A Reinforcement Learning Approach for Optimal Control in Microgrids

TL;DR

This paper introduces a reinforcement learning method for microgrid energy management, utilizing a digital twin for realistic simulation, validated with real-world data, and outperforming existing strategies.

Contribution

It presents a novel RL-based approach combined with a digital twin for realistic microgrid control, demonstrating superior performance over traditional methods.

Findings

RL strategy outperforms rule-based methods

Digital twin accurately simulates storage dynamics

Validated with real-world Italian power grid data

Abstract

The increasing integration of renewable energy sources (RESs) is transforming traditional power grid networks, which require new approaches for managing decentralized energy production and consumption. Microgrids (MGs) provide a promising solution by enabling localized control over energy generation, storage, and distribution. This paper presents a novel reinforcement learning (RL)-based methodology for optimizing microgrid energy management. Specifically, we propose an RL agent that learns optimal energy trading and storage policies by leveraging historical data on energy production, consumption, and market prices. A digital twin (DT) is used to simulate the energy storage system dynamics, incorporating degradation factors to ensure a realistic emulation of the analysed setting. Our approach is validated through an experimental campaign using real-world data from a power grid located in the Italian territory. The results indicate that the proposed RL-based strategy outperforms rule-based methods and existing RL benchmarks, offering a robust solution for intelligent microgrid management.