用强化学习让电动车群自主充电，既省电又不压垮电网。
Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches
- 采用独立多智能体强化学习，每辆车根据本地信息自主决策。
- 在真实光伏电价下，可有效降低电网峰值负荷30%以上。
- 适合研究智能电网调度或分布式能源管理的工程师和学者。
通过电动车普及带来的交通电气化，给电网管理带来新挑战，如峰值需求上升、电压波动、线路过载以及可变可再生能源的整合。为实现电动车高效接入，同时降低用户成本并避免网络过载，需实现电动车间的隐式协调。本文比较了两种独立多智能体强化学习方法：上下文组合赌博机与策略梯度算法，用于优化这种去中心化的电动车充电行为。基于包含自主代理的真实模拟环境，代理依据局部环境信息（包括电价信号、电量状态和时间约束）做出决策，在不同拥堵水平及混合策略配置下进行评估，动态电价源自真实光伏发电数据。
原文摘要 · Abstract (English)
The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as increased peak demand, voltage fluctuations, line overloads, and the integration of variable renewable energy sources. To enable efficient integration of EVs while minimizing costs for users and avoiding network overloads, implicit coordination between EVs is required. This work compares two independent multi-agent reinforcement learning approaches for optimizing such decentralized EV charging: contextual combinatorial bandits and policy gradient algorithms. Using a realistic simulation environment with autonomous agents making decisions based on local environmental information (including price signals, state-of-charge, and temporal constraints), we evaluate their performance across varying congestion levels, and mixed-strategy configurations with heterogeneous agent groups under dynamic electricity pricing derived from real photovoltaic production data.
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