reserve procurement in power systems with high renewable...
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Outline The EU context Model description Case Study Results Results
Reserve procurement in power systems with highrenewable capacity:
How does the time framework matter?
G. Oggioni(1) R. Dominguez(2) Y. Smeers(3)
(1) University of Brescia, Italy (2) Universidad de Castilla-La Mancha, Toledo, Spain(3)CORE, Universite catholique de Louvain, Belgium
Mercati energetici e metodi quantitativi:un ponte tra Universita ed Impresa
PadovaOctober 13th, 2016
Reserve procurement
Outline The EU context Model description Case Study Results Results
Outline
1 The European context
2 Model descriptionModel common assumptionsModel 1: joint procurement of energy and reaserveModel 2: reserve procured before day aheadModel 3: reserve procured after day ahead
3 Case Study
4 Results
5 Conclusions
Reserve procurement
Outline The EU context Model description Case Study Results Results
Reserve procurement and RES integration
Renewable energy integration requires flexibility because of:
Uncertainty;
Variability.
The schedule of an adequate reserve level is becoming extremely importantbecause:
The increasing integration of stochastic (renewable) energy production makespower systems unstable
It guarantees security of supply and system balance in real time!
Reserve procurement
Outline The EU context Model description Case Study Results Results
Towards the Internal European Electricity MarketThird Energy Package and Network Codes
The European Commission envisages the coordination of:
The energy day-ahead markets (Price Coupling of Regions);
The reserve procurement mechanisms;
The congestion management;
The energy balancing markets.
Reserve procurement
Outline The EU context Model description Case Study Results Results
Goals
Reserve procurement
Outline The EU context Model description Case Study Results Results
Our goals in this paper...
Q1: Does the time framework for reserve procurement matter?
We analyze and compare the efficiency levels of three power systems where:
1 Energy and reserves are jointly scheduled by an Independent System Operator(as in the US)
2 Reserves are scheduled before the clearing of the day-ahead energy market(as in Central European countries)
3 Reserves are schedule after the clearing of the day-ahead energy market(as in Italy, Spain, Portugal)
Q2: Does a coordinated reserve procurement increase the system efficiency?
We compare the efficiency levels of the three power systems above assuming acoordinated and not-coordinated reserve schedule.
Reserve procurement
Outline The EU context Model description Case Study Results Results
Models
Reserve procurement
Outline The EU context Model description Case Study Results Results
Common assumptions
Model common assumptions
Spatial granularity: nodal level both in day-ahead energy and ancillaryservice markets
Reserves: Conventional and downward/upward spinning reserves
Generating units: Stochastic (wind and solar PV) vs. dispatchable units(nuclear, coal, CCGT)
Dispatchable units
Qualified Non-qualified
Coal NuclearCCGT
Demand response: demand side management with downward/upwarddeviations in real time
Uncertainty characterization: day-ahead forecasts and real time scenarios fordemand level and renewable power availability
Reserve procurement
Outline The EU context Model description Case Study Results Results
Model 1
Model 1: Energy and reserve needs are jointly scheduled
Model 1 is a two-stage stochastic programming problem as illustrated below:
FirstStage SecondStage
D-1(Dayahead)
ISObalancesthesystemonthebasisof
RTscenarios
ISOco-optimizestheenergyandthereserve
procurement
s1
s2
s3
D(Realtime)
Figure: Decision-making process of Model 1
Reserve procurement
Outline The EU context Model description Case Study Results Results
Model 2
Model 2: Reserve scheduled before the day-ahead energy market
Model 2 is a three-stage stochastic programming problem as illustrated below:
FirstStage SecondStage ThirdStage
f1
PXclearstheenergymarket
TSOprocuresreserves
PXclearstheenergymarket
PXclearstheenergymarket
TSObalancesthesystemonthebasisofRTscenarios
f2
f3
S1f1S2f1
S3f1
S1f2S2f2
S3f2
S1f3
S2f3
S3f3
MODEL2
W-1(Weekahead)
D-1(Dayahead)
D(Realtime)
Figure: Decision-making process of Model 2
Reserve procurement
Outline The EU context Model description Case Study Results Results
Model 3
Model 3: Reserve scheduled after the day-ahead energy market
Model 3 is formulated as illustrated below:
FirstStage SecondStage TSObalancesthe
systemonthebasisofRTscenarios
TSOre-dispatchesenergyandprocures
reservesPXclearstheenergymarket
s1
s2
s3
D-1(Dayahead)
D(Realtime)
D-1(Dayahead)
Figure: Decision-making process of Model 3
Reserve procurement
Outline The EU context Model description Case Study Results Results
Case Study
Reserve procurement
Outline The EU context Model description Case Study Results Results
Case study
Nodal network: IEEE 24-node networkwith 38 transmission lines
Capacity:
Technology Capacity (MW)
CCGT 2250Coal 700
Nuclear 900
Wind 2100Solar 750
Total 6700
Total demand (17 nodes): 3135 MW
Uncertainty: 3 day-ahead forecastsand 3 real time scenarios perday-ahead forecast
18 21 22
17
16 19 20
23
1514 13
11 1224
3 9 10
6
4
5
21 7
8
W
WCCGT
CCGT
N
PVW CCGT CCGT PV
W
CCGT
PV
W WCCGT
CO
Z2
Z1
Z3
PV
CO
Reserve procurement
Outline The EU context Model description Case Study Results Results
Reserve procurement
Coordinated procurement: Reserve need is determined on the whole marketas a unique zone (1 zone);
Not-coordinated procurement: Reserve needs are defined at zonal level (3zones/countries).
Reserve procurement
Outline The EU context Model description Case Study Results Results
Results
Reserve procurement
Outline The EU context Model description Case Study Results Results
Operating costsCoordinated reserve procurement ($):
1 Zone
Model 1 Model 2 Model 3(Expected) (Expected)
Total operating costs 826,180 837,708 827,296
DA operating costs 822,345 851,960 823,532RT operating costs 3,835 -14,252 3,764
Not-coordinated reserve procurement ($):
3 Zones
Model 1 Model 2 Model 3(Expected) (Expected)
Total operating costs 834,007 843,395 5,060,361
DA operating costs 829,937 860,962 858,323DA unserved demand value - - 4,201,153RT operating costs 4,070 -17,568 884RT unserved demand value - - -
Not-coordinated reserve procurement and increased installed capacity ($):
3 Zones (Increased capacity)
Model 1 Model 2 Model 3(Expected) (Expected)
Total operating costs 772,771 775,675 776,195
DA operating costs 786,711 798,431 792,769RT operating costs -13,940 -22,756 -16,574
Reserve procurement
Outline The EU context Model description Case Study Results Results
Conclusions
Reserve procurement
Outline The EU context Model description Case Study Results Results
Conclusions
As expected, the market structure represented through Model 1 (one ISO)results as the most efficient market under all reserve procurementassumptions.
We also verified that the not-coordinated reserve procurement based onmultiple reliability zones leads to higher total operating costs thanconsidering the power system as a whole.
Model 3 in the coordinated reserve procurement case results almost asefficient as Model 1.
But it becomes inefficient (unserved demand) in the not-coordinated reserveprocurement because of the limits imposed on the cross-border exchanges.
Reserve procurement
Outline The EU context Model description Case Study Results Results
Reserve procurement
Outline The EU context Model description Case Study Results Results
Morales, J.M., Conejo, A.J., Madsen, H., Pinson, P., Zugno, M. (2014). Integratingrenewables in electricity markets: Operational Problems. International series inoperations research and management science: 205. New York, NY, USA: Springer.
Fabbri, A., Gomez San Roman, T., Rivier Abbad, J., Mendez Quezada, V. H.
(2005). Assessment of the cost associated with wind generation prediction errorsin a liberalized electricity market, IEEE Transaction on Power Systems, 20(3),1440-1446.
Ortega-Vazquez, M.A., Kirschen, D.S. (2009). Estimating the Spinning Reserve
Requirements in Systems With Significant Wind Power Generation Penetration,IEEE Transaction on Power Systems, 24(1), 114-124.
Papavasiliou, A., Oren, S.S., O’Neill, R.P. (2011). Reserve requirements for wind
power integration: a scenario-based stochastic programming framework. IEEETransaction on Power Systems, 26(4), 2197-2206.
Pineda, S., Morales, J.M. (2016). Capacity expansion of stochastic power
generation under two-stage electricity markets, Computers and OperationsResearch, 70, 101-114.
Reliability Test System Task Force (1999). The IEEE reliability test system-1996,
IEEE Transaction on Power Systems, 14(3), 1010-1020.
Reserve procurement