Optimal Scheduling Method for Integrated Energy Systems with Hydrogen Based on Deep Reinforcement Learning

In order to achieve carbon reduction targets, the combination of hydrogen energy and integrated energy systems has become one of the most potential development directions. Aiming at the problems such as the insufficient flexibility of scheduling strategy of hydrogen integrated energy system and difficulty in solving multi-objective optimization of complex systems, an optimal scheduling method for hydrogen integrated energy systems based on deep reinforcement learning is proposed. First, the variable operation condition model of coupled equipment is used to construct a wind-solar-hydrogen-cooling-heat-electricity integrated energy system, and expand the joint energy supply space of equipment. Secondly, considering the system operation cost, carbon emissions, system self-supply balance and renewable energy utilization rate, a multi-objective optimization model is built based on the optimal solution distance to stimulate the exploration of the agent. Then, the deep reinforcement learning algorithm is optimized by time segment characterization to enhance the estimation accuracy of the system state change. Finally, a simulation case is designed based on the measured data of the source and load. The results show that the proposed method can effectively improve the scheduling flexibility of the hydrogen integrated energy system, fully tap the carbon emission reduction potential of hydrogen energy, and realize the dual optimization of scheduling economy and environmental protection. © 2024 Automation of Electric Power Systems Press. All rights reserved.