Towards the development of risk-constrained optimal bidding strategies for generation companies in electricity markets

In the competitive electricity market environment, generation dispatching is bid-based, and individual generation companies (Gencos) are required to compete with rivals through bidding to the market. Competition implies the opportunities for Gencos to get more profit and, in the meantime, the risk of not being dispatched. As a result, it has become a major concern for Gencos to build optimal bidding strategies so as to maximize profits while minimizing risks associated. In this paper, a new approach is developed for building optimal bidding strategies with risks taken into account for Gencos participating in a pool-based single-buyer electricity market. It is assumed that each Genco bids a linear supply function and that the system is dispatched to minimize the total purchasing cost of the single-buyer. Each Genco chooses the coefficients in the linear supply function for making tradeoff between two conflicting objectives: profit maximization and risk minimization. A stochastic optimization model is established for the purpose and a novel method for solving this problem is presented. Numerical test results for a simulated electricity market with six Gencos show clearly the essential features of the developed model and method.

[1]  G. Shrestha,et al.  Strategic Bidding for Minimum Power Output in the Competitive Power Market , 2001, IEEE Power Engineering Review.

[2]  A. David Competitive bidding in electricity supply , 1993 .

[3]  A. David,et al.  Strategic bidding in competitive electricity markets: a literature survey , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[4]  A. David,et al.  Optimal bidding strategies and modeling of imperfect information among competitive generators , 2001 .

[5]  Y. Ho,et al.  An Ordinal Optimization-Based Bidding Strategy for Electric Power Suppliers in the Daily Energy Market , 2001, IEEE Power Engineering Review.

[6]  Roy Billinton,et al.  A reliability test system for educational purposes-basic distribution system data and results , 1991 .

[7]  S. A. Soman,et al.  Application of actor-critic learning algorithm for optimal bidding problem of a Genco , 2002 .

[8]  Peter B. Luh,et al.  Optimal integrated bidding and hydrothermal scheduling with risk management and self-scheduling requirements , 2000, Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393).

[9]  Eileen Cashman,et al.  Operational risks, bidding strategies and information policies in restructured power markets , 1999, Decis. Support Syst..

[10]  S. Hao A study of basic bidding strategy in clearing pricing auctions , 1999, Proceedings of the 21st International Conference on Power Industry Computer Applications. Connecting Utilities. PICA 99. To the Millennium and Beyond (Cat. No.99CH36351).

[11]  G. Sheblé,et al.  Genetic algorithm evolution of utility bidding strategies for the competitive marketplace , 1998 .

[12]  F. Nogales,et al.  Price-Taker Bidding Strategy under Price Uncertainty , 2002, IEEE Power Engineering Review.

[13]  J. Contreras,et al.  A Cobweb Bidding Model for Competitive Electricity Markets , 2001 .

[14]  Daniel Ashlock,et al.  Comprehensive bidding strategies with genetic programming/finite state automata , 1999 .

[15]  Peter B. Luh,et al.  Optimization based bidding strategies in the deregulated market , 1999 .

[16]  Zuwei Yu A spatial mean-variance MIP model for energy market risk analysis , 2003 .

[17]  Jussi Keppo,et al.  Managing electricity market price risk , 2003, Eur. J. Oper. Res..

[18]  Haili Song,et al.  Optimal electricity supply bidding by Markov decision process , 2000 .

[19]  P. Luh,et al.  Optimal integrated generation bidding and scheduling with risk management under a deregulated daily power market , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).