基于环绕猎物算法的现实埃及系统输电网络规划

A. Shaheen, Ragab. A. Elsehiemy, M. Kharrich, S. Kamel
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引用次数: 2

摘要

输电网规划问题(TNPP)是电力系统规划活动的相关问题之一。它的目的是最佳地挑选新安装的线路,类型和数量,以应对预期的未来负载条件。在此基础上,本研究提出了一个新的TNPP经济模型。该模型的目标是寻找投资和运行成本最小的最优输电路线。最近提出了灰狼优化算法(GWOA)、斑点鬣狗优化算法(SHOA)和鲸鱼优化算法(WOA)来解决TNPP问题。这些算法的概念是基于包围猎物操作。研究了两种现实埃及网络的竞争方法,以寻找最优TNPP解。第一个测试网络是66千伏西三角洲地区(WDR)系统,第二个测试网络是500千伏特高压(EHV)系统。根据自适应神经模糊推理系统(ANFIS)提取了2030年前的需求预测。应用基于围捕的算法处理TNPP具有巨大的技术和经济优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transmission network planning for realistic Egyptian systems via encircling prey based algorithms
: Transmission network planning problem (TNPP) is one of the pertinent issues of the planning activities in power systems. It aims to optimally pick out the routs, types, and number of the new installed lines to confront the expected future loading conditions. In this line, this study proposes a new economic model to the TNPP. The aim of the model is to find the optimal transmission routes at least investment and operating costs. Three recent algorithms called grey wolf optimization algorithm (GWOA), spotted hyena optimization algorithm (SHOA) and whale optimization algorithm (WOA) are developed to solve the TNPP. The concept of these algorithms is based on encircling prey operation. The competitive methods are investigated to find the optimal TNPP solution for two realistic Egyptian networks. The first tested network is the 66 kV West Delta Region (WDR) system while the second one is the extra high voltage (EHV) 500 kV system. Their demand forecasting is extracted forward to 2030 dependent upon the adaptive neuro-fuzzy inference system (ANFIS). Tremendous technical and economic advantages through application of the encircling prey-based algorithms to handle the TNPP.
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