Decision-making Method for Pumped Storage Power Stations in the Electricity Energy and Frequency Regulation Markets

With the establishment of “carbon peaking and carbon neutrality” goals in China, along with the development of new power systems and ongoing electricity market reforms, pumped-storage power stations (PSPSs) will increasingly play a significant role in power systems. Therefore, this study focuses on trading and bidding strategies for PSPSs in the electricity market. Firstly, a comprehensive framework for PSPSs participating in the electricity energy and frequency regulation (FR) ancillary service market is proposed. Subsequently, a two-layer trading model is developed to achieve joint clearing in the energy and frequency regulation markets. The upper-layer model aims to maximize the revenue of the power station by optimizing the bidding strategies using a Q-learning algorithm. The lower-layer model minimized the total electricity purchasing cost of the system. Finally, the proposed bi-level trading model is validated by studying an actual case in which data are obtained from a provincial power system in China. The results indicate that through this decision-making method, PSPSs can achieve higher economic revenue in the market, which will provide a reference for the planning and operation of PSPSs.