文章基本信息

标题：Optimal Operation Strategy for Electric Vehicles Charging Stations with Renewable Energy Integration
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作者：Cesar Diaz-Londono ; Fredy Ruiz ; Andrea Mazza 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：12739-12744
DOI：10.1016/j.ifacol.2020.12.1899
语种：English
出版社：Elsevier
摘要：AbstractThis paper proposes an aggregator for the optimal scheduling of a Electric Vehicle (EV) charging station. Assuming that the station charging can consume energy from the power grid and also from a renewable source at no cost, Day-Ahead and Real-Time strategies are developed for the station, considering uncertainty in the local renewable generation source. First, a scheduler decides the energy to buy in the day ahead market from the power grid considering a renewable source forecast and an EVs schedule. An autoregressive model developed from 5 years of historical solar radiation data is applied. In real time, a model predictive control strategy is designed to follow the scheduled power from the grid, compensating variations in renewable generation by exploiting flexibility in the EVs charging process. The resulting optimization problems are convex programming problems that can be solved efficiently. Simulation analysis show the effectiveness of the strategy in absorbing the variability of the renewable source, minimizing the deviations between the day ahead schedule and the actual real time consumption from the grid.
关键词：KeywordsEnergy ManagementRenewable IntegrationElectric VehiclesFlexible LoadsModel Predictive Control