Risk-Constrained Stochastic Virtual Bidding in Two-Settlement Electricity Markets

Virtual bidding is a financial instrument implemented by most two-settlement electricity markets in the United States in which the profits of the virtual traders are determined by the electricity prices in the day-ahead and real-time energy markets. This paper proposes stochastic optimization models to generate optimal bidding strategies for virtual traders. In the proposed models, the scenarios of electricity prices are generated by using the seasonal autoregressive integrated moving average (SARIMA) model and reduced to a small number by using a fast forward scenario reduction algorithm; and the conditional value at risk (CVaR) is used for risk management for the virtual trader. By using the proposed stochastic optimization models, either an optimal increment or decrement bidding curve can be generated for each hour of the operating day. Case studies using real-world data are carried out for a virtual trader using the proposed models. Results show that the proposed models can help the virtual trader earn profits by using the price differences in the day-ahead and real-time markets effectively, and the bidding strategy of a virtual trader is very sensitive to its risk aversion degree.