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