用强化学习优化电动车充电，让碳排放降八成，还能省电省钱。
Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration
- 基于SAC算法设计多目标奖励，同时控制碳排放、弃电和用户需求
- 50%风电渗透下碳强度降至23.96克/千瓦时，比不控情况低87%
- 适合关注电网低碳化与可再生能源消纳的电力系统研究者
电动汽车快速普及带来充电负荷峰值、电压不稳和变压器过载问题。现有模型预测控制（MPC）和强化学习（RL）方法很少将实时碳强度和波动性可再生能源（RE）供应作为核心调度目标，导致减排潜力未被充分挖掘。本文提出一种基于软演员-批评家（SAC）算法的排放感知强化学习策略，采用多目标奖励函数，惩罚碳排放、就地可再生能源弃用及未满足的用户需求。智能体在EV2Gym平台的统一基准框架中训练，包含屋顶光伏与风电数据、随时间变化的EirGrid碳强度，以及25个充电桩单位的真实工作场所电动车行为。九种控制策略（包括启发式方法、排放感知的MPC变体和所提RL代理）在五种可再生能源渗透率（0%-50%）下进行十次独立实验比较。所提方法在50%风能渗透率下实现最低碳强度23.96克二氧化碳/千瓦时，相较无控制基线最多降低87%；变压器过载低于7千瓦时，远低于最快充电（AFAP）的1093千瓦时；在风光联合供电下自用电率达52%。将碳强度预测嵌入状态与奖励机制，使充电行为与低排放时段对齐，兼顾电网合规与用户满意度。
原文摘要 · Abstract (English)
The rapid growth of Electric Vehicle (EV) adoption challenges power distribution networks through peak load spikes, voltage instability, and transformer overloads from uncoordinated charging. While Model Predictive Control (MPC) and standard Reinforcement Learning (RL) methods have addressed these issues, existing approaches rarely treat real-time carbon intensity or fluctuating renewable energy (RE) availability as primary scheduling objectives, leaving substantial decarbonisation potential unrealised. This paper proposes an emission-aware RL strategy based on the Soft Actor Critic (SAC) algorithm, with a multi-objective reward that penalises carbon emissions, curtailed on-site renewables, and unmet user demand. The agent is trained within a unified benchmarking framework on the EV2Gym platform, incorporating behind-the-meter solar and wind profiles, time-varying EirGrid carbon intensity data, and realistic workplace EV behaviour across 25 Electric Vehicle Supply Equipment (EVSE) units. Nine control strategies, including heuristics, emission-aware MPC variants, and the proposed RL agent, are compared under five renewable penetration scenarios (0%-50%) over ten independent runs each. The RL agent achieves a carbon intensity as low as 23.96 grams of carbon dioxide per kilowatt-hour under 50% wind penetration, representing up to 87% emission reduction versus the uncontrolled baseline, and outperforms the external graph-based Power Distribution Network (PDN) benchmark. Transformer overload remains below 7 kWh across scenarios, against up to 1093 kWh for the As Fast As Possible (AFAP) heuristic, and renewable self-consumption reaches 52% under combined wind and solar supply. Embedding carbon intensity forecasts into the RL state and reward aligns charging with low-emission periods while preserving grid compliance and user satisfaction.
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