• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (8): 22-32.doi: 10.12204/j.issn.1000-7229.2022.08.003

• Planning, Configuration and Operation Control of Energy Storage System under the Background of New Power Systems•Hosted by Professor-level Senior Engineer LI Xiangjun, Professor SHUAI Zhikang and Associate Professor YAN Ning• • Previous Articles     Next Articles

Research on Two-Layer Configuration and Operation Optimization Based on Proximal Policy Optimization for Electrochemical/Hydrogen Hybrid Energy Storage System

YAN Qingyou1,2(), SHI Chaofan1,2(), QIN Guangyu1,2,3(), XU Chuanbo1,2()   

  1. 1. School of Economics and Management, North China Electric Power University, Beijing 102206, China
    2. Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Beijing 102206, China
    3. University of California, Berkeley, Renewable and Appropriate Laboratory, Berkeley 94709, San Francisco, US
  • Received:2022-04-07 Online:2022-08-01 Published:2022-07-27
  • Contact: SHI Chaofan E-mail:yanqingyou@263.net;shichaofan@ncepu.edu.cn;qinguangyu@berkeley.edu;90102479@ncepu.edu.cn
  • Supported by:
    China Scholarship Council Program(202006730045)


According to the complementary characteristics of electrochemical energy storage and hydrogen storage, an integrated optimization model for the configuration and operation of a hybrid energy storage system is given, including electrochemical energy storage, hydrogen storage proposed and an intelligent algorithm. The model is based on a two-layer decision optimization problem, in which two different time dimensions of the hybrid energy storage system configuration and operation are solved in upper and lower layers, and the interaction between them is considered. A reinforcement learning proximal policy optimization (PPO) algorithm is used to solve the two-layer optimization model. By comparing the results of applying various traditional algorithms to solve the scenery data of a region in Gansu Province, it is verified that the used algorithm has the highest adaptability and the fastest convergence speed in a complex environment. The results show that the application of this model can reduce the abandoning rate of wind and solar power by 24% and effectively improve the comprehensive benefit of the system, and that hydrogen storage as a capacity-based energy storage configuration is not limited by topographical factors and is suitable for diverse application scenarios, thus providing an application demonstration for the widespread deployment of hydrogen storage, a new form of energy storage, in the whole country.

Key words: wind-solar consumption, energy storage configuration, two-level optimization, hydrogen energy storage, proximal policy optimization (PPO) algorithm

CLC Number: