{"title":"基于改进蚁狮优化算法的随机可再生能源最优潮流分析","authors":"A. Saini, O. P. Rahi","doi":"10.1177/0309524X231169295","DOIUrl":null,"url":null,"abstract":"The optimal power flow is one of the major area in economic and efficient dispatch of electric power. This research article presents a swarm-based optimization algorithm, known as Modified Ant Lion Optimization (MALO) algorithm to solve optimal power flow (OPF) problems. A holistic approach has been used including thermal, wind, solar, and hydro power plants and results shows minimization of cost, losses, and voltage deviation that amounts for novelty of this paper. The MALO algorithm is validated on IEEE 30-bus and IEEE 57-bus systems, and the result are compared with the state-of-the-art algorithms. It is found that proposed algorithm provides better OPF solutions when compared with other mentioned algorithm namely Antlion Optimization, Graw Wolf Optimization, Salp Swarm Algorithm, and Grasshopper Optimization. The MALO algorithm is useful for electric utilities, researcher, and power system operation.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"75 1","pages":"947 - 972"},"PeriodicalIF":1.5000,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimal power flow analysis including stochastic renewable energy sources using modified ant lion optimization algorithm\",\"authors\":\"A. Saini, O. P. Rahi\",\"doi\":\"10.1177/0309524X231169295\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The optimal power flow is one of the major area in economic and efficient dispatch of electric power. This research article presents a swarm-based optimization algorithm, known as Modified Ant Lion Optimization (MALO) algorithm to solve optimal power flow (OPF) problems. A holistic approach has been used including thermal, wind, solar, and hydro power plants and results shows minimization of cost, losses, and voltage deviation that amounts for novelty of this paper. The MALO algorithm is validated on IEEE 30-bus and IEEE 57-bus systems, and the result are compared with the state-of-the-art algorithms. It is found that proposed algorithm provides better OPF solutions when compared with other mentioned algorithm namely Antlion Optimization, Graw Wolf Optimization, Salp Swarm Algorithm, and Grasshopper Optimization. The MALO algorithm is useful for electric utilities, researcher, and power system operation.\",\"PeriodicalId\":51570,\"journal\":{\"name\":\"Wind Engineering\",\"volume\":\"75 1\",\"pages\":\"947 - 972\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Wind Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/0309524X231169295\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wind Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/0309524X231169295","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
摘要
最优潮流是电力经济高效调度的重要问题之一。本文提出了一种基于群的优化算法——改进蚁狮优化算法(MALO)来解决最优潮流(OPF)问题。采用了包括热能、风能、太阳能和水力发电厂在内的整体方法,结果显示成本、损失和电压偏差最小化,这是本文的新颖性。在IEEE 30总线和IEEE 57总线系统上对MALO算法进行了验证,并与现有算法进行了比较。与Antlion Optimization、Graw Wolf Optimization、Salp Swarm algorithm、Grasshopper Optimization等算法相比,本文算法提供了更好的OPF解。MALO算法对电力公司、研究人员和电力系统运行都很有用。
Optimal power flow analysis including stochastic renewable energy sources using modified ant lion optimization algorithm
The optimal power flow is one of the major area in economic and efficient dispatch of electric power. This research article presents a swarm-based optimization algorithm, known as Modified Ant Lion Optimization (MALO) algorithm to solve optimal power flow (OPF) problems. A holistic approach has been used including thermal, wind, solar, and hydro power plants and results shows minimization of cost, losses, and voltage deviation that amounts for novelty of this paper. The MALO algorithm is validated on IEEE 30-bus and IEEE 57-bus systems, and the result are compared with the state-of-the-art algorithms. It is found that proposed algorithm provides better OPF solutions when compared with other mentioned algorithm namely Antlion Optimization, Graw Wolf Optimization, Salp Swarm Algorithm, and Grasshopper Optimization. The MALO algorithm is useful for electric utilities, researcher, and power system operation.
期刊介绍:
Having been in continuous publication since 1977, Wind Engineering is the oldest and most authoritative English language journal devoted entirely to the technology of wind energy. Under the direction of a distinguished editor and editorial board, Wind Engineering appears bimonthly with fully refereed contributions from active figures in the field, book notices, and summaries of the more interesting papers from other sources. Papers are published in Wind Engineering on: the aerodynamics of rotors and blades; machine subsystems and components; design; test programmes; power generation and transmission; measuring and recording techniques; installations and applications; and economic, environmental and legal aspects.