Upload
others
View
1
Download
0
Embed Size (px)
Citation preview
© 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.
0
10
20
30
40
50
60
2011 2020 2030 2040 2050
Gt
CO
2
Sectors
Power generation 41% Industry 19%Transport 19% Buildings 13%Other transformation 8%
0
10
20
30
40
50
60
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
© OECD/IEA 2013
Decarbonising the global electricity sector
0
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
© OECD/IEA 2013
Strategies to decarbonise electricity vary across regions
© OECD/IEA 2013
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
ele
ctri
city
gen
erat
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.
© OECD/IEA 2013
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
© OECD/IEA 2013
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
© OECD/IEA 2013
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
© OECD/IEA 2013
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
11
1
2
3
4
5
6
7
8
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)
© OECD/IEA 2013
No operational constraints
+ Ramping/scheduling constraints
+ Eff. Penalty
Price duration curve, 2050, 2DS, US
Influence of operational constraints
© OECD/IEA 2014
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
0 6 12 18 24 0 6 12 18 24
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
2
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.
© OECD/IEA 2013
Technical performance of electricity storage technologies
© OECD/IEA 2013
Costs of electricity storage technologies
© OECD/IEA 2013
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
© OECD/IEA 2013
Impact of EVs in the US (2)
© OECD/IEA 2013
Impact of EVs in the US (1)
DR from EVs displacing storage during peak demand periods
© OECD/IEA 2013
0
20
40
60
80
100
120
140
160
180
200
0% 26% 51% 77%
USD
/MW
h
% of the year
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!
www.iea.org/etp2014