integrated modelling of urban energy systems: the case of ......case of singapore: soec and sofc •...

Integrated modelling of urban energy systems: the case of Singapore

Dominik Franjo DominkovićPostdoc at DTU Compute, Denmark

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


Climate change vs. Air pollution

Integrated modelling of urban energy systems3

CO2, CH4, N2O

SOx, NOx, PM

Photo:bbc.com

CO2e


Integrated energy system modelling


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

!!

!!

!!


Limitations

• Spatial representation: transmission and distribution (congestion)

• Temporal resolution: frequency, voltage

• Industry representation

• Socio-economic costs only



Case study: Singapore

• Population, area, GDP, industry

21 September 2018Energy Planning for Integrated Energy Systems7


Primary energy supply of Singapore


Coal1%

Oil60%

Natural gas36%

Bio and waste3%

Source: IEA (2015)


Scenario development


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


Flexibility options in the energy system (4)

Modelling Energy Supply of Future Smart Cities

.

10


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


Storage capacities


(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


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)


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


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


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


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)


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



Thank you!


integrated modelling of urban energy systems: the case of ......case of singapore: soec and sofc •...

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