基于人工蜂群和粒子群优化算法的分布式发电和电动汽车充电站优化配置与规模

Isaac Prempeh, R. El-Sehiemy, Albert K. Awopone, P. Ayambire
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引用次数: 1

摘要

分布式发电机组用于提高电网的可靠性和稳定性。电动汽车充电站(EVCS)在高峰时段消耗更多的电网电力。由于技术的影响,这两个系统实际上不可能位于电网的每个部分。在本研究中,采用了两种元启发式技术,通过同时分配DG单元和EVCS来改善电压分布并最小化功率损失。本研究采用IEEE 33总线测试系统来寻找解决方案。采用标准粒子群算法(PSO)和人工蜂群算法(ABC)对DG和EVCS进行分配。结果表明,在DG单元和EVCS同时分配方面,粒子群算法优于ABC等算法。采用PSO进行分配时,功率损耗减少40.78%。总线2和19是IEEE 33总线系统中EVCS最喜欢的总线。本文的结论是,大容量EVCS的加入应该导致DG机组同时在网络中引入。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Allocation and Sizing of Distributed Generation and Electric Vehicle Charging Stations using Artificial Bee Colony and Particle Swarm Optimization Algorithms
Distributed generation (DG) units are used to improve grid reliability and stability. Electric vehicle charging stations (EVCS) consume more power from the grid at peak periods. These two systems cannot be practically located on every part of the grid due to technical effects. In this study, two metaheuristic techniques are adopted to improve the voltage profile and minimise power losses by simultaneously allocating DG units and EVCS. The study employed the IEEE 33 bus test system in finding the solution. The study used standard Particle Swarm Optimization(PSO) and Artificial Bee Colony(ABC) algorithms for DG and EVCS allocation. The results show that PSO outperformed ABC and other algorithms in terms of the simultaneous allocation of DG units and EVCS. The power losses were 40.78% less when PSO is used for allocation. Buses 2 and 19 are the favorite buses for EVCS on an IEEE 33 bus system. The paper concludes that the addition of high-capacity EVCS should lead to the simultaneous introduction of DG units on the network.
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