A Reinforcement Learning Approach for Optimal Control in Microgrids

2025-06-28

AI Summary

To address the challenges of dynamics, decentralization, and long-term operational reliability in microgrid energy management under high renewable penetration, this paper proposes an intelligent energy management method integrating reinforcement learning (RL) with digital twin (DT) technology. A high-fidelity DT model of the energy storage system (ESS) is developed, explicitly capturing degradation characteristics such as capacity fade. Leveraging real-time inputs—including market electricity prices, wind/solar generation forecasts, and load demand—the framework enables end-to-end joint optimization of ESS dispatch and energy trading. Its key innovation lies in the first-time incorporation of battery aging mechanisms into both the RL reward function and state space, thereby enhancing the long-term economic viability and engineering practicality of the learned policy. Experimental evaluation using real-world grid data demonstrates that the proposed method improves energy utilization by 12.7% and reduces annual operational costs by 9.4%, outperforming conventional rule-based strategies and state-of-the-art RL baselines.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Reinforcement Learning
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Optimize microgrid energy management using reinforcement learning
• Develop RL agent for energy trading and storage policies
• Validate approach with real-world data and digital twin simulation

Innovation

• Reinforcement learning optimizes microgrid energy management
• Digital twin simulates energy storage system dynamics
• RL agent learns optimal trading and storage policies