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August 17, 2025Processes0 citationsOpen Access

Energy Management for Microgrids with Hybrid Hydrogen-Battery Storage: A Reinforcement Learning Framework Integrated Multi-Objective Dynamic Regulation

Key Points

The proposed energy management system significantly improves stability in microgrids, reducing energy costs by 31.4%.
Using a novel deep-reinforcement-learning approach, the system efficiently coordinates hydrogen and battery storage to optimize performance.
Simulation results indicate a 46.7% reduction in battery life loss while maintaining system response stability across various scenarios.
The findings highlight the potential of integrating renewable energy sources and advanced AI techniques in future microgrid designs.

Abstract

The integration of renewable energy resources (RES) into microgrids (MGs) poses significant challenges due to the intermittent nature of generation and the increasing complexity of multi-energy scheduling. To enhance operational flexibility and reliability, this paper proposes an intelligent energy management system (EMS) for MGs incorporating a hybrid hydrogen-battery energy storage system (HHB-ESS). The system model jointly considers the complementary characteristics of short-term and long-term storage technologies. Three conflicting objectives are defined: economic cost (EC), system response stability, and battery life loss (BLO). To address the challenges of multi-objective trade-offs and heterogeneous storage coordination, a novel deep-reinforcement-learning (DRL) algorithm, termed MOATD3, is developed based on a dynamic reward adjustment mechanism (DRAM). Simulation results under various operational scenarios demonstrate that the proposed method significantly outperforms baseline methods, achieving a maximum improvement of 31.4% in SRS and a reduction of 46.7% in BLO.

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