用AI优化社区能源与电动车充电，实测降低9%峰值用电和5%电费。
Control of Renewable Energy Communities using AI and Real-World Data
- 基于MADDPG的多智能体算法，协同控制建筑、光伏、储能和电动车。
- 真实社区测试显示日均峰值用电降9%，能源成本降5%。
- 擅长处理真实数据噪声、用户行为不可预测等实际难题，适合落地部署。
交通电气化和分布式可再生能源的普及使可再生能源社区（RECs）管理更复杂。将电动汽车（EV）充电与建筑暖通空调（HVAC）、光伏发电和电池储能系统整合，带来机遇也伴随挑战。强化学习（RL）中的多智能体深度确定性策略梯度（MADDPG）算法在仿真中表现优异，优于传统规则控制。但现实部署面临数据不全、噪声大、系统异构、同步困难、用户行为不可预测及关键的电动车电池状态（SoC）缺失等问题。本文提出一个专为应对这些复杂性设计的框架，融合EnergAIze这一基于MADDPG的多智能体控制策略，重点解决真实数据采集、系统集成与用户行为建模问题。在包含四栋住宅楼的真实运行REC中初步测试表明，该方法具有可行性，通过优化负荷调度与电动车充电行为，实现日均峰值需求降低9%，能源成本下降5%。结果验证了框架的有效性，推动了智能能源管理在可再生能源社区中的实际应用。
原文摘要 · Abstract (English)
The electrification of transportation and the increased adoption of decentralized renewable energy generation have added complexity to managing Renewable Energy Communities (RECs). Integrating Electric Vehicle (EV) charging with building energy systems like heating, ventilation, air conditioning (HVAC), photovoltaic (PV) generation, and battery storage presents significant opportunities but also practical challenges. Reinforcement learning (RL), particularly MultiAgent Deep Deterministic Policy Gradient (MADDPG) algorithms, have shown promising results in simulation, outperforming heuristic control strategies. However, translating these successes into real-world deployments faces substantial challenges, including incomplete and noisy data, integration of heterogeneous subsystems, synchronization issues, unpredictable occupant behavior, and missing critical EV state-of-charge (SoC) information. This paper introduces a framework designed explicitly to handle these complexities and bridge the simulation to-reality gap. The framework incorporates EnergAIze, a MADDPG-based multi-agent control strategy, and specifically addresses challenges related to real-world data collection, system integration, and user behavior modeling. Preliminary results collected from a real-world operational REC with four residential buildings demonstrate the practical feasibility of our approach, achieving an average 9% reduction in daily peak demand and a 5% decrease in energy costs through optimized load scheduling and EV charging behaviors. These outcomes underscore the framework's effectiveness, advancing the practical deployment of intelligent energy management solutions in RECs.
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