用强化学习优化城市充电商与移动充电桩的协同布局与调度。
Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers
- 结合强化学习与启发式调度，动态规划固定与移动充电桩
- 充电覆盖提升244.4%，用户等待时间缩短79.8%
- 适合智慧交通与新能源基础设施规划者参考
电动交通的成功依赖于高效灵活的充电基础设施。固定充电桩常因需求波动导致利用率低或拥堵，而移动充电桩可随需迁移以提升灵活性。本文研究混合充电设施的最优规划与运营问题，即在城市道路网络中联合优化固定充电桩的选址与移动充电桩的调度。提出混合充电站规划与运营（HCSPO）模型，采用基于模型预测控制（MPC）的充电需求预测支持动态决策。为求解该问题，提出一种增强启发式调度的深度强化学习方法。在真实城市场景下的实验表明，该方法使基础设施覆盖率最高提升244.4%，用户平均等待时间减少高达79.8%，显著优于现有方案。
原文摘要 · Abstract (English)
The success of vehicle electrification relies on efficient and adaptable charging infrastructure. Fixed-location charging stations often suffer from underutilization or congestion due to fluctuating demand, while mobile chargers offer flexibility by relocating as needed. This paper studies the optimal planning and operation of hybrid charging infrastructures that combine both fixed and mobile chargers within urban road networks. We formulate the Hybrid Charging Station Planning and Operation (HCSPO) problem, jointly optimizing the placement of fixed stations and the scheduling of mobile chargers. A charging demand prediction model based on Model Predictive Control (MPC) supports dynamic decision-making. To solve the HCSPO problem, we propose a deep reinforcement learning approach enhanced with heuristic scheduling. Experiments on real-world urban scenarios show that our method improves infrastructure availability - achieving up to 244.4% increase in coverage - and reduces user inconvenience with up to 79.8% shorter waiting times, compared to existing solutions.
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