integrated modelling of urban energy systems: the case of ......case of singapore: soec and sofc •...
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Integrated modelling of urban energy systems: the case of Singapore
Dominik Franjo DominkovićPostdoc at DTU Compute, Denmark
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DTU Compute, Technical University of Denmark
Outline
• Integrated energy planning
• MILP model used» Limitations of the model
• Case study: Singapore
• Results» The role of different storage types» Flexibility provision of industry and buildings» The role of district cooling: optimal capacities» Air pollution and renewable energy sources
• Conclusions
12 March 2019Integrated modelling of urban energy systems2
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DTU Compute, Technical University of Denmark
Climate change vs. Air pollution
Integrated modelling of urban energy systems3
CO2, CH4, N2O
SOx, NOx, PM
Photo:bbc.com
CO2e
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DTU Compute, Technical University of Denmark
Integrated energy system modelling
12 March 2019Integrated modelling of urban energy systems4
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DTU Compute, Technical University of Denmark 12 March 2019Integrated modelling of urban energy systems55
MIXED INTEGER LINEAR OPTIMIZATION MODELEndogenous decisions:
DSM activation in industry and buildingsShare of district and individual cooling
Share of electrified and ICE transportationEnergy efficiency scenario
•INPUT
OUTPUT
Cooling
TransportGas
Electricity
WaterSector interactions
!!
!!
!!
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DTU Compute, Technical University of Denmark
Limitations
• Spatial representation: transmission and distribution (congestion)
• Temporal resolution: frequency, voltage
• Industry representation
• Socio-economic costs only
12 March 2019Integrated modelling of urban energy systems6
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DTU Compute, Technical University of Denmark
Case study: Singapore
• Population, area, GDP, industry
21 September 2018Energy Planning for Integrated Energy Systems7
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DTU Compute, Technical University of Denmark
Primary energy supply of Singapore
12 March 2019Integrated modelling of urban energy systems8
Coal1%
Oil60%
Natural gas36%
Bio and waste3%
Source: IEA (2015)
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DTU Compute, Technical University of Denmark
Scenario development
12 March 2019Integrated modelling of urban energy systems9
Technologies/ constraints
Scenario 1 BAU
Scenario 2 DC
Scenario 3 DC-PV
Scenario 4 DC-PV-el.transp.
Scenario 5 CO2-constr.
Transport Electrification
Photovoltaics District Cooling SOFC, SOEC, synthetic fuels
CO2e emissions
!
!
! !
!
- Demand side management (scenario 5)
- Non-island mode (scenario 7)
6
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DTU Compute, Technical University of Denmark
Flexibility options in the energy system (4)
Modelling Energy Supply of Future Smart Cities
.
10
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DTU Compute, Technical University of Denmark
Demand response – industry and buildings• Load shifting increased peak demand (!) – but optimal
• Load shifting in industry: 9%, 6% and 11% (Scenarios 5-7)
• Load shifting in buildings: 5%, 3% and 6%
• Total: 0.26-0.71% of final electricity demandIntegrated modelling of urban energy systems
-2
0
2
4
6
8
10
0 12 0 12 0 12
(GW
)
(hour)Flexible demand utilized households Flexible demand utilized IndustryFlexible demand charge households Flexible demand charge Industrywaste CHP gas CHPPVs
11
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DTU Compute, Technical University of Denmark
Storage capacities
Integrated modelling of urban energy systems12
(GWh)BAU DC DC-PV
DC-PV-el.transp.
DSM-EnEff
CO2
cons.
non self-suff.
PTES 0 153 297 163 151 96 492Grid batteries 5.9 0 0 0 0 0 20.6Hydrogen 0 0 0 0 0 0 223Methane 7.2 0 24.8 21.8 20.9 0 0EV batteries 1.0 1.5 4.9 15.1 15.0 14.9 23.3
0%20%40%60%80%
100%
Elec
tric
ity g
ener
atio
n by
type
of
the
prod
ucer
(%)
Waste CHP Gas CHP Biomass CHP PVs SOFC
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DTU Compute, Technical University of Denmark
Case of Singapore: SOEC and SOFC• Only Scenario 7 (80% share of PV in ele. generation)
• SOEC: 1,826 MW; SOFC: 434 MW (4% of final ele. demand)
Integrated modelling of urban energy systems13
0%
20%
40%
60%
80%
100%
Scenario 7 (non self-sufficient)El
ectr
icity
gen
erat
ion
by ty
pe o
f the
pr
oduc
er [%
]
Waste CHP Gas CHPBiomass CHP PVsSOFC
0
0.5
1
1.5
2
4114
4146
4178
4210
4242
4274
4306
4338
4370
4402
4434
4466
4498
4530
4562
4594
(GW
)
(hour)
SOEC SOFC
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DTU Compute, Technical University of Denmark
V2G vs. smart charging• Singapore 2030, Scenario 7: 80% PV penetration
• V2G: 16% of the total vehicle discharge• V2G: 4% of the final electricity demand
• Curtailed energy: 4.2%
• Other scenarios: 25%-33% PV(curtailed ele.: 0%-0.3%)
• (V2G): 1.1% - 3.3% of the total vehicle discharge• Other scenarios (V2G): 0.3% - 0.7% of the total vehicle
discharge
Integrated modelling of urban energy systems14Source:wiseguyreports.com
Conclusions:
• PV vs. wind
• High penetration vs low penetration
• Smart charging is enough
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DTU Compute, Technical University of Denmark
District cooling vs individual cooling vs variable renewable energy
Modelling Energy Supply of Future Smart Cities 15
04080
120160200240
Prim
ary
ener
gy s
uppl
y [T
Wh] Waste
consumption
Biomassconsumption
Renewable(without biomass)
Gas consumption
Oil consumption
0
20
40
60
80
100
Cool
ing
ener
gy
dem
and
(TW
h)
District cooling Individual cooling
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DTU Compute, Technical University of Denmark
Biomass and air pollution – the case of Singapore
Socio-economic costs
Scenario1BAU
Scenario2DC
Scenario3DC-PV
Scenario4DC-PV-el.transp.
Scenario5DSM-EnEff
Scenario6CO2
Scenario7non self-sufficient
air pollution (mil €)
682 674 669 31 27 63 10
CO2e emissions (mil €)
910 787 700 614 568 292 294
Modelling Energy Supply of Future Smart Cities 16
05,000
10,00015,00020,00025,00030,000
DSM-EnEff CO2 cons non self-sufficient
NOx (t) SOx (0.1 x t) PM (0.1 x t) CO2e (kt)
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DTU Compute, Technical University of Denmark
Conclusions
1) The role of different storage types
2) Flexibility provision of industry and buildings
3) The role of district cooling: optimal capacities
4) The role of (renewable) gas in future energy system
12 March 2019Integrated modelling of urban energy systems17
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DTU Compute, Technical University of Denmark
Thank you!
Integrated modelling of urban energy systems18