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

机译:分布式风力发电系统在智能电网中对插电式电动汽车充电进行在线协调

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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:插电式电动汽车(PEV)和风电分布式发电(WDG)将代表未来智能电网配置中的关键技术。高渗透水平的PEV充电需要大量的电网能量,而这些能量可以由WDG部分提供。本文研究了WDG对最近实施的基于PEV充电的在线最大灵敏度选择协调算法(OL-MSSCA)的影响。该算法考虑了车辆的随机到达和时变的市场能源价格,以减少用于PEV充电的总发电成本以及相关的电网损耗,同时根据定义的充电时区为用户提供优先级。将对OL-MSSCA进行改进,使其在考虑网络运行标准(例如最大发电极限和电压曲线在其允许极限之内)的同时考虑DG。在经过修改的IEEE 23kV配电系统上进行了详细的仿真,该系统具有3个WDG和22个装有PEV的低压住宅网络。本文的主要贡献是将WDG包含在OL-MSSCA中,并详细研究了其峰值发电时间,穿透深度和位置对装有PEV的智能电网性能的影响。

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