Machine Learning Technique for Hydrogen Fuel Cell Based Microgrid Energy Management

This study explores the application of reinforcement learning (RL) techniques to the integration of hydrogen fuel cells into microgrids, specifically focusing on the Rye Microgrid in Norway, which operates in island mode.The investigation centers on the seamless integration of fuel cells using reinforcement learning (RL) methods, treating the problem as a Markov Decision Process (MDP). The state space includes renewable energy production, current energy storage levels, and spot market prices, while the action space involves decisions on charging and discharging energy storage elements. Forecasted state values support the RL agent's decision-making process.A multi-agent RL framework is employed to optimize the actions for both hydrogen fuel cell and battery with the objective of minimizing costs while satisfying energy demand. The study rigorously investigates various RL algorithms, including Proximal Policy Optimization (PPO) and Deep Q-Network (DQN), identifying DQN as the most effective algorithm. Further analysis of the DQN algorithm, considering different forecasting intervals, demonstrates its robustness and adaptability. The study highlights the potential of advanced RL techniques in improving the economic viability and operational efficiency of microgrids incorporating hydrogen fuel cells.