{"title":"Investigation of optimal power flow solution techniques considering stochastic renewable energy sources: Review and analysis","authors":"Ankur Maheshwari, Y. Sood, Supriya Jaiswal","doi":"10.1177/0309524X221124000","DOIUrl":null,"url":null,"abstract":"Increased penetration of renewable energy sources (RESs) in power system networks poses several challenges in system planning and management due to their uncertain and non-dispatchable nature. Consequently, this paper presents a thorough and precise review of recent solution methodologies for solving the optimal power flow (OPF) problems incorporated with stochastic RESs based on multiple peer-reviewed research publications in reputed journals. The Teaching Learning Based Optimization algorithm has been discussed and implemented to solve the OPF problem considering solar photovoltaic, wind turbine, and tidal energy systems. Weibull, Lognormal, and Gumbel probability density functions representing the uncertainty associated with the availability of wind speed, solar irradiance, and tidal energy systems, respectively. The results obtained from the proposed technique validate its novelty regarding OPF problems like minimization of operating cost, power loss in transmission lines, enhancement of voltage profile, and voltage stability. The proposed solution technique for OPF problems is tested on a modified IEEE 30-bus test system. Thus, this study assists in understanding the OPF problem for new researchers concerned with this domain and also gives the idea of implementing nature-inspired optimization algorithms on a defined test system to solve the OPF problem.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":"103 1","pages":"464 - 490"},"PeriodicalIF":1.5000,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wind Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/0309524X221124000","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: 3
Abstract
Increased penetration of renewable energy sources (RESs) in power system networks poses several challenges in system planning and management due to their uncertain and non-dispatchable nature. Consequently, this paper presents a thorough and precise review of recent solution methodologies for solving the optimal power flow (OPF) problems incorporated with stochastic RESs based on multiple peer-reviewed research publications in reputed journals. The Teaching Learning Based Optimization algorithm has been discussed and implemented to solve the OPF problem considering solar photovoltaic, wind turbine, and tidal energy systems. Weibull, Lognormal, and Gumbel probability density functions representing the uncertainty associated with the availability of wind speed, solar irradiance, and tidal energy systems, respectively. The results obtained from the proposed technique validate its novelty regarding OPF problems like minimization of operating cost, power loss in transmission lines, enhancement of voltage profile, and voltage stability. The proposed solution technique for OPF problems is tested on a modified IEEE 30-bus test system. Thus, this study assists in understanding the OPF problem for new researchers concerned with this domain and also gives the idea of implementing nature-inspired optimization algorithms on a defined test system to solve the OPF problem.
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.