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一种基于凸透镜成像的多目标改进马群优化算法，用于考虑可靠性和不确定性的配电网风能资源随机优化。

A multi-objective improved horse herd optimizer based on convex lens imaging for stochastic optimization of wind energy resources in distribution networks considering reliability and uncertainty.

作者信息

Duan Fude, Basem Ali, Jasim Dheyaa J, Eslami Mahdiyeh, Okati Mustafa

机构信息

School of Intelligent Transportation, Nanjing Vocational College of Information Technology, Nanjing, 210000, Jiangsu, China.

Faculty of Engineering, Warith Al-Anbiyaa University, Karbala, 56001, Iraq.

出版信息

Sci Rep. 2024 Nov 27;14(1):29532. doi: 10.1038/s41598-024-78977-0.

DOI:10.1038/s41598-024-78977-0
PMID:39604424
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11603060/
Abstract

In this study, stochastic multi-objective allocation of wind turbines (WTs) in radial distribution networks is performed using a new multi-objective improved horse herd optimizer (MOIHHO) and an unscented transformation (UT) method for modeling the uncertainties of WTs power and network load. The objective function aims to minimize power loss, improve reliability, and reduce the costs associated with wind turbines (WTs), presenting these goals as a three-dimensional function. The Multi-Objective Improved Horse Herd Optimizer (MOIHHO) is derived from an enhanced version of the traditional horse herd optimizer. This enhancement utilizes mirror imaging based on convex lens principles to address issues of premature convergence. Additionally, the decision-making process is designed to identify the final fuzzy solution among the non-dominant solutions within the Pareto front set. The simulation results are presented with and without considering uncertainty in two scenarios of deterministic and stochastic WT allocation on 33- and 69-bus distribution networks and different objectives are compared. Also, the effect of incorporating uncertainties are evaluated on power loss and reliability using the MOIHHO. Moreover, the superiority of the MOIHHO is investigated in achieving better objective function value compared with conventional MOHHO, multi-objective particle swarm optimization (MOSPO), multi-objective gray wolf optimizer (MOGWO), and multi-objective gazelle optimization algorithm (MOGOA). The obtained results demonstrated that considering the UT-based stochastic scenario, the power losses cost is increased, and the reliability is weakened for 33- and 69-bus networks in comparison with the deterministic scenario.

摘要

在本研究中，采用一种新的多目标改进马群优化器（MOIHHO）和无迹变换（UT）方法对风力发电机组（WT）功率和网络负荷的不确定性进行建模，在径向配电网中进行风力发电机组的随机多目标分配。目标函数旨在最小化功率损耗、提高可靠性并降低与风力发电机组相关的成本，将这些目标表示为一个三维函数。多目标改进马群优化器（MOIHHO）源自传统马群优化器的增强版本。这种增强利用基于凸透镜原理的镜像来解决早熟收敛问题。此外，决策过程旨在从帕累托前沿集内的非主导解中确定最终的模糊解。给出了在33节点和69节点配电网的确定性和随机风力发电机组分配两种情况下，考虑和不考虑不确定性时的仿真结果，并比较了不同目标。此外，使用MOIHHO评估了纳入不确定性对功率损耗和可靠性的影响。此外，与传统的多目标马群优化器（MOHHO）、多目标粒子群优化算法（MOSPO）、多目标灰狼优化器（MOGWO）和多目标瞪羚优化算法（MOGOA）相比，研究了MOIHHO在实现更好目标函数值方面的优越性。所得结果表明，与确定性情况相比，考虑基于UT的随机情况时，33节点和69节点网络的功率损耗成本增加，可靠性降低。

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