Optimal Operation of Green Hydrogen Production System Using a Deep Reinforcement Learning Method

Abstract

This research highlights strategic innovations in advancing net-zero technologies to address the climate crisis, focusing on the integration of green hydrogen within renewable energy frameworks. Power-to-Gas (P2G) technology is identified as a crucial solution for managing the unpredictable outputs from renewable sources such as solar and wind. The core of this study is the development and application of a Deep Reinforcement Learning (DRL)-based operational model that includes an electrolyzer and dynamic energy management among solar, energy storage, and grid sources. This innovative approach leverages real experimental data to adaptively balance energy flows, which significantly improves the handling of variability in renewable energy production and price volatility. The DRL model employs a comprehensive Markov Decision Problem (MDP) framework, utilizing algorithms such as Proximal Policy Optimization (PPO) to refine decision-making in real-time operational scenarios. Unlike traditional dynamic programming or linear optimization methods, which are often limited in handling dynamic environments, DRL provides flexibility and real-time adaptability. The study focuses on employing DRL to optimize the operation of green hydrogen production systems. This approach allows for detailed decision-making in fluctuating energy markets, enhancing the efficiency of green hydrogen systems which face challenges posed by the variability of renewable resources and the volatility of energy prices, facilitating optimal energy management decisions. Results from Chapter 5 demonstrate the effectiveness of the PPO algorithm, which exhibits substantial learning and optimization capabilities, as depicted in the learning curves. This empirical validation confirms the model’s proficiency in dynamic environments, optimizing energy management strategies effectively. In conclusion, the integration of DRL with green hydrogen systems offers robust solutions to enhance renewable energy integration. The DRL framework, rigorously validated through simulation and empirical analysis, supports diverse green hydrogen system configurations, promising substantial improvements in energy efficiency, cost reduction, and sustainability. This study not only confirms the feasibility of DRL in managing complex energy systems but also sets the stage for future advancements in intelligent energy management.