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

Abstract

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.