Plug In Electric Vehicles in Smart Grids - Charging Strategies
Plug In Electric Vehicles in Smart Grids - Charging Strategies
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将电动汽车接入智能电网 - 充电策略
DOI：
10.1007/978-981-287-317-0_5
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Xydas E
中科院分区：
文献类型：
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作者：
Xydas E
据英国据英国交通部统计，英国97%的交通能源消耗来自石油的使用.因此，需要燃料的多样化来提高能源安全性，而插电式电动汽车（PEV）似乎很有希望提供替代解决方案。然而，PEV车主需要来自电网的电力，以便为其车辆的电池充电。PEV充电负荷是一种新型需求，受出行和驾驶模式等其他因素的影响。一天内的平均行驶距离、连接和断开时间以及PEV的功率消耗将直接影响日负荷曲线。本章提出了一种分散控制算法来管理PEV充电请求。控制算法的目的是在需求曲线上实现填谷效应，避免峰值需求的潜在增加。该模型包括PEV短期负荷预测算法。这种预测有助于控制模型的有效性。通过不同的案例研究，所提出的模型的性能进行了评估，并说明了价值的PEV负荷预测的PEV负荷管理过程的一部分。
According to the U.K. Department for Transport, the 97 % of transport energy consumption comes from the usage of oil. Therefore, a fuel diversification is needed to improve the energy security, and plug-in electric vehicles (PEVs) seem promising in giving an alternative solution. However, PEV owners need electric power from the grid in order to recharge the batteries of their vehicles. PEV charging load is a new type of demand, influenced by additional factors such as travel and driving patterns. Average travel distance within a day, the connection and disconnection time and the PEV’s power consumption will directly affect the daily load curve. This chapter proposes a decentralized control algorithm to manage the PEV charging requests. The aim of the control algorithm is to achieve a valley-filling effect on the demand curve, avoiding a potential increase in the peak demand. The proposed model includes an algorithm for PEV short term load forecasting. This forecast contributes to the effectiveness of the control model. Through different case studies, the performance of the proposed model is evaluated and the value of the PEV load forecasting as part of the PEV load management process is illustrated.