Stochastic Virtual Bidding in the Two-settlement Electricity Market

The short-term electricity markets in the United States have a two-settlement structure, which includes a day-ahead (DA) and a real-time (RT) markets. Virtual bidding is a financial tool available for the participants to earn profits by utilizing the price difference between the DA and RT markets. To better utilize this financial tool to help with the electricity market operation, it is necessary to develop decision-making models for virtual bidders to generate optimal virtual bidding strategies while considering the uncertainties related to the electricity prices and the participants’ physical assets. In this dissertation, stochastic optimization-based decision-making models were developed for generating optimal virtual bidding strategies for different types of market participants, and a hybrid electricity price scenario generation method was proposed to improve the virtual bidders’ profits. Firstly, bilevel stochastic optimization models were developed for generating the virtual bidding strategies used by two types of physical participants, i.e., a wind power producer and an electricity retailer, respectively. The proposed models considered the participants’ risk preferences, the impacts of other participants’ bidding strategies on the market clearing processes, and that the physical participants would use virtual bidding at multiple buses, which were not limited to the locations of their generating units or demands, to improve their market power. Case studies were carried out to validate the proposed models for a strategic wind power producer and a retailer, respectively, and the simulation results showed that virtual bidding improved their expected profits. Next, a hybrid electricity price scenario generation method using a seasonal autoregressive integrated moving average (SARIMA) model and historical data was proposed. In the proposed method, the spikes contained in the historical data of the electricity prices were firstly identified by using an outlier detection method; then, the historical data were decomposed into base and spike components; next, the base and spike component scenarios were generated by using the SARIMA- and historical data-based methods, respectively; finally, the electricity price scenarios were obtained by combining the base and spike component scenarios. Case studies were carried out for a virtual bidder in the Pennsylvanian-New Jersey-Maryland (PJM) electricity market to validate the proposed method