Optimal management of green hydrogen production in renewable energy systems using deep reinforcement learning methods

Abstract

This research focuses on developing a deep reinforcement learning (DRL) framework to optimize green hydrogen production within renewable energy systems. By integrating a DRL-based model, the study aims to enhance real-time management of energy supply, storage, and distribution, involving an electrolyzer and balancing energy flows from photovoltaic (PV) sources, an energy storage system (ESS) and grid power. Utilizing real-world data, the DRL model adapts dynamically to fluctuations in renewable energy output and market prices, thereby optimizing operational efficiency. The study compares various DRL algorithms, including proximal policy optimization (PPO), soft actor-critic (SAC), and advantage actor-critic (A2C), assessing their performance in maximizing predefined reward functions. The findings demonstrate the robustness of the PPO algorithm, demonstrating significant reward accumulation and adaptability in managing dynamic environments. This validation is supported by empirical data and learning curves, confirming the DRL model’s proficiency in optimizing energy use and enhancing operational performance in green hydrogen systems. The integration of DRL with the framework for green hydrogen and renewable energy suggests a comprehensive solution that improves energy efficiency, operational costs, and sustainability initiatives. The research highlights the potential of advanced machine learning techniques for enhanced operational efficiency of renewable energy systems. © 2025 Elsevier Ltd.