A Reinforcement Learning Approach for Optimal Control in Microgrids
This provides a robust solution for managing decentralized energy in microgrids, though it is incremental as it builds on existing RL methods with a digital twin for simulation.
The paper tackled the problem of optimizing energy management in microgrids with renewable sources by proposing a reinforcement learning approach, which outperformed rule-based methods and existing RL benchmarks in experiments using real-world data from Italy.
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.