With the increasing penetration of renewable energy sources and electric vehicles (EVs), coordinated operation of EV aggregator (EVA) and distribution system operation (DSO) becomes a complex multistage and multidimensional stochastic problem. The motivation behind this paper is to develop a decentralized mechanism to offer a computationally efficient and almost optimal on-line policy for such problem under the framework of transactive energy control (TEC). First, a heterogeneous decomposition-based TEC is designed by utilizing the heterogeneous interactions between DSO and EVA. Then, a decentralized approximate dynamic programming-based algorithm is proposed to offer almost optimal dynamic TEC policies. A decentralized value function approximation approach with temporal difference learning is further employed for entities to learn how to utilize the flexibilities of their resources in response to the stochastic exogenous information. Case studies demonstrate the effectiveness of the proposed algorithm in terms of optimality, robustness, computation efficiency, and scalability.

中文翻译：

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电动汽车集合体协调的随机主动控制：分散式近似动态规划方法

随着可再生能源和电动汽车（EV）的普及率不断提高，电动汽车聚合器（EVA）和配电系统运行（DSO）的协调运行已成为一个复杂的多级和多维随机问题。本文的目的在于开发一种去中心化机制，以在无功能量控制（TEC）的框架下为此类问题提供计算效率高且几乎最佳的在线策略。首先，利用DSO和EVA之间的异质相互作用设计了基于异质分解的TEC。然后，提出了一种基于分散近似动态规划的算法，以提供几乎最佳的动态TEC策略。带有时差学习的分散价值函数近似方法还被用于实体，以学习如何响应随机的外源信息来利用其资源的灵活性。案例研究证明了该算法在最优性，鲁棒性，计算效率和可伸缩性方面的有效性。