@article{Chen2024, 
author = {Man Chen and Hongtao Zhu and Yumin Peng and Xuan Wang and Xuefeng Zhang and Yijun Xiong and Lianfu Chen and Yikai Li and Bushi Zhao},
title = {Decision-making Method for Pumped Storage Power Stations in the Electricity Energy and Frequency Regulation Markets},
year = {2024},
journal = {Chinese Journal of Electrical Engineering},
volume = {10},
number = {4},
pages = {60-72},
keywords = {Pumped storage power station (PSPSs), electricity energy market, frequency regulation market, bidding strategy, Q-learning algorithm},
url = {https://www.sciopen.com/article/10.23919/CJEE.2024.000084},
doi = {10.23919/CJEE.2024.000084},
abstract = {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.}
}