The mean-WCVaR based model for LDC's optimal portfolio in transmission and distribution separated electricity 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. This is to maximize profits and minimize risks. Conditional Value-at-Risk (CVaR) can measure risk efficiently, but only one kind of price distribution rule could be considered. In fact, the spot electricity price usually does not limited to normal distribution, and it might be shown as logarithmic normal distribution if there was no enough supply at peak load situation. In such case, the 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 has more than one distribution rules. The Mean-WCVaR model is built as a mathematical programming problem to derive the efficient frontier that indicates the optimal tradeoffs available to LDC between expected revenue and purchasing risk in several energy markets. Simulation results show the efficiency of the proposed model. The proposed model paves a new way for LDC to determine the optimal purchasing strategies considering the risk. ©2010 IEEE.