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

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

DTU Compute, Technical University of Denmark

Integrated energy system modelling

12 March 2019Integrated modelling of urban energy systems4

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

!!

!!

!!

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

DTU Compute, Technical University of Denmark

Case study: Singapore

• Population, area, GDP, industry

21 September 2018Energy Planning for Integrated Energy Systems7

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)

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

DTU Compute, Technical University of Denmark

Flexibility options in the energy system (4)

Modelling Energy Supply of Future Smart Cities

.

10

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

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

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

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

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

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)

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

DTU Compute, Technical University of Denmark

Thank you!

Integrated modelling of urban energy systems18