Integrated Energy System Operation Optimization Based on Reinforcement Learning. Issue 1 (1st February 2022)

• Record Type: Journal Article
• Authors: Wang, Yongli; Dong, Huanran; Xu, Miaomiao; Cai, Chengcong; Yao, Suhang; Ma, Yang; Li, Shuquan
• Is Part Of: Journal of physics. Volume 2205:Issue 1(2022)
• Publication Date: 2022-02-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2205/1/012008
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Each subject in the integrated energy system has different interests and demands, and it is necessary to optimize the energy dispatching with the help of multi-subject game theory. In order to solve the above problems, this paper proposes a reinforcement learning-based multi-object operation optimization method for integrated energy systems. Firstly, a multi-subject integrated energy system model including energy suppliers, park service providers and users is constructed; secondly, a game search method based on reinforcement signals is proposed to improve the speed of multi-subject game solution; finally, a simulation is conducted with an integrated energy system as an example to verify the effectiveness and rapidity of the proposed method.