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Online coordination of plug-in electric vehicle charging in smart grid with distributed wind power generation systems

机译:具有分布式风力发电系统的智能电网中插电电动车充电的在线协调

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Plug-in electric vehicles (PEVs) and wind distributed generations (WDGs) will represent key technologies in the future smart grid configurations. PEV charging at high penetration levels requires substantial grid energy that can be partially supplied by WDGs. This paper examines the impacts of WDGs on performance of recently implemented online maximum sensitivities selection based coordination algorithm (OL-MSSCA) for PEV charging. The algorithm considers random arrivals of vehicles and time-varying market energy price to reduce the total cost of energy generation for PEV charging and the associated grid losses while providing consumer priorities based on defined charging time zones. OL-MSSCA will be improved to also consider DGs while maintaining network operation criteria such as maximum generation limits and voltage profiles within their permissible limits. Detailed simulation is performed on the modified IEEE 23kV distribution system with three WDGs and 22 low voltage residential networks populated with PEVs. The main contributions of this paper are inclusion of WDGs in OL-MSSCA, as well as detailed investigations on the impacts of their peak generation times, penetrations and locations on the performance of smart grid populated with PEVs.
机译:插入式电动车(PEVS)和风分布代(WDGS)将代表未来智能电网配置中的关键技术。高渗透水平的PEV充电需要大量的网格能量,可部分由WDGS提供。本文介绍了WDGS对最近实施的在线最大敏感性选择基于在线最大敏感选择的协调算法(OL-MSSCA)的影响,用于PEV充电。该算法考虑了车辆的随机抵达和时变市场能源价格,以降低PEV充电的能量生成总成本和相关网格损耗,同时根据定义的充电时间区提供消费优先级。将改进OL-MSSCA,也可以考虑DGS,同时维持网络操作标准,例如在其允许限制内的最大发电限制和电压配置文件。在具有三个WDG和22个具有PEV的22个低电压住宅网络的改进的IEEE 23KV分配系统上执行了详细的仿真。本文的主要贡献是在OL-MSSCA中纳入WDG,以及对其峰值生成时间,渗透和地点对PEVS填充智能电网性能的影响的详细调查。

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