Multimarket optimal bidding for a power producer

This paper considers a profit-maximizing thermal producer that participates in a sequence of spot markets, namely, day-ahead, automatic generation control (AGC), and balancing markets. The producer behaves as a price-taker in both the day-ahead market and the AGC market but as a potential price-maker in the volatile balancing market. The paper provides a stochastic programming methodology to determine the optimal bidding strategies for the day-ahead market. Uncertainty sources include prices for the day-ahead and AGC markets and balancing market linear price variations with the production of the thermal producer. Results from a realistic case study are reported and analyzed. Conclusions are duly drawn.

[1]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[2]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[3]  David Kendrick,et al.  GAMS, a user's guide , 1988, SGNM.

[4]  L. Escudero,et al.  Hydropower generation management under uncertainty via scenario analysis and parallel computation , 1995, Proceedings of Power Industry Computer Applications Conference.

[5]  Peter Kall,et al.  Stochastic Programming , 1995 .

[6]  Laureano F. Escudero,et al.  Hydropower generation management under uncertainty via scenario analysis and parallel computation , 1995 .

[7]  Julia L. Higle,et al.  Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming , 1996 .

[8]  F. Galiana,et al.  Power systems restructuring : engineering and economics , 1998 .

[9]  John M. Wilson,et al.  Introduction to Stochastic Programming , 1998, J. Oper. Res. Soc..

[10]  G. Sheblé Computational Auction Mechanisms for Restructured Power Industry Operation , 1999 .

[11]  J. Dupacová,et al.  Scenario reduction in stochastic programming: An approach using probability metrics , 2000 .

[12]  Werner Römisch,et al.  Stochastic Lagrangian Relaxation Applied to Power Scheduling in a Hydro-Thermal System under Uncertainty , 2000, Ann. Oper. Res..

[13]  Samer Takriti,et al.  Incorporating Fuel Constraints and Electricity Spot Prices into the Stochastic Unit Commitment Problem , 2000, Oper. Res..

[14]  Mohammad Shahidehpour,et al.  Market operations in electric power systems , 2002 .

[15]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[16]  W. Ziemba,et al.  Hedging electricity portfolios via stochastic programming , 2002 .

[17]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

[18]  K. Kiwiel,et al.  Power management in a hydro-thermal system under uncertainty by Lagrangian relaxation , 2002 .

[19]  Zuyi Li,et al.  Market Operations in Electric Power Systems : Forecasting, Scheduling, and Risk Management , 2002 .

[20]  A. Conejo,et al.  Optimal Response of a Power Generator to Energy, AGC, and Reserve Pool-Based Markets , 2002, IEEE Power Engineering Review.

[21]  A. Conejo,et al.  Network-Constrained Multiperiod Auction for a Pool-Based Electricity Market , 2002, IEEE Power Engineering Review.

[22]  Werner Römisch,et al.  Scenario Reduction Algorithms in Stochastic Programming , 2003, Comput. Optim. Appl..

[23]  Mohammad Shahidehpour,et al.  Communication and Control in Electric Power Systems: Applications of Parallel and Distributed Processing , 2003 .

[24]  Jitka Dupacová,et al.  Scenario reduction in stochastic programming , 2003, Math. Program..

[25]  N. Growe-Kuska,et al.  Scenario reduction and scenario tree construction for power management problems , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[26]  M. Ventosa,et al.  Optimal offering strategies for generation companies operating in electricity spot markets , 2004, IEEE Transactions on Power Systems.

[27]  S. Granville,et al.  Strategic bidding under uncertainty: a binary expansion approach , 2005, IEEE Transactions on Power Systems.