The mean-WCVaR based model for LDC's optimal portfolio in multi-energy markets

Abstract

In a competitive electricity market with highly fluctuated electricity price, local distribution companies (LDCs) need to purchase electric power from several energy markets, such as spot markets, long-term tolling agreements and forward contracts, to maximize profits and minimize risks. Conditional Value-at-Risk (CVaR) can measure risk efficiently, but only one kind of price distribution rule may be considered. In fact, the spot electricity price usually does not follow the normal distribution, and it might be shown as logarithmic normal distribution if there was no enough supply at peak load situation. In this paper, a novel WCVaR method-Weighted Conditional Value-at-Risk-is proposed to measure the purchasing risk of LDC with multiple purchase options, especially when the electricity price follows more than one distribution rules. The Mean-WCVaR model is built as a mathematical programing problem to derive the efficient frontier that indicates the optimal tradeoffs available to LDC between expected revenue and purchasing risk in several energy markets. The existence of optimal solution of proposed WCVaR model is proved mathematically. Simulation results show the efficiency of the proposed model. The proposed model provides a new method for LDC to determine the optimal purchasing strategies considering the risk. Copyright © 2011 John Wiley & Sons, Ltd. Copyright © 2011 John Wiley & Sons, Ltd.