用强化学习优化微电网调度，降低能耗成本，提升可再生能源利用率。
AutoGrid AI: Deep Reinforcement Learning Framework for Autonomous Microgrid Management
- 结合Transformer预测与PPO算法，实现自适应能源调度。
- 相比传统规则方法，能源效率和运行韧性显著提升。
- 开源仿真框架，适合研究微电网与智能电网的开发者。
我们提出一种基于深度强化学习的自主微电网管理框架，专为偏远社区设计。通过深度强化学习与时间序列预测模型，优化微电网能源调度策略，以降低运营成本并最大化太阳能、风能等可再生能源的利用。方法融合Transformer架构进行可再生能源发电预测，并采用近端策略优化（PPO）代理在模拟环境中做出决策。实验结果表明，相较于传统规则基方法，本方案在能源效率和运行韧性方面均有显著提升。该研究推动了零碳能源系统的智能电网技术发展。最后，我们开源了一个支持多种微电网环境仿真的框架。
原文摘要 · Abstract (English)
We present a deep reinforcement learning-based framework for autonomous microgrid management. tailored for remote communities. Using deep reinforcement learning and time-series forecasting models, we optimize microgrid energy dispatch strategies to minimize costs and maximize the utilization of renewable energy sources such as solar and wind. Our approach integrates the transformer architecture for forecasting of renewable generation and a proximal-policy optimization (PPO) agent to make decisions in a simulated environment. Our experimental results demonstrate significant improvements in both energy efficiency and operational resilience when compared to traditional rule-based methods. This work contributes to advancing smart-grid technologies in pursuit of zero-carbon energy systems. We finally provide an open-source framework for simulating several microgrid environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。