Wind Power Bidding Strategy Based on the Minimax Regret Criterion with Limited Distribution Information


In optimal wind bidding strategy related literatures, it is usually assumed that the full distribution information (for example, the cumulative distribution function or the probability density function) of wind power output is known. In real world applications, however, only very limited distribution information can be obtained. Therefore, the “optimal bidding strategy” obtained based on the hypothetical distribution may be far away from the true optimal one. In this paper, an optimal bidding strategy is obtained based on the minimax regret criterion. The salient feature of the new approach is that it requires only partial information of wind power distribution, for example, the expectation and the support set. Numerical test is then performed and the results suggest that the method established in this paper is effective.

Share and Cite:

Mao, Y. , Tian, J. and Zhai, Q. (2014) Wind Power Bidding Strategy Based on the Minimax Regret Criterion with Limited Distribution Information. Journal of Power and Energy Engineering, 2, 169-175. doi: 10.4236/jpee.2014.24024.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Galloway, S., Bell, G., Burt, G., McDonald, J. and Siewierski, T. (2006) Managing the Risk of Trading Wind Energy in a Competitive Market. IEEE Proceedings of Generation, Transmission and Distribution, 153, 106-114.
[2] Matevosyan, J. and Soder, L. (2006) Minimization of Imbalance Cost Trading Wind Power on the Short-Term Power market. IEEE Transactions on Power System, 21, 1396-1404.
[3] Zhang, X. (2012) Optimal Wind Bidding Strategy Considering Imbalance Cost and Allowed Imbalance Band. IEEE, EnergyTech, 1-5.
[4] Zhang, H., Gao, F., Wu, J., Liu, K. and Zhai, Q. (2011) A Stochastic Cournot Bidding Model for Wind Power Producers. IEEE International Conference on Automation and Logistics (ICAL), Chongqing, August 2011, 319-324.
[5] Fabbri, A., Gomez San Roman, T., River Abbad, J. and Mendez Quezada, V.H. (2005) Assessment of the Cost Associated with Wind Generation Prediction Errors in a Liberalized Electricity Market. IEEE Transactions on Power System, 20, 1440-1446.
[6] Usaola, J. and Moreno, M.A. (2009) Optimal Bidding of Wind Energy in Intraday Markets. 6th International Conference on the European Energy Market, 1-7.
[7] Bueno, M., Moreno, M.A., Usaola, J. and Nogales, F.J. (2010) Strategic Wind Energy Bidding in Adjustment Markets. 45th International IEEE Universities Power Engineering Conference (UPEC), August 2010, 1-6.
[8] Hill, A.V. (2011) The Newsvendor Problem. Minneapolis: Clamshell Beach Press.
[9] Zhu, Z., Zhang, J. and Ye, Y. (2006) Newsvendor Optimization with Limited Distribution Information. Working Paper, Stanford University, Stanford.
[10] Natarajan, K., Shi, D. and Toh, K.C. (2012) A Probabilistic Model for Minmax Regret in Combinatorial Optimization. Working Paper, Singapore University of Technology and Design.
[11] Yue, J., Chen, B. and Wang, M.C. (2006) Expected Value of Distribution Information for the Newsvendor Problem. Operations Research, 54, 1128-1136.
[12] Perakis, G. and Roels, G. (2006) Regret in the Newsvendor Model with Partial Information. Working Paper, Operations Research Center, Massachusetts Institute of Technology.
[13] Xue, Y., Venkatesh, B. and Chang, L. (2008) Bidding Wind Power in Short-Term Electricity Market Based on Multiple-Objective Fuzzy Optimization. Canadian Conference on Electrical and Computer Engineering (CCECE 2008), 4-7 May 2008, 1135-1138.
[14] Ramana, M.V., Tuncel, L. and Wolkowicz, H. (1997) Strong Duality for Semidefinite Programming. SIAM Journal on Optimization, 7, 641-662.
[15] Al-Awami, A.T. and El-Sharkawi, M.A. (2011) Coordinated Trading of Wind and Thermal Energy. IEEE Transactions on Sustainable Energy, 2, 277-287.
[16] (2014) Web of the Information System of the Spanish TSO.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.