文献类型：期刊文献

英文题名：Optimal Scheduling of a Hydrogen-Based Microgrid for an Industrial Park: A Reinforcement Learning Approach

作者：He, Wangli[1,2,3];Cai, Chenhao[1,2,3];Han, Qing-Long[4];Qing, Xiangyun[1,2,3];Du, Wenli[1,2,3];Qian, Feng[1,2,3]

机构：[1]East China Univ Sci & Technol, Minist Educ, State Key Lab Ind Control Technol, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[3]Huzhou Inst Ind Control Technol, Huzhou 313099, Peoples R China;[4]Swinburne Univ Technol, Sch Engn, Melbourne, Vic 3122, Australia

年份：2025

卷号：55

期号：6

起止页码：4348

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

收录：;EI(收录号：20251518196175);WOS:【SCI-EXPANDED(收录号:WOS:001470620900001)】；

基金：This work was supported in part by the Shanghai Pilot Program for Basic Research under Grant 22TQ1400100-3; in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017; in part by the State Key Laboratory of Industrial Control Technology, China under Grant ICT2024A14; and in part by the Fundamental Research Funds for the Central Universities under Grant 222202517006.

语种：英文

外文关键词：Microgrids; Fuel cells; Job shop scheduling; Hydrogen; Costs; Optimal scheduling; Renewable energy sources; Voltage control; Reinforcement learning; Fifth Industrial Revolution; Hydrogen-based microgrid; industrial park; multilearning rate reinforcement learning; optimal day-ahead scheduling

摘要：Many industrial parks, which are connected to the main grid, have integrated renewable energy to reduce carbon emission for achieving the goal of Industry 5.0. However, the optimal scheduling is challenging due to fluctuations in renewable energy generation. Hydrogen, which plays an important role in the future development of the power grid in Industry 5.0, offers an attractive option to coordinate with the batteries. This work focuses on the day-ahead scheduling of a hydrogen-based microgrid for an industrial park. A day-ahead scheduling model is established by taking into consideration the detailed nonlinear energy conversion behavior of the electrolyzer and fuel cell, as well as the two-timescale property of a battery energy storage system (BESS) and the hydrogen system, including an electrolyzer, a hydrogen energy storage system (HESS), and a fuel cell. Note that the optimization problem is a mixed integer nonlinear programming, which is challenging to be solved. A novel multilearning rate reinforcement learning algorithm is proposed and its convergence is also proved based on two-timescale stochastic approximation theory. Simulation results, based on real-world traces in Belgium at a 15-min resolution, are presented, which shows that the proposed method has a higher reward, lower-operating costs and less computing time. It is also found that the shorter scheduling period for the BESS can lead to reduced operating costs by decreasing the required purchasing power and the renewable energy curtailment power.