LAUSR

To comprehensively consider the actual spatial‐temporal transfer process of electric vehicles (EVs) and enhance the computation efficiency of scheduling, this article proposes a spatial‐temporal transfer model of EVs and an improved Lagrange dual relaxation method (ILDRM) for the decentralized scheduling of a charging‐storage station (CSS). Specifically, with the application of trip chain technology, Monte Carlo, and Markov decision process (MDP), the spatial‐temporal transfer model of EVs is constructed, taking into account multiple factors including temperatures, traffic conditions, and transfer randomness. Subsequently, by introducing ILDRM, a decentralized optimization model is proposed which converts the traditional centralized optimization model into a set of sub‐problems. Moreover, the optimization model aims to maximize the profit of CSS under the constraints of vehicle‐to‐grid behavior and the operation of both CSS and distribution network. To validate the proposed spatial‐temporal transfer model and the decentralized optimization method for CSS, a series of simulations in various scenarios are performed regarding the load curve and computation efficiency. The comprehensive and systematical study indicates that the proposed spatial‐temporal transfer model enables to reflect EVs transfer randomness and it is more factually practical than the classical Dijkstra algorithm. Besides, ILDRM can provide a high computationally efficient solution to the operation of CSS.