Oppositional arithmetic optimization algorithm for network reconfiguration and simultaneous placement of DG and capacitor in radial distribution networks

IF 1.6 4区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Numerical Modelling-Electronic Networks Devices and Fields Pub Date : 2024-09-30 DOI:10.1002/jnm.3298

Indrajit Dey, Provas Kumar Roy

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引用次数: 0

Abstract

The prime objective of this study is the simultaneous network reconfiguration with distributed generation (DG) and capacitor placement in radial distribution networks (RDN) to get the techno and economic benefits for two separate objectives, which are the minimization of actual power loss and annual economic loss as well as a multi objective combining these two single objectives using an oppositional arithmetic optimization algorithm (OAOA). It is an improved version of the currently suggested arithmetic optimization algorithm (AOA) used in the field of engineering for the optimization task. Though the recently developed AOA shows its efficacy in different optimization tasks but to improve the quality of solutions, convergence behavior, and to avoid the local optima, oppositional behavior is added to AOA. The efficacy and exactness of OAOA are tested on three test systems (33-bus, 69-bus, and 118-bus). For the reduction of power loss and annual economic loss as well as the multi objective optimization, two scenarios with different cases are executed using OAOA in RDNs. In scenario 1, the installation of the capacitor (case 1), the installation of unity power factor (UPF) based DG (case 2), and the placement of optimal power factor (OPF) based DG (case 3) have been executed. In scenario 2, allocation of UPF based DG and capacitors simultaneously (case 1), placement of OPF based DG and capacitors simultaneously (case 2) and simultaneous reconfiguration with installation of OPF based DG and capacitor (case 3) has been executed. This recommended OAOA algorithm provides the percentage improvement in real power loss and yearly economic loss for all cases of 33-bus, and 69-bus systems (34.28%, 65.50%, 94.43%, 93.26%, 94.89%, and 95.11%), (28.54%, 56.69%, 83.42%, 79.62%, 83.65%, and 83.71%), and (35.51%, 69.16%, 98.10%, 97.52%, 98.22%, and 98.25%), (30.26%, 61.68%, 88.75%, 85.42%, 88.81%, and 88.98%), respectively. The results and comparative study reveal that the OAOA is better than several optimization algorithms in terms of solution quality and good results. This algorithm has a good speed of response and convergence behavior.

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用于径向配电网络中的网络重构和 DG 与电容器同步布置的对立算术优化算法

本研究的主要目标是在径向配电网（RDN）中同时进行分布式发电（DG）和电容器布置的网络重新配置，以获得两个独立目标的技术和经济效益，这两个目标是实际电力损失和年度经济损失最小化，以及使用对立算术优化算法（OAOA）将这两个单一目标结合起来的多目标。它是目前工程领域用于优化任务的算术优化算法（AOA）的改进版。虽然最近开发的算术优化算法在不同的优化任务中都显示出了它的功效，但为了提高解的质量、收敛行为和避免局部最优，算术优化算法中加入了对立行为。在三个测试系统（33 总线、69 总线和 118 总线）上测试了 OAOA 的有效性和精确性。为了减少电能损耗和年度经济损失以及进行多目标优化，在 RDN 中使用 OAOA 执行了两种不同情况的方案。在方案 1 中，执行了电容器安装（方案 1）、基于统一功率因数（UPF）的 DG 安装（方案 2）和基于最佳功率因数（OPF）的 DG 布置（方案 3）。在方案 2 中，同时分配基于 UPF 的 DG 和电容器（方案 1），同时放置基于 OPF 的 DG 和电容器（方案 2），同时重新配置基于 OPF 的 DG 和电容器（方案 3）。在 33 总线和 69 总线系统的所有情况下，推荐的 OAOA 算法都能提高实际电能损失和年度经济损失的百分比（34.28%、65.50%、94.43%、93.26%、94.89% 和 95.11%）。89%和 95.11%）、（28.54%、56.69%、83.42%、79.62%、83.65%和 83.71%）以及（35.51%、69.16%、98.10%、97.52%、98.22%和 98.25%）和（30.26%、61.68%、88.75%、85.42%、88.81%和 88.98%）。结果和比较研究表明，OAOA 在求解质量和良好结果方面优于几种优化算法。该算法具有良好的响应速度和收敛行为。

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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来源期刊

International Journal of Numerical Modelling-Electronic Networks Devices and Fields 工程技术-工程：电子与电气

CiteScore

4.60

自引率

6.20%

发文量

101

审稿时长

>12 weeks

期刊介绍： Prediction through modelling forms the basis of engineering design. The computational power at the fingertips of the professional engineer is increasing enormously and techniques for computer simulation are changing rapidly. Engineers need models which relate to their design area and which are adaptable to new design concepts. They also need efficient and friendly ways of presenting, viewing and transmitting the data associated with their models. The International Journal of Numerical Modelling: Electronic Networks, Devices and Fields provides a communication vehicle for numerical modelling methods and data preparation methods associated with electrical and electronic circuits and fields. It concentrates on numerical modelling rather than abstract numerical mathematics. Contributions on numerical modelling will cover the entire subject of electrical and electronic engineering. They will range from electrical distribution networks to integrated circuits on VLSI design, and from static electric and magnetic fields through microwaves to optical design. They will also include the use of electrical networks as a modelling medium.