Effectively operating electrical vehicle charging station (EVCS) is crucial for enabling the rapid transition of electrified transportation. To solve this problem using reinforcement learning (RL), the dimension of state/action spaces scales with the number of EVs and is thus very large and time-varying. This dimensionality issue affects the efficiency and convergence properties of generic RL algorithms. We develop aggregation schemes that are based on the emergency of EV charging, namely the laxity value. A least-laxity first (LLF) rule is adopted to consider only the total charging power of the EVCS which ensures the feasibility of individual EV schedules. In addition, we propose an equivalent state aggregation that can guarantee to attain the same optimal policy. Based on the proposed representation, policy gradient method is used to find the best parameters for the linear Gaussian policy . Numerical results have validated the performance improvement of the proposed representation approaches in attaining higher rewards and more effective policies as compared to existing approximation based approach.
Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks
East China Normal University（华东师范大学）；EPFL（洛桑联邦理工学院）；ShanghaiTech University（上海科技大学）；State Grid Beijing Electric Power Company（国网北京市电力公司）；Tsinghua University（清华大学）
Deep Information Fusion for Electric Vehicle Charging Station Occupancy Forecasting
L3S Research Center, Leibniz University Hannover（莱布尼茨汉诺威大学L3S研究中心）；Data Science & Intelligent Systems (DSIS) Research Group, University of Bonn（波恩大学数据科学与智能系统（DSIS）研究组）
Predicting Electric Vehicle Charging Station Usage: Using Machine Learning to Estimate Individual Station Statistics from Physical Configurations of Charging Station Networks
Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences
School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能工程学院）；Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能工程学院广东省智能交通系统重点实验室）
