© OECD/IEA 2013

Role of Storage in Decarbonised Electricity Systems

Uwe Remme, Luis Munuera

JRC-EU-TIMES Workshop “Addressing Flexibility in Energy System Models”

© OECD/IEA 2013

A transformation is needed…

..and we to have the tools to develop a strategy and be proactive.

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2011 2020 2030 2040 2050

Gt

CO

2

Sectors

Power generation 41% Industry 19%Transport 19% Buildings 13%Other transformation 8%

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2011 2020 2030 2040 2050

Technologies

End-use fuel and electricity efficiency 38% CCS 14%End-use fuel switching 9% Renewables 30%Power generation efficiency and fuel switching 2% Nuclear 7%

6DS

2DS

6DS

2DS

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Decarbonising the global electricity sector

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10 000

20 000

30 000

40 000

50 000

2011 2020 2030 2040 2050

TWh

Other

Wind

Solar

Hydro

Nuclear

Biomass and waste

Oil

Gas

Coal

In the 6DS, fossil technologies continue to dominate electricity generation with a share of 66% in 2050, whereas in the 2DS more than 90% of electricity are generated from low-carbon sources (renewables, nuclear, CCS).

6DS

2DS

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Strategies to decarbonise electricity vary across regions

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Challenge of integrating increasing shares of variable renewables

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

2011 2030 2050 2011 2030 2050 2011 2030 2050 2011 2030 2050

China India European Union United States

Shar

e o

f va

riab

le r

enew

able

s in

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ctri

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gen

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ion

(%

)

© OECD/IEA 2013

We have the flexible resources

Grid infrastructure

Dispatchable generation Storage

Demand side integration

Four sources of flexibility …

No one flexible resource meets all the needs.

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Complementing a long-term model wtih a linear dispatch model

Enhanced ETP-TIMES model (long-term; horizon up to 2050):

28 regions, 3h-load segments for a typical day (8 per day, four typical days per year)

Large-scale storage: electricity, hydrogen

Considering other flexibility options for the electricity system:

Flexible generation technologies

Inclusion of demand response, e.g. V2G

Investment decisions in generation technologies and first estimate on storage needs

Linear TIMES dispatch model for operational analysis (short-term; one year):

1h-timeslice resolution

Analysing operation of electricity system within a year for specific region with investment decisions for generation technologies from long-term model

Additional operational constraints (ramp-up/-down, min load, min up/down times)

Option to include investment decisions for generation and storage technologies

Improved analysis on storage needs and role of competing flexibility options

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Flexible uses in conversion sector

Long-term ETP-TIMES model for the power sector

Flexible generation

Demand side management

DSM

DSM

DSM

Potentials

Fuel costs Technical and economic

characteristics Electricity and heat demands

Load curves

Fuel demand

Generation mix New capacities

Electricity prices Average generation costs

Emissions

Electricity storage

Pumped storage

CAES

District heat storage

Process heat storage

Energy storage

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Soft-link to linearised dispatch analysis of ETP long-term decarbonisation scenarios

• DR at the individual technology level • Electric vehicles • Under development

• End-use heat storage • Flexible district heating • Industrial processes

Dispatchablegenera on

• Operational constraints for a range of plants: • Ramping • Minimum up/down times • Turn-down ratios • Part-load efficiency approximation

Storage

• Storage modelled for: • Load following • Arbitrage/load-levelling • Seasonal storage

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Linear dispatch model: Model formulation (1)

Objective function

p

t,ppp

pp

ppp VAR_GENVarOMCstFuelPricEff

VAR_NCAPFomCstAnnInvCstMin1

Constraints

t,ppt,p AvailFacVAR_CAPVAR_GEN )(

t

p

t,p DemandVAR_GEN

Decision variables

pt,p VAR_NCAPVAR_GEN ,

tp

p

pp DemandPkRsvVAR_CAPPkCnt

Generation-capacity constraint

Demand constraint

Peaking constraint

Electricity generation New capacity

ppp VAR_NCAPCapResidVAR_CAP Total installed capacity

Indices

p

t

generation technologies

hours

Existing capacity

Availability factor

Peak reserve margin

Annualised investment costs

Fixed OM costs Efficiency

Fuel price Variable OM costs

© OECD/IEA 2013

Operational constraints

t

MW

t

Available capacity for a technology MW

Online capacity 1

t

MW

Generation 2

Minimum load

3

Ramp-up constraint

4 Ramp-down constraint 5

Minimum operation time

6

Minimum down time

7

Part-load efficiency 8

Start-up costs

9

© OECD/IEA 2013

Linear dispatch model: Model formulation (2)

t,ppt,p AvailFacVAR_CAPVAR_CAPON

ptt,p VAR_CAPONVAR_GEN ,

pt,pt,p MinGenVAR_CAPONVAR_GEN

t,p1,ptt,p TRVAR_CAPONSVAR_CAPONVAR_CAPON

pt,p1,ptt,p RmpUpVAR_CAPONVAR_GENVAR_GEN

pt,pt,p1,pt RmpDnVAR_CAPONVAR_GENVAR_GEN

p,p1,ptt,p MinOpTimetVAR_CAPONVAR_CAPONVAR_CAPON

p,pt,pt,p MinDnTimetVAR_CAPONVAR_CAPTRVAR_CAPONS

Defining capacity online

Minimum generation

Ramp-up and –down constraints

Minimum operation hours

Minimum downtime hours constrain start-up of online capacity

Approximating part-load efficiency by adding fuel cost penalty term in objective function

pt,pp

pp

MinGenVAR_CAPONFuelCstdEffPartLoadEffFullLoa

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1

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t,pp TRVAR_CAPONSCSTNCAP_STRUP 9 Start-up costs to bring capacity online

© OECD/IEA 2013

Comparing different modelling approaches Small example with four technologies (coal, CCGT, OCGT, wind) on an hourly basis

MIP OprCon: unit commitment with operational constraints, lumpy investments

LP No OprCon: no operational constraints (= standard TIMES)

LP OprCon: linearised operational constraints, continuous investments

Existing system Given capacities (1000 GW coal, 400 GW gas) Adding wind capacity 1100 GW

LP NoOpr: very flexible use of coal plants to avoid curtailment

MIP & LP OprCon: curtailment of 23% & 15%

Greenfield system Optimised system for given wind capacity 1100 GW

MIP vs LP: lumpiness of investments

LP No OprCon vs LP OprCon: similar investments, but no curtailment in No OprCon

Operational constraints more important in systems with existing capacity or cases with high shares of renewables (-> curtailment -> flexibility needs)

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No operational constraints

+ Ramping/scheduling constraints

+ Eff. Penalty

Price duration curve, 2050, 2DS, US

Influence of operational constraints

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0 6 12 18 24

Dispatch model - Model structure 19/08/14 HYDROGEN CC

TURBINE/FUEL CELL

ELCH2NGCC00

HARD COAL USC PLANT

ELCHCOUSC00

HYDROELECTRIC PLANT

ELCHYD00

NATURAL GAS OC TURBINE

ELCNGAT00

NATURAL GAS CC TURBINE

ELCNGACC00

NUCLEAR PLANT ELCNUC00

SOLAR CSP TURBINE

ELCSOLCSPTUR00

ONSHORE WIND

ELCWINON00

HARD COAL USC + CCS

ELCZHCOUSC00

NATURAL GAS OC + CCS ELCZNGACC00

HYDROGEN ELECTROLISER

ELCH2PROD00

DEMAND PROCESS FOR EVs

DEMPREV

DEMAND PROCESS FOR ELECTRICITY

DEMPRELC

HV ELECTRICITY

ELCHV

ELECTRICITY DEMAND (EVs)

DEMELCEV

HYDROGEN FROM ELEC

ELCH2

ELECTRICITY DEMAND

DEMELC2

PSH PUMP

ELCHYDPMP

PSH TURBINE

ELCHYDTUR00

STORAGE

ELCSTG

Electricity storage

RESERVOIR

SOLAR PV PLANT ELCSOLPV00

HYDRO RESOURCE

ELCHYD

SOLAR RESOURCE

ELCSOL

NAT. GAS RESOURCE

ELCNGA

URANIUM RESOURCE

ELCNUC

WIND RESOURCE

ELCWIN

CSP INPUT

HETCSP

HARD COAL RESOURCE

ELCHCO

H2 FROM ELEC

ELCH2

Hourly wind profiles

Solar irradiation + Panel model

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HEAT STORAGE

SOLAR FIELD

CHP PLANT

ELCCHP00

VARIOUS FUELS DH GRADE

HEAT

HEATDH LOW GRADE

HEAT DEMAND

DEMHEAT

DEMAND PROCESS FOR LGH

DEMPRELC

HEAT STORAGE

BOILER ELCCHP00

HEAT PUMP ELCCHP00

DH DISTRIBUTION

DEMPRELC LOW

GRADE HEAT

HEATDH

Hourly demand pprofiles

Hourly demand profiles

Areal Heat distribution costs

Demand response

HEAT ONLY PLANTS ELCCHP00

BIOMASS B ELCCHP00

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1

Heat mapping

© OECD/IEA 2013

Modelling of storage

Pump

Turbine

Storage reservoir

Electricity stored Electricity

Storage in term of capacity (“MW”)

Storage in term of energy (“MWh”)

Generic storage: Pumped storage analogy

For existing pumped storage, often lack of data on storage volume in terms of energy

DNI category

Solar field Power

generation Thermal storage

DNI Heat Electricity

Concentrated solar power plants with thermal storage

CSP plant broken down in solar field, heat storage and turbine block

© OECD/IEA 2013

PV at sunrise, STE at sunset

CSP plants would generate electricity for peak and mid-peak demand; after sunset, their capacity complements PV generation from earlier in the day.

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Technical performance of electricity storage technologies

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Costs of electricity storage technologies

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Analysis of storage potential in 2DS

Regions/Countries: China, EU, India, US

Year 2050 analysed on a 1h time resolution (arbitrage, load following, integration of solar PV and wind)

Based on capacity mix from long-term planning model

3 cases:

2DS: estimation of storage requirements

Breakthrough: as 2DS plus agressive cost reductions for storage comparable to CCGT for arbitrage (e.g. LCOE of USD 90/MWh in EU)

EV/DR: 2DS plus smart charging (G2V) based on 2DS projection of number of EVs (e.g. for the US: 45 million EVs in 2050 resulting in a total storage capacity of 1 TWh)

© OECD/IEA 2013

Potential for storage in key regions

Attractiveness of storage is highly context-specific, but increases greatly across all scenarios

Depending on the system, flexibility from demand response could provide greater returns

68 GW 15 GW 22 GW 53 GW Long-term model results for storage in 2DS

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Impact of EVs in the US (2)

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Impact of EVs in the US (1)

DR from EVs displacing storage during peak demand periods

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USD

/MW

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2DS

DR

Full DR+Storage

Avoidance of extreme prices during scarcity periods with more DR, storage

Reduced curtailment, other technologies setting price during periods where low SRMC was price setter

Key changes in wholesale price duration curve

© OECD/IEA 2013

Findings, limitations of analysis and future work

Conclusions: Storage is an important option, but not the sole solution for flexibility needs, as it

competes with other options (DR, flexible generation, grids)

DR from EVs in this analysis appears attractive, but hinges on smarter grid infrastructure

Key question for DR is hidden/transaction costs and uncertainty over achievable DR take-up

Limitations in storage analysis: Flexibility from interconnections through enlarged balancing areas difficult to capture,

without explicitly consider spatial system structure

Uncertainty over profile of future load curves, particularly in emerging economies, coupled with difficulty of modelling individual end-uses

Plans for future work: Including investment decisions for analysis of a “greenfield” system

Expanding DR options

Including flexibility options outside of electricity sector (especially CHP and thermal storage)

© OECD/IEA 2013

Thank you!

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