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Modeling of Load Demand Due to EV Battery Charging in Distribution Systems

机译:配电系统中电动汽车电池充电引起的负载需求建模

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This paper presents a methodology for modeling and analyzing the load demand in a distribution system due to electric vehicle (EV) battery charging. Following a brief introduction to the common types of EV batteries and their charging characteristics, an analytical solution for predicting the EV charging load is developed. The method is stochastically formulated so as to account for the stochastic nature of the start time of individual battery charging and the initial battery state-of-charge. A comparative study is carried out by simulating four EV charging scenarios, i.e., uncontrolled domestic charging, uncontrolled off-peak domestic charging, “smart” domestic charging and uncontrolled public charging—commuters capable of recharging at the workplace. The proposed four EVs charging scenarios take into account the expected future changes to the electricity tariffs in the electricity market place and appropriate regulation of EVs battery charging loads. A typical U.K. distribution system is adopted as an example. The time-series data of EV charging loads is taken from two commercially available EV batteries: lead-acid and lithium-ion. Results show that a 10% market penetration of EVs in the studied system would result in an increase in daily peak demand by up to 17.9%, while a 20% level of EV penetration would lead to a 35.8% increase in peak load, for the scenario of uncontrolled domestic charging—the “worst-case” scenario.
机译:本文介绍了一种用于建模和分析配电系统中由于电动汽车(EV)电池充电而引起的负载需求的方法。在简要介绍了EV电池的常见类型及其充电特性之后,开发了一种用于预测EV充电负载的分析解决方案。该方法是随机制定的,以考虑单个电池充电开始时间和初始电池充电状态的随机性。通过模拟四种电动汽车充电场景进行比较研究,即不受控制的家用充电,不受控制的非高峰期家用充电,“智能”家用充电和不受控制的通勤者-能够在工作场所充电的通勤者。拟议的四种电动汽车充电方案考虑了电力市场中电价的预期未来变化以及对电动汽车电池充电负荷的适当规定。以典型的英国分销系统为例。电动汽车充电负载的时间序列数据来自两个市售的电动汽车电池:铅酸和锂离子。结果表明,在所研究的系统中,电动汽车的10%的市场渗透率将导致每日峰值需求增加多达17.9%,而20%的电动汽车渗透率将导致峰值负荷增加35.8%。家庭收费不受控制的情况-“最坏的情况”。

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