Portfolio optimization in electricity market using a novel risk based decision making approach

This paper provides generation companies (GENCOs) with a novel decisionmaking tool that accounts for both long-term and short-term risk aversion preferences and devises optimal strategies to participate in energy and ancillary services markets and forward contracts, in which the possibility of involvement in arbitrage opportunities is also considered. Because of the imprecise nature of the decision maker's judgment, appropriate modelling of risk aversion attitude of the GENCO is another challenge. This paper uses fuzzy satisfaction theory to express decision maker's attitude toward risk. Conditional Value at Risk methodology (CVaR) is utilized as the measure of risk and uncertainty sources include prices for the day-ahead energy market, Automatic Generation Control (AGC), and reserve markets. By applying the proposed method, not only trading loss over the whole scheduling horizon can be controlled, but also the amount of imposed loss during every time period can be reduced. An illustrative case study is provided for further analysis.