用强化学习优化微电网能源管理，提升自适应调控能力。
A Reinforcement Learning Approach for Optimal Control in Microgrids
- 基于强化学习构建智能代理，从历史数据中学习储能与交易策略。
- 实验显示其在真实意大利电网数据上优于规则方法与现有基准。
- 融合数字孪生模拟电池退化，提升仿真真实性与实用性。
可再生能源的持续接入正在重塑传统电网结构，亟需新的方式应对分布式发电与用电的管理挑战。微电网通过本地化控制发电、储能与配电，提供了一种有效解决方案。本文提出一种基于强化学习（RL）的微电网能源管理新方法，设计一个能够利用能源生产、消耗及市场价格历史数据学习最优储能与交易策略的智能体。采用数字孪生（DT）模拟储能系统动态特性，引入退化因素以实现更真实的场景再现。通过在意大利实际电网数据上的实验验证，结果表明该方法显著优于规则基方法及现有强化学习基准，展现出对智能微电网管理的强大鲁棒性。
原文摘要 · Abstract (English)
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.
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