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

作　　者：Man Chen Hongtao Zhu Yumin Peng Xuan Wang Xuefeng Zhang Yijun Xiong Lianfu Chen Yikai Li Bushi Zhao 

机构地区：[1]CSG PGC Power Storage Research Institute,Guangzhou 510635,China [2]Sichuan Energy Internet Research Institute,Tsinghua University,Chengdu 610213,China

出　　处：《Chinese Journal of Electrical Engineering》2024年第4期60-72,共13页中国电气工程学报(英文)

基　　金：Supported by the Innovation Project of the China Southern Power Grid Co.,Ltd.(020000KK52210005).

摘　　要：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.

关 键 词：Pumped storage power station(PSPSs) electricity energy market frequency regulation market bidding strategy Q-learning algorithm 

分 类 号：TM732[电气工程—电力系统及自动化]