Outline The EU context Model description Case Study Results Results Reserve procurement in power systems with high renewable 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, Universit´ e catholique de Louvain, Belgium Mercati energetici e metodi quantitativi: un ponte tra Universit` a ed Impresa Padova October 13th, 2016 Reserve procurement
Outline The EU context Model description Case Study Results Results Outline 1 The European context 2 Model description Model common assumptions Model 1: joint procurement of energy and reaserve Model 2: reserve procured before day ahead Model 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 important because: The increasing integration of stochastic (renewable) energy production makes power 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 Market Third 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 a coordinated 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 ancillary service 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 Nuclear CCGT Demand response: demand side management with downward/upward deviations in real time Uncertainty characterization: day-ahead forecasts and real time scenarios for demand 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: First Stage Second Stage s 1 ISO co-optimizes the energy and the reserve procurement ISO balances the s 2 system on the basis of RT scenarios s 3 D-1 D (Day ahead) (Real time) 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: First Stage Second Stage Third Stage PX clears the energy S1f1 market S2f1 S3f1 f1 PX clears the energy S1f2 TSO market S2f2 f2 TSO balances the procures system on the basis reserves S3f2 of RT scenarios S1f3 f3 PX clears the energy market S2f3 S3f3 W-1 D-1 D (Week ahead) (Day ahead) (Real time) 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: First Stage Second Stage s 1 TSO re-dispatches PX clears the energy and procures energy market TSO balances the reserves s 2 system on the basis of RT scenarios s 3 D-1 D-1 D (Day ahead) (Day ahead) (Real time) 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 network PV W CCGT W CCGT W with 38 transmission lines 18 21 22 17 Capacity: W CO 23 CCGT PV Technology Capacity (MW) 16 19 20 CCGT 2250 Coal 700 14 13 15 Nuclear 900 N Wind 2100 W Solar 750 CCGT Total 6700 Z2 12 24 11 Z3 Total demand (17 nodes): 3135 MW 3 9 10 6 Uncertainty: 3 day-ahead forecasts 4 and 3 real time scenarios per day-ahead forecast Z1 5 8 1 2 7 W CCGT PV CCGT CO PV Reserve procurement
Outline The EU context Model description Case Study Results Results Reserve procurement Coordinated procurement: Reserve need is determined on the whole market as a unique zone (1 zone); Not-coordinated procurement: Reserve needs are defined at zonal level (3 zones/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 costs Coordinated reserve procurement ($): 1 Zone Model 1 Model 2 Model 3 (Expected) (Expected) Total operating costs 826,180 837,708 827,296 DA operating costs 822,345 851,960 823,532 RT operating costs 3,835 -14,252 3,764 Not-coordinated reserve procurement ($): 3 Zones Model 1 Model 2 Model 3 (Expected) (Expected) Total operating costs 834,007 843,395 5,060,361 DA operating costs 829,937 860,962 858,323 DA unserved demand value - - 4,201,153 RT operating costs 4,070 -17,568 884 RT 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 786,711 798,431 792,769 RT 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 procurement assumptions. We also verified that the not-coordinated reserve procurement based on multiple reliability zones leads to higher total operating costs than considering the power system as a whole. Model 3 in the coordinated reserve procurement case results almost as efficient as Model 1. But it becomes inefficient (unserved demand) in the not-coordinated reserve procurement 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). Integrating renewables in electricity markets: Operational Problems. International series in operations 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 errors in 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. IEEE Transaction 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 Operations Research , 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
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