用多智能体强化学习优化光伏充电站，抗故障且降成本。
Agent-Based Decentralized Energy Management of EV Charging Station with Solar Photovoltaics via Multi-Agent Reinforcement Learning
- 每个充电桩为智能体，协同调度应对故障与行为波动。
- 实测降低充电成本，提升服务满意度，抗扰动能力强。
- 适合关注智慧能源与分布式管理的工程师和研究者。
为实现智慧城市能源净零目标，交通电气化至关重要。随着电动汽车（EV）数量持续增长，充电站能源管理日益关键。现有研究虽能降低充电成本并维持电网稳定，却常忽视充电行为差异及部分充电桩故障等不确定性。为此，提出一种基于多智能体强化学习（MARL）的新方法，将每个充电桩视为独立智能体，在含光伏系统的场景下协同调度，更贴近真实运行环境。在算法中引入长短期记忆网络（LSTM）提取时序特征，并设计密集奖励机制以优化充电体验。基于真实数据集验证表明，该方法对系统不确定性与故障具有鲁棒性，有效降低充电成本，同时最大化服务满意度。
原文摘要 · Abstract (English)
In the pursuit of energy net zero within smart cities, transportation electrification plays a pivotal role. The adoption of Electric Vehicles (EVs) keeps increasing, making energy management of EV charging stations critically important. While previous studies have managed to reduce energy cost of EV charging while maintaining grid stability, they often overlook the robustness of EV charging management against uncertainties of various forms, such as varying charging behaviors and possible faults in faults in some chargers. To address the gap, a novel Multi-Agent Reinforcement Learning (MARL) approach is proposed treating each charger to be an agent and coordinate all the agents in the EV charging station with solar photovoltaics in a more realistic scenario, where system faults may occur. A Long Short-Term Memory (LSTM) network is incorporated in the MARL algorithm to extract temporal features from time-series. Additionally, a dense reward mechanism is designed for training the agents in the MARL algorithm to improve EV charging experience. Through validation on a real-world dataset, we show that our approach is robust against system uncertainties and faults and also effective in minimizing EV charging costs and maximizing charging service satisfaction.
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。