AFCPOA-based optimal dispatch of hybrid PV-wind DGs for voltage stability and loss reduction in radial distribution network

Sunil Ankeshwarapu
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Abstract

This study proposes the Adaptive Fuzzy Campus Placement-based Optimization Algorithm (AFCPOA) for optimal dispatch of Renewable Distributed Generators (RDGs) — Solar PV, Wind, and Hybrid (PV + Wind) in a Radial Distribution Network (RDN) considering dynamic hourly and seasonal load variations. AFCPOA minimizes total power losses and enhances voltage stability using a Network Topology-based Load Flow approach. Its performance was evaluated on the IEEE 33-bus system and benchmarked against Hybrid Genetic Algorithm (GA)-Jaya, Jaya Algorithm, Shuffled Frog-Leaping Algorithm (SFLA), Particle Swarm Optimization (PSO), and GA. Results show that AFCPOA achieved a 42.6% reduction in total losses compared to the base case and outperformed other algorithms by 9–18% in loss reduction, with an average voltage profile improvement of 5.3%. These findings demonstrate AFCPOA’s superior ability to handle seasonal load variability and optimize RDG integration efficiently.
基于afcpoa的径向配电网PV-wind混合dg优化调度
本研究提出了一种基于自适应模糊校园布局优化算法(AFCPOA),用于考虑每小时和季节负荷变化的径向配电网(RDN)中可再生分布式发电机(rdg)——太阳能光伏、风能和混合动力(PV +风能)的优化调度。AFCPOA采用基于网络拓扑的负载流方法,最大限度地降低了总功率损耗,提高了电压稳定性。在IEEE 33总线系统上对其性能进行了评估,并对混合遗传算法(GA)-Jaya、Jaya算法、shuffle青蛙跳跃算法(SFLA)、粒子群优化(PSO)和遗传算法进行了基准测试。结果表明,与基本情况相比,AFCPOA的总损耗降低了42.6%,损耗降低率比其他算法高9-18%,平均电压分布提高了5.3%。这些结果证明了AFCPOA处理季节性负荷变化和优化RDG集成的卓越能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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