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A Novel Charging and Discharging Algorithm of Plug-in Hybrid Electric Vehicles Considering Vehicle-to-Grid and Photovoltaic Generation

机译:考虑车辆到电网和光伏发电的插入式混合动力电动汽车的新型充电和放电算法

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摘要

Considering, the high penetration of plug-in electric vehicles (PHEVs), the charging and discharging of PHEVs may lead to technical problems on electricity distribution networks. Therefore, the management of PHEV charging and discharging needs to be addressed to coordinate the time of PHEVs so as to be charged or discharged. This paper presents a management control method called the charging and discharging control algorithm (CDCA) to determine when and which of the PHEVs can be activated to consume power from the grid or supply power back to grid through the vehicle-to-grid technology. The proposed control algorithm considers fast charging scenario and photovoltaic generation during peak load to mitigate the impact of the vehicles. One of the important parameters considered in the CDCA is the PHEV battery state of charge (SOC). To predict the PHEV battery SOC, a particle swarm optimization-based artificial neural network is developed. Results show that the proposed CDCA gives better performance as compared to the uncoordinated charging method of vehicles in terms of maintaining the bus voltage profile during fast charging.
机译:考虑到PhEV的插入电动车辆(PHEV)的高渗透可能导致电力分配网络上的技术问题。因此,需要解决PHEV充电和放电的管理,以协调PHEV的时间,以便充电或放电。本文介绍了一种称为充电和放电控制算法(CDCA)的管理控制方法,以确定PHEV的何时何种,并且通过车辆到电网技术将电力从电网或供电电源消耗电源。所提出的控制算法考虑了峰值负荷期间的快速充电情景和光伏发电,以减轻车辆的影响。 CDCA中考虑的重要参数之一是PHEV电池充电状态(SOC)。为了预测PHEV电池SOC,开发了基于粒子群优化的人工神经网络。结果表明,与在快速充电期间维护总线电压曲线方面的车辆的不协调充电方法相比,所提出的CDCA提供了更好的性能。

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