmark (DCB) prices and LCOEs for distributed solar

PV projects in 344 cities, and after making this comparison, as in the United

States, the study of Paper II identified four levels of cost-risk for operating

coal plants (Gimon et al., 2019):

1) Deemed no cost-risk: if DCB is > 25% lower than LCOE.

2) Potentially at cost-risk: if DCB is 0-25% lower than LCOE.

3) At cost-risk: if DCB is 0-25% higher than LCOE.

4) Substantially at cost-risk: if DCB is > 25% higher than LCOE.

These levels of cost-risk can be reliable means to determine the less expen-

sive option between traditional desulfurized coal generation and distributed

solar PV power generation.

3.1.2 Levelized Profit of Electricity and other three indices

As an analogue and complement to LCOE, the Levelized Profit of Electricity

(LPOE) was proposed, which measured the net revenue per kWh of solar PV

generation without subsidies. It took into account not only the project costs

over the life of the project, but also the various potential revenues, i.e., savings

from selling surplus electricity to the grid and savings from avoiding power

purchases from the grid. In the calculation, the LPOE was the discounted net

profit divided by the electricity generation, where the discounted net profit

included both discounted net revenue and net costs. The LPOE formula in

Paper I (Equation (3)) was developed on the basis of the LCOE.

𝐿𝑃𝑂𝐸 = ∑ (

(𝑝𝑟𝑜𝑓𝑖𝑡)𝑡(1+𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒)𝑡 )𝑇

𝑡=0



(3)

Net Present Value (NPV) is the difference between the present value of all

cash inflows and outflows associated with the investment (Equation (4))

44

26

(Yujia and Finenko, 2016). It measures if an investment project is profitable

or not, which depends on a designated return required by an investor. Internal

Rate of Return (IRR) is the value of the discount rate at which the NPV equals

to zero. It is the weighted average cost of financing a distributed PV project,

taking into account the time value of money. From an economic point of view,

a distributed solar PV project is profitable if the IRR exceeds the cost of cap-

ital. Discounted Payback Period (DPBP) represents the number of years re-

quired to recover the investment cost, which means the year when the cumu-

lative cashflows turn positive. A shorter DPBP is preferred than a longer one.

Like the NPV and IRR, the DPBP takes into account the value of money over

time (Rodrigues, Chen and Morgado-Dias, 2017).

NPV = −𝐶𝐹0 + 𝐶𝐹1

1+𝑟+

𝐶𝐹2

(1+𝑟)2 + ⋯ + 𝐶𝐹𝑇

(1+𝑟)𝑇 = ∑𝐶𝐹𝑡

(1+𝑟)𝑡𝑇𝑡=0 − 𝐶𝐹0 (4)

Where, 𝑟 is the discount rate, 𝐶𝐹0 is the initial investment, 𝐶𝐹1, 𝐶𝐹2, … 𝐶𝐹𝑡

are the cash flow of year 1, 2, … , 𝑡

3.1.3 Grid parity indices

Grid parity is a phenomenon that the cost of electricity from deploying a solar

PV system becomes lower than or equal to the conventional electricity price.

Specifically, it was defined as the intersection of solar PV LCOE with: the

electricity market price for the end-users (Spertino, Leo and Cocina, 2014)

(Breyer and Gerlach, 2013), or the electricity price from the power grid

(Biondi and Moretto, 2015) (Bhandari and Stadler, 2009). This was always

analyzed with the consideration of all kinds of subsidies in the previous liter-

ature. In Paper I and Paper II, we defined the grid parity as an unsubsidized

LCOE lower than or equal to the electricity market price, or the conventional

coal-fired power price. Therefore, the grid parity was developed into two in-

dices based on two different scenarios: the Grid Parity Index of User-side

(GPIu) and the Grid Parity Index of Plant-side (GPIp). In Equation (5), if GPIu

is lower than or equal to 1, the unsubsidized LCOE is lower than or equal to

the local electricity purchasing cost, and the region is considered to have

reached the level of User-side Grid Parity.

GPIu= 𝐿𝐶𝑂𝐸𝑀𝑃⁄ (5)

Where 𝑀𝑃 is the electricity market price.

In Equation (6), if GPIp is lower than or equal to 1, the unsubsidized LCOE

is lower than or equal to the local desulfurized coal benchmark price, and the

region is considered to have reached the level of Plant-side Grid Parity.

GPIp= 𝐿𝐶𝑂𝐸𝐷𝐶𝐵⁄ (6)

Where 𝐷𝐶𝐵 is the desulfurized coal benchmark price.

26

(Yujia and Finenko, 2016). It measures if an investment project is profitable

or not, which depends on a designated return required by an investor. Internal

Rate of Return (IRR) is the value of the discount rate at which the NPV equals

to zero. It is the weighted average cost of financing a distributed PV project,

taking into account the time value of money. From an economic point of view,

a distributed solar PV project is profitable if the IRR exceeds the cost of cap-

ital. Discounted Payback Period (DPBP) represents the number of years re-

quired to recover the investment cost, which means the year when the cumu-

lative cashflows turn positive. A shorter DPBP is preferred than a longer one.

Like the NPV and IRR, the DPBP takes into account the value of money over

time (Rodrigues, Chen and Morgado-Dias, 2017).

NPV = −𝐶𝐹0 + 𝐶𝐹1

1+𝑟+

𝐶𝐹2

(1+𝑟)2 + ⋯ + 𝐶𝐹𝑇

(1+𝑟)𝑇 = ∑𝐶𝐹𝑡

(1+𝑟)𝑡𝑇𝑡=0 − 𝐶𝐹0 (4)

Where, 𝑟 is the discount rate, 𝐶𝐹0 is the initial investment, 𝐶𝐹1, 𝐶𝐹2, … 𝐶𝐹𝑡

are the cash flow of year 1, 2, … , 𝑡

3.1.3 Grid parity indices

Grid parity is a phenomenon that the cost of electricity from deploying a solar

PV system becomes lower than or equal to the conventional electricity price.

Specifically, it was defined as the intersection of solar PV LCOE with: the

electricity market price for the end-users (Spertino, Leo and Cocina, 2014)

(Breyer and Gerlach, 2013), or the electricity price from the power grid

(Biondi and Moretto, 2015) (Bhandari and Stadler, 2009). This was always

analyzed with the consideration of all kinds of subsidies in the previous liter-

ature. In Paper I and Paper II, we defined the grid parity as an unsubsidized

LCOE lower than or equal to the electricity market price, or the conventional

coal-fired power price. Therefore, the grid parity was developed into two in-

dices based on two different scenarios: the Grid Parity Index of User-side

(GPIu) and the Grid Parity Index of Plant-side (GPIp). In Equation (5), if GPIu

is lower than or equal to 1, the unsubsidized LCOE is lower than or equal to

the local electricity purchasing cost, and the region is considered to have

reached the level of User-side Grid Parity.

GPIu= 𝐿𝐶𝑂𝐸𝑀𝑃⁄ (5)

Where 𝑀𝑃 is the electricity market price.

In Equation (6), if GPIp is lower than or equal to 1, the unsubsidized LCOE

is lower than or equal to the local desulfurized coal benchmark price, and the

region is considered to have reached the level of Plant-side Grid Parity.

GPIp= 𝐿𝐶𝑂𝐸𝐷𝐶𝐵⁄ (6)

Where 𝐷𝐶𝐵 is the desulfurized coal benchmark price.

45

27

3.2 Monte Carlo Analysis

In a techno-economic analysis model on solar power generation, many varia-

bles are subject to variability over time, consequently, will cause variations in

economic indicators. For example, the market prices for electricity will vary

over time. In Monte Carlo Analysis (MCA), the inputs are chosen randomly

from a range of values based on a probability distribution or generated using

a random model. Then multiple iterations (ranging from thousands to mil-

lions) of a given mathematical simulation are performed (Sommerfeldt and

Madani, 2017). Likewise, Paper I used Monte Carlo simulations for the sen-

sitivity analysis as a useful tool for approximating reality.

Such a sensitivity analysis can determine the extent to which risk factors

affect the objectives of the investment project and is therefore a critical part

of the risk analysis of investment projects. Suppose there are 𝑘 risk factors,

which are represented by 𝑥1, 𝑥2, … , 𝑥𝑘 respectively. Indicator 𝑌 indicates the

financial indicators of the strengths and weaknesses of investment projects,

such as NPV, IRR, etc. 𝑌 is a function of these risk factors, namely: 𝑌 =

f(𝑥1, 𝑥2, … , 𝑥𝑘), where 𝑥𝑖 represents the i-th risk factor of the model. Sensitiv-

ity analysis is the study and prediction of the extent to which these inputs af-

fect the output value. The magnitude of the degree of influence can be referred

to as the sensitivity coefficient of the input. Obviously, the greater the absolute

value of the sensitivity coefficient, the greater the impact of the risk factor on

the investment project. The key to sensitivity analysis is to find the sensitivity

coefficient of each risk factor, and then get the importance ranking of each

risk factor.

In Paper I, bank interest rate, PV module cost, rooftop rent, power genera-

tion, desulfurized coal price, electricity market price, and self-consumption

ratio were used as hypothetical risk variables for the project. We performed

10,000 Monte Carlo simulations to study the effects of those variables on the

LCOE and LPOE. We assumed a) Uniform Distribution for PV module cost,

rooftop rent, self-consumption ratio, desulfurized coal benchmark price, and

electricity market price; b) Normal Distribution for bank interest rates, and

power generation.

3.3 K-means clustering algorithm

Data clustering is an effective approach to discover the structure in certain

datasets. For example, researchers have used the clustering algorithm to find

distinctive groups of electricity consumption profiles (Kim, Ko and Choi,

2011), to find the driving factors behind those groups (Rhodes et al., 2014),

and further to forecast the loads (Kwac, Flora and Rajagopal, 2014) (Zhou,

Yang and Shen, 2013). The K-means clustering algorithm is an incremental

approach to cluster data points, in which the “clustering” could be understood

27

3.2 Monte Carlo Analysis

In a techno-economic analysis model on solar power generation, many varia-

bles are subject to variability over time, consequently, will cause variations in

economic indicators. For example, the market prices for electricity will vary

over time. In Monte Carlo Analysis (MCA), the inputs are chosen randomly

from a range of values based on a probability distribution or generated using

a random model. Then multiple iterations (ranging from thousands to mil-

lions) of a given mathematical simulation are performed (Sommerfeldt and

Madani, 2017). Likewise, Paper I used Monte Carlo simulations for the sen-

sitivity analysis as a useful tool for approximating reality.

Such a sensitivity analysis can determine the extent to which risk factors

affect the objectives of the investment project and is therefore a critical part

of the risk analysis of investment projects. Suppose there are 𝑘 risk factors,

which are represented by 𝑥1, 𝑥2, … , 𝑥𝑘 respectively. Indicator 𝑌 indicates the

financial indicators of the strengths and weaknesses of investment projects,

such as NPV, IRR, etc. 𝑌 is a function of these risk factors, namely: 𝑌 =

f(𝑥1, 𝑥2, … , 𝑥𝑘), where 𝑥𝑖 represents the i-th risk factor of the model. Sensitiv-

ity analysis is the study and prediction of the extent to which these inputs af-

fect the output value. The magnitude of the degree of influence can be referred

to as the sensitivity coefficient of the input. Obviously, the greater the absolute

value of the sensitivity coefficient, the greater the impact of the risk factor on

the investment project. The key to sensitivity analysis is to find the sensitivity

coefficient of each risk factor, and then get the importance ranking of each

risk factor.

In Paper I, bank interest rate, PV module cost, rooftop rent, power genera-

tion, desulfurized coal price, electricity market price, and self-consumption

ratio were used as hypothetical risk variables for the project. We performed

10,000 Monte Carlo simulations to study the effects of those variables on the

LCOE and LPOE. We assumed a) Uniform Distribution for PV module cost,

rooftop rent, self-consumption ratio, desulfurized coal benchmark price, and

electricity market price; b) Normal Distribution for bank interest rates, and

power generation.

3.3 K-means clustering algorithm

Data clustering is an effective approach to discover the structure in certain

datasets. For example, researchers have used the clustering algorithm to find

distinctive groups of electricity consumption profiles (Kim, Ko and Choi,

2011), to find the driving factors behind those groups (Rhodes et al., 2014),

and further to forecast the loads (Kwac, Flora and Rajagopal, 2014) (Zhou,

Yang and Shen, 2013). The K-means clustering algorithm is an incremental

approach to cluster data points, in which the “clustering” could be understood

46

28

as finding homogeneous groups in a given dataset. The main idea of K-means

clustering is to select some initial partition of data units and then change the

membership of the clusters to obtain better partitions. It uses an iterative pro-

cedure in the clustering process.

In statistical analysis, cluster analysis can be divided into hierarchical and

non-hierarchical clustering. The classification of clustering algorithms can be

described as in Figure 4 (Ma and Chow, 2004). The difference is that the for-

mer data structure has hierarchical classes (in a bottom-up or top-down man-

ner). The data used in Paper II were cross-sectional data of cities, without sub-

ordination or overlap. Therefore, non-hierarchical clustering was appropriate.

Figure 4. Categorization of clustering algorithms.

Before the clustering, an underlying assumption is that these numerical

measures of distance are comparable to each other (Das, 2003). Then the fol-

lowing sequential steps were conducted.

1) Each item in the dataset is assigned into one specified cluster on the

basis that it has the nearest distance to the assigned cluster centroid

(mean). The traditional K-means clustering algorithm uses the Euclid-

ean Distance (Equation (7) (Su and Chou, 2001) ) to measure similar-

ity.

d(p, q) = √∑ |𝑥𝑝𝑖 − 𝑥𝑞𝑖|2𝑛𝑖=1 (7)

where, i =1,2,...,n

The Euclidean Distance treats the difference between different attrib-

utes of the sample equally, which ignores the variables’ dimensions.

Moreover, it does not consider the correlation between variables, or

the relative importance of each variable when processing data. In or-

der to remedy these disadvantages, Mahalanobis Distance was used

in Paper II instead of the traditional Euclidean Distance (Equation (8)).

28

as finding homogeneous groups in a given dataset. The main idea of K-means

clustering is to select some initial partition of data units and then change the

membership of the clusters to obtain better partitions. It uses an iterative pro-

cedure in the clustering process.

In statistical analysis, cluster analysis can be divided into hierarchical and

non-hierarchical clustering. The classification of clustering algorithms can be

described as in Figure 4 (Ma and Chow, 2004). The difference is that the for-

mer data structure has hierarchical classes (in a bottom-up or top-down man-

ner). The data used in Paper II were cross-sectional data of cities, without sub-

ordination or overlap. Therefore, non-hierarchical clustering was appropriate.

Figure 4. Categorization of clustering algorithms.

Before the clustering, an underlying assumption is that these numerical

measures of distance are comparable to each other (Das, 2003). Then the fol-

lowing sequential steps were conducted.

1) Each item in the dataset is assigned into one specified cluster on the

basis that it has the nearest distance to the assigned cluster centroid

(mean). The traditional K-means clustering algorithm uses the Euclid-

ean Distance (Equation (7) (Su and Chou, 2001) ) to measure similar-

ity.

d(p, q) = √∑ |𝑥𝑝𝑖 − 𝑥𝑞𝑖|2𝑛𝑖=1 (7)

where, i =1,2,...,n

The Euclidean Distance treats the difference between different attrib-

utes of the sample equally, which ignores the variables’ dimensions.

Moreover, it does not consider the correlation between variables, or

the relative importance of each variable when processing data. In or-

der to remedy these disadvantages, Mahalanobis Distance was used

in Paper II instead of the traditional Euclidean Distance (Equation (8)).

47

29

m(p, q) = √∑ (𝑥𝑝𝑖 − 𝑥𝑞𝑖)𝑇𝑛𝑖=1 Σ−1(𝑥𝑝𝑖 − 𝑥𝑞𝑖) (8)

In Paper II, p, q were any two points; p, q =1,2,…,344. i was the di-

mension (i.e. LPOE, NPV, IRR, and DPBP); i =1,2,3,4.

2) Recalculate the centroid (mean) for each cluster and decide the new

center. If √∑ (𝑥𝑝𝑖 − 𝑐𝑘(𝑏)

)𝑇𝑛𝑖=1 Σ−1(𝑥𝑝𝑖 − 𝑐𝑘

(𝑏)) <

√∑ (𝑥𝑝𝑖 − 𝑐𝑘(𝑎)


(𝑎)), then 𝑥𝑝𝑖 ∈ 𝑐𝑘

(𝑏). a and b are

𝑎𝑡ℎ and 𝑏𝑡ℎ iteration (a ≠ b). k is the 𝑘𝑡ℎ cluster, k =1,2,...,4.

In this Step, four new centroids (mean) were generated.

3) Then Step 2) was re-conducted so that each cluster receives new items.

This process was iterated until no more reassignments take place, i.e.

minimizing the distance between each item and the centroids (mean).

The process of K-means clustering algorithm in Paper II can be graphically

shown in Figure 5.

Figure 5. K-means clustering algorithm procedure in Paper II.

29

m(p, q) = √∑ (𝑥𝑝𝑖 − 𝑥𝑞𝑖)𝑇𝑛𝑖=1 Σ−1(𝑥𝑝𝑖 − 𝑥𝑞𝑖) (8)

In Paper II, p, q were any two points; p, q =1,2,…,344. i was the di-

mension (i.e. LPOE, NPV, IRR, and DPBP); i =1,2,3,4.

2) Recalculate the centroid (mean) for each cluster and decide the new

center. If √∑ (𝑥𝑝𝑖 − 𝑐𝑘(𝑏)


(𝑏)) <

√∑ (𝑥𝑝𝑖 − 𝑐𝑘(𝑎)


(𝑎)), then 𝑥𝑝𝑖 ∈ 𝑐𝑘

(𝑏). a and b are

𝑎𝑡ℎ and 𝑏𝑡ℎ iteration (a ≠ b). k is the 𝑘𝑡ℎ cluster, k =1,2,...,4.

In this Step, four new centroids (mean) were generated.

3) Then Step 2) was re-conducted so that each cluster receives new items.

This process was iterated until no more reassignments take place, i.e.

minimizing the distance between each item and the centroids (mean).

The process of K-means clustering algorithm in Paper II can be graphically

shown in Figure 5.

Figure 5. K-means clustering algorithm procedure in Paper II.

48

30

3.4 Case study and questionnaire survey

3.4.1 Case study

Case studies can actually use qualitative or quantitative data that may come

from different sources such as fieldwork, archival records, oral reports, obser-

vations, etc. (Yin, 2003). The main difference between quantitative and qual-

itative research methods is that when using case studies, quantitative research

uses only a few variables but many cases, while qualitative research uses many

variables but only a few cases. A case study can be either a single case or

multiple cases, numerous levels of analysis, either at the company level or at

the industry level, or a cross-case analysis. In this sense, it can be a good re-

search design that gives the researcher a holistic, in-depth view of a phenom-

enon or series of events that can provide a rounded picture. Case studies be-

come particularly useful when one needs to gain insight into particular issues

or situations, and informative cases can be identified.

As Ragin and Becker put it (Ragin and Becker, 1992), the empirical

world is infinite in its detail, complexity, specificity, and uniqueness. In

contrast, theoretical concepts are relatively simple and general. Empirical

research is often conducted without the explicit guidance of theory. Mak-

ing something into a case or casing it can bring operational closure to some

problematic relationship between ideas and evidence, between theory and

data. When cases are made, the process of casing consists of matching

ideas and evidence.

In Paper III, we conducted a case study of the Swedish energy market

transition to explore the theoretical explanation of new actors in smart city

transformation (Yin, 2003). In this case study, the managers, policymakers,

and other practitioners were involved in the research process. The study

followed the Swedish VR/CODEX ethical guidelines (inform, consent,

acknowledge and use requirements) and the case descriptions did not include

the names and positions of the participants (i.e., all participants were anony-

mous).

Sweden aspires to reach 100% renewable energy system in 2040 and

has the highest share (more than 55 % of energy consumption in 2019) of

renewable energy of all EU countries (Eurostat, 2021). Sweden also scored

high in terms of multiple fields including innovativeness, ICT, and sus-

tainability (Strand, Freeman and Hockerts, 2015). These make Sweden a

suitable case to understand future electricity market transformation. The

archival materials, such as data gathered by other researchers, electricity

suppliers, real estate organizations, and service organizations, and the in-

terviews on the project participants were taken into account. The abundant

information was repeatedly revisited to disentangle empirical findings

(Dubois and Gadde, 2002). In this process, we iteratively refined the the-

oretical explanation of future electricity transformation, which implied the

30

3.4 Case study and questionnaire survey

3.4.1 Case study

Case studies can actually use qualitative or quantitative data that may come

from different sources such as fieldwork, archival records, oral reports, obser-

vations, etc. (Yin, 2003). The main difference between quantitative and qual-

itative research methods is that when using case studies, quantitative research

uses only a few variables but many cases, while qualitative research uses many

variables but only a few cases. A case study can be either a single case or

multiple cases, numerous levels of analysis, either at the company level or at

the industry level, or a cross-case analysis. In this sense, it can be a good re-

search design that gives the researcher a holistic, in-depth view of a phenom-

enon or series of events that can provide a rounded picture. Case studies be-

come particularly useful when one needs to gain insight into particular issues

or situations, and informative cases can be identified.

As Ragin and Becker put it (Ragin and Becker, 1992), the empirical

world is infinite in its detail, complexity, specificity, and uniqueness. In

contrast, theoretical concepts are relatively simple and general. Empirical

research is often conducted without the explicit guidance of theory. Mak-

ing something into a case or casing it can bring operational closure to some

problematic relationship between ideas and evidence, between theory and

data. When cases are made, the process of casing consists of matching

ideas and evidence.

In Paper III, we conducted a case study of the Swedish energy market

transition to explore the theoretical explanation of new actors in smart city

transformation (Yin, 2003). In this case study, the managers, policymakers,

and other practitioners were involved in the research process. The study

followed the Swedish VR/CODEX ethical guidelines (inform, consent,

acknowledge and use requirements) and the case descriptions did not include

the names and positions of the participants (i.e., all participants were anony-

mous).

Sweden aspires to reach 100% renewable energy system in 2040 and

has the highest share (more than 55 % of energy consumption in 2019) of

renewable energy of all EU countries (Eurostat, 2021). Sweden also scored

high in terms of multiple fields including innovativeness, ICT, and sus-

tainability (Strand, Freeman and Hockerts, 2015). These make Sweden a

suitable case to understand future electricity market transformation. The

archival materials, such as data gathered by other researchers, electricity

suppliers, real estate organizations, and service organizations, and the in-

terviews on the project participants were taken into account. The abundant

information was repeatedly revisited to disentangle empirical findings

(Dubois and Gadde, 2002). In this process, we iteratively refined the the-

oretical explanation of future electricity transformation, which implied the

49

31

establishment of a smart market. Paper III adopted an explanation-oriented

lens in several analytical iterations, and produced the finalized and con-

densed empirical renderings.

3.4.2 Questionnaire survey

The methodology used in the key performance indicator (KPI) analysis sec-

tion consisted of four steps in Paper IV. First, we aggregated potential KPIs

from the literature based on four different categories (i.e., technical, economic,

environmental, and social/policy). Second, a questionnaire was formed based

on the literature survey, which was then sent to different stakeholders. Third,

we collected feedback and analyzed the data to identify the most important

KPIs. finally, the list of selected KPIs was determined based on the results of

the statistical analysis. There was a rich body of literature on the appropriate

strategy for selecting the right set of KPIs. Due to its complex nature, while

being important to all parties, selecting the proper set of KPIs was a critical

process. The literature review generated a library of KPIs; and the survey pro-

vided important assistance in the selection of KPIs. A summary of the KPI

categories, issue numbers for each category, stakeholders and their roles is

shown in Figure 6.

Figure 6. Summary of KPI categories, stakeholders and their roles.

The purpose of the questionnaire was to conduct an initial screening of

KPIs and to make a draft selection by screening a certain number of KPIs in

each category. Subsequently, the different levels of importance of the KPI

candidates can be determined. The questionnaire should include the organiza-

tion responsible for the flexibility solution or its representatives; the parties

affected by the flexibility selection; and experts in various fields as consultants

and evaluators of the analysis results. It was also important to decide on the

number of KPIs that should be sufficient to demonstrate the flexibility

31

establishment of a smart market. Paper III adopted an explanation-oriented

lens in several analytical iterations, and produced the finalized and con-

densed empirical renderings.

3.4.2 Questionnaire survey

The methodology used in the key performance indicator (KPI) analysis sec-

tion consisted of four steps in Paper IV. First, we aggregated potential KPIs

from the literature based on four different categories (i.e., technical, economic,

environmental, and social/policy). Second, a questionnaire was formed based

on the literature survey, which was then sent to different stakeholders. Third,

we collected feedback and analyzed the data to identify the most important

KPIs. finally, the list of selected KPIs was determined based on the results of

the statistical analysis. There was a rich body of literature on the appropriate

strategy for selecting the right set of KPIs. Due to its complex nature, while

being important to all parties, selecting the proper set of KPIs was a critical

process. The literature review generated a library of KPIs; and the survey pro-

vided important assistance in the selection of KPIs. A summary of the KPI

categories, issue numbers for each category, stakeholders and their roles is

shown in Figure 6.

Figure 6. Summary of KPI categories, stakeholders and their roles.

The purpose of the questionnaire was to conduct an initial screening of

KPIs and to make a draft selection by screening a certain number of KPIs in

each category. Subsequently, the different levels of importance of the KPI

candidates can be determined. The questionnaire should include the organiza-

tion responsible for the flexibility solution or its representatives; the parties

affected by the flexibility selection; and experts in various fields as consultants

and evaluators of the analysis results. It was also important to decide on the

number of KPIs that should be sufficient to demonstrate the flexibility

50

32

performance of the multi-energy systems. The greater the number of KPIs, the

less focused they were on the specific objectives they were intended to demon-

strate. Therefore, five KPIs in the "Technical" category, five KPIs in the "Eco-

nomic" category, three KPIs in the "Environmental" category, and three KPIs

in the "Social/Policy" category should be selected from the questionnaire anal-

ysis.

The selection of the most important KPIs was identified based on the num-

ber of votes, the mean of votes (ranging from 0 to 7), and the standard devia-

tion of votes (STD). The Coefficient of Variation (CV) could very well repre-

sent the mean of votes and the STD, defining as the ratio of the STD 𝜎 to the

mean 𝜇 (Equation (9)). CV was a measure that showed the degree of variation

relative to the population mean, and in Paper IV, it showed the overall agree-

ment of respondents on the importance of the KPI.

𝐶𝑉 =𝜎

𝜇 (9)

For each KPI, a 𝑍 value was calculated as Equation (10). The goal was to

find the highest value of 𝑍-value. This objective ensured that the selected KPIs

have both: a) the highest number of respondents selecting them and b) the

lowest CV value (combining the mean and STD of the scores). This meant

that the questionnaire respondents were consistent in their importance for the

given KPIs.

𝑍 = 𝑁𝑣𝑜𝑡𝑒 + (1-𝑁𝐶𝑉) (10)

Here, 𝑁𝑣𝑜𝑡𝑒 represents the normalized numbers of votes, and. 𝑁𝐶𝑉 repre-

sents the normalized CV (“min-max” normalization was performed).

3.5 Potential availability analysis

Paper V proposed a GIS-based approach combined with energy system mod-

eling to facilitate a feasibility analysis of the geographic and technical poten-

tial of roof-mounted solar PV systems. The methodology consisted of three

phases. The first phase was to assess the geographical potential for the exploi-

tation of rooftop solar energy on buildings in Västerås, Sweden, including res-

idential buildings, industrial buildings, buildings of social function, buildings

of commercial function, buildings of economic/agricultural function, build-

ings of complementary function, and buildings of other unknown function. A

step-by-step procedure for estimating the total rooftop solar PV potential was

established, including geographic data division and classification, surface area

calculation, roof orientation analysis, and roof shading and obstruction analy-

sis. The second stage was to assess the technical potential for installing solar

PV systems. For flat roofs, the distance between rows of solar panels and the

tilt angle were designed according to three scenarios. And the electricity gen-

eration could be calculated corresponding to different scenarios, based on the

32

performance of the multi-energy systems. The greater the number of KPIs, the

less focused they were on the specific objectives they were intended to demon-

strate. Therefore, five KPIs in the "Technical" category, five KPIs in the "Eco-

nomic" category, three KPIs in the "Environmental" category, and three KPIs

in the "Social/Policy" category should be selected from the questionnaire anal-

ysis.

The selection of the most important KPIs was identified based on the num-

ber of votes, the mean of votes (ranging from 0 to 7), and the standard devia-

tion of votes (STD). The Coefficient of Variation (CV) could very well repre-

sent the mean of votes and the STD, defining as the ratio of the STD 𝜎 to the

mean 𝜇 (Equation (9)). CV was a measure that showed the degree of variation

relative to the population mean, and in Paper IV, it showed the overall agree-

ment of respondents on the importance of the KPI.

𝐶𝑉 =𝜎

𝜇 (9)

For each KPI, a 𝑍 value was calculated as Equation (10). The goal was to

find the highest value of 𝑍-value. This objective ensured that the selected KPIs

have both: a) the highest number of respondents selecting them and b) the

lowest CV value (combining the mean and STD of the scores). This meant

that the questionnaire respondents were consistent in their importance for the

given KPIs.

𝑍 = 𝑁𝑣𝑜𝑡𝑒 + (1-𝑁𝐶𝑉) (10)

Here, 𝑁𝑣𝑜𝑡𝑒 represents the normalized numbers of votes, and. 𝑁𝐶𝑉 repre-

sents the normalized CV (“min-max” normalization was performed).

3.5 Potential availability analysis

Paper V proposed a GIS-based approach combined with energy system mod-

eling to facilitate a feasibility analysis of the geographic and technical poten-

tial of roof-mounted solar PV systems. The methodology consisted of three

phases. The first phase was to assess the geographical potential for the exploi-

tation of rooftop solar energy on buildings in Västerås, Sweden, including res-

idential buildings, industrial buildings, buildings of social function, buildings

of commercial function, buildings of economic/agricultural function, build-

ings of complementary function, and buildings of other unknown function. A

step-by-step procedure for estimating the total rooftop solar PV potential was

established, including geographic data division and classification, surface area

calculation, roof orientation analysis, and roof shading and obstruction analy-

sis. The second stage was to assess the technical potential for installing solar

PV systems. For flat roofs, the distance between rows of solar panels and the

tilt angle were designed according to three scenarios. And the electricity gen-

eration could be calculated corresponding to different scenarios, based on the

51

33

commercial solar PV modules. The third stage was to extrapolate the method-

ology from the city scale to the national scale to reveal the potential available

roof area and installed capacity for the entire country.

3.5.1 Usable roof area estimation

In the geographical potential analysis, both absolute and relative reductions of

the roof area were performed. The absolute reductions were applied to the

roofs of special use, e.g. with cultural-heritage value, and thus difficult to ob-

tain building permits. The relative reductions were applied to the roofs with

different utilization factors, due to orientation, shadows and obstructions.

Different roof shapes exist in Sweden, for example, gable roofs, mansard

roofs, flat roofs, and so on (Swedish Wood (Svenskt Trä), 2020) (Kamp,

2013). In Paper V, an assumption about the roof type and the slope of pitched

roofs was obtained from Lingfors and Widén (Lingfors and Widén, 2014)

(Lingfors and Widén, 2018b) and Kamp (Kamp, 2013). The pitched roof sur-

face area could be calculated according to Equation (11).

𝐴𝑟𝑜𝑜𝑓 = 2 ∙ 𝑚 ∙ 𝑞 = 2 ∙𝑝

𝑐𝑜𝑠 𝛼𝑟𝑜𝑜𝑓∙ 𝑞 =

𝐴𝑏𝑎𝑠𝑒

𝑐𝑜𝑠 𝛼𝑟𝑜𝑜𝑓 (11)

Where 𝐴𝑟𝑜𝑜𝑓 is the surface roof area, 𝐴𝑏𝑎𝑠𝑒 is the base area, 𝛼𝑟𝑜𝑜𝑓 is the

slope angle of the roof. 𝑚 , 𝑝 , and 𝑞 are the length of the roof ridge, counter

beam, and hanging beam. The calculations gave the total area of ideal ceiling

surfaces. More information of calculating could be found in Paper V.

To determine the orientation of the roofs, Paper V used the sampling

method within the first ring road of the city of Västerås as a sample, where six

types of buildings were involved (excluding the industrial buildings). Then

the roof orientation was obtained by the use of Google Earth ProTM. Only the

roofs facing South, South West, South East, West, and East were considered

to be applicable for solar PV systems. Considering building orientations, the

fraction of property oriented roof area can be computed as follows (Equation

(12)), using the approach (Mansouri et al., 2019):

𝑈𝑜𝑟𝑖 = 𝑅𝑓𝑙𝑎 ∙ 𝑟𝑓𝑙𝑎 + 𝑅𝑢_𝑝𝑖𝑡 ∙ 𝑟𝑢_𝑝𝑖𝑡 + 𝑅𝑛_𝑝𝑖𝑡 ∙ 𝑟𝑛_𝑝𝑖𝑡 (12)

Where 𝑈𝑜𝑟𝑖 is the utilization factor of orientation, 𝑅𝑓𝑙𝑎 is the flat roof per-

cent, 𝑟𝑓𝑙𝑎 is the orientation role for flat roofs, 𝑅𝑢_𝑝𝑖𝑡 is the percent of applica-

ble orientations for solar PV installations,𝑟𝑢_𝑝𝑖𝑡 is the orientation role for roofs

with applicable orientations, 𝑅𝑛_𝑝𝑖𝑡 is the percent of not-applicable orienta-

tions for solar PV installations, and 𝑟𝑛_𝑝𝑖𝑡 is the orientation role for roofs with

not-applicable orientations. 𝑅𝑢_𝑝𝑖𝑡 = 𝑅𝑠 + 𝑅𝑠𝑤 + 𝑅𝑠𝑒 + 𝑅𝑤 + 𝑅𝑒 , and

𝑅𝑛_𝑝𝑖𝑡 = 𝑅𝑛 + 𝑅𝑛𝑒 + 𝑅𝑛𝑤 ; 𝑅𝑠 , 𝑅𝑠𝑤 , 𝑅𝑠𝑒 ,𝑅𝑤 , 𝑅𝑒 , 𝑅𝑛 , 𝑅𝑛𝑒 , 𝑅𝑛𝑤 are the per-

cent with the orientation of South, South West, South East, West, East, North,

North East, and North West, respectively. The sampling took place only on

non-industrial buildings.

In considering the shadows and obstacles, different utilization factors were

extracted from literatures (Equations (13) and (14)(Kamp, 2013))

33

commercial solar PV modules. The third stage was to extrapolate the method-

ology from the city scale to the national scale to reveal the potential available

roof area and installed capacity for the entire country.

3.5.1 Usable roof area estimation

In the geographical potential analysis, both absolute and relative reductions of

the roof area were performed. The absolute reductions were applied to the

roofs of special use, e.g. with cultural-heritage value, and thus difficult to ob-

tain building permits. The relative reductions were applied to the roofs with

different utilization factors, due to orientation, shadows and obstructions.

Different roof shapes exist in Sweden, for example, gable roofs, mansard

roofs, flat roofs, and so on (Swedish Wood (Svenskt Trä), 2020) (Kamp,

2013). In Paper V, an assumption about the roof type and the slope of pitched

roofs was obtained from Lingfors and Widén (Lingfors and Widén, 2014)

(Lingfors and Widén, 2018b) and Kamp (Kamp, 2013). The pitched roof sur-

face area could be calculated according to Equation (11).

𝐴𝑟𝑜𝑜𝑓 = 2 ∙ 𝑚 ∙ 𝑞 = 2 ∙𝑝

𝑐𝑜𝑠 𝛼𝑟𝑜𝑜𝑓∙ 𝑞 =

𝐴𝑏𝑎𝑠𝑒

𝑐𝑜𝑠 𝛼𝑟𝑜𝑜𝑓 (11)

Where 𝐴𝑟𝑜𝑜𝑓 is the surface roof area, 𝐴𝑏𝑎𝑠𝑒 is the base area, 𝛼𝑟𝑜𝑜𝑓 is the

slope angle of the roof. 𝑚 , 𝑝 , and 𝑞 are the length of the roof ridge, counter

beam, and hanging beam. The calculations gave the total area of ideal ceiling

surfaces. More information of calculating could be found in Paper V.

To determine the orientation of the roofs, Paper V used the sampling

method within the first ring road of the city of Västerås as a sample, where six

types of buildings were involved (excluding the industrial buildings). Then

the roof orientation was obtained by the use of Google Earth ProTM. Only the

roofs facing South, South West, South East, West, and East were considered

to be applicable for solar PV systems. Considering building orientations, the

fraction of property oriented roof area can be computed as follows (Equation

(12)), using the approach (Mansouri et al., 2019):

𝑈𝑜𝑟𝑖 = 𝑅𝑓𝑙𝑎 ∙ 𝑟𝑓𝑙𝑎 + 𝑅𝑢_𝑝𝑖𝑡 ∙ 𝑟𝑢_𝑝𝑖𝑡 + 𝑅𝑛_𝑝𝑖𝑡 ∙ 𝑟𝑛_𝑝𝑖𝑡 (12)

Where 𝑈𝑜𝑟𝑖 is the utilization factor of orientation, 𝑅𝑓𝑙𝑎 is the flat roof per-

cent, 𝑟𝑓𝑙𝑎 is the orientation role for flat roofs, 𝑅𝑢_𝑝𝑖𝑡 is the percent of applica-

ble orientations for solar PV installations,𝑟𝑢_𝑝𝑖𝑡 is the orientation role for roofs

with applicable orientations, 𝑅𝑛_𝑝𝑖𝑡 is the percent of not-applicable orienta-

tions for solar PV installations, and 𝑟𝑛_𝑝𝑖𝑡 is the orientation role for roofs with

not-applicable orientations. 𝑅𝑢_𝑝𝑖𝑡 = 𝑅𝑠 + 𝑅𝑠𝑤 + 𝑅𝑠𝑒 + 𝑅𝑤 + 𝑅𝑒 , and

𝑅𝑛_𝑝𝑖𝑡 = 𝑅𝑛 + 𝑅𝑛𝑒 + 𝑅𝑛𝑤 ; 𝑅𝑠 , 𝑅𝑠𝑤 , 𝑅𝑠𝑒 ,𝑅𝑤 , 𝑅𝑒 , 𝑅𝑛 , 𝑅𝑛𝑒 , 𝑅𝑛𝑤 are the per-

cent with the orientation of South, South West, South East, West, East, North,

North East, and North West, respectively. The sampling took place only on

non-industrial buildings.

In considering the shadows and obstacles, different utilization factors were

extracted from literatures (Equations (13) and (14)(Kamp, 2013))

52

34

𝑈𝑠𝑜𝑖𝑛𝑑 = 1 − 0.3 = 0.7 (13)

𝑈𝑠𝑜𝑛𝑜_𝑖𝑛𝑑 = 1 − 0.25 = 0.75 (14)

Where, 𝑈𝑠𝑜𝑖𝑛𝑑 is the utilization factor due to shadows and obstacles for in-

dustrial buildings. 𝑈𝑠𝑜𝑛𝑜_𝑖𝑛𝑑

is the utilization factor due to shadows and obsta-

cles for non-industrial buildings.

The total useable roof area for solar PV systems in entire Sweden was cal-

culated based on Equation (15).

𝐴𝑝𝑣𝑆𝐸 = ∑ (

𝐴𝑏𝑎𝑠𝑒𝑚 ∙𝑈𝑎𝑏𝑠

cos 𝛼𝑟𝑜𝑜𝑓 ∙ 𝑈𝑜𝑟𝑖 ∙ 𝑈𝑠𝑜)290𝑚𝑢=1 (15)

Where 𝐴𝑝𝑣𝑆𝐸 is the usable area for roof-mounted PV systems in Sweden,

𝑚𝑢 = 1,2,3 … ,290 (290 municipalities), 𝐴𝑏𝑎𝑠𝑒𝑚 is the building base area of

the 𝑚𝑡ℎ municipality, 𝑈𝑎𝑏𝑠 is the absolute reduction factor due to the cultural

and religious purpose of buildings, 𝛼𝑟𝑜𝑜𝑓 is the slope angle of the roof, 𝑈𝑜𝑟𝑖

is the utilization factor of orientation, 𝑈𝑠𝑜 is the shadows and obstacles factor.

3.5.2 Configurations of PV module and energy conversion

In paper V, the angle of PV modules was in parallel to the roof plane for in-

stallations on pitched roofs (Mainzer et al., 2014). Three scenarios, i.e., Sce-

nario A, Scenario B, and Scenario C, were applied to determine the angles of

PV modules and appropriate inter-row distances, for those mounted on flat

roofs. This was to consider that power generation could be affected by mutual

shading effects between each row. In Scenarios A and B, the PV modules were

installed in rows and oriented true South. In scenario C, the PV modules were

installed in the East-West direction with a relatively small tilt angle. These

three scenarios were compared as theoretical design (Scenario A) and com-

mercial solutions (Scenario B and Scenario C).

Scenarios A and Scenario B investigated two different combinations of tilt

angle and row spacing to cope with different inter-row shading effects. In Sce-

nario A, Paper V employed the longer row spacing to minimize the mutual

shading effects. Firstly, the tilt angle was calculated in Equation (16)

(Campana et al., 2020) (Swedish Meteorological and Hydrological Institute

(SMHI), 2017). Then the distance between two rows was determined by Pack-

ing Factor (PF) (Equation (17)) (Martín-chivelet, 2016) to avoid mutual shad-

ing at noon of winter solstice. The Figure 7 shows the schematic diagram of

the required distance between rows of solar panels corresponding to different

solar elevation angles in Scenario A.

𝛽𝑜𝑝𝑡 = −0.1101𝜙2 + 14.003𝜙 − 404.77 (16)

Where 𝛽𝑜𝑝𝑡 is the optimal tilt angle of PV module. 𝜙 is the latitude.

𝑃𝐹 =𝑙

𝑑= (𝑐𝑜𝑠 𝛽 +

𝑠𝑖𝑛 𝛽

𝑡𝑎𝑛 𝛼𝑐𝑜𝑠 𝛾)−1 (17)

Where 𝛽 is the tilt angle of the PV array, 𝛼 is the solar elevation defined

by Equation (18) and 𝛾 is the solar azimuth.

𝑐𝑜𝑠 𝛼 = 𝑠𝑖𝑛 𝛿 𝑠𝑖𝑛 𝜙 + 𝑐𝑜𝑠 𝛿 𝑐𝑜𝑠 𝜙 𝑐𝑜𝑠 𝜔 (18)

34

𝑈𝑠𝑜𝑖𝑛𝑑 = 1 − 0.3 = 0.7 (13)

𝑈𝑠𝑜𝑛𝑜_𝑖𝑛𝑑 = 1 − 0.25 = 0.75 (14)

Where, 𝑈𝑠𝑜𝑖𝑛𝑑 is the utilization factor due to shadows and obstacles for in-

dustrial buildings. 𝑈𝑠𝑜𝑛𝑜_𝑖𝑛𝑑

is the utilization factor due to shadows and obsta-

cles for non-industrial buildings.

The total useable roof area for solar PV systems in entire Sweden was cal-

culated based on Equation (15).

𝐴𝑝𝑣𝑆𝐸 = ∑ (

𝐴𝑏𝑎𝑠𝑒𝑚 ∙𝑈𝑎𝑏𝑠

cos 𝛼𝑟𝑜𝑜𝑓 ∙ 𝑈𝑜𝑟𝑖 ∙ 𝑈𝑠𝑜)290𝑚𝑢=1 (15)

Where 𝐴𝑝𝑣𝑆𝐸 is the usable area for roof-mounted PV systems in Sweden,

𝑚𝑢 = 1,2,3 … ,290 (290 municipalities), 𝐴𝑏𝑎𝑠𝑒𝑚 is the building base area of

the 𝑚𝑡ℎ municipality, 𝑈𝑎𝑏𝑠 is the absolute reduction factor due to the cultural

and religious purpose of buildings, 𝛼𝑟𝑜𝑜𝑓 is the slope angle of the roof, 𝑈𝑜𝑟𝑖

is the utilization factor of orientation, 𝑈𝑠𝑜 is the shadows and obstacles factor.

3.5.2 Configurations of PV module and energy conversion

In paper V, the angle of PV modules was in parallel to the roof plane for in-

stallations on pitched roofs (Mainzer et al., 2014). Three scenarios, i.e., Sce-

nario A, Scenario B, and Scenario C, were applied to determine the angles of

PV modules and appropriate inter-row distances, for those mounted on flat

roofs. This was to consider that power generation could be affected by mutual

shading effects between each row. In Scenarios A and B, the PV modules were

installed in rows and oriented true South. In scenario C, the PV modules were

installed in the East-West direction with a relatively small tilt angle. These

three scenarios were compared as theoretical design (Scenario A) and com-

mercial solutions (Scenario B and Scenario C).

Scenarios A and Scenario B investigated two different combinations of tilt

angle and row spacing to cope with different inter-row shading effects. In Sce-

nario A, Paper V employed the longer row spacing to minimize the mutual

shading effects. Firstly, the tilt angle was calculated in Equation (16)

(Campana et al., 2020) (Swedish Meteorological and Hydrological Institute

(SMHI), 2017). Then the distance between two rows was determined by Pack-

ing Factor (PF) (Equation (17)) (Martín-chivelet, 2016) to avoid mutual shad-

ing at noon of winter solstice. The Figure 7 shows the schematic diagram of

the required distance between rows of solar panels corresponding to different

solar elevation angles in Scenario A.

𝛽𝑜𝑝𝑡 = −0.1101𝜙2 + 14.003𝜙 − 404.77 (16)

Where 𝛽𝑜𝑝𝑡 is the optimal tilt angle of PV module. 𝜙 is the latitude.

𝑃𝐹 =𝑙

𝑑= (𝑐𝑜𝑠 𝛽 +

𝑠𝑖𝑛 𝛽

𝑡𝑎𝑛 𝛼𝑐𝑜𝑠 𝛾)−1 (17)

Where 𝛽 is the tilt angle of the PV array, 𝛼 is the solar elevation defined

by Equation (18) and 𝛾 is the solar azimuth.

𝑐𝑜𝑠 𝛼 = 𝑠𝑖𝑛 𝛿 𝑠𝑖𝑛 𝜙 + 𝑐𝑜𝑠 𝛿 𝑐𝑜𝑠 𝜙 𝑐𝑜𝑠 𝜔 (18)

53

35

Where 𝛿 is the earth’s declination, and 𝜔 is the hour angle.

Figure 7. Schematic diagram of the required distance between rows of solar panels

in scenario A.

In scenario B, Paper V redefined a 35 kWp solar PV system in 10 rows as

the reference system. In this scenario, we looked for the tilt angle and row

distance which could enable higher electricity generation in a given area

(higher value of kWh/m2), higher potential installed capacity (with smaller

inter-row distances), and lower shading losses. Parametric simulations were

run in PVsyst software (V6.87) using the most accurate simulation method

that considered shadings according to modules orientation and strings defini-

tion.

In Scenario C shown in Figure 8 as a comparison to scenario A and B, an

East-West orientation with a low tilt angle was employed in the analysis.

Proven by previous studies (Asgharzadeh et al., 2018) and (Hartner et al.,

2015), this configuration has become popular on flat roofs. Because it enabled

the modules to fit more tightly together, therefore, gave higher installed ca-

pacities with more economic land-use. Although such an installation gener-

ated less electricity per installed kWp comparing to South-oriented system, it

also made less significant mutual shadings.

35

Where 𝛿 is the earth’s declination, and 𝜔 is the hour angle.

Figure 7. Schematic diagram of the required distance between rows of solar panels

in scenario A.

In scenario B, Paper V redefined a 35 kWp solar PV system in 10 rows as

the reference system. In this scenario, we looked for the tilt angle and row

distance which could enable higher electricity generation in a given area

(higher value of kWh/m2), higher potential installed capacity (with smaller

inter-row distances), and lower shading losses. Parametric simulations were

run in PVsyst software (V6.87) using the most accurate simulation method

that considered shadings according to modules orientation and strings defini-

tion.

In Scenario C shown in Figure 8 as a comparison to scenario A and B, an

East-West orientation with a low tilt angle was employed in the analysis.

Proven by previous studies (Asgharzadeh et al., 2018) and (Hartner et al.,

2015), this configuration has become popular on flat roofs. Because it enabled

the modules to fit more tightly together, therefore, gave higher installed ca-

pacities with more economic land-use. Although such an installation gener-

ated less electricity per installed kWp comparing to South-oriented system, it

also made less significant mutual shadings.

54

36

Figure 8. East-West orientation configuration with a low tilt angle in scenario C.

In Paper V, Jinko Solar PV modules (JKMS 350M-72V Maxim) were used

with 350Wp of STC Power Rating, 20.5 % of cells area efficiency, and a 0.992

x 1.956 m² module size. The potential capacity and electricity generation were

calculated in PVsyst (V6.87) with the geographical data, module data, and

system assumptions.

36

Figure 8. East-West orientation configuration with a low tilt angle in scenario C.

In Paper V, Jinko Solar PV modules (JKMS 350M-72V Maxim) were used

with 350Wp of STC Power Rating, 20.5 % of cells area efficiency, and a 0.992

x 1.956 m² module size. The potential capacity and electricity generation were

calculated in PVsyst (V6.87) with the geographical data, module data, and

system assumptions.

55

37

4 Results

This chapter presents the results from the analysis of the grid party capability,

economic feasibility, and investment values in the market of China, Service-

Dominant logic explanation in smart city transformation in Sweden, and po-

tential availability. This chapter also presents the interpretation of the results

from the investigations.

4.1 Grid parity of user-side and plant-side (Paper I)

In Paper I, the results revealed all Chinese cities could achieve 100% grid

parity of user-side as shown in Figure 9(a). The darker the color, the better the

User-side Grid Parity condition. The cities marked with name and locations

were those with GPIu ≤ 0.40 or ≥ 0.80. The MCA showed the electricity gen-

eration was the most sensitive to the LCOE values. This factor together with

electricity market price determined the GPIu value. Cities with better GPIu

conditions located in northwestern Tibet, northeastern provinces, Inner Mon-

golia, and so on, due to the higher solar radiation and higher electricity market

prices. In Inner Mongolia, four cities of East Grid had better GPIu conditions

than those eight cities of West Grid under similar solar radiation resources

(Resource Zone I). This could be explained by the higher electricity market

prices in the East Grid (0.77 CNY/kWh compared to 0.59 CNY/kWh). An-

other example is that four cities in the province of Sichuan performed signifi-

cantly better than the other 17 cities with the same electricity market price,

since those four cities located on a plateau with more abundant solar radiation.

Cities in Chongqing province, locating in the eastern part of the Sichuan Ba-

sin, had the worst GPIu values. Chongqing province had the lowest average

solar availability capacity, less than 100 GW. Still, despite its meteorological

disadvantages, Chongqing could nevertheless achieve a lower LCOE than the

electricity market price.

37

4 Results

This chapter presents the results from the analysis of the grid party capability,

economic feasibility, and investment values in the market of China, Service-

Dominant logic explanation in smart city transformation in Sweden, and po-

tential availability. This chapter also presents the interpretation of the results

from the investigations.

4.1 Grid parity of user-side and plant-side (Paper I)

In Paper I, the results revealed all Chinese cities could achieve 100% grid

parity of user-side as shown in Figure 9(a). The darker the color, the better the

User-side Grid Parity condition. The cities marked with name and locations

were those with GPIu ≤ 0.40 or ≥ 0.80. The MCA showed the electricity gen-

eration was the most sensitive to the LCOE values. This factor together with

electricity market price determined the GPIu value. Cities with better GPIu

conditions located in northwestern Tibet, northeastern provinces, Inner Mon-

golia, and so on, due to the higher solar radiation and higher electricity market

prices. In Inner Mongolia, four cities of East Grid had better GPIu conditions

than those eight cities of West Grid under similar solar radiation resources

(Resource Zone I). This could be explained by the higher electricity market

prices in the East Grid (0.77 CNY/kWh compared to 0.59 CNY/kWh). An-

other example is that four cities in the province of Sichuan performed signifi-

cantly better than the other 17 cities with the same electricity market price,

since those four cities located on a plateau with more abundant solar radiation.

Cities in Chongqing province, locating in the eastern part of the Sichuan Ba-

sin, had the worst GPIu values. Chongqing province had the lowest average

solar availability capacity, less than 100 GW. Still, despite its meteorological

disadvantages, Chongqing could nevertheless achieve a lower LCOE than the

electricity market price.

56

38

Figure 9. Grid Parity Indices for distributed PV projects in 344 cities without subsi-

dies.

Among 344 cities, 76 cities (22.09%) had GPIp strictly below 1, i.e., LCOE

achieved below the desulfurized coal benchmark price (Figure 9(b)). Dalian,

Liaoning Province, had the highest GPIp level, followed by cities in Hainan,

Heilongjiang, Sichuan, and Qinghai provinces. Accordingly, 58.6% of prov-

inces (18 out of 31) have achieved plant-side grid parity, including most of the

northeastern provinces, northern provinces, western provinces, and some

southern provinces. Through the MCA, the abundance level of solar radiation

was one of the main contributors to GPIp, similar to GPIu. The local desulfu-

rized coal benchmark price was another sensitive factor. Chongqing had the

worst Plant-side Grid Parity condition because of low average solar radiation

and relatively low desulfurized coal benchmark price. Apart from the 76 cities

with Plant-side Grid Parity, notably, another 99 cities had GPIp values slightly

higher than but very close to 1.0 (they range from 1.01 to 1.09). That was a

positive sign that the expected grid parity of solar PV was close to becoming

widespread in many cities.

4.2 Investment profitability (Paper I & II)

Both GPIu and GPIp compared the cost of solar PV generation to external

costs, and neither included revenue in Paper I. Therefore, the Levelized Profit

of Electricity (LPOE) was proposed which measured the net revenue per kWh

of electricity from solar PV generation without subsidies. LPOE was defined

as the discounted net profit divided by the discounted net power generation,

where the discounted net profit included the discounted net income and net

cost. In the analysis, there were 92.73% of the cities reached the net profit

status of electricity based on 50% self-generation and 50% feed-in (See Figure

10(a). The cities marked with name and locations were those with: LPOE ≤

38

Figure 9. Grid Parity Indices for distributed PV projects in 344 cities without subsi-

dies.

Among 344 cities, 76 cities (22.09%) had GPIp strictly below 1, i.e., LCOE

achieved below the desulfurized coal benchmark price (Figure 9(b)). Dalian,

Liaoning Province, had the highest GPIp level, followed by cities in Hainan,

Heilongjiang, Sichuan, and Qinghai provinces. Accordingly, 58.6% of prov-

inces (18 out of 31) have achieved plant-side grid parity, including most of the

northeastern provinces, northern provinces, western provinces, and some

southern provinces. Through the MCA, the abundance level of solar radiation

was one of the main contributors to GPIp, similar to GPIu. The local desulfu-

rized coal benchmark price was another sensitive factor. Chongqing had the

worst Plant-side Grid Parity condition because of low average solar radiation

and relatively low desulfurized coal benchmark price. Apart from the 76 cities

with Plant-side Grid Parity, notably, another 99 cities had GPIp values slightly

higher than but very close to 1.0 (they range from 1.01 to 1.09). That was a

positive sign that the expected grid parity of solar PV was close to becoming

widespread in many cities.

4.2 Investment profitability (Paper I & II)

Both GPIu and GPIp compared the cost of solar PV generation to external

costs, and neither included revenue in Paper I. Therefore, the Levelized Profit

of Electricity (LPOE) was proposed which measured the net revenue per kWh

of electricity from solar PV generation without subsidies. LPOE was defined

as the discounted net profit divided by the discounted net power generation,

where the discounted net profit included the discounted net income and net

cost. In the analysis, there were 92.73% of the cities reached the net profit

status of electricity based on 50% self-generation and 50% feed-in (See Figure

10(a). The cities marked with name and locations were those with: LPOE ≤

57

39

0.0, or LPOE ≥ 0.20). A more detailed city analysis could be found in Paper

I.

For the investors, the investment indices, such as IRR, could be more pre-

ferred, as it indicated that an applicable investment when the IRR was higher

than the cost of capital. In Paper I, 83.72% of the cities had an IRR higher than

8%. This indicated that investing in distributed solar PV systems in those cities

could bring a higher economic return based on pre-assumed investment costs

(e.g. an 8% bank interest rate and a 5-year loan period) (See Figure 10(b). The

cities marked with name and locations were those with: IRR ≤ 5.0% or IRR ≥

20.0%). Approximately, there were 83% of the cities with an IRR higher than

8% and 10% of the cities with an IRR higher than 20% - very high profitabil-

ity. 67% of the cities’ DPBPs were less than 15 years (See Figure 10(c). The

cities marked with name and locations were those with: DPBP ≤5.0, or DPBP

≥ 20.0).

Figure 10. Economic feasibility and profitability of solar PV in 344 cities without

subsidies.

39

0.0, or LPOE ≥ 0.20). A more detailed city analysis could be found in Paper

I.

For the investors, the investment indices, such as IRR, could be more pre-

ferred, as it indicated that an applicable investment when the IRR was higher

than the cost of capital. In Paper I, 83.72% of the cities had an IRR higher than

8%. This indicated that investing in distributed solar PV systems in those cities

could bring a higher economic return based on pre-assumed investment costs

(e.g. an 8% bank interest rate and a 5-year loan period) (See Figure 10(b). The

cities marked with name and locations were those with: IRR ≤ 5.0% or IRR ≥

20.0%). Approximately, there were 83% of the cities with an IRR higher than

8% and 10% of the cities with an IRR higher than 20% - very high profitabil-

ity. 67% of the cities’ DPBPs were less than 15 years (See Figure 10(c). The

cities marked with name and locations were those with: DPBP ≤5.0, or DPBP

≥ 20.0).

Figure 10. Economic feasibility and profitability of solar PV in 344 cities without

subsidies.

58

40

In the United States and China, two of the world's most important econo-

mies, coal remained firmly the largest (66.54% by 2018) and second-largest

(27.90% by 2018) source of electricity among other energy sources (Dudley,

2018). In the coming years, coal-fired power plants were scheduled to be shut

down as non-fossil fuels such as solar energy gradually replace them. In the

United States, coal plants have already been decommissioned since 2010. One

of the main reasons for these continued retirements was the poor economics

of coal power plants. In China, the retirement of coal plants has not yet started

despite the expansion of alternative energy sources. In this section, the cost-

risk analysis was used to determine the different levels of cost-risk for cur-

rently operating coal-fired power plants versus the distributed solar PV pro-

jects.

Using a cost crossover algorithm, this study identified 76 cities with oper-

ating coal-fired power plants that were at cost-risk from distributed solar PV

projects, i.e., these operating coal-fired power plants can be replaced by dis-

tributed solar PV projects that were 0-25% cheaper in cost. There were 217

cities with coal-fired power plants that were potentially at cost-risk from dis-

tributed solar PV projects, i.e., where the local DCB price was 0-25% lower

than the distributed PV LCOE. Finally, 51 cities had operating coal-fired

power plants that were considered to be deemed no cost-risk, i.e., the local

DCB price was more than 25% lower than the local solar PV LCOE. Overall,

85% of the coal-fired power plants in the surveyed cities were currently gen-

erally at risk. The results are shown in Figure 11. A more detailed cost com-

parison analysis between China and the United States could be found in Paper

II.

Figure 11. Current running coal-fired power plants cost-risk levels and city number

percent in China and the US.

40

In the United States and China, two of the world's most important econo-

mies, coal remained firmly the largest (66.54% by 2018) and second-largest

(27.90% by 2018) source of electricity among other energy sources (Dudley,

2018). In the coming years, coal-fired power plants were scheduled to be shut

down as non-fossil fuels such as solar energy gradually replace them. In the

United States, coal plants have already been decommissioned since 2010. One

of the main reasons for these continued retirements was the poor economics

of coal power plants. In China, the retirement of coal plants has not yet started

despite the expansion of alternative energy sources. In this section, the cost-

risk analysis was used to determine the different levels of cost-risk for cur-

rently operating coal-fired power plants versus the distributed solar PV pro-

jects.

Using a cost crossover algorithm, this study identified 76 cities with oper-

ating coal-fired power plants that were at cost-risk from distributed solar PV

projects, i.e., these operating coal-fired power plants can be replaced by dis-

tributed solar PV projects that were 0-25% cheaper in cost. There were 217

cities with coal-fired power plants that were potentially at cost-risk from dis-

tributed solar PV projects, i.e., where the local DCB price was 0-25% lower

than the distributed PV LCOE. Finally, 51 cities had operating coal-fired

power plants that were considered to be deemed no cost-risk, i.e., the local

DCB price was more than 25% lower than the local solar PV LCOE. Overall,

85% of the coal-fired power plants in the surveyed cities were currently gen-

erally at risk. The results are shown in Figure 11. A more detailed cost com-

parison analysis between China and the United States could be found in Paper

II.

Figure 11. Current running coal-fired power plants cost-risk levels and city number

percent in China and the US.

59

41

From the geographical map below (Figure 12), operating coal-fired power

plants in the northwest and Sichuan basin were at low-level risk in costs. A

more detailed geographical analysis could be found in Paper II.

Figure 12. The four cost-risk levels of current coal-fired power plants and their cor-

responding cities in China.

After running the K-means clustering algorithm in SPSS® software, all 344

cities were divided into four clusters. The clustering results can be shown in

Figure 13. City cluster 1 had the superiority of investment-profits over the

other three city clusters.

41

From the geographical map below (Figure 12), operating coal-fired power

plants in the northwest and Sichuan basin were at low-level risk in costs. A

more detailed geographical analysis could be found in Paper II.

Figure 12. The four cost-risk levels of current coal-fired power plants and their cor-

responding cities in China.

After running the K-means clustering algorithm in SPSS® software, all 344

cities were divided into four clusters. The clustering results can be shown in

Figure 13. City cluster 1 had the superiority of investment-profits over the

other three city clusters.

60

42

Figure 13. Four city clusters of distributed solar PV investment-profits.

The cities in clusters 1 and 2 - concentrated in the northwest and northeast

- were characterized by large land areas, abundant solar resources, and rela-

tively sparse populations. In these regions, the curtailment of large-scale solar

energy production was an important issue. In 2017, the curtailment rates in

Xinjiang and Gansu were 21.6% and 20.8%, respectively, accounting for 70%

of the curtailment rate in the northwest of China (Tang et al., 2018). However,

distributed solar PV could be a better solution. Investing in urban clusters 1

and 2 had the following advantages: higher levels of solar resources and com-

petitive market prices. According to MCA, solar radiation, market price of

electricity, and self-consumption ratio were the most sensitive variables re-

garding project benefits (represented as LPOE). The annual solar power pro-

duction of clusters 1 and 2 varied from 1,280 to 1,976 kWh/kWp with an av-

erage of 1,509 kWh/kWp. The market price can be as high as 1.8 CNY/kWh

with an average price of 0.74 CNY/kWh. The strong solar radiation and the

electricity market price made it possible to obtain a high return on investment.

However, a strong grid infrastructure to support inter-provincial transmission

was expected due to the relatively low demand for electricity. Investing in cluster 3 - containing cities scattered in the southeast, south-

west, and parts of the north - had the advantage of higher electricity demand.

Many manufacturing companies were regionally clustered in these areas, es-

pecially in the eastern coastal cities with good market access (He, Wei and

Pan, 2007). Investing in distributed solar PV applications would benefit from

high electricity demand and abundant rooftop area, achieving economies of

scale. However, a well-designed and market-based solar power trading system

was necessary for cities in those cities. Specifically, this referred to the design

42

Figure 13. Four city clusters of distributed solar PV investment-profits.

The cities in clusters 1 and 2 - concentrated in the northwest and northeast

- were characterized by large land areas, abundant solar resources, and rela-

tively sparse populations. In these regions, the curtailment of large-scale solar

energy production was an important issue. In 2017, the curtailment rates in

Xinjiang and Gansu were 21.6% and 20.8%, respectively, accounting for 70%

of the curtailment rate in the northwest of China (Tang et al., 2018). However,

distributed solar PV could be a better solution. Investing in urban clusters 1

and 2 had the following advantages: higher levels of solar resources and com-

petitive market prices. According to MCA, solar radiation, market price of

electricity, and self-consumption ratio were the most sensitive variables re-

garding project benefits (represented as LPOE). The annual solar power pro-

duction of clusters 1 and 2 varied from 1,280 to 1,976 kWh/kWp with an av-

erage of 1,509 kWh/kWp. The market price can be as high as 1.8 CNY/kWh

with an average price of 0.74 CNY/kWh. The strong solar radiation and the

electricity market price made it possible to obtain a high return on investment.

However, a strong grid infrastructure to support inter-provincial transmission

was expected due to the relatively low demand for electricity. Investing in cluster 3 - containing cities scattered in the southeast, south-

west, and parts of the north - had the advantage of higher electricity demand.

Many manufacturing companies were regionally clustered in these areas, es-

pecially in the eastern coastal cities with good market access (He, Wei and

Pan, 2007). Investing in distributed solar PV applications would benefit from

high electricity demand and abundant rooftop area, achieving economies of

scale. However, a well-designed and market-based solar power trading system

was necessary for cities in those cities. Specifically, this referred to the design

61

43

of tariffs regarding load matching and differences between grid inputs/out-

puts. This required the deregulation of the retail electricity market and the de-

velopment of interregional electricity trading, which would change the inter-

action between grid companies, generators, and end-users (Zhang, Andrews-

Speed and Li, 2018).

Compared to the other three city clusters, investing in cluster 4 was less

advantageous. However, investments can still be made in poverty alleviation

projects and public use projects, such as in urban lighting and traffic signals.

The PV poverty alleviation project launched in 2014 had an ambitious goal of

adding more than 10 GWp and benefiting more than 2 million households by

2020 (Zhang, Andrews-Speed and Li, 2018). Qinghai, Sichuan, Yunnan, and

Shaanxi provinces were among the major provinces with various incentives,

such as credit support from China Development Bank and special funds from

local governments (Li et al., 2018).

4.3 Electricity market in transformation (Paper III)

4.3.1 The traditional linear value chain market logic

Most forms of renewable energy production are spatially distributed, highly

variable, and less predictable than non-renewable energy sources such as oil

and coal as well as nuclear energy. In addition, some forms of renewable en-

ergy allow for small-scale, such as solar PV systems, and medium-scale en-

ergy production, which present challenges to current energy producers who are

often in oligopolistic positions. This challenges the operational and manage-

ment control of the power system, including the distribution of the generated

electricity, the financial results, and the laws and regulations to be followed.

Through the application of renewable energy, customers (households, compa-

nies, and public organizations) can move from being "pure" electricity users

and consumers to - through the installation of renewable energy products -

having ownership of energy production equipment and real-time monitoring

through mobile "apps". However, this also challenges the behavior of other

established actors (e.g. roles and practices) and the structure of the distribution

system. This new environment means that households, commercial com-

panies, and public organizations can be independent of the grid and can

also contribute to the overall energy production of the city.

The Swedish electricity market is technically and commercially part of

the Nordic electricity market. Since the 1990s it has been a deregulated

market, formalized by the Norwegian-Swedish Exchange (Nord Pool), the

world's first multinational exchange for trading electricity and utility

power contracts. The deregulation in the Nordic countries initially led to a

marked increase in price competition. However, the Swedish government

decided to separate the generation and supply of electricity (which became

43

of tariffs regarding load matching and differences between grid inputs/out-

puts. This required the deregulation of the retail electricity market and the de-

velopment of interregional electricity trading, which would change the inter-

action between grid companies, generators, and end-users (Zhang, Andrews-

Speed and Li, 2018).

Compared to the other three city clusters, investing in cluster 4 was less

advantageous. However, investments can still be made in poverty alleviation

projects and public use projects, such as in urban lighting and traffic signals.

The PV poverty alleviation project launched in 2014 had an ambitious goal of

adding more than 10 GWp and benefiting more than 2 million households by

2020 (Zhang, Andrews-Speed and Li, 2018). Qinghai, Sichuan, Yunnan, and

Shaanxi provinces were among the major provinces with various incentives,

such as credit support from China Development Bank and special funds from

local governments (Li et al., 2018).

4.3 Electricity market in transformation (Paper III)

4.3.1 The traditional linear value chain market logic

Most forms of renewable energy production are spatially distributed, highly

variable, and less predictable than non-renewable energy sources such as oil

and coal as well as nuclear energy. In addition, some forms of renewable en-

ergy allow for small-scale, such as solar PV systems, and medium-scale en-

ergy production, which present challenges to current energy producers who are

often in oligopolistic positions. This challenges the operational and manage-

ment control of the power system, including the distribution of the generated

electricity, the financial results, and the laws and regulations to be followed.

Through the application of renewable energy, customers (households, compa-

nies, and public organizations) can move from being "pure" electricity users

and consumers to - through the installation of renewable energy products -

having ownership of energy production equipment and real-time monitoring

through mobile "apps". However, this also challenges the behavior of other

established actors (e.g. roles and practices) and the structure of the distribution

system. This new environment means that households, commercial com-

panies, and public organizations can be independent of the grid and can

also contribute to the overall energy production of the city.

The Swedish electricity market is technically and commercially part of

the Nordic electricity market. Since the 1990s it has been a deregulated

market, formalized by the Norwegian-Swedish Exchange (Nord Pool), the

world's first multinational exchange for trading electricity and utility

power contracts. The deregulation in the Nordic countries initially led to a

marked increase in price competition. However, the Swedish government

decided to separate the generation and supply of electricity (which became

62

44

competitive) from grid and distribution activities (which remained non-

competitive).

The Swedish electricity market has traditionally followed a value chain

design (see Figure 14), starting with power generators and delivering elec-

tricity to the distribution network. The main part of electricity has tradi-

tionally been produced by hydro and nuclear power plants, while recently

there has been an increase in the production of wind power, biofuels, and

solar PV power. Producers sell the generated electricity to electricity trad-

ers or end-users. The five largest electricity producers in Sweden are Vat-

tenfall, Fortum, E.ON Sverige, Statkraft Sverige, and Skellefteå Kraft.

The electricity network established by distributors transports electricity

from the production sites to the end-users. This grid network is divided

into a national grid, a regional grid, and a local grid. Svenska Kraftnät

owns the national grid and plays the role of Transmission System Operator

(TSO), which means ensuring that power plants work together in a reliable

way and that production and imports correspond to consumption and ex-

ports. The TSO is responsible for maintaining a balance between national

production and consumption. It is regulated and reviewed by the Swedish

Energy Market Inspectorate. The regional network transmits electricity

from the grid to the local network and, in some cases, to large users such

as industry. Three large regional grid owners - E.ON Elnät Sverige, Vat-

tenfall Eldistribution, and Fortum Distribution - own most of the regional

grid in Sweden. As a result, these companies have both production and

distribution business units, which need to be strictly separated for legal

and regulatory reasons. Local grids distribute electricity to end-users

within a certain geographical area. Owners of local networks include gov-

ernments, municipalities, companies and non-governmental organizations,

and licenses for grid concessions are issued by the Swedish Energy Market

Inspectorate.

44

competitive) from grid and distribution activities (which remained non-

competitive).

The Swedish electricity market has traditionally followed a value chain

design (see Figure 14), starting with power generators and delivering elec-

tricity to the distribution network. The main part of electricity has tradi-

tionally been produced by hydro and nuclear power plants, while recently

there has been an increase in the production of wind power, biofuels, and

solar PV power. Producers sell the generated electricity to electricity trad-

ers or end-users. The five largest electricity producers in Sweden are Vat-

tenfall, Fortum, E.ON Sverige, Statkraft Sverige, and Skellefteå Kraft.

The electricity network established by distributors transports electricity

from the production sites to the end-users. This grid network is divided

into a national grid, a regional grid, and a local grid. Svenska Kraftnät

owns the national grid and plays the role of Transmission System Operator

(TSO), which means ensuring that power plants work together in a reliable

way and that production and imports correspond to consumption and ex-

ports. The TSO is responsible for maintaining a balance between national

production and consumption. It is regulated and reviewed by the Swedish

Energy Market Inspectorate. The regional network transmits electricity

from the grid to the local network and, in some cases, to large users such

as industry. Three large regional grid owners - E.ON Elnät Sverige, Vat-

tenfall Eldistribution, and Fortum Distribution - own most of the regional

grid in Sweden. As a result, these companies have both production and

distribution business units, which need to be strictly separated for legal

and regulatory reasons. Local grids distribute electricity to end-users

within a certain geographical area. Owners of local networks include gov-

ernments, municipalities, companies and non-governmental organizations,

and licenses for grid concessions are issued by the Swedish Energy Market

Inspectorate.

63

45

Figure 14. A simplified illustration of the Swedish energy system’s traditional linear

value chain market logic.

Further down the line, electricity retailers trade in the electricity market,

competing with other retailers for sales under different electricity con-

tracts. They thus act as intermediaries between producers and consumers,

which means that this role is also known as a distribution system operator

(DSO). In order to assume balancing responsibilities, the DSO first has to

enter into a balancing service contract with the TSO and then legally bal-

ance all electricity produced or consumed at the input and output points of

the network, in accordance with the provisions of the Swedish Electricity

Act.

At the end of the chain are the electricity customers (various actors in-

cluding households, companies, consumers, etc.), who are the end-users

of electricity. Customers buy electricity from retailers or producers and

use it for their own value creation. Customers need to enter into agree-

ments with grid owners and electricity traders to gain access to the grid

and to purchase electricity at an agreed price. Such agreements are often

arranged by a single actor, such as a power distributor, and the service

resulting from the agreement are limited. Given that traditional metering

45

Figure 14. A simplified illustration of the Swedish energy system’s traditional linear

value chain market logic.

Further down the line, electricity retailers trade in the electricity market,

competing with other retailers for sales under different electricity con-

tracts. They thus act as intermediaries between producers and consumers,

which means that this role is also known as a distribution system operator

(DSO). In order to assume balancing responsibilities, the DSO first has to

enter into a balancing service contract with the TSO and then legally bal-

ance all electricity produced or consumed at the input and output points of

the network, in accordance with the provisions of the Swedish Electricity

Act.

At the end of the chain are the electricity customers (various actors in-

cluding households, companies, consumers, etc.), who are the end-users

of electricity. Customers buy electricity from retailers or producers and

use it for their own value creation. Customers need to enter into agree-

ments with grid owners and electricity traders to gain access to the grid

and to purchase electricity at an agreed price. Such agreements are often

arranged by a single actor, such as a power distributor, and the service

resulting from the agreement are limited. Given that traditional metering

64

46

systems are based on invoice accuracy rather than user-friendliness, cus-

tomers who are interested in their real-time energy usage have historically

received little such service from electricity producers or retailers. As a re-

sult, customers who want to know their energy usage in real-time need to

purchase third-party solutions (e.g., generic mobile "apps") that have lim-

ited control possibilities.

4.3.2 The networked market and an emerging new role - aggregator

The need to coordinate and deal with decentralized energy systems has given

rise to so-called "aggregators" (i.e. third parties). These aggregators are both

assemblers and operators of renewable energy sources, as well as connectors,

balancers, and re-constructors of electricity codes/rules. In addition to instal-

lation, operation and maintenance services for distributed energy systems, ag-

gregators frame distributed energy systems as controllable and manageable

smart grid communities. Physical electricity is produced, traded, consumed,

and balanced within the local microgrid and is complementary to the larger

regional grid. Electricity is offered as a package for customers to choose from,

usually below the retail market price. The collector is responsible for the over-

all electricity price balancing and price negotiation for its customers, thus be-

coming a kind of energy system ombudsman.

Aggregators participate in the process of energy conversion and distribu-

tion, thus acting as intermediaries between the actors in the grid (see Figure

15). Power aggregators bring in resources in the form of expertise, information

distribution and new technologies to take advantage of the ongoing electricity

market failures. The growth of energy micro-production, such as PV systems,

has led to information asymmetries and imperfect coordination of actors in

terms of economic and technical (electricity) signals (i.e., mismatch between

real-time demand and supply), resulting in irrational and unstable prices. Ag-

gregators are coordinating the load and generation of all demand and supply

units with the aim of making the production and use of electricity technically

and economically optimal.

46

systems are based on invoice accuracy rather than user-friendliness, cus-

tomers who are interested in their real-time energy usage have historically

received little such service from electricity producers or retailers. As a re-

sult, customers who want to know their energy usage in real-time need to

purchase third-party solutions (e.g., generic mobile "apps") that have lim-

ited control possibilities.

4.3.2 The networked market and an emerging new role - aggregator

The need to coordinate and deal with decentralized energy systems has given

rise to so-called "aggregators" (i.e. third parties). These aggregators are both

assemblers and operators of renewable energy sources, as well as connectors,

balancers, and re-constructors of electricity codes/rules. In addition to instal-

lation, operation and maintenance services for distributed energy systems, ag-

gregators frame distributed energy systems as controllable and manageable

smart grid communities. Physical electricity is produced, traded, consumed,

and balanced within the local microgrid and is complementary to the larger

regional grid. Electricity is offered as a package for customers to choose from,

usually below the retail market price. The collector is responsible for the over-

all electricity price balancing and price negotiation for its customers, thus be-

coming a kind of energy system ombudsman.

Aggregators participate in the process of energy conversion and distribu-

tion, thus acting as intermediaries between the actors in the grid (see Figure

15). Power aggregators bring in resources in the form of expertise, information

distribution and new technologies to take advantage of the ongoing electricity

market failures. The growth of energy micro-production, such as PV systems,

has led to information asymmetries and imperfect coordination of actors in

terms of economic and technical (electricity) signals (i.e., mismatch between

real-time demand and supply), resulting in irrational and unstable prices. Ag-

gregators are coordinating the load and generation of all demand and supply

units with the aim of making the production and use of electricity technically

and economically optimal.

65

47

Figure 15. A future smart and dynamic networked market logic.

As shown in Figure 15, different actors interact with each other based on

power flows, information flows, and cash flows. The function of the aggrega-

tor is to secure and transfer the dynamic flows of the system and the value

generated by the system to ensure that each actor in the system receives a fair

share of the costs and benefits for its part of the value generated. For example,

aggregators can offer leases or "power purchase agreements" to households,

companies, or organizations to enable them to access PV systems or other re-

newable power systems. Aggregators can also install and maintain renewable

power systems and provide electricity to customers when the PV system's out-

put is too low. In addition, aggregators can control the demand for electricity

from the aggregated consumption of the system. Thus, the aggregator physi-

cally connects the PV system owner to the grid, but matches this with an in-

creased level of information generation among all partners. Basically, from

interconnection and net metering, aggregators import power from the grid or

export power when needed or when dealing with a surplus. From power trad-

ing, aggregators gain a share by coordinating the costs and benefits of relevant

actors. In addition, there may be additional key ancillary transactions to stabi-

lize the power system. In this sense, aggregators act as a bridge between end-

users and other actors, providing services to end-users, grid owners, and large

power companies. Finally, aggregators also have the ability to deal with laws

and regulations related to energy production and distribution-something that

users do not have to consider before becoming their own generators.

Due to the increasing penetration of renewable energy sources in smart cit-

ies, there is a lack of connection between end-users, the grid, and energy pro-

ducers. The introduction of renewable energy resources changes the tradi-

tional roles and there will be no clear boundaries between who is a producer

and who is a user (in fact, it can be the same actor). Aggregators will thus

reduce the transaction costs of individual participation and provide ancillary

47

Figure 15. A future smart and dynamic networked market logic.

As shown in Figure 15, different actors interact with each other based on

power flows, information flows, and cash flows. The function of the aggrega-

tor is to secure and transfer the dynamic flows of the system and the value

generated by the system to ensure that each actor in the system receives a fair

share of the costs and benefits for its part of the value generated. For example,

aggregators can offer leases or "power purchase agreements" to households,

companies, or organizations to enable them to access PV systems or other re-

newable power systems. Aggregators can also install and maintain renewable

power systems and provide electricity to customers when the PV system's out-

put is too low. In addition, aggregators can control the demand for electricity

from the aggregated consumption of the system. Thus, the aggregator physi-

cally connects the PV system owner to the grid, but matches this with an in-

creased level of information generation among all partners. Basically, from

interconnection and net metering, aggregators import power from the grid or

export power when needed or when dealing with a surplus. From power trad-

ing, aggregators gain a share by coordinating the costs and benefits of relevant

actors. In addition, there may be additional key ancillary transactions to stabi-

lize the power system. In this sense, aggregators act as a bridge between end-

users and other actors, providing services to end-users, grid owners, and large

power companies. Finally, aggregators also have the ability to deal with laws

and regulations related to energy production and distribution-something that

users do not have to consider before becoming their own generators.

Due to the increasing penetration of renewable energy sources in smart cit-

ies, there is a lack of connection between end-users, the grid, and energy pro-

ducers. The introduction of renewable energy resources changes the tradi-

tional roles and there will be no clear boundaries between who is a producer

and who is a user (in fact, it can be the same actor). Aggregators will thus

reduce the transaction costs of individual participation and provide ancillary

66

48

services. From the grid perspective, aggregators help organize demand and

supply and maintain grid stability by "peak-shaving" (i.e., controlling some of

the energy used by grid actors) and "load shifting" (distributing energy among

actors). Thus, aggregators act as intermediary actors between energy suppliers

and users by collecting distributed generation from grid actors and organizing

intra-grid transactions.

4.4 Selected key performance indicators (Paper IV)

In order to distinguish the five most important KPIs, the entire pool of poten-

tial candidates was analyzed. The Table 2 summarizes the statistical parame-

ters of the questionnaire results. Due to content limitations, this table only

shows the results for the top 5 and the last 5 KPI candidates.

Table 2. Summary results of statistical parameters. (T: technology, En: environmen-tal, Ec: economics, S/P: social/policy)

No. KPI candidate Category Count STD AVG CV Z

value

1 Energy efficiency T 16 1.06 5.94 0.18 1.95

2 Greenhouse gasses

(GHG) emission

En 16 1.63 5.63 0.29 1.87

3 Ramp rate T 14 1.45 5.50 0.26 1.74

4 Availability Factor T 13 1.12 5.62 0.20 1.72

5 Energy Price Ec 11 1.29 5.64 0.23 1.56

… … … … … … … …

35 Revenue for the net-

work operator from

the service

Ec 4 1.89 3.75 0.50 0.84

36 Duration and fre-

quency of interrup-

tions per customer

T 4 2.71 5.00 0.54 0.82

37 Public safety S/P 4 2.65 4.50 0.59 0.78

38 Reduction of the

number of communi-

cation channels

S/P 2 2.12 1.50 1.41 0.00

39 System minutes lost T 2 4.95 3.50 1.41 0.00

Based on the results in Table 2, it was found that the most important KPIs

were the energy efficiency followed by GHG emission. The top five KPIs are

presented in Table 3. The selected KPIs were characterized by the lowest co-

efficient of variation and highest number of votes (Count) both normalized

and presented as objective (Normalized objective).

48

services. From the grid perspective, aggregators help organize demand and

supply and maintain grid stability by "peak-shaving" (i.e., controlling some of

the energy used by grid actors) and "load shifting" (distributing energy among

actors). Thus, aggregators act as intermediary actors between energy suppliers

and users by collecting distributed generation from grid actors and organizing

intra-grid transactions.

4.4 Selected key performance indicators (Paper IV)

In order to distinguish the five most important KPIs, the entire pool of poten-

tial candidates was analyzed. The Table 2 summarizes the statistical parame-

ters of the questionnaire results. Due to content limitations, this table only

shows the results for the top 5 and the last 5 KPI candidates.

Table 2. Summary results of statistical parameters. (T: technology, En: environmen-tal, Ec: economics, S/P: social/policy)

No. KPI candidate Category Count STD AVG CV Z

value

1 Energy efficiency T 16 1.06 5.94 0.18 1.95

2 Greenhouse gasses

(GHG) emission

En 16 1.63 5.63 0.29 1.87

3 Ramp rate T 14 1.45 5.50 0.26 1.74

4 Availability Factor T 13 1.12 5.62 0.20 1.72

5 Energy Price Ec 11 1.29 5.64 0.23 1.56

… … … … … … … …

35 Revenue for the net-

work operator from

the service

Ec 4 1.89 3.75 0.50 0.84

36 Duration and fre-

quency of interrup-

tions per customer

T 4 2.71 5.00 0.54 0.82

37 Public safety S/P 4 2.65 4.50 0.59 0.78

38 Reduction of the

number of communi-

cation channels

S/P 2 2.12 1.50 1.41 0.00

39 System minutes lost T 2 4.95 3.50 1.41 0.00

Based on the results in Table 2, it was found that the most important KPIs

were the energy efficiency followed by GHG emission. The top five KPIs are

presented in Table 3. The selected KPIs were characterized by the lowest co-

efficient of variation and highest number of votes (Count) both normalized

and presented as objective (Normalized objective).

67

49

Table 3. Five most important KPIs without categories.

No. Chosen KPIs

1 Energy efficiency

2 GHG emission

3 Availability factor

4 Ramp rate

5 Market price of provided energy and services

In the second part of the analysis, the five most important KPIs were dis-

tinguished from each category (technical, economic, environmental, and so-

cial/policy). The results of this analysis are as the following Table 4.

Table 4. The selected KPIs from the questionnaire analysis in different categories.

Category Chosen KPIs

Technical Energy efficiency

Ramp rate

Availability factor

Start up time

Share of electrical energy produced by renewable sources

Economic Market price of provided energy and services

Production cost

Energy conversion plant profitability

Costs and revenues arising from system operation

Operational failure risk

Environmental GHG emission

Fuel energy savings ratio

Generated pollutant element

Social/policy Abiotic depletion potential

Public safety and acceptability

Reduction of the number of communication channels

Paper IV summarized the process of identifying and selecting the most im-

portant KPIs that can be used to assess the achievement of a simulated flexi-

bility program. The selection of KPIs was based on a mixed-method approach,

including literature review, questionnaire analysis, and expert knowledge.

Considering the range of the nature of multi-energy systems, the KPIs were

classified into four categories: technical, economic, environmental, and so-

cial/policy. In Paper IV, 16 KPIs were selected by ranking the KPI candidates

of different importance through statistical analysis of the questionnaire. These

indicators were selected based on the statistical importance of the respondents'

votes.

49

Table 3. Five most important KPIs without categories.

No. Chosen KPIs

1 Energy efficiency

2 GHG emission

3 Availability factor

4 Ramp rate

5 Market price of provided energy and services

In the second part of the analysis, the five most important KPIs were dis-

tinguished from each category (technical, economic, environmental, and so-

cial/policy). The results of this analysis are as the following Table 4.

Table 4. The selected KPIs from the questionnaire analysis in different categories.

Category Chosen KPIs

Technical Energy efficiency

Ramp rate

Availability factor

Start up time

Share of electrical energy produced by renewable sources

Economic Market price of provided energy and services

Production cost

Energy conversion plant profitability

Costs and revenues arising from system operation

Operational failure risk

Environmental GHG emission

Fuel energy savings ratio

Generated pollutant element

Social/policy Abiotic depletion potential

Public safety and acceptability

Reduction of the number of communication channels

Paper IV summarized the process of identifying and selecting the most im-

portant KPIs that can be used to assess the achievement of a simulated flexi-

bility program. The selection of KPIs was based on a mixed-method approach,

including literature review, questionnaire analysis, and expert knowledge.

Considering the range of the nature of multi-energy systems, the KPIs were

classified into four categories: technical, economic, environmental, and so-

cial/policy. In Paper IV, 16 KPIs were selected by ranking the KPI candidates

of different importance through statistical analysis of the questionnaire. These

indicators were selected based on the statistical importance of the respondents'

votes.

68

50

KPIs are used to communicate goals, progress, and room for improvement

in flexible performance. Therefore, the compiled list of recommended KPIs

should be quantifiable and clearly defined. The implementation of KPIs and

further feedback was essential to obtain the necessary data and information.

In order to achieve an efficient and stable multi-energy system, more techno-

logical innovations are needed to maximize the flexibility potential of variable

energy sources. In addition, decisionmakers should encourage progress on all

KPIs through appropriate incentives.

4.5 Potential availability analysis (Paper V)

The estimated roof area for solar PV systems in Västerås was 5.74 𝑘𝑚2, with

1.12 𝑘𝑚2 area of industrial buildings and 4.63 𝑘𝑚2 area of non-industrial

buildings. For building types, it was investigated that 3.68 𝑘𝑚2 were pitched

roofs, and 2.06 𝑘𝑚2 were flat roofs.

For the pitched roofs, different orientation gave different electricity yields,

which ranged from 814 kWh/kWp (West-oriented) to 1022 kWh/kWp (South-

oriented). The total potential installed capacity was estimated to be 664 MWp

and yearly electricity generation 565 GWh. The results are presented in Table

5.

Table 5. The usable area, potential installed capacity, and electricity generation for pitched roofs.

Roof type: pitched roofs

Usable area (𝑘𝑚2)

Tilt angle of PV modules (°)

Specific yield (kWh/kWp)

Potential in-stalled capa-city (MWp)

Potential yearly electric-ity generation (GWh)

East 1.06 30 820 191 149

South East 0.16 30 966 28 26

South 1.08 30 1023 195 190

South West 0.31 30 966 56 51

West 1.07 30 814 193 150 Total 3.68 N/A N/A 664 565

For scenario A of the flat roofs, a 5.9-meter row distance was calculated

with 39° tilt angle. The potential installed capacity was estimated to be 63

MWp and yearly electricity generation 60 GWh (Table 6).

For scenario B of the flat roofs, the simulation results of a 35 kWp system

(Figure 16) suggested a 20° tilt angle and 2-meter row distance that gave the

lowest shading losses. This design was also consistent with existing commer-

cial solutions, for example, IBC Solar suggested 1.6-meter and 10°, 1.8-meter

and 15° on flat roofs for the South-oriented solar PV systems in Germany (IBC

Solar, 2019). In this particular scenario, the losses due to mutual-shading

50

KPIs are used to communicate goals, progress, and room for improvement

in flexible performance. Therefore, the compiled list of recommended KPIs

should be quantifiable and clearly defined. The implementation of KPIs and

further feedback was essential to obtain the necessary data and information.

In order to achieve an efficient and stable multi-energy system, more techno-

logical innovations are needed to maximize the flexibility potential of variable

energy sources. In addition, decisionmakers should encourage progress on all

KPIs through appropriate incentives.

4.5 Potential availability analysis (Paper V)

The estimated roof area for solar PV systems in Västerås was 5.74 𝑘𝑚2, with

1.12 𝑘𝑚2 area of industrial buildings and 4.63 𝑘𝑚2 area of non-industrial

buildings. For building types, it was investigated that 3.68 𝑘𝑚2 were pitched

roofs, and 2.06 𝑘𝑚2 were flat roofs.

For the pitched roofs, different orientation gave different electricity yields,

which ranged from 814 kWh/kWp (West-oriented) to 1022 kWh/kWp (South-

oriented). The total potential installed capacity was estimated to be 664 MWp

and yearly electricity generation 565 GWh. The results are presented in Table

5.

Table 5. The usable area, potential installed capacity, and electricity generation for pitched roofs.

Roof type: pitched roofs


Tilt angle of PV modules (°)


Potential in-stalled capa-city (MWp)

Potential yearly electric-ity generation (GWh)

East 1.06 30 820 191 149

South East 0.16 30 966 28 26

South 1.08 30 1023 195 190

South West 0.31 30 966 56 51

West 1.07 30 814 193 150 Total 3.68 N/A N/A 664 565

For scenario A of the flat roofs, a 5.9-meter row distance was calculated

with 39° tilt angle. The potential installed capacity was estimated to be 63

MWp and yearly electricity generation 60 GWh (Table 6).

For scenario B of the flat roofs, the simulation results of a 35 kWp system

(Figure 16) suggested a 20° tilt angle and 2-meter row distance that gave the

lowest shading losses. This design was also consistent with existing commer-

cial solutions, for example, IBC Solar suggested 1.6-meter and 10°, 1.8-meter

and 15° on flat roofs for the South-oriented solar PV systems in Germany (IBC

Solar, 2019). In this particular scenario, the losses due to mutual-shading

69

51

effect were estimated to be 5.3%. The potential installed capacity was esti-

mated to be 184 MWp and yearly electricity generation 155 GWh (Table 6).

Figure 16. The percent of electricity losses using different row distances and tilt an-

gles due to mutual shading.

For scenario C of the flat roofs, a 2.5-meter row distance and 0.05-meter

top spacing were employed. Suggested by (Malmsten, 2015) and (IBC Solar,

2019), we used the 10° as tilt angle. With the same module parameters and

technical assumptions, this configuration was estimated to be 292 MWp and

yearly electricity generation 235 GWh (Table 6).

To sum up, 5.74 𝑘𝑚2 usable area in Västerås gave potential installed ca-

pacity as 727 MWp, 848 MWp, and 956 MWp, and potential yearly electricity

generation as 626 GWh, 720 GWh, and 801 GWh on pitched roofs and flat

roofs with three scenarios, respectively. This potential generation corres-

ponded to 55%-70% of Västerås’ annual electricity demand (Statistics

Sweden (SCB), 2019). It was noteworthy that this did not consider the hourly

or seasonal electricity supply and demand balance. Around noon in summer,

there would be an excess of PV electricity that had to be exported. According

to the statistics from Swedish Energy Agency, 14.46 MWp of grid-connected

systems was installed in Västerås municipality at the end of 2019 (Swedish

Energy Agency (Energimyndigheten), 2020a). Comparing with the potential,

this was still a very small share.

51

effect were estimated to be 5.3%. The potential installed capacity was esti-

mated to be 184 MWp and yearly electricity generation 155 GWh (Table 6).

Figure 16. The percent of electricity losses using different row distances and tilt an-

gles due to mutual shading.

For scenario C of the flat roofs, a 2.5-meter row distance and 0.05-meter

top spacing were employed. Suggested by (Malmsten, 2015) and (IBC Solar,

2019), we used the 10° as tilt angle. With the same module parameters and

technical assumptions, this configuration was estimated to be 292 MWp and

yearly electricity generation 235 GWh (Table 6).

To sum up, 5.74 𝑘𝑚2 usable area in Västerås gave potential installed ca-

pacity as 727 MWp, 848 MWp, and 956 MWp, and potential yearly electricity

generation as 626 GWh, 720 GWh, and 801 GWh on pitched roofs and flat

roofs with three scenarios, respectively. This potential generation corres-

ponded to 55%-70% of Västerås’ annual electricity demand (Statistics

Sweden (SCB), 2019). It was noteworthy that this did not consider the hourly

or seasonal electricity supply and demand balance. Around noon in summer,

there would be an excess of PV electricity that had to be exported. According

to the statistics from Swedish Energy Agency, 14.46 MWp of grid-connected

systems was installed in Västerås municipality at the end of 2019 (Swedish

Energy Agency (Energimyndigheten), 2020a). Comparing with the potential,

this was still a very small share.

70

52

Table 6. The usable area, potential installed capacity, and electricity generation for flat roofs in three scenarios.

Roof type: flat roofs


Tilt an-gle of PV modules (°)

PV mo-dule orientat-ion

Row distance (𝑚)


Potential installed capacity (MWp)

Potential yearly elec-tricity gener-ation (GWh)

Scenario A

2.06 39 South 5.9 1010 63 60

Scenario B

2.06 20 South 2 933 184 155

Scenario C

2.06 10 East-West

2.5 849 292 235

The overall results for the roof area in Sweden and the geographical distri-

bution for each municipality can be found in Figure 17(a). The total available

roof area for different building types in Sweden was estimated to be 504 km2,

with 327 km2 pitched roofs and 178 km2 flat roofs.

Based on this method, the potential installed capacity was calculated for

290 cities. Three scenarios (Scenarios A, B, and C) were designed on flat

roofs. Nationwide, the total potential installed capacity of solar PV systems

under the three scenarios for pitched and flat roofs was 65, 75, and 84 GWp,

respectively. The geographical distribution of the potential installed capacity

of roof-mounted solar PV systems is in Figure 17(b) - (d). In Paper V, the

latest geographic data and statistics were used as much as possible to calculate

the solar PV power potential and technical software was used to simulate solar

power generation.

52

Table 6. The usable area, potential installed capacity, and electricity generation for flat roofs in three scenarios.

Roof type: flat roofs


Tilt an-gle of PV modules (°)

PV mo-dule orientat-ion

Row distance (𝑚)


Potential installed capacity (MWp)

Potential yearly elec-tricity gener-ation (GWh)

Scenario A

2.06 39 South 5.9 1010 63 60

Scenario B

2.06 20 South 2 933 184 155

Scenario C

2.06 10 East-West

2.5 849 292 235

The overall results for the roof area in Sweden and the geographical distri-

bution for each municipality can be found in Figure 17(a). The total available

roof area for different building types in Sweden was estimated to be 504 km2,

with 327 km2 pitched roofs and 178 km2 flat roofs.

Based on this method, the potential installed capacity was calculated for

290 cities. Three scenarios (Scenarios A, B, and C) were designed on flat

roofs. Nationwide, the total potential installed capacity of solar PV systems

under the three scenarios for pitched and flat roofs was 65, 75, and 84 GWp,

respectively. The geographical distribution of the potential installed capacity

of roof-mounted solar PV systems is in Figure 17(b) - (d). In Paper V, the

latest geographic data and statistics were used as much as possible to calculate

the solar PV power potential and technical software was used to simulate solar

power generation.

71

53

Figure 17. The potential area for roof-mounted solar PV systems (a), and the poten-

tial installed capacity on pitched roofs and flat roofs with three scenarios (b-d).

The usable roof area for solar PV installation per capita was 49 m2 for Swe-

den on average, and 38 m2 for Västerås municipality, which were within the

range of results obtained by other authors. Izquierdo et al. (Izquierdo,

Rodrigues and Fueyo, 2008) reported an available roof area per capita to be

14.0±4.5 m2/capita for Spain. The values ranged from 6.2 m2/capita to 76.4

m2/capita. Wiginton et al. (Wiginton, Nguyen and Pearce, 2010) reported a

roof area per capita of 70.0 m2/ca±6.2% in the Canadian context, after reduc-

tion of constraint factors. Analysis of potential installed capacity yielded a

value of 6-8 kWp/capita for Sweden and 5-6 kWp/capita for Västerås munici-

pality. The results also showed a potential of 129-167 MWp/km2 for Sweden

and 127-166 MWp/km2 for Västerås. As a comparison, a value of 149

MWp/km2 was found in the US (Phillips et al., 2018), 95.06 MWp/km2 in the

residential rooftop area in Baden-Württemberg, Germany (Mainzer et al.,

2014), and 91.98 MWp/km2 in Lethbridge, Canada (Mansouri et al., 2019).

This indicated that Sweden might have better potential for PV system instal-

lation than Germany and Canada. By the end of 2019, a record of 287 MWp

53

Figure 17. The potential area for roof-mounted solar PV systems (a), and the poten-

tial installed capacity on pitched roofs and flat roofs with three scenarios (b-d).

The usable roof area for solar PV installation per capita was 49 m2 for Swe-

den on average, and 38 m2 for Västerås municipality, which were within the

range of results obtained by other authors. Izquierdo et al. (Izquierdo,

Rodrigues and Fueyo, 2008) reported an available roof area per capita to be

14.0±4.5 m2/capita for Spain. The values ranged from 6.2 m2/capita to 76.4

m2/capita. Wiginton et al. (Wiginton, Nguyen and Pearce, 2010) reported a

roof area per capita of 70.0 m2/ca±6.2% in the Canadian context, after reduc-

tion of constraint factors. Analysis of potential installed capacity yielded a

value of 6-8 kWp/capita for Sweden and 5-6 kWp/capita for Västerås munici-

pality. The results also showed a potential of 129-167 MWp/km2 for Sweden

and 127-166 MWp/km2 for Västerås. As a comparison, a value of 149

MWp/km2 was found in the US (Phillips et al., 2018), 95.06 MWp/km2 in the

residential rooftop area in Baden-Württemberg, Germany (Mainzer et al.,

2014), and 91.98 MWp/km2 in Lethbridge, Canada (Mansouri et al., 2019).

This indicated that Sweden might have better potential for PV system instal-

lation than Germany and Canada. By the end of 2019, a record of 287 MWp

72

54

annual installation was made, which brought the total capacity to 698 MWp

(Swedish Energy Agency (Energimyndigheten), 2020b). However, compared

with the potential, there was still huge space for the solar PV market in Swe-

den to grow.

54

annual installation was made, which brought the total capacity to 698 MWp

(Swedish Energy Agency (Energimyndigheten), 2020b). However, compared

with the potential, there was still huge space for the solar PV market in Swe-

den to grow.

73

55

5 Discussions and policy implications

This chapter highlights the main discussion points and policy implications.

5.1 “Soft” cost

In Paper I and Paper II, solar PV hardware costs (including PV modules, inte-

gration, inverters, wiring, and junction boxes) accounted for slightly less than

50% of the total costs, while the remaining 50 % were non-hardware or "soft"

costs - such as roof rent, financing, design and installation, and operations and

maintenance. In the United States, this figure was 59% in the first quarter of

2017. While hardware costs (especially PV module costs) have fallen signifi-

cantly, high soft costs combined with low electricity prices in some cities

made grid parity more difficult to achieve. For large projects (utility-scale),

soft costs had a greater impact, but to a relatively lesser extent. For smaller

projects in the industrial and commercial (I&C) market, the impact of soft

costs was very high and can actually make many projects financially dis-

tressed. In China, soft costs and their reduction potential were clearly high-

lighted in January 2019 in China's new policy "Notice on actively promoting

the work related to subsidy-free wind power and PV power generation for grid

parity". Soft costs involved in China's solar PV industry may include financing

costs, land acquisition costs, grid connection costs, and various fees for per-

mitting, inspection, and grid connection. Labor costs were not high due to the

relatively low wages of direct labor and related installation overhead. In

China, customer acquisition has been largely achieved due to market maturity,

customer familiarity with PV systems, and the perception that PV systems

were a reliable technology. However, policymakers should consider strength-

ening targeted policies for the following soft costs.

Financing cost referred to the cost of raising funds from banks, insurance

companies, investment cooperation, and other financial institutions. In China,

the existing financing mechanisms for distributed solar PV projects were tra-

ditional bank loans, local financing platforms, industrial investment funds,

lease financing, Internet financing, etc. Because, first of all, distributed solar

PV owners were usually small and medium-sized enterprises or natural per-

sons with low credibility; secondly, the roof property rights were complicated

to define, the cost of land acquisition was high, and traditional banks needed

a long process to identify the project risks and assess the investment returns.

55

5 Discussions and policy implications

This chapter highlights the main discussion points and policy implications.

5.1 “Soft” cost

In Paper I and Paper II, solar PV hardware costs (including PV modules, inte-

gration, inverters, wiring, and junction boxes) accounted for slightly less than

50% of the total costs, while the remaining 50 % were non-hardware or "soft"

costs - such as roof rent, financing, design and installation, and operations and

maintenance. In the United States, this figure was 59% in the first quarter of

2017. While hardware costs (especially PV module costs) have fallen signifi-

cantly, high soft costs combined with low electricity prices in some cities

made grid parity more difficult to achieve. For large projects (utility-scale),

soft costs had a greater impact, but to a relatively lesser extent. For smaller

projects in the industrial and commercial (I&C) market, the impact of soft

costs was very high and can actually make many projects financially dis-

tressed. In China, soft costs and their reduction potential were clearly high-

lighted in January 2019 in China's new policy "Notice on actively promoting

the work related to subsidy-free wind power and PV power generation for grid

parity". Soft costs involved in China's solar PV industry may include financing

costs, land acquisition costs, grid connection costs, and various fees for per-

mitting, inspection, and grid connection. Labor costs were not high due to the

relatively low wages of direct labor and related installation overhead. In

China, customer acquisition has been largely achieved due to market maturity,

customer familiarity with PV systems, and the perception that PV systems

were a reliable technology. However, policymakers should consider strength-

ening targeted policies for the following soft costs.

Financing cost referred to the cost of raising funds from banks, insurance

companies, investment cooperation, and other financial institutions. In China,

the existing financing mechanisms for distributed solar PV projects were tra-

ditional bank loans, local financing platforms, industrial investment funds,

lease financing, Internet financing, etc. Because, first of all, distributed solar

PV owners were usually small and medium-sized enterprises or natural per-

sons with low credibility; secondly, the roof property rights were complicated

to define, the cost of land acquisition was high, and traditional banks needed

a long process to identify the project risks and assess the investment returns.

74

56

In addition, traditional banks had high interest rates and short loan terms. The

sensitivity analysis in Paper I also proved that bank interest was one of the

most critical factors in determining the cost and return of distributed solar PV

projects. As for other financing methods, to some extent they failed in practice

due to low performance, limited funding sources, legality issues, or invest-

ment security issues. Although as early as 2013, the National Energy Admin-

istration and the National Development Bank jointly issued a policy calling

for a supportive financing environment for the distributed solar PV industry,

until recently, this policy did not provide for specific measures. Therefore, the

provision of innovative financing solutions and business modules at both pol-

icy and commercial levels was believed to be of critical importance in achiev-

ing grid parity.

The cost of grid connection referred to the reluctance and complicated pro-

cedures for grid companies to integrate solar PV. To provide undifferentiated

and convenient access to solar PV, the State Grid Corporation of China and

China Southern Power Grid - two of China's largest state-owned power com-

panies - have been offering free and full access to the grid for distributed solar

PV projects since 2012. They also provided free services including grid con-

nection solutions, metering, equipment testing, and purchase of all electricity

generated by the solar PV projects. This was also clearly confirmed and writ-

ten into policy by the NDRC in 2013. However, every kilowatt hour of solar

PV power generated for self-consumption would directly reduce grid revenue.

Assuming 50% self-generation and an average power purchase price of 0.74

CNY/kWh, this reduction could be as much as 19 billion CNY by the end of

September 2018. This was a concern for grid companies facing a high pene-

tration of solar PV. In addition, grid companies needed to pay for the addi-

tional costs of system integration and grid reinforcement. Assuming an invest-

ment of 8 CNY/Watt, this amount was estimated to reach 11-19 billion CNY

by the end of September 2018. Thus, the passive attitude and reluctance of

grid companies indirectly added soft costs to the grid connection approval pro-

cess. For example, additional procedures and "lobbying" costs were added to

distributed solar PV project owners. We recommended that the government

may design a comprehensive operating cost compensation mechanism for grid

companies. The compensation could come from the current FiT and renewable

energy subsidies for the solar PV industry.

The various tax costs were the taxes and fees for solar PV projects and the

administrative costs for project permitting, inspection, and grid connection.

The various types of taxes and fees for solar PV projects were income tax

15%, Value-added Tax (VAT) 17%, land-use tax 1.2%, city construction tax

5%, and so on. The taxes and fees significantly increased the soft cost of solar

PV power. The approval process for a typical solar PV project may involve

multiple local departments and companies, such as the local development and

reform commission, city planning department, energy management depart-

ment, ecology department, construction department, grid company, banks, etc.

56

In addition, traditional banks had high interest rates and short loan terms. The

sensitivity analysis in Paper I also proved that bank interest was one of the

most critical factors in determining the cost and return of distributed solar PV

projects. As for other financing methods, to some extent they failed in practice

due to low performance, limited funding sources, legality issues, or invest-

ment security issues. Although as early as 2013, the National Energy Admin-

istration and the National Development Bank jointly issued a policy calling

for a supportive financing environment for the distributed solar PV industry,

until recently, this policy did not provide for specific measures. Therefore, the

provision of innovative financing solutions and business modules at both pol-

icy and commercial levels was believed to be of critical importance in achiev-

ing grid parity.

The cost of grid connection referred to the reluctance and complicated pro-

cedures for grid companies to integrate solar PV. To provide undifferentiated

and convenient access to solar PV, the State Grid Corporation of China and

China Southern Power Grid - two of China's largest state-owned power com-

panies - have been offering free and full access to the grid for distributed solar

PV projects since 2012. They also provided free services including grid con-

nection solutions, metering, equipment testing, and purchase of all electricity

generated by the solar PV projects. This was also clearly confirmed and writ-

ten into policy by the NDRC in 2013. However, every kilowatt hour of solar

PV power generated for self-consumption would directly reduce grid revenue.

Assuming 50% self-generation and an average power purchase price of 0.74

CNY/kWh, this reduction could be as much as 19 billion CNY by the end of

September 2018. This was a concern for grid companies facing a high pene-

tration of solar PV. In addition, grid companies needed to pay for the addi-

tional costs of system integration and grid reinforcement. Assuming an invest-

ment of 8 CNY/Watt, this amount was estimated to reach 11-19 billion CNY

by the end of September 2018. Thus, the passive attitude and reluctance of

grid companies indirectly added soft costs to the grid connection approval pro-

cess. For example, additional procedures and "lobbying" costs were added to

distributed solar PV project owners. We recommended that the government

may design a comprehensive operating cost compensation mechanism for grid

companies. The compensation could come from the current FiT and renewable

energy subsidies for the solar PV industry.

The various tax costs were the taxes and fees for solar PV projects and the

administrative costs for project permitting, inspection, and grid connection.

The various types of taxes and fees for solar PV projects were income tax

15%, Value-added Tax (VAT) 17%, land-use tax 1.2%, city construction tax

5%, and so on. The taxes and fees significantly increased the soft cost of solar

PV power. The approval process for a typical solar PV project may involve

multiple local departments and companies, such as the local development and

reform commission, city planning department, energy management depart-

ment, ecology department, construction department, grid company, banks, etc.

75

57

The diversity of permit requirements, zoning applications, physical inspec-

tions, and various fees further increased the soft costs of solar PV projects. It

was recommended that more targeted policies be introduced to simplify these

procedures and thus reduce taxes and fees.

5.2 “Additional cost”

On the one hand, the reduced cost of solar PV provided cheaper and cleaner

electricity for end-users. Local regulators and utilities should consider

smoothing the closure of older coal-fired power plants. On the other hand, the

high share of solar and other renewable energy sources posed a significant

flexibility problem for the power system (Denholm, Clark and O’Connell,

2016). Specifically, solar power generation can vary with weather fluctuations

or the passage of clouds, resulting in inaccurate power supply forecasts

(Creutzig et al., 2017). "Over-generation" and "under-generation" may occur

spontaneously, leading to curtailment and rapid adjustment of power. "Addi-

tional costs" were incurred to maintain the balance between electricity supply

and demand. The spread of solar PV also added challenges to the frequency

of the grid system. Therefore, another "additional cost" was required to main-

tain the stability of the power system. These "additional costs" increased as

the penetration of solar power increased (Merei et al., 2016). When solar PV

generation increased to about 15% (in kWh of generation), the average whole-

sale price of solar PV decreased by 45% to 55% per kWh relative to zero pen-

etration. When penetration increased to 30%, the price decrease can be around

70% (Sivaram and Kann, 2016). According to a study by UKERC

(Heptonstall and Gross, 2017), when renewable energy penetration reached

50%, there would be an additional cost of 15-45 GBP/MWh (132-397

CNY/MWh (OECD, 2018)) for reserve requirements and an additional cost of

5-20 GBP/MWh (44-176 CNY/MWh) for upgrading the transmission net-

work. As the price of solar PV systems fell, less money was spent on energy,

but more money was spent on these "services".

As noted in Paper II, 85.17% of coal-fired power plants in operation were

at cost-risk due to solar PV applications. Investing in city clusters 1, 2, and 3

- which accounted for 66% of China's cities - can yield an IRR above 8% and

a DPBP of up to 15 years. however, this only meant that the investment was

profitable for individual investors and did not indicate that solar power was

profitable on a societal level. The full energy value of solar technology can

only be understood in the context of the entire generation system, taking into

account the flexibility challenges described above.

In this case, technical and management options should be considered to

mitigate these challenges. First, it was recommended to increase the self-con-

sumption rate to directly offset the demand. Currently, a higher self-consump-

tion rate not only provided a cheaper source of electricity, but also implied a

57

The diversity of permit requirements, zoning applications, physical inspec-

tions, and various fees further increased the soft costs of solar PV projects. It

was recommended that more targeted policies be introduced to simplify these

procedures and thus reduce taxes and fees.

5.2 “Additional cost”

On the one hand, the reduced cost of solar PV provided cheaper and cleaner

electricity for end-users. Local regulators and utilities should consider

smoothing the closure of older coal-fired power plants. On the other hand, the

high share of solar and other renewable energy sources posed a significant

flexibility problem for the power system (Denholm, Clark and O’Connell,

2016). Specifically, solar power generation can vary with weather fluctuations

or the passage of clouds, resulting in inaccurate power supply forecasts

(Creutzig et al., 2017). "Over-generation" and "under-generation" may occur

spontaneously, leading to curtailment and rapid adjustment of power. "Addi-

tional costs" were incurred to maintain the balance between electricity supply

and demand. The spread of solar PV also added challenges to the frequency

of the grid system. Therefore, another "additional cost" was required to main-

tain the stability of the power system. These "additional costs" increased as

the penetration of solar power increased (Merei et al., 2016). When solar PV

generation increased to about 15% (in kWh of generation), the average whole-

sale price of solar PV decreased by 45% to 55% per kWh relative to zero pen-

etration. When penetration increased to 30%, the price decrease can be around

70% (Sivaram and Kann, 2016). According to a study by UKERC

(Heptonstall and Gross, 2017), when renewable energy penetration reached

50%, there would be an additional cost of 15-45 GBP/MWh (132-397

CNY/MWh (OECD, 2018)) for reserve requirements and an additional cost of

5-20 GBP/MWh (44-176 CNY/MWh) for upgrading the transmission net-

work. As the price of solar PV systems fell, less money was spent on energy,

but more money was spent on these "services".

As noted in Paper II, 85.17% of coal-fired power plants in operation were

at cost-risk due to solar PV applications. Investing in city clusters 1, 2, and 3

- which accounted for 66% of China's cities - can yield an IRR above 8% and

a DPBP of up to 15 years. however, this only meant that the investment was

profitable for individual investors and did not indicate that solar power was

profitable on a societal level. The full energy value of solar technology can

only be understood in the context of the entire generation system, taking into

account the flexibility challenges described above.

In this case, technical and management options should be considered to

mitigate these challenges. First, it was recommended to increase the self-con-

sumption rate to directly offset the demand. Currently, a higher self-consump-

tion rate not only provided a cheaper source of electricity, but also implied a

76

58

stable power supply. The use of battery storage and/or electric-to-thermal

(thermal storage) schemes can be beneficial to increase the self-consumption

rate (Creutzig et al., 2017). Second, from a policy perspective, well-designed

incentive schemes can increase self-consumption, reduce the use of transmis-

sion lines, and increase the independence of customers from central grid

power. China was taking steps towards peer-to-peer trading of distributed gen-

eration (National Development and Reform Commission (NDRC); National

Energy Administration (NEA), 2017). Three business models between distrib-

uted generators, end-users, and local grid companies were proposed for the

pilot: direct sales, entrust sales, and sales to the grid. This was expected to

redefine the distributed generation business model and bring new opportuni-

ties for investments to serve commercial and industrial customers. A new

player, the aggregator, who would integrate customers' electricity consump-

tion behavior and grid services, was expected to emerge in a mature market

(Ekman, Röndell and Yang, 2019), which was discussed in Paper III. A single

aggregator can provide a limited number of services to customers and the grid.

However, a relatively large number of aggregators can form a single, large,

predictable entity and provide new services to the system over a longer period

of time. However, due to its complexity, this policy only gave an overview of

new ideas, and many details remained to be decided. For example, the ability

to coordinate the grid for inter-provincial and inter-regional transactions, the

compensation mechanism for distributed resources, etc.

5.3 Self-consumption rate

Sensitivity analysis showed that analyzing different self-consumption rates

would help to understand how these rates affect various economic parameters.

The decentralized nature of solar PV technology allowed producers to con-

sume the generated electricity directly on site. In Paper II, the self-consump-

tion rate was defined as the ratio of the direct consumption of solar PV elec-

tricity by end-users to the total amount of solar PV electricity generated

(Luthander et al., 2015). This was an attractive proposition for commercial

energy users, especially when the cost of solar power generation was lower

than the cost of purchased electricity. The self-consumption share varied

widely among users. From the results, the cost of solar power generation was

lower than the cost of purchasing power from the grid in all cities. In China,

self-generation was more economical than purchasing power from the grid. It

was predicted that the continued decline in the cost of solar PV generation

would provide more economic incentives for self-generation than purchasing

power from the grid.

Increasing the self-consumption rate can also increase the profitability of

solar PV systems. In Paper II, a self-consumption rate of 50% was assumed.

Since the market price of electricity in all Chinese cities was higher than the

58

stable power supply. The use of battery storage and/or electric-to-thermal

(thermal storage) schemes can be beneficial to increase the self-consumption

rate (Creutzig et al., 2017). Second, from a policy perspective, well-designed

incentive schemes can increase self-consumption, reduce the use of transmis-

sion lines, and increase the independence of customers from central grid

power. China was taking steps towards peer-to-peer trading of distributed gen-

eration (National Development and Reform Commission (NDRC); National

Energy Administration (NEA), 2017). Three business models between distrib-

uted generators, end-users, and local grid companies were proposed for the

pilot: direct sales, entrust sales, and sales to the grid. This was expected to

redefine the distributed generation business model and bring new opportuni-

ties for investments to serve commercial and industrial customers. A new

player, the aggregator, who would integrate customers' electricity consump-

tion behavior and grid services, was expected to emerge in a mature market

(Ekman, Röndell and Yang, 2019), which was discussed in Paper III. A single

aggregator can provide a limited number of services to customers and the grid.

However, a relatively large number of aggregators can form a single, large,

predictable entity and provide new services to the system over a longer period

of time. However, due to its complexity, this policy only gave an overview of

new ideas, and many details remained to be decided. For example, the ability

to coordinate the grid for inter-provincial and inter-regional transactions, the

compensation mechanism for distributed resources, etc.

5.3 Self-consumption rate

Sensitivity analysis showed that analyzing different self-consumption rates

would help to understand how these rates affect various economic parameters.

The decentralized nature of solar PV technology allowed producers to con-

sume the generated electricity directly on site. In Paper II, the self-consump-

tion rate was defined as the ratio of the direct consumption of solar PV elec-

tricity by end-users to the total amount of solar PV electricity generated

(Luthander et al., 2015). This was an attractive proposition for commercial

energy users, especially when the cost of solar power generation was lower

than the cost of purchased electricity. The self-consumption share varied

widely among users. From the results, the cost of solar power generation was

lower than the cost of purchasing power from the grid in all cities. In China,

self-generation was more economical than purchasing power from the grid. It

was predicted that the continued decline in the cost of solar PV generation

would provide more economic incentives for self-generation than purchasing

power from the grid.

Increasing the self-consumption rate can also increase the profitability of

solar PV systems. In Paper II, a self-consumption rate of 50% was assumed.

Since the market price of electricity in all Chinese cities was higher than the

77

59

DCB price, increasing the self-consumption rate implied more financial sav-

ings, which can be considered as a potential benefit. Looking at the five dif-

ferent self-consumption rate scenarios below (Figure 18), the higher the self-

consumption rate, the better the financial performance. In fact, studies in many

cases have shown that self-consumption was economically viable, although

different regulatory policies may affect its profitability (Kästel and Gilroy-

Scott, 2015). Self-consumption provided a degree of independence against un-

stable grid and electricity market price increases. It also reduced the pressure

on the distribution grid by offsetting demand on the generation side (Creutzig

et al., 2017).

Figure 18. Different self-consumption rates of 0%, 25%, 50%, 75% and 100% im-

pact economic performances.

5.4 Capital subsidy

Since 2009, Sweden has provided direct capital subsidies for the installation

of grid-connected PV systems at 60% of the installation cost in 2009 and 20%

in 2020. Along with this direct capital subsidy, a tax credit of SEK 0.6

SEK/kWh on electricity sold was introduced in 2015 for purchases of up to

30,000 kWh or so per year (Palm, Eidenskog and Luthander, 2018). To meet

the increased interest in solar PV in Sweden, the current authorities decided

in autumn 2015 to significantly increase the annual budget for 2016-2019 by

235, 390, and 390 million SEK, respectively. In 2017, it was decided to further

59

DCB price, increasing the self-consumption rate implied more financial sav-

ings, which can be considered as a potential benefit. Looking at the five dif-

ferent self-consumption rate scenarios below (Figure 18), the higher the self-

consumption rate, the better the financial performance. In fact, studies in many

cases have shown that self-consumption was economically viable, although

different regulatory policies may affect its profitability (Kästel and Gilroy-

Scott, 2015). Self-consumption provided a degree of independence against un-

stable grid and electricity market price increases. It also reduced the pressure

on the distribution grid by offsetting demand on the generation side (Creutzig

et al., 2017).

Figure 18. Different self-consumption rates of 0%, 25%, 50%, 75% and 100% im-

pact economic performances.

5.4 Capital subsidy

Since 2009, Sweden has provided direct capital subsidies for the installation

of grid-connected PV systems at 60% of the installation cost in 2009 and 20%

in 2020. Along with this direct capital subsidy, a tax credit of SEK 0.6

SEK/kWh on electricity sold was introduced in 2015 for purchases of up to

30,000 kWh or so per year (Palm, Eidenskog and Luthander, 2018). To meet

the increased interest in solar PV in Sweden, the current authorities decided

in autumn 2015 to significantly increase the annual budget for 2016-2019 by

235, 390, and 390 million SEK, respectively. In 2017, it was decided to further

78

60

increase the budget for 2017 and 2018 to 585.6 - 1,085 million SEK. Follow-

ing revisions in autumn 2018 and spring 2019, a total of 1.2 billion SEK was

allocated for 2019. The historical budgets and historical direct capital subsi-

dies are summarized in Figure 19 (Lindahl et al., 2019).

Figure 19. The historical direct subsidy percent and the budgets for solar industry.

As a young and growing industry, the Swedish solar PV industry still

needed access to external funding at various times. Subsidies were an im-

portant tool for the government to support the renewable energy industry. By

using prudent policy measures, policymakers had the means to increase the

use of PV and thus stimulated related innovation and increase economic com-

petitiveness through economies of scale.

Sweden's ambition is to become one of the world's fossil-free welfare

states. The government will continue to promote the expansion of solar energy

as a suitable energy source that can mitigate aspects of the current climate

crisis (Ministry of the Environment - Government Offices of Sweden, 2016).

According to the results of this study, the maximum installed capacity of Swe-

dish rooftops had a theoretical potential of 65 GWp to 84 GWp. Although it

was not possible to fully utilize the theoretically available rooftop resources,

these unrealized quantities highlighted key issues for investors and authorities.

At the same time, the increased interest and awareness of solar PV projects

have significantly accelerated their deployment. Since 2009, the direct capital

subsidy program has attracted many investors, which has always resulted in

the authorities' budget allocations being less than the amount requested by so-

lar PV owners. For example, the average waiting time that occurred in 2016

was 722 days.

As in the early stages, subsidies (especially in the form of direct capital

subsidies) were sufficient and necessary to strengthen a nascent industry.

60

increase the budget for 2017 and 2018 to 585.6 - 1,085 million SEK. Follow-

ing revisions in autumn 2018 and spring 2019, a total of 1.2 billion SEK was

allocated for 2019. The historical budgets and historical direct capital subsi-

dies are summarized in Figure 19 (Lindahl et al., 2019).

Figure 19. The historical direct subsidy percent and the budgets for solar industry.

As a young and growing industry, the Swedish solar PV industry still

needed access to external funding at various times. Subsidies were an im-

portant tool for the government to support the renewable energy industry. By

using prudent policy measures, policymakers had the means to increase the

use of PV and thus stimulated related innovation and increase economic com-

petitiveness through economies of scale.

Sweden's ambition is to become one of the world's fossil-free welfare

states. The government will continue to promote the expansion of solar energy

as a suitable energy source that can mitigate aspects of the current climate

crisis (Ministry of the Environment - Government Offices of Sweden, 2016).

According to the results of this study, the maximum installed capacity of Swe-

dish rooftops had a theoretical potential of 65 GWp to 84 GWp. Although it

was not possible to fully utilize the theoretically available rooftop resources,

these unrealized quantities highlighted key issues for investors and authorities.

At the same time, the increased interest and awareness of solar PV projects

have significantly accelerated their deployment. Since 2009, the direct capital

subsidy program has attracted many investors, which has always resulted in

the authorities' budget allocations being less than the amount requested by so-

lar PV owners. For example, the average waiting time that occurred in 2016

was 722 days.

As in the early stages, subsidies (especially in the form of direct capital

subsidies) were sufficient and necessary to strengthen a nascent industry.

79

61

Examples such as China, Greece and Spain showed how strong support from

the authorities can strengthen the solar PV industry. In Sweden, similar effects

have been seen in the solar PV industry in recent years. Until 2018, subsidies

have supported about 79 % (337 MW) of the total installed capacity (Lindahl

et al., 2019). However, subsidies should be designed to be realistic and con-

trollable. In the later stage, the authorities should phase out direct financial

subsidies. More efforts should be focused on other forms of incentives, such

as reducing or eliminating energy taxes for customers with PV systems larger

than 255 kWp, enhancing self-consumption, and, as some have suggested (Fan

et al., 2019) to strengthen tax credits for "green" investments. Otherwise, the

policy risks associated with the policy may lead to overcapacity and subsidy

shortfalls.

Reflected on Paper I and II, it was recommended that government policies

should be more geographically targeted. Financial subsidies were an effective

economic lever to promote new industries by facilitating technological break-

throughs, lowering cost curves, and increasing investment. The cost of solar

PV has been reduced to the point where it was competitive with market prices

in all cities studied, with 22% of them being able to compete with the cost of

traditional forms of energy. Some 83% of the cities achieved IRRs higher than

8%, and 67% have a DPBP of less than 15 years. Considering the widening

subsidy gap (45.5 billion CNY by the end of 2017), it was suggested that sub-

sidies for the solar PV industry should be more targeted to different cities to

maximize benefits. For cities that have achieved the desired grid parity and

therefore did not "need" subsidies, market-based programs ("invisible hand")

rather than government intervention ("visible hand") were needed. The devel-

opment of market-based trading for distributed generation was an actor-based

framework that allowed for the bottom-up design of future electricity markets.

Distributed generation was encouraged to be traded directly with electricity

consumers or power sales companies under bilateral or centralized auction

mechanisms. Market-based trading will reduce transmission and distribution

charges, provide more options for purchasing electricity at favorable prices,

and stimulate the spread of solar PV. At the same time, green certificate trad-

ing combined with a mandatory quota system could serve as an alternative

economic incentive to increase the use of solar PV and generate profits for

solar PV project owners. In short, there was a high priority on consolidation

and stabilization of the solar industry, rather than retaining top-down hierar-

chical government subsidies. In cities that have not achieved grid parity on the

plant side, local governments can continue to provide appropriate financial

incentives. In addition, as mentioned above, policymakers should develop pol-

icies to further reduce the soft costs of PV projects wherever possible. More

targeted policies would take into account the different geographic PV markets

across the country with the goal of promoting sustainable development of the

solar industry and grid parity.

61

Examples such as China, Greece and Spain showed how strong support from

the authorities can strengthen the solar PV industry. In Sweden, similar effects

have been seen in the solar PV industry in recent years. Until 2018, subsidies

have supported about 79 % (337 MW) of the total installed capacity (Lindahl

et al., 2019). However, subsidies should be designed to be realistic and con-

trollable. In the later stage, the authorities should phase out direct financial

subsidies. More efforts should be focused on other forms of incentives, such

as reducing or eliminating energy taxes for customers with PV systems larger

than 255 kWp, enhancing self-consumption, and, as some have suggested (Fan

et al., 2019) to strengthen tax credits for "green" investments. Otherwise, the

policy risks associated with the policy may lead to overcapacity and subsidy

shortfalls.

Reflected on Paper I and II, it was recommended that government policies

should be more geographically targeted. Financial subsidies were an effective

economic lever to promote new industries by facilitating technological break-

throughs, lowering cost curves, and increasing investment. The cost of solar

PV has been reduced to the point where it was competitive with market prices

in all cities studied, with 22% of them being able to compete with the cost of

traditional forms of energy. Some 83% of the cities achieved IRRs higher than

8%, and 67% have a DPBP of less than 15 years. Considering the widening

subsidy gap (45.5 billion CNY by the end of 2017), it was suggested that sub-

sidies for the solar PV industry should be more targeted to different cities to

maximize benefits. For cities that have achieved the desired grid parity and

therefore did not "need" subsidies, market-based programs ("invisible hand")

rather than government intervention ("visible hand") were needed. The devel-

opment of market-based trading for distributed generation was an actor-based

framework that allowed for the bottom-up design of future electricity markets.

Distributed generation was encouraged to be traded directly with electricity

consumers or power sales companies under bilateral or centralized auction

mechanisms. Market-based trading will reduce transmission and distribution

charges, provide more options for purchasing electricity at favorable prices,

and stimulate the spread of solar PV. At the same time, green certificate trad-

ing combined with a mandatory quota system could serve as an alternative

economic incentive to increase the use of solar PV and generate profits for

solar PV project owners. In short, there was a high priority on consolidation

and stabilization of the solar industry, rather than retaining top-down hierar-

chical government subsidies. In cities that have not achieved grid parity on the

plant side, local governments can continue to provide appropriate financial

incentives. In addition, as mentioned above, policymakers should develop pol-

icies to further reduce the soft costs of PV projects wherever possible. More

targeted policies would take into account the different geographic PV markets

across the country with the goal of promoting sustainable development of the

solar industry and grid parity.

80

62

6 Conclusions

This chapter presents the major conclusions of the thesis. RQ 1: how do subsidy-free solar PV projects in China compete eco-

nomically with conventional coal-fired generation?

This study revealed a clearer and more accurate discussion based on the

analysis of the latest data. This study focused on the grid parity and investment

value of I&C distributed PV projects in the absence of state financial subsi-

dies. Under the same circumstances, with certain assumptions on PV system

investment and operating costs, 344 cities can all achieve grid parity on the

user side, and about 76 cities (22.09%) can achieve grid parity on the plant

side. Among them, cities in Tibet, Gansu, Inner Mongolia and Heilongjiang

had the best conditions for user-side grid parity. Cities in Tibet, Qinghai,

northeastern provinces, northern provinces and some southern provinces had

the highest level of plant-side grid parity. 92.73% of cities can achieve positive

net profit of electricity, and the average net profit was 0.13 CNY/kWh based

on 50% self-generation and 50% feed-into-grid. In general, investments in cit-

ies of Tibet, northeastern, and Gansu can be profitable, but not in Sichuan,

Guizhou, and Xinjiang.

RQ 2: how do investment profits of unsubsidized solar PV projects dif-

fer from city to city?

The results showed that 85.17% of the currently operating coal-fired power

plants were under cost-risk considering the LCOE comparison with distrib-

uted solar PV projects. This revealed that most distributed solar PV projects

in different cities in China can reach a levelized cost that was sufficiently com-

petitive with the current published DCB prices. Approximately 36.63% of

China's coal-fired power plants can currently be replaced by distributed solar

PV projects.

Examining the return on investment, 21.8% of the surveyed cities had a

medium or high rate of return. 44.19% of the cities can generate a medium

return. Investing in city clusters 1, 2, and 3 found in this study can lead to an

IRR higher than 8% and a DPBP of up to 15 years. Investing in distributed

solar PV facilities in these cities can benefit from: abundant solar resources,

competitive electricity market prices, high electricity demand, and sufficient

rooftop area. At the same time, investing in city cluster 4 would not guarantee

capital recovery over the life of a solar PV project. However, investing in pov-

erty-alleviation projects can be a beneficiary with preferential support from

local governments.

62

6 Conclusions

This chapter presents the major conclusions of the thesis. RQ 1: how do subsidy-free solar PV projects in China compete eco-

nomically with conventional coal-fired generation?

This study revealed a clearer and more accurate discussion based on the

analysis of the latest data. This study focused on the grid parity and investment

value of I&C distributed PV projects in the absence of state financial subsi-

dies. Under the same circumstances, with certain assumptions on PV system

investment and operating costs, 344 cities can all achieve grid parity on the

user side, and about 76 cities (22.09%) can achieve grid parity on the plant

side. Among them, cities in Tibet, Gansu, Inner Mongolia and Heilongjiang

had the best conditions for user-side grid parity. Cities in Tibet, Qinghai,

northeastern provinces, northern provinces and some southern provinces had

the highest level of plant-side grid parity. 92.73% of cities can achieve positive

net profit of electricity, and the average net profit was 0.13 CNY/kWh based

on 50% self-generation and 50% feed-into-grid. In general, investments in cit-

ies of Tibet, northeastern, and Gansu can be profitable, but not in Sichuan,

Guizhou, and Xinjiang.

RQ 2: how do investment profits of unsubsidized solar PV projects dif-

fer from city to city?

The results showed that 85.17% of the currently operating coal-fired power

plants were under cost-risk considering the LCOE comparison with distrib-

uted solar PV projects. This revealed that most distributed solar PV projects

in different cities in China can reach a levelized cost that was sufficiently com-

petitive with the current published DCB prices. Approximately 36.63% of

China's coal-fired power plants can currently be replaced by distributed solar

PV projects.

Examining the return on investment, 21.8% of the surveyed cities had a

medium or high rate of return. 44.19% of the cities can generate a medium

return. Investing in city clusters 1, 2, and 3 found in this study can lead to an

IRR higher than 8% and a DPBP of up to 15 years. Investing in distributed

solar PV facilities in these cities can benefit from: abundant solar resources,

competitive electricity market prices, high electricity demand, and sufficient

rooftop area. At the same time, investing in city cluster 4 would not guarantee

capital recovery over the life of a solar PV project. However, investing in pov-

erty-alleviation projects can be a beneficiary with preferential support from

local governments.

81

63

Different self-generation and self-consumption rates affected profitability.

In China, the LCOE of distributed solar PV projects has reached a level that

was excessively competitive with the purchase price of electricity. Therefore,

a higher self-generation rate meant more financial savings. Battery and/or

thermal storage could be a technical solution. A well-developed electricity

trading market mechanism can fully exploit the economic potential of PV self-

generation and self-consumption. Distributed generation was encouraged at

the demand-side with self-consumption and excess electricity fed into the grid,

so as to realize the optimum allocation of distributed resources.

The "additional costs" due to distributed generation penetration should be

taken into account, e.g., the cost of rapid power adjustment, the cost of system

reserves to meet short-term balancing requirements, and the cost of strength-

ening the transmission and distribution network. In this sense, the current low

LCOE of distributed solar PV applications cannot simply be considered as an

absolute economic advantage. More research should continue on how to max-

imize the use of variable renewable energy generation while minimizing "ad-

ditional costs" by increasing system flexibility.

RQ 3: how do the solar PV applications change the Swedish electricity

market and alter the actor roles in the smart city transformation?

The study showed that the underlying logic for understanding the possibil-

ities and challenges associated with smart city development from the perspec-

tive of policy and business practice was related to understanding the logic of

the 'new' market. Shifts in activities and actor roles (e.g., the need for

knowledge brokers) were interlinked with the introduction of "new" actors,

resources, and service provision that can move beyond the traditional view of

the market (actors in a variety of roles engaged in linear transactions) toward

a service ecosystem view of the market. In this service ecosystem view, the

role of knowledge brokers constituted an example of the need to revise the

understanding of actors in the transformation of smart cities.

Building on the S-D logic, our research suggested that further attention

needed to be paid to the "intermediary" functions in such service ecosystems,

exploring how knowledge-mediating functions (e.g., actors and activities)

contributed to the conceptualization of how markets were transformed and

why new combinations of actors emerged in policy and management prac-

tices. For example, knowledge about energy systems for sustainable urban de-

velopment often lacked intermediary actors that "translated" the possibilities

and constraints of such transformations. Regardless of which part of the smart

city was studied, a high level of understanding of evolving markets and po-

tential new actors would facilitate future smart city research, as well as offer

conceptual accounts of smart markets for smart cities.

The recognition of the need to assess new services that went beyond tradi-

tional value creation changed the need, nature and quality of information, and

how this information can be translated into applicable knowledge (as a re-

source). This change was likely to generate a need for higher intensity of

63

Different self-generation and self-consumption rates affected profitability.

In China, the LCOE of distributed solar PV projects has reached a level that

was excessively competitive with the purchase price of electricity. Therefore,

a higher self-generation rate meant more financial savings. Battery and/or

thermal storage could be a technical solution. A well-developed electricity

trading market mechanism can fully exploit the economic potential of PV self-

generation and self-consumption. Distributed generation was encouraged at

the demand-side with self-consumption and excess electricity fed into the grid,

so as to realize the optimum allocation of distributed resources.

The "additional costs" due to distributed generation penetration should be

taken into account, e.g., the cost of rapid power adjustment, the cost of system

reserves to meet short-term balancing requirements, and the cost of strength-

ening the transmission and distribution network. In this sense, the current low

LCOE of distributed solar PV applications cannot simply be considered as an

absolute economic advantage. More research should continue on how to max-

imize the use of variable renewable energy generation while minimizing "ad-

ditional costs" by increasing system flexibility.

RQ 3: how do the solar PV applications change the Swedish electricity

market and alter the actor roles in the smart city transformation?

The study showed that the underlying logic for understanding the possibil-

ities and challenges associated with smart city development from the perspec-

tive of policy and business practice was related to understanding the logic of

the 'new' market. Shifts in activities and actor roles (e.g., the need for

knowledge brokers) were interlinked with the introduction of "new" actors,

resources, and service provision that can move beyond the traditional view of

the market (actors in a variety of roles engaged in linear transactions) toward

a service ecosystem view of the market. In this service ecosystem view, the

role of knowledge brokers constituted an example of the need to revise the

understanding of actors in the transformation of smart cities.

Building on the S-D logic, our research suggested that further attention

needed to be paid to the "intermediary" functions in such service ecosystems,

exploring how knowledge-mediating functions (e.g., actors and activities)

contributed to the conceptualization of how markets were transformed and

why new combinations of actors emerged in policy and management prac-

tices. For example, knowledge about energy systems for sustainable urban de-

velopment often lacked intermediary actors that "translated" the possibilities

and constraints of such transformations. Regardless of which part of the smart

city was studied, a high level of understanding of evolving markets and po-

tential new actors would facilitate future smart city research, as well as offer

conceptual accounts of smart markets for smart cities.

The recognition of the need to assess new services that went beyond tradi-

tional value creation changed the need, nature and quality of information, and

how this information can be translated into applicable knowledge (as a re-

source). This change was likely to generate a need for higher intensity of

82

64

interaction, not only among existing actors, but also with other actors in the

service ecosystem. The concept of "service ecosystems" in the S-D logic was

therefore essential to facilitate the development of "new" practices and to in-

ject smart markets into the concept of smart cities.

The need for improved information quality and interaction requirements

would also increase the potential knowledge demand for future energy sys-

tems. On the one hand, this may raise the barriers to entry for actors, but this

can be mitigated if actors play the role of intermediaries and knowledge bro-

kers. Research has shown that such knowledge brokers function to both facil-

itate and ease the burden for actors by managing the integration of some re-

sources at the actor and service ecosystem levels. Thus, the future energy mar-

ket and smart city transformation would benefit from being viewed as a ser-

vice ecosystem rather than as an isolated, pre-defined market. By

understanding the energy systems of smart cities as service ecosystems, future

energy markets would be understood as more dynamic, as they develop sym-

biotically with the market and smart city transformation. There would be a

need to support third-party actions, which have the potential to increase the

total value generation of future energy markets. A common description of

these new players in smart cities was as knowledge brokers.

RQ 4: what is the potential availability of solar PV energy in the Swe-

dish built environment?

In this study, the results showed that the city of Västerås had a huge poten-

tial for utilizing solar energy on its building surfaces. A total of 5.74 km2 of

roof area was identified as available for solar panels on building roofs. Sec-

ondly, this study calculated the technical potential by considering the technical

characteristics and estimated the available solar PV capacity, as well as the

geographical potential. The amount of electricity that can be generated by pre-

dicting the roof utilization was calculated by considering the PV inter-row

distance design and the characteristics of the panels. The total power genera-

tion capacity of the available roof area converted into three cases of pitched

and flat roofs ranged from 727 MWp to 956 MWp. The potential annual PV

power generation capacity of rooftop solar applications in Västerås was 626-

801 GWh, which corresponded to 55%-70% of the annual electricity demand

in Västerås. It was worth noting that this only took into account the total elec-

tricity consumption. In order to achieve a real-time power balance, hourly

power demand and supply profiles should be analyzed. Third, this study ex-

trapolated the methodology from the municipal level to the national level. Ap-

proximately 504 km2 available rooftop area was identified, and a total poten-

tial capacity of 65 GWp to 84 GWp calculated for Sweden. Current policies,

especially direct capital subsidies in Sweden, have been helping to meet the

prerequisites for further promotion of solar technology in Sweden. The anal-

ysis showed that the PV potential was huge, which suggested that the author-

ities needed to take prudent, realistic, and manageable policy measures. The

64

interaction, not only among existing actors, but also with other actors in the

service ecosystem. The concept of "service ecosystems" in the S-D logic was

therefore essential to facilitate the development of "new" practices and to in-

ject smart markets into the concept of smart cities.

The need for improved information quality and interaction requirements

would also increase the potential knowledge demand for future energy sys-

tems. On the one hand, this may raise the barriers to entry for actors, but this

can be mitigated if actors play the role of intermediaries and knowledge bro-

kers. Research has shown that such knowledge brokers function to both facil-

itate and ease the burden for actors by managing the integration of some re-

sources at the actor and service ecosystem levels. Thus, the future energy mar-

ket and smart city transformation would benefit from being viewed as a ser-

vice ecosystem rather than as an isolated, pre-defined market. By

understanding the energy systems of smart cities as service ecosystems, future

energy markets would be understood as more dynamic, as they develop sym-

biotically with the market and smart city transformation. There would be a

need to support third-party actions, which have the potential to increase the

total value generation of future energy markets. A common description of

these new players in smart cities was as knowledge brokers.

RQ 4: what is the potential availability of solar PV energy in the Swe-

dish built environment?

In this study, the results showed that the city of Västerås had a huge poten-

tial for utilizing solar energy on its building surfaces. A total of 5.74 km2 of

roof area was identified as available for solar panels on building roofs. Sec-

ondly, this study calculated the technical potential by considering the technical

characteristics and estimated the available solar PV capacity, as well as the

geographical potential. The amount of electricity that can be generated by pre-

dicting the roof utilization was calculated by considering the PV inter-row

distance design and the characteristics of the panels. The total power genera-

tion capacity of the available roof area converted into three cases of pitched

and flat roofs ranged from 727 MWp to 956 MWp. The potential annual PV

power generation capacity of rooftop solar applications in Västerås was 626-

801 GWh, which corresponded to 55%-70% of the annual electricity demand

in Västerås. It was worth noting that this only took into account the total elec-

tricity consumption. In order to achieve a real-time power balance, hourly

power demand and supply profiles should be analyzed. Third, this study ex-

trapolated the methodology from the municipal level to the national level. Ap-

proximately 504 km2 available rooftop area was identified, and a total poten-

tial capacity of 65 GWp to 84 GWp calculated for Sweden. Current policies,

especially direct capital subsidies in Sweden, have been helping to meet the

prerequisites for further promotion of solar technology in Sweden. The anal-

ysis showed that the PV potential was huge, which suggested that the author-

ities needed to take prudent, realistic, and manageable policy measures. The

83

65

further market formation should be time-limited and subsidy-independent in

order to stimulate market-based entrepreneurship.

These policy implications can also be transferred to other countries, both

developed and developing. Solar energy would continue to grow globally and

in Sweden in the coming decades. At this stage, technological barriers to the

solar industry have been significantly reduced due to advances in technology.

In countries with relatively mature market infrastructures, more emphasis can

be placed on the development of potential solar resources. As early adopter

countries, it was recommended that the authorities continued to provide in-

centives. Policy instruments can take many forms, such as subsidies, loans,

tax exemptions and other financial and non-financial support.

65

further market formation should be time-limited and subsidy-independent in

order to stimulate market-based entrepreneurship.

These policy implications can also be transferred to other countries, both

developed and developing. Solar energy would continue to grow globally and

in Sweden in the coming decades. At this stage, technological barriers to the

solar industry have been significantly reduced due to advances in technology.

In countries with relatively mature market infrastructures, more emphasis can

be placed on the development of potential solar resources. As early adopter

countries, it was recommended that the authorities continued to provide in-

centives. Policy instruments can take many forms, such as subsidies, loans,

tax exemptions and other financial and non-financial support.

84

66

7 Future work

This chapter discusses some of the limitations of this study and the issues that

require further investigation.

This thesis provides a first attempt on the transition to lower-emitting and

more flexible electricity systems while delivering economic benefits to con-

sumers and investors. The novelty of this research is the establishment of sub-

stitution possibility from an economic perspective and the estimation of in-

vestment profitability in all cities in China, which is unique. In the future, the

work could be enhanced by performing a more complete and robust analysis

by combining technical, economic, and social factors. The analysis could be

extended to both the utility-scale and distributed generation projects. It is also

worthy of discussing more market designs in order to safeguard the integration

of renewables without harassing the economic viability and system flexibility

in current power generation units.

The outcomes of this study will also be an established reference point for

validating with other methods and LiDAR data at the regional and national

levels. Further research might usefully consider the temporal diffusion pat-

terns of PV uptake interacting with other socio-economic factors. Further re-

search could also adopt a more local level analysis to explore how the potential

of solar PV energy generation could be integrated with load profiles of differ-

ent uses.

66

7 Future work

This chapter discusses some of the limitations of this study and the issues that

require further investigation.

This thesis provides a first attempt on the transition to lower-emitting and

more flexible electricity systems while delivering economic benefits to con-

sumers and investors. The novelty of this research is the establishment of sub-

stitution possibility from an economic perspective and the estimation of in-

vestment profitability in all cities in China, which is unique. In the future, the

work could be enhanced by performing a more complete and robust analysis

by combining technical, economic, and social factors. The analysis could be

extended to both the utility-scale and distributed generation projects. It is also

worthy of discussing more market designs in order to safeguard the integration

of renewables without harassing the economic viability and system flexibility

in current power generation units.

The outcomes of this study will also be an established reference point for

validating with other methods and LiDAR data at the regional and national

levels. Further research might usefully consider the temporal diffusion pat-

terns of PV uptake interacting with other socio-economic factors. Further re-

search could also adopt a more local level analysis to explore how the potential

of solar PV energy generation could be integrated with load profiles of differ-

ent uses.

85

67

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Armaroli, N. and Balzani, V. (2016) ‘Solar Electricity and Solar Fuels: Status and Perspectives in the Context of the Energy Transition’, Chemistry - A European Journal, 22(1), pp. 32–57. doi: 10.1002/chem.201503580.

Asgharzadeh, A. et al. (2018) ‘A Comparison Study of the Performance of South/North-facing vs East/West-facing Bifacial Modules under Shading Conditi’, 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion, WCPEC 2018 - A Joint Conference of 45th IEEE PVSC, 28th PVSEC and 34th EU PVSEC. IEEE, pp. 1730–1734. doi: 10.1109/PVSC.2018.8548185.

Balta-ozkan, N., Yildirim, J. and Connor, P. M. (2020) ‘Regional distribution of photovoltaic deployment in the UK and its determinants : A spatial econometric approach’, Energy Economics. Elsevier B.V., 51(2015), pp. 417–429. doi: 10.1016/j.eneco.2015.08.003.

Bergamasco, L. and Asinari, P. (2011) ‘Scalable methodology for the photovoltaic solar energy potential assessment based on available roof surface area : Application to Piedmont Region (Italy)’, Solar Energy. Elsevier Ltd, 85(5), pp. 1041–1055. doi: 10.1016/j.solener.2011.02.022.

Bhandari, R. and Stadler, I. (2009) ‘Grid parity analysis of solar photovoltaic systems in Germany using experience curves’, Solar Energy. Elsevier Ltd, 83(9), pp. 1634–1644. doi: 10.1016/j.solener.2009.06.001.

Biondi, T. and Moretto, M. (2015) ‘Solar Grid Parity dynamics in Italy: A real option approach’, Energy. Elsevier Ltd, 80, pp. 293–302. doi: 10.1016/j.energy.2014.11.072.

Bocca, R. (2020) ‘Fostering Effective Energy Transition. 2020 edition.’, World Economic Forum, March(May), pp. 1–40. Available at: www.weforum.org.

Branker, K., Pathak, M. J. M. and Pearce, J. M. (2011) ‘A review of solar photovoltaic levelized cost of electricity’, Renewable and Sustainable Energy Reviews. Elsevier Ltd, 15(9), pp. 4470–4482. doi: 10.1016/j.rser.2011.07.104.

Breyer, C. and Gerlach, A. (2013) ‘Global overview on grid‐parity’, Progress in photovoltaics: Research and Applications. Wiley Online Library, 21(1), pp. 121–136.

Bu, R., Grassi, S. and Raubal, M. (2018) ‘A scalable method for estimating rooftop solar irradiation potential over large regions’, Applied Energy, 216(November 2017), pp. 389–401. doi: 10.1016/j.apenergy.2018.02.008.

Burger, S. et al. (2016) ‘The value of aggregators in electricity systems’, MIT Center for Energy and Environment Policy Research: Cambridge, MA, USA.

Byrne, J. et al. (2015) ‘A review of the solar city concept and methods to assess rooftop solar electric potential, with an illustrative application to the city of

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68

Seoul’, Renewable and Sustainable Energy Reviews. Elsevier, 41, pp. 830–844. doi: 10.1016/j.rser.2014.08.023.

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Seoul’, Renewable and Sustainable Energy Reviews. Elsevier, 41, pp. 830–844. doi: 10.1016/j.rser.2014.08.023.

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Campana, P. E. et al. (2020) ‘A gridded optimization model for photovoltaic applications’, Solar Energy. Elsevier, 202(September 2019), pp. 465–484. doi: 10.1016/j.solener.2020.03.076.

Carreiro, A. M., Jorge, H. M. and Antunes, C. H. (2017) ‘Energy management systems aggregators: A literature survey’, Renewable and Sustainable Energy Reviews. Elsevier Ltd, 73(January), pp. 1160–1172. doi: 10.1016/j.rser.2017.01.179.

Chen, X. et al. (2015) ‘Increasing the Flexibility of Combined Heat and Power for Wind Power Integration in China: Modeling and Implications’, IEEE Transactions on Power Systems. IEEE, 30(4), pp. 1848–1857. doi: 10.1109/TPWRS.2014.2356723.

Chen, X. et al. (2018) ‘Aggregate Power Flexibility in Unbalanced Distribution Systems’, arXiv, 11(1), pp. 258–269.

Chen, Z. (2019) ‘Supporting or Not: Does Government Subsidies Enhance the Value of Photovoltaic Enterprises? Research on Quantile Regression on Least Square Regression’, Journal of Management Policy and Practice, 20(4). doi: 10.33423/jmpp.v20i4.2377.

Cherp, A. et al. (2018) ‘Integrating techno-economic, socio-technical and political perspectives on national energy transitions: A meta-theoretical framework’, Energy Research and Social Science. Elsevier, 37(September), pp. 175–190. doi: 10.1016/j.erss.2017.09.015.

Choi, Y. et al. (2011) ‘PV Analyst: Coupling ArcGIS with TRNSYS to assess distributed photovoltaic potential in urban areas’, Solar Energy. Elsevier Ltd, 85(11), pp. 2924–2939. doi: 10.1016/j.solener.2011.08.034.

Chong, Z., Zhou, D. and Wang, Q. (2019) ‘Without Subsidy, Will Chinese Renewable Energy Power Generation Have a Bright Future?’, Emerging Markets Finance and Trade. Routledge, 00(00), pp. 1–34. doi: 10.1080/1540496X.2019.1696191.

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Deng, R. et al. (2020) ‘Exploring flexibility of electric vehicle aggregators as energy reserve’, Electric Power Systems Research, 184(March). doi: 10.1016/j.epsr.2020.106305.

Denholm, P., Clark, K. and O’Connell, M. (2016) ‘On the Path to Sunshot: Emerging Issues and Challenges in Integrating High Levels of Solar into the Electrical Generation and Transmission System’, (May), p. 68. doi: 10.2172/1344204.

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Dubois, A. and Gadde, L. E. (2002) ‘Systematic combining: An abductive approach to case research’, Journal of Business Research, 55(7), pp. 553–560. doi: 10.1016/S0148-2963(00)00195-8.

Dudley, B. (2018) ‘BP statistical review of world energy’, BP Statistical Review, London, UK, accessed Aug, 6, p. 2018.

Ekman, P. and Lindh, C. (2013) A smart city study: the status of companies in the Mälardalen business region. School of Business, Society and Technology, Mälardalen University.

Ekman, P., Raggio, R. D. and Thompson, S. M. (2016) ‘Service network value co-creation: Defining the roles of the generic actor’, Industrial Marketing Management. Elsevier Inc., 56, pp. 51–62. doi: 10.1016/j.indmarman.2016.03.002.

Ekman, P., Röndell, J. and Yang, Y. (2019) ‘Exploring smart cities and market transformations from a service-dominant logic perspective’, Sustainable Cities and Society. Elsevier, 51(June), p. 101731. doi: 10.1016/j.scs.2019.101731.

Emmanuel, M. et al. (2020) ‘A review of power system planning and operational models for flexibility assessment in high solar energy penetration scenarios’, Solar Energy, 210(July), pp. 169–180. doi: 10.1016/j.solener.2020.07.017.

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Yuan, J. et al. (2018) ‘Coal use for power generation in China’, Resources, Conservation and Recycling. doi: 10.1016/j.resconrec.2016.03.021.

Yujia, J. and Finenko, A. (2016) ‘Moving beyond LCOE : impact of various fi nancing methods on PV pro fi tability for SIDS’, 98, pp. 749–758. doi: 10.1016/j.enpol.2016.03.021.

Zhang, H. et al. (2016) ‘The impact of subsidies on overcapacity: A comparison of wind and solar energy companies in China’, Energy. Elsevier Ltd, 94, pp. 821–827. doi: 10.1016/j.energy.2015.11.054.

Zhang, L., Qin, Q. and Wei, Y. M. (2019) ‘China’s distributed energy policies: Evolution, instruments and recommendation’, Energy Policy. Elsevier Ltd, 125(September 2018), pp. 55–64. doi: 10.1016/j.enpol.2018.10.028.

Zhang, M. et al. (2016) ‘Optimal feed-in tariff for solar photovoltaic power generation in China: A real options analysis’, Energy Policy. Elsevier, 97, pp. 181–192. doi: 10.1016/j.enpol.2016.07.028.

Zhang, S., Andrews-Speed, P. and Ji, M. (2014) ‘The erratic path of the low-carbon transition in China: Evolution of solar PV policy’, Energy Policy. Elsevier, 67, pp. 903–912. doi: 10.1016/j.enpol.2013.12.063.

Zhang, S., Andrews-Speed, P. and Li, S. (2018) ‘To what extent will China’s ongoing electricity market reforms assist the integration of renewable energy?’, Energy Policy. doi: 10.1016/j.enpol.2017.12.002.

Zhang, X. et al. (2011) ‘Economic dispatch considering volatile wind power generation with lower-semi-deviation risk measure’, in 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), pp. 140–144. doi: 10.1109/DRPT.2011.5993877.

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97

The Swedish Research School of Management and Information Technology (MIT) is one of 16

national research schools supported by the Swedish Government. MIT is jointly operated by the

following institutions: Blekinge Institute of Technology, Chalmers University of Technology,

University of Gothenburg, Jönköping International Business School, Karlstad University,

Linköping University, Linnaeus University Växjö, Lund University, Mälardalen University

College, Stockholm University, Umeå University, Örebro University, and Uppsala University, host

to the research school. At the Swedish Research School of Management and Information

Technology (MIT), research is conducted, and doctoral education provided, in three fields:

management information systems, business administration, and informatics.

DISSERTATIONS FROM THE SWEDISH RESEARCH

SCHOOL OF MANAGEMENT AND INFORMATION

TECHNOLOGY

Doctoral theses (2003- )

1. Baraldi, Enrico (2003), When Information Technology Faces Resource Interaction: Using IT

Tools to Handle Products at IKEA and Edsbyn. Department of Business Studies, Uppsala

University, Doctoral Thesis No. 105.

2. Wang, Zhiping (2004), Capacity-Constrained Production-Inventory Systems: Modelling and

Analysis in both a Traditional and an E-Business Context. IDA-EIS, Linköpings universitet

och Tekniska Högskolan i Linköping, Dissertation No. 889

3. Ekman, Peter (2006), Enterprise Systems & Business Relationships: The Utilization of IT in the

Business with Customers and Suppliers. School of Business, Mälardalen University, Doctoral

Dissertation No 29.

4. Lindh, Cecilia (2006), Business Relationships and Integration of Information Technology.

School of Business, Mälardalen University, Doctoral Dissertation No 28.

5. Frimanson, Lars (2006), Management Accounting and Business Relationships from a Supplier

Perspective. Department of Business Studies, Uppsala University, Doctoral Thesis No. 119.

6. Johansson, Niklas (2007), Self-Service Recovery. Information Systems, Faculty of Economic

Sciences, Communication and IT, Karlstad University, Dissertation KUS 2006:68.

7. Sonesson, Olle (2007), Tjänsteutveckling med personal medverkan: En studie av banktjänster.

Företagsekonomi, Fakulteten för ekonomi, kommunikation och IT, Karlstads universitet,

Doktorsavhandling, Karlstad University Studies 2007:9.

The Swedish Research School of

Management and Information Technology

MIT

The Swedish Research School of Management and Information Technology (MIT) is one of 16

national research schools supported by the Swedish Government. MIT is jointly operated by the

following institutions: Blekinge Institute of Technology, Chalmers University of Technology,

University of Gothenburg, Jönköping International Business School, Karlstad University,

Linköping University, Linnaeus University Växjö, Lund University, Mälardalen University

College, Stockholm University, Umeå University, Örebro University, and Uppsala University, host

to the research school. At the Swedish Research School of Management and Information

Technology (MIT), research is conducted, and doctoral education provided, in three fields:

management information systems, business administration, and informatics.

DISSERTATIONS FROM THE SWEDISH RESEARCH

SCHOOL OF MANAGEMENT AND INFORMATION

TECHNOLOGY

Doctoral theses (2003- )

1. Baraldi, Enrico (2003), When Information Technology Faces Resource Interaction: Using IT

Tools to Handle Products at IKEA and Edsbyn. Department of Business Studies, Uppsala


2. Wang, Zhiping (2004), Capacity-Constrained Production-Inventory Systems: Modelling and

Analysis in both a Traditional and an E-Business Context. IDA-EIS, Linköpings universitet

och Tekniska Högskolan i Linköping, Dissertation No. 889

3. Ekman, Peter (2006), Enterprise Systems & Business Relationships: The Utilization of IT in the

Business with Customers and Suppliers. School of Business, Mälardalen University, Doctoral

Dissertation No 29.

4. Lindh, Cecilia (2006), Business Relationships and Integration of Information Technology.

School of Business, Mälardalen University, Doctoral Dissertation No 28.

5. Frimanson, Lars (2006), Management Accounting and Business Relationships from a Supplier

Perspective. Department of Business Studies, Uppsala University, Doctoral Thesis No. 119.

6. Johansson, Niklas (2007), Self-Service Recovery. Information Systems, Faculty of Economic

Sciences, Communication and IT, Karlstad University, Dissertation KUS 2006:68.

7. Sonesson, Olle (2007), Tjänsteutveckling med personal medverkan: En studie av banktjänster.

Företagsekonomi, Fakulteten för ekonomi, kommunikation och IT, Karlstads universitet,

Doktorsavhandling, Karlstad University Studies 2007:9.

The Swedish Research School of

Management and Information Technology

MIT

98

8. Maaninen-Olsson, Eva (2007), Projekt i tid och rum: Kunskapsintegrering mellan

projektet och dess historiska och organisatoriska kontext. Företagsekonomiska

institutionen, Uppsala universitet, Doctoral Thesis No. 126.

9. Keller, Christina (2007), Virtual learning environments in higher education: A study of

user acceptance. Linköping Studies in Science and Technology, Dissertation No. 1114.

10. Abelli, Björn (2007), On Stage! Playwriting, Directing and Enacting the Informing Processes.

School of Business, Mälardalen University, Doctoral Dissertation No. 46.

11. Cöster, Mathias (2007), The Digital Transformation of the Swedish Graphic Industry.

Linköping Studies in Science and Technology, Linköping University, Dissertation No. 1126.

12. Dahlin, Peter (2007), Turbulence in Business Networks: A Longitudinal Study of Mergers,

Acquisitions and Bankruptcies Involving Swedish IT-companies. School of Business,

Mälardalen University, Doctoral Thesis No. 53.

13. Myreteg, Gunilla (2007), Förändringens vindar: En studie om aktörsgrupper och konsten att

välja och införa ett affärssystem. Företagsekonomiska institutionen, Uppsala universitet,

Doctoral Thesis No. 131.

14. Hrastinski, Stefan (2007), Participating in Synchronous Online Education. School of

Economics and Management, Lund University, Lund Studies in Informatics No. 6.

15. Granebring, Annika (2007), Service-Oriented Architecture: An Innovation Process

Perspective. School of Business, Mälardalen University, Doctoral Thesis No. 51.

16. Lövstål, Eva (2008), Management Control Systems in Entrepreneurial Organizations: A

Balancing Challenge. Jönköping International Business School, Jönköping University, JIBS

Dissertation Series No. 045.

17. Hansson, Magnus (2008), On Closedowns: Towards a Pattern of Explanation to the

Closedown Effect. Örebro University School of Business, Örebro University, Doctoral Thesis

No. 1.

18. Fridriksson, Helgi-Valur (2008), Learning processes in an inter-organizational context: A

study of krAft project. Jönköping International Business School, Jönköping University, JIBS


19. Selander, Lisen (2008), Call Me Call Me for some Overtime: On Organizational

Consequences of System Changes. Institute of Economic Research, Lund Studies in Economics

and Management No. 99.

20. Henningsson, Stefan (2008), Managing Information Systems Integration in Corporate

Mergers & Acquisitions. Institute of Economic Research, Lund Studies in Economics and

Management No. 101.

21. Ahlström, Petter (2008), Strategier och styrsystem för seniorboende-marknaden. IEI-EIS,

Linköping universitetet och Tekniska Högskolan i Linköping, Doktorsavhandling, Nr. 1188.

22. Sörhammar, David (2008), Consumer-firm business relationship and network: The case of

”Store” versus Internet. Department of Business Studies, Uppsala University, Doctoral Thesis

No. 137.

8. Maaninen-Olsson, Eva (2007), Projekt i tid och rum: Kunskapsintegrering mellan

projektet och dess historiska och organisatoriska kontext. Företagsekonomiska

institutionen, Uppsala universitet, Doctoral Thesis No. 126.

9. Keller, Christina (2007), Virtual learning environments in higher education: A study of

user acceptance. Linköping Studies in Science and Technology, Dissertation No. 1114.

10. Abelli, Björn (2007), On Stage! Playwriting, Directing and Enacting the Informing Processes.

School of Business, Mälardalen University, Doctoral Dissertation No. 46.

11. Cöster, Mathias (2007), The Digital Transformation of the Swedish Graphic Industry.

Linköping Studies in Science and Technology, Linköping University, Dissertation No. 1126.

12. Dahlin, Peter (2007), Turbulence in Business Networks: A Longitudinal Study of Mergers,

Acquisitions and Bankruptcies Involving Swedish IT-companies. School of Business,

Mälardalen University, Doctoral Thesis No. 53.

13. Myreteg, Gunilla (2007), Förändringens vindar: En studie om aktörsgrupper och konsten att

välja och införa ett affärssystem. Företagsekonomiska institutionen, Uppsala universitet,

Doctoral Thesis No. 131.

14. Hrastinski, Stefan (2007), Participating in Synchronous Online Education. School of

Economics and Management, Lund University, Lund Studies in Informatics No. 6.

15. Granebring, Annika (2007), Service-Oriented Architecture: An Innovation Process

Perspective. School of Business, Mälardalen University, Doctoral Thesis No. 51.

16. Lövstål, Eva (2008), Management Control Systems in Entrepreneurial Organizations: A

Balancing Challenge. Jönköping International Business School, Jönköping University, JIBS


17. Hansson, Magnus (2008), On Closedowns: Towards a Pattern of Explanation to the

Closedown Effect. Örebro University School of Business, Örebro University, Doctoral Thesis

No. 1.

18. Fridriksson, Helgi-Valur (2008), Learning processes in an inter-organizational context: A

study of krAft project. Jönköping International Business School, Jönköping University, JIBS


19. Selander, Lisen (2008), Call Me Call Me for some Overtime: On Organizational

Consequences of System Changes. Institute of Economic Research, Lund Studies in Economics

and Management No. 99.

20. Henningsson, Stefan (2008), Managing Information Systems Integration in Corporate

Mergers & Acquisitions. Institute of Economic Research, Lund Studies in Economics and

Management No. 101.

21. Ahlström, Petter (2008), Strategier och styrsystem för seniorboende-marknaden. IEI-EIS,

Linköping universitetet och Tekniska Högskolan i Linköping, Doktorsavhandling, Nr. 1188.

22. Sörhammar, David (2008), Consumer-firm business relationship and network: The case of

”Store” versus Internet. Department of Business Studies, Uppsala University, Doctoral Thesis

No. 137.

99

23. Caesarius, Leon Michael (2008), In Search of Known Unknowns: An Empirical Investigation

of the Peripety of a Knowledge Management System. Department of Business Studies, Uppsala


24. Cederström, Carl (2009), The Other Side of Technology: Lacan and the Desire for the Purity

of Non-Being. Institute of Economic Research, Lund University, Doctoral Thesis, ISBN: 91-

85113-37-9.

25. Fryk, Pontus, (2009), Modern Perspectives on the Digital Economy: With Insights from the

Health Care Sector. Department of Business Studies, Uppsala University, Doctoral Thesis No.

145.

26. Wingkvist, Anna (2009), Understanding Scalability and Sustainability in Mobile Learning: A

Systems Development Framework. School of Mathematics and Systems Engineering, Växjö

University, Acta Wexionesia, No. 192, ISBN: 978-91-7636-687-5.

27. Sällberg, Henrik (2010), Customer Rewards Programs: Designing Incentives for Repeated

Purchase. Blekinge Institute of Technology, School of Management, Doctoral Dissertation

Series No. 2010:01.

28. Verma, Sanjay (2010), New Product Newness and Benefits: A Study of Software Products

from the Firms’ Perspective. Mälardalen University Press, Doctoral Thesis.

29. Iveroth, Einar (2010), Leading IT-Enabled Change Inside Ericsson: A Transformation Into a

Global Network of Shared Service Centres. Department of Business Studies, Uppsala


30. Nilsson, Erik (2010), Strategi, styrning och konkurrenskraft: En longitudinell studie av

Saab AB. IEI-EIS, Linköpings universitet och Tekniska Högskolan i Linköping,

Doktorsavhandling, Nr. 1318.

31. Sjöström, Jonas (2010), Designing Information Systems: A pragmatic account. Department

of Informatics and Media, Uppsala University, Doctoral Thesis.

32. Numminen, Emil (2010), On the Economic Return of a Software Investment: Managing Cost,

Benefit and Uncertainty. Blekinge Institute of Technology, School of Management, Doctoral

Thesis.

33. Frisk, Elisabeth (2011), Evaluating as Designing: Towards a Balanced IT Investment

Approach. IT University, Göteborg, Doctoral Thesis.

34. Karlsudd, Peter (2011), Support for Learning: Possibilities and Obstacles in Learning

Applications. Mälardalen University, Doctoral Thesis.

35. Wicander, Gudrun (2011), Mobile Supported e-Government Systems: Analysis of the

Education Management Information System (EMIS) in Tanzania. Karlstad University,

Doctoral Thesis. Karlstad University Studies 2011:49.

36. Åkesson, Maria (2011), Role Constellations in Value Co-Creation: A Study of Resource

Integration in an e-Government Context. Karlstad University, Doctoral Thesis. Karlstad

University Studies 2011:36.

23. Caesarius, Leon Michael (2008), In Search of Known Unknowns: An Empirical Investigation

of the Peripety of a Knowledge Management System. Department of Business Studies, Uppsala


24. Cederström, Carl (2009), The Other Side of Technology: Lacan and the Desire for the Purity

of Non-Being. Institute of Economic Research, Lund University, Doctoral Thesis, ISBN: 91-

85113-37-9.

25. Fryk, Pontus, (2009), Modern Perspectives on the Digital Economy: With Insights from the

Health Care Sector. Department of Business Studies, Uppsala University, Doctoral Thesis No.

145.

26. Wingkvist, Anna (2009), Understanding Scalability and Sustainability in Mobile Learning: A

Systems Development Framework. School of Mathematics and Systems Engineering, Växjö

University, Acta Wexionesia, No. 192, ISBN: 978-91-7636-687-5.

27. Sällberg, Henrik (2010), Customer Rewards Programs: Designing Incentives for Repeated

Purchase. Blekinge Institute of Technology, School of Management, Doctoral Dissertation

Series No. 2010:01.

28. Verma, Sanjay (2010), New Product Newness and Benefits: A Study of Software Products

from the Firms’ Perspective. Mälardalen University Press, Doctoral Thesis.

29. Iveroth, Einar (2010), Leading IT-Enabled Change Inside Ericsson: A Transformation Into a

Global Network of Shared Service Centres. Department of Business Studies, Uppsala


30. Nilsson, Erik (2010), Strategi, styrning och konkurrenskraft: En longitudinell studie av

Saab AB. IEI-EIS, Linköpings universitet och Tekniska Högskolan i Linköping,

Doktorsavhandling, Nr. 1318.

31. Sjöström, Jonas (2010), Designing Information Systems: A pragmatic account. Department

of Informatics and Media, Uppsala University, Doctoral Thesis.

32. Numminen, Emil (2010), On the Economic Return of a Software Investment: Managing Cost,

Benefit and Uncertainty. Blekinge Institute of Technology, School of Management, Doctoral

Thesis.

33. Frisk, Elisabeth (2011), Evaluating as Designing: Towards a Balanced IT Investment

Approach. IT University, Göteborg, Doctoral Thesis.

34. Karlsudd, Peter (2011), Support for Learning: Possibilities and Obstacles in Learning

Applications. Mälardalen University, Doctoral Thesis.

35. Wicander, Gudrun (2011), Mobile Supported e-Government Systems: Analysis of the

Education Management Information System (EMIS) in Tanzania. Karlstad University,


36. Åkesson, Maria (2011), Role Constellations in Value Co-Creation: A Study of Resource

Integration in an e-Government Context. Karlstad University, Doctoral Thesis. Karlstad


100

37. Nfuka, Edephonce N. (2012), IT Governance in Tanzanian Public Sector Organisations.

Department of Computer and Systems Sciences, Stockholm University, Doctoral Thesis.

38. Larsson, Anders Olof (2012), Doing Things in Relation to Machines: Studies on Online

Interactivity. Department of Informatics and Media, Uppsala University, Doctoral Thesis.

39. Andersson, Bo (2012), Harnessing Handheld Computing: Framework, Toolkit and Design

Propositions. Lund University, Doctoral Thesis.

40. Erixon, Cecilia (2012), Information System Providers and Business Relationships: A Study on

the Impact of Connections. Mälardalen University, Doctoral Thesis.

41. Svensson, Martin (2012), Routes, Routines and Emotions in Decision Making of Emergency

Call Takers. Blekinge Institute of Technology, Doctoral Dissertation Series No. 2012:04.

42. Svensson, Ann (2012), Kunskapsintegrering med informationssystem I professionsorienterade

praktiker. Institutionen för tillämpad IT, Göteborgs universitet, Doktorsavhandling.

43. Pareigis, Jörg (2012), Customer Experiences of Resource Integration: Reframing

Servicescapes Using Scripts and Practices. Karlstad University, Doctoral Thesis. Karlstad


44. Röndell, Jimmie (2012), From Marketing to, to Marketing with Consumers. Department of

Business Studies, Uppsala University, Doctoral Thesis No. 155.

45. Lippert, Marcus (2013), Communities in the Digital Age: Towards a Theoretical Model of

Communities of Practice and Information Technology. Department of Business Studies,

Uppsala University, Doctoral Thesis No. 156.

46. Netz, Joakim (2013), Diffusa spänningar eller spännande tillväxt? Företagsledning i tider av

snabb förändring. Mälardalens högskola, Doktorsavhandling nr 135.

47. Thorén, Claes (2013), Print or Perish? A Study of Inertia in a Regional Newspaper Industry.

Karlstad University, Doctoral Thesis. Karlstad University Studies 2014:10 (Ny uppl.).

Stockhult, Helén (2013), Medarbetare i dialog: en studie om viljan att göra mer än det

formellt förväntade. Örebro universitet, Örebro Studies in Business Dissertations, 4.

48. Mihailescu, Daniela (2013), Explaining the Use of Implementation Methodology in Enterprise

Systems Implementation Context: A Critical Realist Perspective. Lund University, Doctoral

Thesis.

49. Ghazawneh, Ahmad (2012), Towards a Boundary Resources Theory of Software Platforms.

Jönköping International Business School, Doctoral Thesis.

50. Shams, Poja (2013), What Does it Take to Get your Attention? The Influence of In-Store and

Out-of-Store Factors on Visual Attention and Decision Making for Fast-Moving Consumer

Goods. Karlstad University, Doctoral Thesis. Karlstad University Studies 2013:5.

51. Osowski, Dariusz (2013), From Illusiveness to Genuineness: Routines, Trading Zones, Tools

and Emotions in Sales Work. Department of Business Studies, Uppsala University, Doctoral

Thesis No. 160.

37. Nfuka, Edephonce N. (2012), IT Governance in Tanzanian Public Sector Organisations.

Department of Computer and Systems Sciences, Stockholm University, Doctoral Thesis.

38. Larsson, Anders Olof (2012), Doing Things in Relation to Machines: Studies on Online

Interactivity. Department of Informatics and Media, Uppsala University, Doctoral Thesis.

39. Andersson, Bo (2012), Harnessing Handheld Computing: Framework, Toolkit and Design

Propositions. Lund University, Doctoral Thesis.

40. Erixon, Cecilia (2012), Information System Providers and Business Relationships: A Study on

the Impact of Connections. Mälardalen University, Doctoral Thesis.

41. Svensson, Martin (2012), Routes, Routines and Emotions in Decision Making of Emergency

Call Takers. Blekinge Institute of Technology, Doctoral Dissertation Series No. 2012:04.

42. Svensson, Ann (2012), Kunskapsintegrering med informationssystem I professionsorienterade

praktiker. Institutionen för tillämpad IT, Göteborgs universitet, Doktorsavhandling.

43. Pareigis, Jörg (2012), Customer Experiences of Resource Integration: Reframing

Servicescapes Using Scripts and Practices. Karlstad University, Doctoral Thesis. Karlstad


44. Röndell, Jimmie (2012), From Marketing to, to Marketing with Consumers. Department of

Business Studies, Uppsala University, Doctoral Thesis No. 155.

45. Lippert, Marcus (2013), Communities in the Digital Age: Towards a Theoretical Model of

Communities of Practice and Information Technology. Department of Business Studies,


46. Netz, Joakim (2013), Diffusa spänningar eller spännande tillväxt? Företagsledning i tider av

snabb förändring. Mälardalens högskola, Doktorsavhandling nr 135.

47. Thorén, Claes (2013), Print or Perish? A Study of Inertia in a Regional Newspaper Industry.

Karlstad University, Doctoral Thesis. Karlstad University Studies 2014:10 (Ny uppl.).

Stockhult, Helén (2013), Medarbetare i dialog: en studie om viljan att göra mer än det

formellt förväntade. Örebro universitet, Örebro Studies in Business Dissertations, 4.

48. Mihailescu, Daniela (2013), Explaining the Use of Implementation Methodology in Enterprise

Systems Implementation Context: A Critical Realist Perspective. Lund University, Doctoral

Thesis.

49. Ghazawneh, Ahmad (2012), Towards a Boundary Resources Theory of Software Platforms.

Jönköping International Business School, Doctoral Thesis.

50. Shams, Poja (2013), What Does it Take to Get your Attention? The Influence of In-Store and

Out-of-Store Factors on Visual Attention and Decision Making for Fast-Moving Consumer

Goods. Karlstad University, Doctoral Thesis. Karlstad University Studies 2013:5.

51. Osowski, Dariusz (2013), From Illusiveness to Genuineness: Routines, Trading Zones, Tools

and Emotions in Sales Work. Department of Business Studies, Uppsala University, Doctoral

Thesis No. 160.

101

52. Höglund, Linda (2013), Discursive Practises in Strategic Entrepreneurship: Discourses and

Repertoires in Two Firms. Örebro University, Doctoral Thesis.

53. Persson Ridell, Oscar (2013), Who is the Active Consumer? Insight into Contemporary

Innovation and Marketing Practices. Department of Business Studies, Uppsala University,

Doctoral Thesis.

54. Kask, Johan (2013), On business relationships as Darwinian systems: An exploration into

how Darwinian systems thinking can support business relationship research. Örebro

University, Doctoral Thesis.

55. Paulsson, Wipawee Victoria (2013), The Complementary Use of IS Technologies to Support

Flexibility and Integration Needs in Budgeting. Lund University, Doctoral Thesis.

56. Kajtazi, Miranda (2013), Assessing Escalation of Commitment as an Antecedent of

Noncompliance with Information Security Policy. Linnaeus University, Doctoral Thesis.

57. Hasche, Nina (2013), Value Co-Creating Processes in International Business Relationships:

Three empirical stories of co-operation between Chinese customers and Swedish suppliers.

Örebro University, Doctoral Thesis.

58. Pierce, Paul (2013), Using Alliances to Increase ICT Capabilities. Lund University, Doctoral

Thesis.

59. Mansour, Osama (2013), The Bureaucracy of Social Media: An Empirical Account in

Organizations. Linnaeus University, Doctoral Thesis.

60. Osmonalieva, Zarina (2013), Factors Determining Exploitation of Innovative Venture Ideas:

A study of nascent entrepreneurs in an advisory system. Mälardalen University, Doctoral

Thesis.

61. Holmberg, Nicklas (2014), The Purity of Separation of Concerns: The Service Oriented

Business Process - a Design Approach for Business Agility. Lund University, Doctoral Thesis.

62. Poth, Susanna (2014), Competitive Advantage in the Service Industry. The Importance of

Strategic Congruence, Integrated Control and Coherent Organisational Structure: A

Longitudinal Case Study of an Insurance Company. Department of Business Studies, Uppsala


63. Safari, Aswo (2014), Consumer Foreign Online Purchasing: Uncertainty in the Consumer-

Retailer Relationship. Department of Business Studies, Uppsala University, Doctoral Thesis.

64. Sandberg, Johan (2014), Digital Capability: Investigating Coevolution of IT and Business

Strategies. Umeå University, Doctoral Thesis.

65. Eklinder Frick, Jens (2014), Sowing Seeds for Innovation: The Impact of Social Capital in

Regional Strategic Networks. Mälardalen University, Doctoral Thesis.

66. Löfberg, Nina (2014), Service Orientation in Manufacturing Firms: Understanding

Challenges with Service Business Logic. Karlstad University, Doctoral Thesis. Karlstad


52. Höglund, Linda (2013), Discursive Practises in Strategic Entrepreneurship: Discourses and

Repertoires in Two Firms. Örebro University, Doctoral Thesis.

53. Persson Ridell, Oscar (2013), Who is the Active Consumer? Insight into Contemporary

Innovation and Marketing Practices. Department of Business Studies, Uppsala University,

Doctoral Thesis.

54. Kask, Johan (2013), On business relationships as Darwinian systems: An exploration into

how Darwinian systems thinking can support business relationship research. Örebro


55. Paulsson, Wipawee Victoria (2013), The Complementary Use of IS Technologies to Support

Flexibility and Integration Needs in Budgeting. Lund University, Doctoral Thesis.

56. Kajtazi, Miranda (2013), Assessing Escalation of Commitment as an Antecedent of

Noncompliance with Information Security Policy. Linnaeus University, Doctoral Thesis.

57. Hasche, Nina (2013), Value Co-Creating Processes in International Business Relationships:

Three empirical stories of co-operation between Chinese customers and Swedish suppliers.


58. Pierce, Paul (2013), Using Alliances to Increase ICT Capabilities. Lund University, Doctoral

Thesis.

59. Mansour, Osama (2013), The Bureaucracy of Social Media: An Empirical Account in

Organizations. Linnaeus University, Doctoral Thesis.

60. Osmonalieva, Zarina (2013), Factors Determining Exploitation of Innovative Venture Ideas:

A study of nascent entrepreneurs in an advisory system. Mälardalen University, Doctoral

Thesis.

61. Holmberg, Nicklas (2014), The Purity of Separation of Concerns: The Service Oriented

Business Process - a Design Approach for Business Agility. Lund University, Doctoral Thesis.

62. Poth, Susanna (2014), Competitive Advantage in the Service Industry. The Importance of

Strategic Congruence, Integrated Control and Coherent Organisational Structure: A

Longitudinal Case Study of an Insurance Company. Department of Business Studies, Uppsala


63. Safari, Aswo (2014), Consumer Foreign Online Purchasing: Uncertainty in the Consumer-

Retailer Relationship. Department of Business Studies, Uppsala University, Doctoral Thesis.

64. Sandberg, Johan (2014), Digital Capability: Investigating Coevolution of IT and Business

Strategies. Umeå University, Doctoral Thesis.

65. Eklinder Frick, Jens (2014), Sowing Seeds for Innovation: The Impact of Social Capital in

Regional Strategic Networks. Mälardalen University, Doctoral Thesis.

66. Löfberg, Nina (2014), Service Orientation in Manufacturing Firms: Understanding

Challenges with Service Business Logic. Karlstad University, Doctoral Thesis. Karlstad


102

67. Gullberg, Cecilia (2014), Roles of Accounting Information in Managerial Work. Department

of Business Studies, Uppsala University, Doctoral Thesis No. 171.

68. Bergkvist, Linda (2014), Towards a Framework for Relational-Oriented Management of

Information Systems Outsourcing: Key Conditions Connected to Actors, Relationships and

Process. Karlstad University, Doctoral Thesis. Karlstad University Studies 2014:31.

69. Tavassoli, Sam (2014), Determinants and Effects of Innovation: Context Matters. Blekinge

Institute of Technology, Doctoral Thesis No. 2014:10.

70. Högström, Claes (2014), Fit In to Stand Out: An Experience Perspective on Value Creation.

Karlstad University, Doctoral Thesis. Karlstad University Studies 2014:44.

71. Jansson, Tomas (2015), Agila projektledningsmetoder och motivation. Karlstads universitet,


72. Ryzhkova, Natalia (2015), Web-Enabled Customer Involvement: A Firms’ Perspective.

Blekinge Institute of Technology, Doctoral Thesis.

73. Sundberg, Klas (2015), Strategisk utveckling och ekonomistyrning: Ett livscykelperspektiv.

Företagsekonomiska institutionen, Uppsala universitet, Doctoral Thesis No. 173.

74. Nylén, Daniel (2015), Digital Innovation and Changing Identities: Investigating

Organizational Implications of Digitalization. Umeå University, Doctoral Thesis.

75. Chowdhury, Soumitra (2015), Service Logic in Digitalized Product Platforms: A Study of

Digital Service Innovation in the Vehicle Industry. Gothenburg University, Doctoral Thesis.

76. Jogmark, Marina (2015), Den regionala transformationsprocessens sociala dimension.

Karlskrona 1989-2002. Blekinge Tekniska Högskola, Doctoral Thesis.

77. Sundström, Angelina (2015), Old Swedish Business in New International Clothes: Case

Studies on the Management of Strategic Resources in Foreign-Acquired Swedish R&D Firms.

Mälardalen University, Doctoral Thesis.

78. Öbrand, Lars (2015), Information Infrastructure Risk: Perspectives, Practices &

Technologies. Umeå University, Doctoral Thesis.

79. Brozović, Danilo (2016), Service Provider Flexibility: A Strategic Perspective. Stockholm


80. Siegert, Steffi (2016), Enacting Boundaries through Social Technologies: A Dance between

Work and Private Life. Stockholm University, Doctoral Thesis.

81. Linton, Gabriel (2016), Entrepreneurial Orientation: Reflections from a Contingency

Perspective. Örebro University, Doctoral Thesis.

82. Akram, Asif (2016), Value Network Transformation: Digital Service Innovation in the Vehicle

Industry. Department of Applied Information Technology, Chalmers University of Technology

and University of Gothenburg, Doctoral Thesis.

67. Gullberg, Cecilia (2014), Roles of Accounting Information in Managerial Work. Department


68. Bergkvist, Linda (2014), Towards a Framework for Relational-Oriented Management of

Information Systems Outsourcing: Key Conditions Connected to Actors, Relationships and

Process. Karlstad University, Doctoral Thesis. Karlstad University Studies 2014:31.

69. Tavassoli, Sam (2014), Determinants and Effects of Innovation: Context Matters. Blekinge

Institute of Technology, Doctoral Thesis No. 2014:10.

70. Högström, Claes (2014), Fit In to Stand Out: An Experience Perspective on Value Creation.

Karlstad University, Doctoral Thesis. Karlstad University Studies 2014:44.

71. Jansson, Tomas (2015), Agila projektledningsmetoder och motivation. Karlstads universitet,


72. Ryzhkova, Natalia (2015), Web-Enabled Customer Involvement: A Firms’ Perspective.

Blekinge Institute of Technology, Doctoral Thesis.

73. Sundberg, Klas (2015), Strategisk utveckling och ekonomistyrning: Ett livscykelperspektiv.

Företagsekonomiska institutionen, Uppsala universitet, Doctoral Thesis No. 173.

74. Nylén, Daniel (2015), Digital Innovation and Changing Identities: Investigating

Organizational Implications of Digitalization. Umeå University, Doctoral Thesis.

75. Chowdhury, Soumitra (2015), Service Logic in Digitalized Product Platforms: A Study of

Digital Service Innovation in the Vehicle Industry. Gothenburg University, Doctoral Thesis.

76. Jogmark, Marina (2015), Den regionala transformationsprocessens sociala dimension.

Karlskrona 1989-2002. Blekinge Tekniska Högskola, Doctoral Thesis.

77. Sundström, Angelina (2015), Old Swedish Business in New International Clothes: Case

Studies on the Management of Strategic Resources in Foreign-Acquired Swedish R&D Firms.


78. Öbrand, Lars (2015), Information Infrastructure Risk: Perspectives, Practices &

Technologies. Umeå University, Doctoral Thesis.

79. Brozović, Danilo (2016), Service Provider Flexibility: A Strategic Perspective. Stockholm


80. Siegert, Steffi (2016), Enacting Boundaries through Social Technologies: A Dance between

Work and Private Life. Stockholm University, Doctoral Thesis.

81. Linton, Gabriel (2016), Entrepreneurial Orientation: Reflections from a Contingency

Perspective. Örebro University, Doctoral Thesis.

82. Akram, Asif (2016), Value Network Transformation: Digital Service Innovation in the Vehicle

Industry. Department of Applied Information Technology, Chalmers University of Technology

and University of Gothenburg, Doctoral Thesis.

103

83. Hadjikhani, Annoch (2016), Executive Expectation in the Internationalization Process of

Banks: The Study of Two Swedish Banks Foreign Activities. Department of Business Studies,


84. El-Mekawy, Mohamed (2016), From Theory to Practice of Business-IT Alignment: Barriers,

an Evaluation Framework and Relationships with Organizational Culture. DSV, Stockholm


85. Salavati, Sadaf (2016), Use of Digital Technologies in Education: The Complexity of

Teachers’ Everyday Practice. Linnaeus University, Doctoral Thesis.

86. Pashkevich, Natallia (2016), Information Worker Productivity Enabled by IT System Usage:

A Complementary-Based Approach. Stockholm Business School, Stockholm University,

Doctoral Thesis.

87. Stone, Trudy-Ann (2016), Firms in Global Value Chains. Blekinge Institute of Technology

(BTH), Doctoral Thesis.

88. Saarikko, Ted (2016), An Inquiry into the Nature and Causes of Digital Platforms. Umeå


89. Tona, Olgerta (2017), The Journey of Mobile Business Intelligence: From Vision to Use.

Lund University, Doctoral Thesis.

90. Fredin, Sabrina (2017), History and Geography Matter: The Cultural Dimension of

Entrepreneurship. Blekinge Institute of Technology, Doctoral Thesis.

91. Giovacchini, Elia (2017), Weaving the Symbiotic Relationship: A Longitudinal Study of a

Firm-Sponsored Open Source Community Relationship Maintenance. Stockholm Business

School, Stockholm University, Doctoral Thesis.

92. Gillmore, Edward (2017), Four Essays on Subsidiary Evolution: Exploring the Antecedents,

Contexts and Outcomes of Mandate Loss. School of Business, Mälardalen University, Doctoral

Thesis.

93. Crawford, Jason (2017), Regulation’s Influence on Risk Management and Management

Control Systems in Banks. Department of Business Studies, Uppsala University, Doctoral

Thesis.

94. Von Schantz, Hanna (2017), Well, that makes sense! Investigating opportunity development

in a technology start-up. Stockholm Business School, Stockholm University, Doctoral Thesis.

95. Wass, Sofie (2017), The Importance of eHealth Innovations: Lessons about Patient Accessible

Information. Jönköping International Business School, Doctoral Thesis.

96. Imre, Özgün (2018), Adopting Information Systems: Perspectives from Small Organizations.

Department of Management and Engineering (IEI), Linköping University, Doctoral Thesis.

97. Lövgren, Daniel (2017), Dancing Together Alone: Inconsistencies and Contradictions of

Strategic Communication in Swedish Universities. Informatics and Media, Uppsala University,

Doctoral Thesis.

83. Hadjikhani, Annoch (2016), Executive Expectation in the Internationalization Process of

Banks: The Study of Two Swedish Banks Foreign Activities. Department of Business Studies,


84. El-Mekawy, Mohamed (2016), From Theory to Practice of Business-IT Alignment: Barriers,

an Evaluation Framework and Relationships with Organizational Culture. DSV, Stockholm


85. Salavati, Sadaf (2016), Use of Digital Technologies in Education: The Complexity of

Teachers’ Everyday Practice. Linnaeus University, Doctoral Thesis.

86. Pashkevich, Natallia (2016), Information Worker Productivity Enabled by IT System Usage:

A Complementary-Based Approach. Stockholm Business School, Stockholm University,

Doctoral Thesis.

87. Stone, Trudy-Ann (2016), Firms in Global Value Chains. Blekinge Institute of Technology

(BTH), Doctoral Thesis.

88. Saarikko, Ted (2016), An Inquiry into the Nature and Causes of Digital Platforms. Umeå


89. Tona, Olgerta (2017), The Journey of Mobile Business Intelligence: From Vision to Use.

Lund University, Doctoral Thesis.

90. Fredin, Sabrina (2017), History and Geography Matter: The Cultural Dimension of

Entrepreneurship. Blekinge Institute of Technology, Doctoral Thesis.

91. Giovacchini, Elia (2017), Weaving the Symbiotic Relationship: A Longitudinal Study of a

Firm-Sponsored Open Source Community Relationship Maintenance. Stockholm Business

School, Stockholm University, Doctoral Thesis.

92. Gillmore, Edward (2017), Four Essays on Subsidiary Evolution: Exploring the Antecedents,

Contexts and Outcomes of Mandate Loss. School of Business, Mälardalen University, Doctoral

Thesis.

93. Crawford, Jason (2017), Regulation’s Influence on Risk Management and Management

Control Systems in Banks. Department of Business Studies, Uppsala University, Doctoral

Thesis.

94. Von Schantz, Hanna (2017), Well, that makes sense! Investigating opportunity development

in a technology start-up. Stockholm Business School, Stockholm University, Doctoral Thesis.

95. Wass, Sofie (2017), The Importance of eHealth Innovations: Lessons about Patient Accessible

Information. Jönköping International Business School, Doctoral Thesis.

96. Imre, Özgün (2018), Adopting Information Systems: Perspectives from Small Organizations.

Department of Management and Engineering (IEI), Linköping University, Doctoral Thesis.

97. Lövgren, Daniel (2017), Dancing Together Alone: Inconsistencies and Contradictions of

Strategic Communication in Swedish Universities. Informatics and Media, Uppsala University,

Doctoral Thesis.

104

98. Charitsis, Vasileios (2018), Self-Tracking, Datafication and the Biopolitical Prosumption of

Life. Karlstad University, Doctoral Thesis.

99. Lammi, Inti (2018), A Practice Theory in Practice: Analytical Consequences in the Study of

Organization and Socio-Technical Change. Department of Business Studies, Uppsala


100. Leite, Emilene (2018), Complexity in the ‘Extended’ Business Network: A study of Business,

Social and Political Relationships in Smart City Solutions. Department of Business Studies,

Uppsala University, Doctoral Thesis.

101. Aasi, Parisa (2018), Information Technology Governance: The Role of Organizational

Culture and Structure. Department of Computer and Systems Sciences, Stockholm


102. Servadio, Luigi (2018), Customer Rituals: Ethnographic Explorations of Wine Rituals with

Families and Friends. Stockholm Business School, Stockholm University, Doctoral Thesis.

103. Ahlgren, Kajsa (2018), Travelling Business Models: On Adapting Business Models to New

Contexts. Design Sciences, Faculty of Engineering, Lund University, Doctoral Thesis.

104. Markowski, Peter (2018), Collaboration Routines: A Study of Interdisciplinary Healthcare.

Stockholm Business School, Stockholm University, Doctoral Thesis.

105. Zaffar, Fahd Omair (2018), The Value of Social Media: What Social Networking Sites

Afford Organizations. Division of Informatics, Department of Applied Information

Technology, University of Gothenburg, Doctoral Thesis.

106. Stendahl, Emma (2018), Headquarters Involvement in Managing Subsidiaries.


107. Fischer, Christian (2018), Business Intelligence through a Sociomaterial Lens: The

Imbrication of People and Technology in a Sales Process. Department of Business Studies,


108. Lagin, Madelen (2018), The Price We Pay: The Autonomy of Store Managers in Making

Price Decisions. Department of Business Studies, Örebro University, Doctoral Thesis.

109. Odar, Susanne (2019), Managementinitiativ, mening och verksamhetsresultat: En

retrospektiv studie av en teknikintensiv verksamhet. Department of Management and

Engineering (IEI), Linköping University, Linköping Studies in Science and Technology,

Doctoral Thesis.

110. Radits, Markus (2019), A Business Ecology Perspective on Community-Driven Open

Source: The Case of the Free and Open Source Content Management System Joomla.

Department of Management and Engineering (IEI), Linköping University, Linköping Studies

in Science and Technology, Doctoral Thesis No. 1937.

111. Skog, Daniel A. (2019), The Dynamics of Digital Transformation: The Role of Digital

Innovation, Ecosystems and Logics in Fundamental Organizational Change. Umeå


98. Charitsis, Vasileios (2018), Self-Tracking, Datafication and the Biopolitical Prosumption of

Life. Karlstad University, Doctoral Thesis.

99. Lammi, Inti (2018), A Practice Theory in Practice: Analytical Consequences in the Study of

Organization and Socio-Technical Change. Department of Business Studies, Uppsala


100. Leite, Emilene (2018), Complexity in the ‘Extended’ Business Network: A study of Business,

Social and Political Relationships in Smart City Solutions. Department of Business Studies,


101. Aasi, Parisa (2018), Information Technology Governance: The Role of Organizational

Culture and Structure. Department of Computer and Systems Sciences, Stockholm


102. Servadio, Luigi (2018), Customer Rituals: Ethnographic Explorations of Wine Rituals with

Families and Friends. Stockholm Business School, Stockholm University, Doctoral Thesis.

103. Ahlgren, Kajsa (2018), Travelling Business Models: On Adapting Business Models to New

Contexts. Design Sciences, Faculty of Engineering, Lund University, Doctoral Thesis.

104. Markowski, Peter (2018), Collaboration Routines: A Study of Interdisciplinary Healthcare.


105. Zaffar, Fahd Omair (2018), The Value of Social Media: What Social Networking Sites

Afford Organizations. Division of Informatics, Department of Applied Information

Technology, University of Gothenburg, Doctoral Thesis.

106. Stendahl, Emma (2018), Headquarters Involvement in Managing Subsidiaries.


107. Fischer, Christian (2018), Business Intelligence through a Sociomaterial Lens: The

Imbrication of People and Technology in a Sales Process. Department of Business Studies,


108. Lagin, Madelen (2018), The Price We Pay: The Autonomy of Store Managers in Making

Price Decisions. Department of Business Studies, Örebro University, Doctoral Thesis.

109. Odar, Susanne (2019), Managementinitiativ, mening och verksamhetsresultat: En

retrospektiv studie av en teknikintensiv verksamhet. Department of Management and

Engineering (IEI), Linköping University, Linköping Studies in Science and Technology,

Doctoral Thesis.

110. Radits, Markus (2019), A Business Ecology Perspective on Community-Driven Open

Source: The Case of the Free and Open Source Content Management System Joomla.

Department of Management and Engineering (IEI), Linköping University, Linköping Studies

in Science and Technology, Doctoral Thesis No. 1937.

111. Skog, Daniel A. (2019), The Dynamics of Digital Transformation: The Role of Digital

Innovation, Ecosystems and Logics in Fundamental Organizational Change. Umeå


105

112. Ek, Peter (2019), Managing Digital Open Innovation with User Communities: A Study

of Community Sensing and Product Openness Capabilities in the Video Game Industry.

Department of Business Studies, Uppsala University, Doctoral Thesis No. 199

113. Muhic, Mirella (2019), Transition to Cloud sourcing – Innovation and competitive

advantage. Design Sciences, Faculty of Engineering, Lund University, Doctoral Thesis.

114. Mankevich, Vasili (2019), Digital Innovation Management: Investigating Digital Trace

Data in Online Communities. Umeå University, Doctoral Thesis.

115. Vink, Josina (2019), In/visible - Conceptualizing Service Ecosystem Design. Karlstad


116. Bäckström, Izabelle (2019), Mirror, mirror on the wall, who’s the innovator after all?

An explorative study of a management-initiated employee innovation process.

Department of Design Sciences, Faculty of Engineering, Lund University, Doctoral

Thesis No. 116.

117. Bani-Hani, Imad (2020), Self-Service Business Analytics and the Path to Insights:

Integrating Resources for Generating Insights, Department of Informatics, School of

Economics and Management, Lund University, Doctoral Thesis.

118. Kashyap, Shruti Rangan (2020), Monsoon Paper Dragons: Transparency,

Accountability, Risk, and Compliance in Banking Regulation and Practice, Department


119. Havemo, Emelie (2020), Den visuella bilden av organisationen: Perspektiv på visualitet

i accounting, Linköping Studies in Science and Technology, Institutionen för ekonomisk

och industriell utveckling, Linköpings universitet, Doktorsavhandling nr. 2075.

120. Nyende, Hawa (2020), Maternal Healthcare in Low Resource Settings: Investigations of

IT as a resource, Department of Applied Information Technology, University of

Gothenburg, Doctoral Thesis.

121. Kizito, Michael (2020), Enacting ambidextrous IT Governance in healthcare,

Department of Applied Information Technology, University of Gothenburg, Doctoral

Thesis.

122. Ofe, Hosea Ayaba (2020), Orchestrating Emerging Digital Ecosystems: Investigating

the Establishment of an Open Data Platform in the Swedish Public Transport Industry.,

Department of Informatics, Umeå University, Doctoral Thesis.

123. Kurti, Erdelina (2020), Institutional Tensions and Complexity in the Digital Innovation

of Incumbent Business Models, Linnaeus University, Doctoral Thesis.

124. Gustavsson, Tomas (2020), Inter-team Coordination in Large-Scale Agile Software

Development Projects, Karlstad University, Doctoral Thesis.

125. Hedrén, Andreas (2021), With Lives on the Line: How Users Respond to a Highly

Mandated Information System Implementation – A Longitudinal Study, Department of

Informatics and Media, Uppsala University, Doctoral Thesis.

112. Ek, Peter (2019), Managing Digital Open Innovation with User Communities: A Study

of Community Sensing and Product Openness Capabilities in the Video Game Industry.

Department of Business Studies, Uppsala University, Doctoral Thesis No. 199

113. Muhic, Mirella (2019), Transition to Cloud sourcing – Innovation and competitive

advantage. Design Sciences, Faculty of Engineering, Lund University, Doctoral Thesis.

114. Mankevich, Vasili (2019), Digital Innovation Management: Investigating Digital Trace

Data in Online Communities. Umeå University, Doctoral Thesis.

115. Vink, Josina (2019), In/visible - Conceptualizing Service Ecosystem Design. Karlstad


116. Bäckström, Izabelle (2019), Mirror, mirror on the wall, who’s the innovator after all?

An explorative study of a management-initiated employee innovation process.

Department of Design Sciences, Faculty of Engineering, Lund University, Doctoral

Thesis No. 116.

117. Bani-Hani, Imad (2020), Self-Service Business Analytics and the Path to Insights:

Integrating Resources for Generating Insights, Department of Informatics, School of

Economics and Management, Lund University, Doctoral Thesis.

118. Kashyap, Shruti Rangan (2020), Monsoon Paper Dragons: Transparency,

Accountability, Risk, and Compliance in Banking Regulation and Practice, Department


119. Havemo, Emelie (2020), Den visuella bilden av organisationen: Perspektiv på visualitet

i accounting, Linköping Studies in Science and Technology, Institutionen för ekonomisk

och industriell utveckling, Linköpings universitet, Doktorsavhandling nr. 2075.

120. Nyende, Hawa (2020), Maternal Healthcare in Low Resource Settings: Investigations of

IT as a resource, Department of Applied Information Technology, University of

Gothenburg, Doctoral Thesis.

121. Kizito, Michael (2020), Enacting ambidextrous IT Governance in healthcare,

Department of Applied Information Technology, University of Gothenburg, Doctoral

Thesis.

122. Ofe, Hosea Ayaba (2020), Orchestrating Emerging Digital Ecosystems: Investigating

the Establishment of an Open Data Platform in the Swedish Public Transport Industry.,

Department of Informatics, Umeå University, Doctoral Thesis.

123. Kurti, Erdelina (2020), Institutional Tensions and Complexity in the Digital Innovation

of Incumbent Business Models, Linnaeus University, Doctoral Thesis.

124. Gustavsson, Tomas (2020), Inter-team Coordination in Large-Scale Agile Software

Development Projects, Karlstad University, Doctoral Thesis.

125. Hedrén, Andreas (2021), With Lives on the Line: How Users Respond to a Highly

Mandated Information System Implementation – A Longitudinal Study, Department of

Informatics and Media, Uppsala University, Doctoral Thesis.

106

126. Nykvist, Rasmus (2021), Essays on the Interaction Between Regulation and

Technology: Understanding Agency and Context Through Multiple Levels of Inquiry,

Örebro University School of Business, Örebro University, Doctoral Thesis.

127. Geissinger, Andrea (2021), Platforms in Liquid Modernity: Essays about the Sharing

Economy, Digital Platforms, and Institutions, Örebro University School of Business,


128. Yang, Ying (2021), The Arrival of the Tipping Point of Solar Photovoltaic Technology,


Contact person: Professor Christina Keller, Director of MIT, Uppsala University [email protected] Address: The Swedish Research School of Management and Information Technology, Department of Informatics and Media, Uppsala University, Box 513, 751 20 Uppsala Web site: www.mit.uu.se

126. Nykvist, Rasmus (2021), Essays on the Interaction Between Regulation and

Technology: Understanding Agency and Context Through Multiple Levels of Inquiry,

Örebro University School of Business, Örebro University, Doctoral Thesis.

127. Geissinger, Andrea (2021), Platforms in Liquid Modernity: Essays about the Sharing

Economy, Digital Platforms, and Institutions, Örebro University School of Business,


128. Yang, Ying (2021), The Arrival of the Tipping Point of Solar Photovoltaic Technology,


Contact person: Professor Christina Keller, Director of MIT, Uppsala University [email protected] Address: The Swedish Research School of Management and Information Technology, Department of Informatics and Media, Uppsala University, Box 513, 751 20 Uppsala Web site: www.mit.uu.se

107

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