光伏发电增强型配电网中电动汽车充放电调度的优势

Pritam Das, Partha Kayal
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引用次数: 0

摘要

电动汽车(EV)因其无污染的技术和低运行成本,将在交通领域占据主导地位。然而,电动汽车充电会对电力输送网络造成巨大的电力需求和压力。如果充电和放电调度与智能电动汽车路由相协调,就能很好地解决这一难题。本文提出了两阶段充放电调度。在第一阶段,采用时间调度算法确定不同时段的电动汽车充电/放电时段;在第二阶段,在不同充电站之间优化分配这些时段。设计了电动汽车驶向电动汽车充电站的路线,以提高电动汽车对充放电计划的有效参与。为此,利用回归模型预测了测试区域内电动汽车的可能数量。在典型的光伏增强型 28 总线印度配电网络上测试了充放电和位置调度组合模型的适当性。进行了三个案例研究,每个案例包含三个子案例,结合了电动汽车车主在一天中不同时段充电和放电的选择。案例研究的结果是,案例-1a、案例-1b、案例-1c、案例-2a、案例-2b、案例-2c、案例-3a、案例-3b 和案例-1c 中 24 小时需求模式的峰均比(PAR)分别为 1.151.0、1.165.0、1.196.8、1.165.0、1.180.9、1.196.8、1.196.8、1.196.8 和 1.196.8,而基础案例中 24 小时需求模式的峰均比(PAR)为 1.2。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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An advantageous charging/discharging scheduling of electric vehicles in a PV energy enhanced power distribution grid

Electric vehicles (EVs) are going to overrule the transportation sector due to their pollution-free technology and low running costs. However, charging the EVs causes significant power demand and stress on the power delivery network. The challenge can be tackled well when charging and discharging scheduling are coordinated with intelligent EV routing. In this work, two-stage charging and discharging scheduling are proposed. In the first stage, a time scheduling algorithm is structured to identify EV charging/discharging slots at different hours, and at a later stage, the slots are optimally distributed among different charging stations. Routing of the EVs towards the EVCSs has been designed to enhance the useful participation of the EVs in the charging and discharging program. In this regard, a possible number of EVs in the test region has been forecasted with a regression model. The adequacy of the combined charging-discharging and location scheduling model is tested on a typical PV-enhanced 28-bus Indian distribution network. Three case studies containing three sub-cases in each have been performed incorporating the choice of the EV owners towards charging and discharging in different time slots in a day. The case studies have resulted in a peak-to-average ratio (PAR) of 1.151,0, 1.165,0, 1.196,8, 1.165,0, 1.180,9, 1.196,8, 1.196,8, 1.196,8 and 1.196,8 for the 24-h demand pattern in Case-1a, Case-1b, Case-1c, Case-2a, Case-2b, Case-2c, Case-3a, Case-3b and Case-1c respectively in comparison to a PAR of 1.2 for the 24-h demand in base case.

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