Optimal location and sizing of multiple distributed generators in radial distribution network using metaheuristic optimization algorithms

IF 0.6 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
N. Belbachir, M. Zellagui, B. Bekkouche
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引用次数: 0

Abstract

The satisfaction of electricity customers and environmental constraints imposed have made the trend towards renewable energies more essential given its advantages such as reducing power losses and enhancing voltage profiles. This study addresses the optimal sizing and setting of Photovoltaic Distributed Generator (PVDG) connected to Radial Distribution Network (RDN) using various novel optimization algorithms. These algorithms are implemented to minimize the Multi-Objective Function (MOF), which devoted to optimize the Total Active Power Loss (TAPL), the Total Voltage Deviation (TVD), and the overcurrent protection relays (OCRs)?s Total Operation Time (TOT). The effectiveness of the proposed algorithms is validated on the test system standard IEEE 33-bus RDN. In this paper is presented a recent meta-heuristic optimization algorithm of the Slime Mould Algorithm (SMA), where the results reveal its effectiveness and robustness among all the applied optimization algorithms, in identifying the optimal allocation (locate and size) of the PVDG units into RDN for mitigating the power losses, enhance the RDN system's voltage profiles and improve the overcurrent protection system. Accordingly, the SMA approach can be a very favorable algorithm to cope with the optimal PVDG allocation problem.
基于元启发式优化算法的径向配电网中多台分布式发电机的最优选址与规模优化
由于可再生能源具有减少电力损耗和提高电压分布等优势,电力客户的满意度和环境约束使得可再生能源的趋势变得更加重要。本研究利用各种新颖的优化算法,解决了光伏分布式发电机(PVDG)与径向配电网(RDN)连接的最佳尺寸和设置问题。这些算法的实现是为了最小化多目标函数(MOF),该函数致力于优化总有功功率损耗(TAPL),总电压偏差(TVD)和过流保护继电器(ocr)?s总操作时间(TOT)。在测试系统标准IEEE 33总线RDN上验证了算法的有效性。本文提出了一种基于黏菌算法(SMA)的元启发式优化算法,结果表明其在所有应用的优化算法中具有有效性和鲁棒性,可以确定PVDG单元在RDN中的最佳配置(位置和大小),以减轻功率损耗,增强RDN系统的电压分布,并改进过流保护系统。因此,SMA方法是解决PVDG最优分配问题的一种很好的算法。
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来源期刊
Facta Universitatis-Series Electronics and Energetics
Facta Universitatis-Series Electronics and Energetics ENGINEERING, ELECTRICAL & ELECTRONIC-
自引率
16.70%
发文量
10
审稿时长
20 weeks
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