Optimal Bidding Strategies for Generation Companies in a Day-Ahead Electricity Market with Risk Management Taken into Account
Azmi Saleh, Takao Tsuji and Tsutomu Oyama
DOI : 10.3844/ajeassp.2009.8.16
American Journal of Engineering and Applied Sciences
Volume 2, Issue 1
Problem statement: In a competitive electricity market with limited number of producers, Generation Companies (Gencos) is facing an oligopoly market rather than a perfect competition. Under oligopoly market environment, each Genco may increase its own profit through a favorable bidding strategy. The objective of a Genco is to maximize its profit and minimize the associated risk. In order to achieve this goal, it is necessary and important for the Genco to make optimal bidding strategies with risk management before bidding into spot market to get an expected high profit, since spot prices are substantially volatile. This study propose a method to build optimal bidding strategies in a day-ahead electricity market with incomplete information and considering both risk management and unit commitment. Approach: The proposed methodology employs the Monte Carlo simulation for modeling a risk management and a strategic behavior of rival. A probability density function (pdf), Value at Risk (VaR) and Monte Carlo simulation used to build optimal bidding strategies for a Genco. Results: The result of the proposed method shows that a Genco can build optimal bidding strategies to maximize expected total profit considering unit commitment and risk management. The Genco controls the risk by setting the confidence level. If the Genco increase the confidence level, the expected total VaR of profit decrease. Conclusions/Recommendations: The proposed method for building optimal bidding strategies in a day-ahead electricity market to maximize expected total profit considering unit commitment and risk management is helpful for a Genco to make a decision to submit bidding to the Independent System Operator (ISO).
© 2009 Azmi Saleh, Takao Tsuji and Tsutomu Oyama. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.