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Impact of PEV Charging Loads on Distribution System Operations and Optimal Siting and Sizing of PEV Charging Stations

机译:PEV充电负荷对配电系统运行的影响以及PEV充电站的最佳选址和选型

摘要

Smart grid has emerged as a promising paradigm to promote and deliver a clean, modern and efficient electricity grid to all customers, and it allows Local Distribution Companies (LDC) to integrate renewable sources more reliably, efficiently, safely and economically. Smart grid realizes Plug-in Electric Vehicles (PEVs) as a potential solution to reduce green house gas (GHG) emissions. However, large scale penetration of PEVs can significantly impact distribution system operations.This thesis first presents an extensive study of PEV characteristics such as, owner driving behavior, mobility trends of the system as a whole, battery capacity, State of Charge (SOC), different charging levels and energy required for charging the battery. The US National Household Travel Survey (NHTS) 2009 data set is explored to model the PEV load characteristics by representing customers' charging behavior in close to reality. This includes the study of the number of trips covered each day, during weekdays and weekends, over different seasons, the miles traveled, and the home arrival and departure times. Using the developed PEV load profiles, distribution system impact analysis and optimal operational studies are carried out to examine how the LDC can accommodate such loads.The NHTS data set is also used to develop probability density functions (pdfs) of certain mobility patterns such as initial SOC and starting time of charging. Using these pdfs, a Stochastic Distribution Optimal Power Flow (SDOPF) model with various objectives such as minimization of feeder loss, minimization of energy drawn and minimization of PEV charging cost, subject to feeder operational constraints is presented. Various scenarios of uncontrolled and smart charging are studied. In the uncontrolled charging case, the worst case scenarios are discussed. The smart charging scenarios provides with the optimal charging schedules which result in flattening the load profile.This thesis further presents an approach to optimally siting and sizing of Electric Vehicle Charging Stations (EVCS). Various aspects in identifying the optimal location of EVCS, from both the LDC's and customers' perspectives are discussed. A new approach to modeling the initial SOC of PEVs considering the travel distance from home to EVCS in relation to the feeder sections' electrical parameters is presented. A heuristic approach to determine the optimal siting and sizing of EVCS considering minimum feeder loss, peak demand and customer charging cost is proposed.
机译:智能电网已经成为向所有客户推广和提供清洁,现代化和高效的电网的有希望的典范,它使本地配电公司(LDC)能够更可靠,高效,安全和经济地集成可再生能源。智能电网将插电式电动汽车(PEV)实现为减少温室气体(GHG)排放的潜在解决方案。然而,PEV的大规模渗透会严重影响配电系统的运行。本文首先对PEV的特性进行了广泛的研究,例如,车主的驾驶行为,整个系统的移动性趋势,电池容量,充电状态(SOC),不同的充电水平和为电池充电所需的能量。探索了美国国家家庭旅行调查(NHTS)2009数据集,以通过逼近客户的充电行为来模拟PEV负载特征。这包括对工作日,工作日和周末,不同季节中每天旅行的次数,行进的英里数以及到达和离开的时间的研究。使用已开发的PEV负荷曲线,进行配电系统影响分析和最佳运营研究,以检查LDC如何适应此类负荷.NHTS数据集还用于开发某些流动性模式(如初始)的概率密度函数(pdfs) SOC和充电开始时间。使用这些pdf文件,提出了一种随机分配的最佳功率流(SDOPF)模型,其模型具有各种目标,例如,受馈线操作约束的影响,使馈线损耗最小,能量消耗最小和PEV充电成本最小。研究了不受控制的智能充电的各种情况。在不受控制的充电情况下,将讨论最坏的情况。智能充电方案提供了最佳的充电时间表,从而使负载曲线变得平坦。本文进一步提出了一种优化电动汽车充电站(EVCS)选址和尺寸的方法。从最不发达国家和客户的角度讨论了确定EVCS最佳位置的各个方面。提出了一种新的PEV初始SOC建模方法,该方法考虑了从家到EVCS的行进距离(与馈线部分的电气参数有关)。提出了一种启发式方法来确定EVCS的最佳选址和规模,同时考虑最小的馈线损耗,高峰需求和客户收费成本。

著录项

  • 作者

    Shetty Shubhalakshmi;

  • 作者单位
  • 年度 2016
  • 总页数
  • 原文格式 PDF
  • 正文语种 en
  • 中图分类

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