Bilevel Optimal Bidding Strategy for Energy Storage Power Stations Participating in the Joint Clearing Market with Uncertainty

Energy storage can provide flexibility in power systems with high penetration of renewable energy, but how to reasonably price different energy storage services has drawn wide attentions. This paper proposes a bilevel model for energy storage participating in the joint clearing market considering uncertainty. In the upper level, energy storage aggregators develop energy and reserve pricing strategies to participate in the market with the goal of maximizing revenue. In the lower level, the day-ahead-real-time two-stage stochastic model is firstly established by system operator, and then joint market clearing is completed to get the minimum total cost. Futhermore, the two-layer model is converted into a single-layer model through KKT conditions, and the difficulty of solving the model is reduced through linearization methods such as binary expansion. Through numerical studies on IEEE RTS 24-bus line, it is shown that the proposed method can improve the benefits of energy storage operators participating in joint clearing market. The revenue size and pricing levels vary with different energy storage types.