Energy Management of Electric–Hydrogen Coupled Integrated Energy System Based on Improved Proximal Policy Optimization Algorithm

The electric–hydrogen coupled integrated energy system (EHCS) is a critical pathway for the low-carbon transition of energy systems. However, the inherent uncertainties of renewable energy sources present significant challenges to optimal energy management in the EHCS. To address these challenges, this paper proposes an energy management method for the EHCS based on an improved proximal policy optimization (IPPO) algorithm. This method aims to overcome the limitations of traditional heuristic algorithms, such as low solution accuracy, and the inefficiencies of mathematical programming methods. First, a mathematical model for the EHCS is established. Then, by introducing the Markov decision process (MDP), this mathematical model is transformed into a deep reinforcement learning framework. On this basis, the state space and action space of the system are defined, and a reward function is designed to guide the agent to learn to the optimal strategy, which takes into account the constraints of the system. Finally, the efficacy and economic viability of the proposed method are validated through numerical simulation.