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arXiv · 2506.22931

Real-Time Energy Management Strategies for Community Microgrids

Moslem Uddin·Huadong Mo·Daoyi Dong
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Abstract

This study presents a real-time energy management framework for hybrid community microgrids integrating photovoltaic, wind, battery energy storage systems, diesel generators, and grid interconnection. The proposed approach formulates the dispatch problem as a multi-objective optimization task that aims to minimize operational costs. Two control strategies are proposed and evaluated: a conventional rule-based control (RBC) method and an advanced deep reinforcement learning (DRL) approach utilizing proximal policy optimization (PPO). A realistic case study based on Australian load and generation profiles is used to validate the framework. Simulation results demonstrate that DRL-PPO reduces operational costs by 18%, CO_2 emissions by 20%, and improves system reliability by 87.5% compared to RBC. Beside, DRL-PPO increases renewable energy utilization by 13%, effectively reducing dependence on diesel generation and grid imports. These findings demonstrate the potential of DRL-based approaches to enable cost-effective and resilient microgrid operations, particularly in regional and remote communities.

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Moslem Uddin, Huadong Mo, Daoyi Dong. 2025-06-28. Real-Time Energy Management Strategies for Community Microgrids. https://arxiv.org/abs/2506.22931

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