Optimization of energy systems for a sustainable district in stockholm using genetic algorithms, the...

Optimization of Energy Systems for a Sustainable District in Stockholm Using Genetic Algorithms

The case study of Albano

Supervisors:

Marco Perino (Italy)

Ivo Martinac, Aumnad Phdungsilp (Sweden)

Student:

Alessandro Magny

18/03/2014

MASTER THESIS PRESENTATION Double degree at KTH Royal Institute of Technology & Politecnico di Torino

Summary

• Introduction

• Background on multi-objective optimization

• Formulation of the Albano case study

• Results

• Discussion

• Conclusion and further works

18/03/2014 Alessandro Magny – Politecnico di Torino 2

Introduction

• Research problem

Given the project of a district, how to find in the preliminary design phase the best energy mix to supply the district energy needs?

• Case study

The new Albano campus (Sweden): a mixed-use urban development project for Stockholm University (150 000 m2, expected completion in 2020)

18/03/2014 3 Alessandro Magny – Politecnico di Torino

Background

• Approach

Multi-objective optimization problem

Minimize

Subject to

x = decision variables (system sizes and existence)

hi,gj = constraint functions (limited area)

fm = objective functions (environmental, energetic, economical)


Background

• Pareto solutions Conflicting objectives ->

No single optimal solution, but a set of infinite solutions

so-called “Pareto front”.


Objective 2

Ob

ject

ive

1

(Alarcon-Rodriguez et al., 2010)

Background


• Genetic algorithms (Holland, 1975; Goldberg, 1989)

GAs find the Pareto front by means of a set of solutions convergent for a certain number of generations (i.e. iterations)

GAs create the new population using concepts coming from evolutionary biology, such as selection, crossover and mutation

Previous studies have applied GAs for solving district energy supply systems, e.g. Weber, 2008 and Hai Lu, 2012.

Background


• Implementation MOBO, a multi-objective optimization software (Palonen et al., 2013)

+ Matlab® script

Albano: Input data

• Wind speed and solar radiation

• System performances

• Costs

• Energy uses: Electricity

Space heating

Domestic hot water

Cooling


Albano: Decision variables

• 17 decision variables represent the sizes and and existence of the energy systems


Decision variables Unit Name Type Range or acceptable values

Ground source heat pump (reversible) kWth S_RGSHP integer [0,2000]

Ground source heat pump (hot water) kWth S_HWGSHP integer [0,2000]

Solar cells area m2 A_PV integer [0,11000]

Solar thermal collectors area m2 A_TC integer [0,11000]

Biomass boiler size kWth S_BB integer [100,2000]

Reciprocating engine size kWel S_RE integer [10,3000]

Molten carbonate fuel cell size kWel S_MCFC integer [240,2800]

Absorption chiller size kWc S_AC integer [100,2000]

Absorption chiller existence - X_AC binary {0;1}

Anaerobic digester existence - X_AD binary {0;1}

On-site wind turbines number - N_WO discrete {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}

On-site wind turbine size kW S_WO discrete {0,16; 0,49; 0,77; 0,8; 0,93;

2,2; 4; 13,1; 25,4; 30;60}

Nearby wind turbine size kW S_WN discrete {0; 150; 250; 500; 600; 601;

800; 1300; 1650; 2000}

0: no CHP

1: reciprocating

engines

2: MC fuel cell

1: natural gas

2: biogas

0: no boiler

1: pellet

2: wood residues

{0; 1; 2}discreteF_BBBiomass boiler fuel type

CHP type T_CHP discrete {0; 1; 2}

{1; 2}discreteF_CHPCHP fuel type

Albano: Energy fluxes


CALCULATION PROCEDURE

EN

ER

GY

US

ED

IN T

HE

BU

ILD

ING

SY

ST

EM

S

DE

LIV

ER

ED

EN

ER

GY

Albano: Objective functions

• Non-renewable primary energy consumption

• Operational global warming potential

• Levelized life-cycle costs


i = energy carrier (electricity, pellets, wood, biogas, district heating, district cooling) fi =non-renewable primary energy factor for each energy carrier Ki = CO2-eq emission coefficient

(REHVA, 2013)

Albano: Modelling assumptions


f k c

- gCO2/kWh SEK/kWh

Electricity grid 2,10 36,40 1,05

Pellets 0,11 5,76 0,36

Wood residues 0,03 0,72 0,53

Natural gas 1,09 252-454 1,73

Biogas (delivered) 0,15 0,00 1,53

District heating 0,21 68,70 0,68

District cooling 0,00 0,00 0,40

Energy carrier

System Lifetime (years)

PV cells 25 35000-31100 SEK/m2

Thermal collectors 20 7000 SEK/m2

Wind turbines (small scale) 20 195000-27000 SEK/kW

Wind turbines (large scale) 20 10625 SEK/kW

Biomass boiler 20 6500 SEK/kW

GS heat pump 20 16000 SEK/kW

GS heat pump (boreholes) 50 350 SEK/m

Reciprocating engines 20 15000-7500 SEK/kW

MC fuel cell 5 80000-25000 SEK/kW

Anaerobic digester 25 730000 SEK

Absorption cooling 23 15000-1200 SEK/kW

Investment cost

The exported energy compensates the delivered energy (same price, same coefficients). Only electricity can be exported.

Market discount rate 5% Inflation rate 2% Business real discount rate = 3%

The layout of the distribution network is not optimized

Albano: Implementation

• CASE I - All technologies

• CASE II - No wind turbines

• CASE III - No district heating and cooling

• CASE IV - No district heating and cooling and no wind turbines


Albano: Data processing


-25 -20 -15 -10 -5 0 5 10 150

10

20

30

40

50

60

Non-renewable Primary Energy [GWh/year]

An

nual

ised

Lif

e C

ycle

Co

st [

MSE

K/

year

]

RESULTS (CASE I)

5000 function evaluations (50x100)

Visited points

Initial random points

Gen 20

Gen 50

Gen 100

Optimal points

-500 0 500 1000 1500 2000 2500 3000 3500 4000 45000

10

20

30

40

50

60

Greenhouse Gas Emissions [tCO2/year]

An

nua

lised

Lif

e C

ycle

Co

st [

MSE

K/

year

]

RESULTS (CASE I)

5000 function evaluations (50x100)

Visited points

Initial random points

Gen 20

Gen 50

Gen 100

Optimal points

• Pareto front for CASE I

Decision variables trends (case I - 379 optimal solutions)

-> identify clusters of solutions -> results selection

Albano: Results


• Pareto fronts

-500 -400 -300 -200 -100 0 100 200 3000

10

20

30

40

50

60

I.1

I.2

I.3

I.4I.5I.6

I.7

I.8

I.9I.10

Greenhouse Gas Emissions [tCO2/year]

Annual

ised

Lif

e C

ycle

Cost

[M

SE

K/

year

]

OPTIMAL SOLUTIONS

II.1

II.2

II.3II.4

II.5II.6

II.7II.8

II.9 II.10

III.1

III.2

III.3

III.4

III.5

III.6

III.7

IV.1

IV.2

IV.3

IV.4

IV.5

IV.6

IV.7 IV.8

CASE I - All

CASE II - no wind

CASE III - no DH or DC

CASE IV - no DH, DC or wind

Selected points in blue

Large MCFC(bg)

+AC +PV +Wind

GSHP+BB

RE+BB+

DC+PV

Small MCFC+PV

+GSHP

RE+BB+

Wind+PV

GSHP+BB+Wind

Small MCFC

+Wind+PV

Large MCFC

+AC+PV

Small MCFC

+AC+PV

Albano: Discussion

• Results analysis

– Wind turbines can be avoided if other electricity sources are available. In particular, MC Fuel cell could be further investigated (very clean tech. but also new, very expensive and with a short lifetime)

– Reciprocating engines are found only at small and medium sizes

– Pellet/wood boiler appears in almost all optimal cases

– Photovoltaic cells are often present at large scales (however, would be penalized by a full LCA)

– Heat pumps: less advantageous from a primary energy point of view, but less expensive and more reliable solution

– District heating is almost never used in optimal solution except to cover peak loads -> storage systems or backup boilers could be used instead


Discussion & further work

• Advantages: – Flexibility, easy to set-up MOBO, simple input data

– Possibility to implement other objectives (exergy, LPSP), and more energy systems

– Provides a large set of optimal solutions, can asses different stakeholders perspectives, hourly calculation allow to represent the variability over the year

• Limitations: – Sensitivity to economic parameters to be evaluated (taxation rate, inflation, variation

of costs of buying and selling energy)

– Time-consuming, complexity of post-processing multi-objective data

– Difficulty to find some data for the life-cycle cost analysis (decommissioning) or for operation of some innovative technologies

• Possible improvements: – Energy quality management (exergy analysis)

– Full life-cycle GHG emissions

– Include storage systems

– Optimize the distribution network as in Weber, 2012

– Include energy exchange between buildings and between districts

– Holistic optimization of both energy supply and building designs

– Validate against measured data



Thank you for listening!

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