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A framework for stochastic estimation of electric vehicle charging behavior for risk assessment of distribution networks
Frontiers in Energy ( IF 2.9 ) Pub Date : 2019-11-30 , DOI: 10.1007/s11708-019-0648-5
Salman Habib , Muhammad Mansoor Khan , Farukh Abbas , Muhammad Numan , Yaqoob Ali , Houjun Tang , Xuhui Yan

Power systems are being transformed to enhance the sustainability. This paper contributes to the knowledge regarding the operational process of future power networks by developing a realistic and stochastic charging model of electric vehicles (EVs). Large-scale integration of EVs into residential distribution networks (RDNs) is an evolving issue of paramount significance for utility operators. Unbalanced voltages prevent effective and reliable operation of RDNs. Diversified EV loads require a stochastic approach to predict EVs charging demand, consequently, a probabilistic model is developed to account several realistic aspects comprising charging time, battery capacity, driving mileage, state-of-charge, traveling frequency, charging power, and time-of-use mechanism under peak and off-peak charging strategies. An attempt is made to examine risks associated with RDNs by applying a stochastic model of EVs charging pattern. The output of EV stochastic model obtained from Monte-Carlo simulations is utilized to evaluate the power quality parameters of RDNs. The equipment capability of RDNs must be evaluated to determine the potential overloads. Performance specifications of RDNs including voltage unbalance factor, voltage behavior, domestic transformer limits and feeder losses are assessed in context to EV charging scenarios with various charging power levels at different penetration levels. Moreover, the impact assessment of EVs on RDNs is found to majorly rely on the type and location of a power network.

中文翻译:

配电网络风险评估的电动汽车充电行为随机估计框架

电力系统正在转型,以增强可持续性。本文通过开发一种现实且随机的电动汽车(EV)充电模型,为有关未来电力网络运行过程的知识做出了贡献。将电动汽车大规模集成到住宅配电网(RDN)中,对于公用事业运营商来说,这是一个至关重要的不断发展的问题。电压不平衡会阻碍RDN的有效和可靠运行。多样化的电动汽车负载需要一种随机方法来预测电动汽车的充电需求,因此,开发了一个概率模型来说明几个实际方面,包括充电时间,电池容量,行驶里程,充电状态,行驶频率,充电功率和时间高峰和非高峰充电策略下的使用机制。尝试通过应用电动汽车充电模式的随机模型来检查与RDN相关的风险。从蒙特卡洛模拟获得的EV随机模型的输出用于评估RDN的电能质量参数。必须评估RDN的设备能力,以确定潜在的过载。RDN的性能规格包括电压不平衡因数,电压行为,家用变压器极限和馈线损耗,是根据具有不同穿透水平的各种充电功率水平的EV充电场景评估的。此外,发现电动汽车对RDN的影响评估主要取决于电网的类型和位置。从蒙特卡洛模拟获得的EV随机模型的输出用于评估RDN的电能质量参数。必须评估RDN的设备能力,以确定潜在的过载。RDN的性能规格包括电压不平衡因数,电压行为,家用变压器极限和馈线损耗,是根据具有不同穿透水平的各种充电功率水平的EV充电场景评估的。此外,发现电动汽车对RDN的影响评估主要取决于电网的类型和位置。从蒙特卡洛模拟获得的EV随机模型的输出用于评估RDN的电能质量参数。必须评估RDN的设备能力,以确定潜在的过载。RDN的性能规格包括电压不平衡因子,电压行为,家用变压器极限和馈线损耗,是根据具有不同穿透水平的各种充电功率水平的EV充电场景进行评估的。此外,发现电动汽车对RDN的影响评估主要取决于电网的类型和位置。针对不同充电功率水平,不同穿透水平的电动汽车充电场景,评估了家用变压器的限值和馈线损耗。此外,发现电动汽车对RDN的影响评估主要取决于电网的类型和位置。针对不同充电功率水平,不同穿透水平的电动汽车充电场景,评估了家用变压器的限值和馈线损耗。此外,发现电动汽车对RDN的影响评估主要取决于电网的类型和位置。
更新日期:2019-11-30
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