Real-Time Energy Management Strategies for Community Microgrids

TL;DR

This paper develops a real-time energy management framework for hybrid community microgrids, comparing rule-based control and deep reinforcement learning, demonstrating significant cost, emission, and reliability improvements with DRL.

Contribution

It introduces a novel DRL-based control strategy for microgrid management and validates its effectiveness against traditional methods using realistic Australian data.

Findings

DRL-PPO reduces operational costs by 18%

DRL-PPO cuts CO2 emissions by 20%

DRL-PPO enhances system reliability by 87.5%

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.