A Stochastic Market Design With Revenue Adequacy and Cost Recovery by Scenario: Benefits and Costs

Two desirable properties of electricity market mechanisms include: 1) revenue adequacy for the market, and 2) cost recovery for all generators. Previously proposed stochastic market-clearing mechanisms satisfy both properties in expectation only, or satisfy one property by scenario and another in expectation. Consequently, market parties may perceive significant risks to participating in the market since they may lose money in one or more scenarios, and therefore be discouraged from offering in the market or perhaps even from investing. We develop a stochastic two-stage market-clearing model including day-ahead and real-time settlements with an energy-only pricing scheme that ensures both properties by scenario. However, this approach is cost-inefficient in general and may sacrifice other desirable market attributes. Undesirable consequences include: One group of participants will have to pay more to ensure that all other participants have their costs covered, and thus their prices will not be equilibrium supporting; and day-ahead and real-time prices are not arbitraged in expectation, although this can be fixed by allowing virtual bidders to arbitrage but at the potential cost of increased market inefficiency. Considering these pros and cons, we propose our model as an appropriate tool for market analysis, and not for clearing actual markets. Numerical results from case studies illustrate the benefits and costs of the proposed stochastic market design.

[1]  Yves Smeers,et al.  A Stochastic Two Settlement Equilibrium Model for Electricity Markets With Wind Generation , 2015, IEEE Transactions on Power Systems.

[2]  Alan G. Isemonger,et al.  The Benefits and Risks of Virtual Bidding in Multi-Settlement Markets , 2006 .

[3]  Pierre Pinson,et al.  Electricity market clearing with improved scheduling of stochastic production , 2014, Eur. J. Oper. Res..

[4]  Eugene Litvinov,et al.  Multistage Adaptive Robust Optimization for the Unit Commitment Problem , 2016, Oper. Res..

[5]  Yunjian Xu,et al.  An Efficient and Incentive Compatible Mechanism for Wholesale Electricity Markets , 2017, IEEE Transactions on Smart Grid.

[6]  Golbon Zakeri,et al.  Pricing Wind: A Revenue Adequate, Cost Recovering Uniform Price for Electricity Markets with Intermittent Generation , 2016 .

[7]  José Fortuny-Amat,et al.  A Representation and Economic Interpretation of a Two-Level Programming Problem , 1981 .

[8]  S. Gabriel,et al.  An SOS1-Based Approach for Solving MPECs with a Natural Gas Market Application , 2013 .

[9]  Shmuel S. Oren,et al.  Efficiency impact of convergence bidding in the california electricity market , 2015 .

[10]  Xu Andy Sun,et al.  Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem , 2013, IEEE Transactions on Power Systems.

[11]  Benjamin F. Hobbs,et al.  Real-Time Markets for Flexiramp: A Stochastic Unit Commitment-Based Analysis , 2016, IEEE Transactions on Power Systems.

[12]  Antonio J. Conejo,et al.  A robust optimization approach to energy and reserve dispatch in electricity markets , 2015, Eur. J. Oper. Res..

[13]  B. Hobbs,et al.  Linear Complementarity Models of Nash-Cournot Competition in Bilateral and POOLCO Power Markets , 2001, IEEE Power Engineering Review.

[14]  J. Fuller,et al.  Pricing Energy and Reserves Using Stochastic Optimization in an Alternative Electricity Market , 2007, IEEE Transactions on Power Systems.

[15]  B. Hobbs,et al.  Complementarity Modeling in Energy Markets , 2012 .

[16]  Geoffrey Pritchard,et al.  A Single-Settlement, Energy-Only Electric Power Market for Unpredictable and Intermittent Participants , 2010, Oper. Res..

[17]  Benjamin F. Hobbs,et al.  Efficient market-clearing prices in markets with nonconvexities , 2005, Eur. J. Oper. Res..

[18]  Antonio J. Conejo,et al.  Pricing Electricity Through a Stochastic Non-Convex Market-Clearing Model , 2017 .

[19]  Congcong Wang,et al.  Ramp Requirement Design for Reliable and Efficient Integration of Renewable Energy , 2017, IEEE Transactions on Power Systems.

[20]  Mohammad Shahidehpour,et al.  The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee , 1999 .

[21]  William W. Hogan,et al.  Virtual bidding and electricity market design , 2016 .

[22]  S. Gabriel,et al.  Pricing Non-Convexities in an Electricity Pool , 2012, IEEE Transactions on Power Systems.

[23]  Georgios B. Giannakis,et al.  Distributed Stochastic Market Clearing With High-Penetration Wind Power , 2015, IEEE Transactions on Power Systems.

[24]  Robert B. Wilson,et al.  Research Paper Series Graduate School of Business Stanford University Architecture of Power Markets Architecture of Power Markets 1 , 2022 .

[25]  A. Conejo,et al.  On Walrasian Equilibrium for Pool-Based Electricity Markets , 2002, IEEE Power Engineering Review.

[26]  Benjamin F. Hobbs,et al.  Value of Flexible Resources, Virtual Bidding, and Self-Scheduling in Two-Settlement Electricity Markets With Wind Generation—Part II: ISO Models and Application , 2018, IEEE Transactions on Power Systems.

[27]  Benjamin F. Hobbs,et al.  Value of Flexible Resources, Virtual Bidding, and Self-Scheduling in Two-Settlement Electricity Markets with Wind Generation—Part I: Principles and Competitive Model , 2018, IEEE Transactions on Power Systems.

[28]  B. Hobbs,et al.  Electricity Markets and Renewables: A Survey of Potential Design Changes and Their Consequences , 2017, IEEE Power and Energy Magazine.

[29]  Jin Zhong,et al.  Pricing Electricity in Pools With Wind Producers , 2012, IEEE Transactions on Power Systems.

[30]  Victor M. Zavala,et al.  A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties , 2015, Oper. Res..