Optimal allocation of distributed generation units and fast electric vehicle charging stations for sustainable cities

IF 16.4
Isaac Prempeh , Albert K. Awopone , Patrick N. Ayambire , Ragab A. El-Sehiemy
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Abstract

The rise of electric vehicles (EVs) in sustainable cities has fuelled interest in Distributed Generation (DG) units allocation. A well-planned and efficient charging infrastructure is required for effective e-mobility. The paper examined the single-objective frameworks of optimal simultaneous allocation of DG units and fast EV charging stations (EVCS). The applications are employed on the IEEE 69 bus network and a real part of the Ghana network in the Ashanti region. The optimization tasks are carried out by using Particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms. The impact of optimal placement on the networks was analysed. The results show that with high penetration levels of DG units (up to 40%) and fast EVCS, PSO, and ABC can achieve a significant power loss reduction that reaches 68%. Furthermore, PSO outperforms ABC in relation to the voltage deviation index on both the test network and the 33 ​kV Ashanti region network, while still satisfying the IEC standards' 5% margins. The results indicate that PSO and ABC are viable swarm algorithms for mitigating active power loss and enhancing the voltage profile of a system through concurrent allocation.

Abstract Image

可持续城市分布式发电机组和快速电动汽车充电站的优化配置
电动汽车(ev)在可持续发展城市中的兴起,激发了人们对分布式发电(DG)机组分配的兴趣。有效的电动交通需要精心规划和高效的充电基础设施。研究了快速电动汽车充电站与燃气发电机组同时优化配置的单目标框架。这些应用程序被用于IEEE 69总线网络和阿散蒂地区加纳网络的实际部分。采用粒子群算法(PSO)和人工蜂群算法(ABC)进行优化。分析了最优布局对网络的影响。结果表明,DG单元的高穿透水平(高达40%)和快速EVCS, PSO和ABC可以实现显著的功率损耗降低,达到68%。此外,PSO在测试网络和33 kV阿散蒂地区网络的电压偏差指数方面优于ABC,同时仍然满足IEC标准的5%裕度。结果表明,粒子群优化算法和蚁群优化算法是一种可行的群算法,可以通过并行分配来降低有功损耗,改善系统的电压分布。
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CiteScore
6.40
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