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首页> 外文期刊>IEEJ Transactions on Electrical and Electronic Engineering >Performance analysis of future PEA distribution networks under high penetration of PEV home charging using the Monte Carlo method
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Performance analysis of future PEA distribution networks under high penetration of PEV home charging using the Monte Carlo method

机译:使用Monte Carlo方法在PEV房屋充电高渗透率下对未来PEA分销网络的绩效分析

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摘要

This article presents the performance analysis of future Provincial Electricity Authority of Thailand (PEA) distribution networks under high penetration of plug‐in electric vehicle (PEV) home charging using the Monte Carlo method. Network performance indices considered in this study are the voltage profile, power losses, load factor, and voltage unbalance. The voltage profile and power losses are evaluated based on the PEA criteria. In addition, the voltage unbalance factors (%VUF) are evaluated according to the IEC 61000‐2‐4: 2000‐06 standards. A selected PEA distribution network based on the existing data is used for the simulation study with the DIgSILENT PowerFactory. The penetration of PEV home charging at each node and phase is assigned using the Monte Carlo method, which in particular is used to randomly generate patterns of PEV charging behaviors. The simulation study presents five cases; the average points of charge, max‐to‐min charge, min‐to‐max charge, central feeder charge, and random point of charge. All cases are compared with a base case (without PEVs). Charging scenarios at phases ‘a’, ‘b’, and ‘c’ are assumed having overlapped time duration. The PEV charging duration per phase is assumed 6 h. Simulation results indicate that PEV charging time, charging point, and penetration levels at each phase and node are significant factors impacting the network performance. Voltage profiles slightly drop in all cases, meanwhile the average voltage drop increases by 7% compared with the base case; case 3 has the maximum voltage drop, which is deeper than PEA criteria. The power losses increase in all cases. The average power losses have increased by 103.52%. The %VUF increases gradually with the length of the feeder. The maximum %VUF occurs at the end of the feeder for all cases and is higher than 2.0% of the IEC standard. On the other hand, PEV charging improves the load factor. ? 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
机译:本文介绍了泰国未来省级电力管理局的绩效分析(PEA) )使用Monte Carlo方法,插件电动汽车(PEV)房屋充电的分配网络。本研究中考虑的网络性能指数是电压曲线,功率损耗,负载因子和电压不平衡。根据PEA标准评估电压曲线和功率损耗。此外,根据IEC 61000-2-4:2000-06标准评估电压不平衡因子(%VUF)。基于现有数据的选定的PEA分销网络与Digsilent PowerFactory一起用于仿真研究。使用蒙特卡洛法分配了PEV房屋充电在每个节点和相位的渗透,尤其用于随机生成PEV充电行为的模式。仿真研究提出了五个病例;平均电荷点,最大至分钟电荷,最小到玛克斯电荷,中央进料器充电和随机充电点。将所有情况与基本案例(无PEV)进行比较。假定在“ A”,“ B”和“ C”的阶段充电场景被认为持续时间重叠。假定每相的PEV充电持续时间为6 h。仿真结果表明,每个阶段的PEV充电时间,充电点和渗透水平和节点是影响网络性能的重要因素。在所有情况下,电压曲线略有下降,与基本情况相比,平均电压下降增加了7%。案例3具有最大电压下降,比PEA标准更深。在所有情况下,功率损失都会增加。平均功率损失增加了103.52%。 VUF%随馈线的长度逐渐增加。对于所有情况,最大%VUF发生在馈线的末尾,高于IEC标准的2.0%。另一方面,PEV充电可改善负载系数。 ? 2018年日本电气工程师研究所。由John Wiley&amp出版Sons,Inc。

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