Post on 21-Jan-2023
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
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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)
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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)
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Background
• Pareto solutions Conflicting objectives ->
No single optimal solution, but a set of infinite solutions
so-called “Pareto front”.
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Objective 2
Ob
ject
ive
1
(Alarcon-Rodriguez et al., 2010)
Background
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• 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
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• 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
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Albano: Decision variables
• 17 decision variables represent the sizes and and existence of the energy systems
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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
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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
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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
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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
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Albano: Data processing
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-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
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• 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
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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
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