A novel consumer-friendly electric vehicle charging scheme with vehicle to grid provision supported by genetic algorithm based optimization
A novel consumer-friendly electric vehicle charging scheme with vehicle to grid provision supported by genetic algorithm based optimization
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DOI：
10.1016/j.est.2022.104655
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发表时间：
2022-05-06
影响因子：
9.4
通讯作者：
Aziz, Tareq
中科院分区：
文献类型：
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作者：
Abdullah-Al-Nahid, Syed;Khan, Tafsir Ahmed;Aziz, Tareq
由于全球交通领域对电动汽车（EV）的需求不断上升，由跨电力网络的高效充电方案支持的充电设施对电力系统的运行具有重大影响。电动汽车的无监督和分散充电对电力系统的各种运行特性有一些不利影响。另一方面，具有适当充电方案的集中式充电系统在电力系统操作中提供较低的复杂性以避免不必要的电力系统网络压力。本文提出了一种基于山谷填充技术的电动汽车充电方案，通过集中充电系统为居民用户提供便利。充电方案包括基于遗传算法的EV接受优化，以最佳方式利用供应网络的可用能量。充电方案还包括车辆到电网（V2G）的便利化和用于转移电动汽车的重新分配技术，以解决充电槽的任何过载问题。基于遗传算法（GA）的优化的仿真结果表明，达到接近最佳的EV数据集，小于50次迭代次数和使用超过99.5%的可用千瓦时充电。其他结果段表明，通过将电动汽车纳入需求曲线的谷时间段，有效地利用了可用的供应能源。此外，研究结果指出，充电方案成功地遇到了不必要的网络压力问题，在电动汽车集成V2G提供和重新分配技术，优先考虑消费者满意度在过载的充电时隙。测试案例的仿真结果表明，“平均峰值”的需求曲线中的比例从68%增加到90%，将建议的充电计划电动汽车充电。此外，结果表明，在考虑网络压力和客户优先级等复杂驱动参数的情况下，所提出的分配充电时段的方法具有新奇。
Due to escalating demand for electric vehicles (EVs) in the worldwide transportation sector, the charging facilities supported by an efficient charging scheme across the power network have a significant impact on the operation of the power system. The unsupervised and decentralized charging of the EVs have several adverse effects on various operational features of the power system. On the other hand, the centralized charging system with proper charging schemes offers less complexity in power system operation to avoid unnecessary power system network stress. This article proposes a valley-filling technique-based EV charging scheme for residential consumers facilitated by a centralized charging system. The charging scheme comprises a genetic algorithmbased optimization for EV acceptance to utilize the available energy of the supply network in the best way. The charging scheme also includes vehicle to grid (V2G) facilitation and the reallocation technique for shifting EVs to resolve any overloading of charging slots. Simulation results of Genetic Algorithm (GA) based optimization show the attainment of near-optimum EV data set with less than 50 iteration numbers and usage of more than 99.5% of available kWh for charging. Other result segments indicate the efficient use of available supply energy by incorporating EVs in the valley time slab of the demand curve. Also, the findings point out that the charging scheme successfully encounters unwanted network stress issues during EV integration by V2G provision and reallocation technique, prioritizing consumer satisfaction at the overloaded charging time slots. The test case simulation results indicate that the 'average to peak' demand ratio in the demand curve is increased to 90% from 68% by incorporating the proposed charging scheme for EV charging. Moreover, the outcomes indicate the novelty of the proposed methodology in allocating charging slots considering complex driving parameters like network stress and customer prioritization.