Prisma Megantoro , Syahirah Abd Halim , Nor Azwan Mohamed Kamari , Lilik Jamilatul Awalin , Ramizi Mohamed , Hazwani Mohd Rosli
{"title":"An enhanced multi-objective reactive power dispatch for hybrid Wind-Solar power system using Archimedes optimization algorithm","authors":"Prisma Megantoro , Syahirah Abd Halim , Nor Azwan Mohamed Kamari , Lilik Jamilatul Awalin , Ramizi Mohamed , Hazwani Mohd Rosli","doi":"10.1016/j.ijepes.2025.110676","DOIUrl":null,"url":null,"abstract":"<div><div>Optimal reactive power dispatch (ORPD) is essential for addressing power system challenges related to distributed generation (DG), particularly from renewable energy (RE) sources such as wind and solar. The intermittent nature and uncertainty of these energy sources, influenced by varying wind speeds and solar irradiation, complicate their integration into power systems. This paper proposes a solution to the ORPD problem in systems with RE-DG integration using the Archimedes Optimization Algorithm (AOA). The uncertainties of wind and solar power generation were modelled using Weibull and lognormal probability density functions (PDFs), respectively, and the optimization model was tested using a scenario-based method. The AOA was applied to the IEEE 57 bus system to minimize power loss, voltage deviation, and voltage stability index (VSI). The results demonstrated that AOA contributed to a 15.7% reduction in power loss, and an 83.9% enhancement in VSI compared to the base case. In the multi-objective optimization scenario, AOA achieved a 7.1% reduction in power loss, with an additional 11.6% improvement upon the integration of DGs. The performance of AOA was also compared with other metaheuristic algorithms, demonstrating superior results in terms of tracking accuracy and convergence speed. AOA outperformed the multi-objective ant lion optimization (MOALO) and the Levy-based Interior Search Algorithm (LISA) in terms of power loss reduction and voltage stability. AOA achieved a 1.83% lower power loss and a 29.67% lower VSI compared to MOALO. When compared to LISA, AOA achieved a 1.68% lower power loss, demonstrating its superior optimization capabilities. These findings confirm that AOA is a highly effective method for solving the ORPD problem, accounting for renewable energy uncertainties and improving overall system performance.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110676"},"PeriodicalIF":5.0000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Electrical Power & Energy Systems","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0142061525002273","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Optimal reactive power dispatch (ORPD) is essential for addressing power system challenges related to distributed generation (DG), particularly from renewable energy (RE) sources such as wind and solar. The intermittent nature and uncertainty of these energy sources, influenced by varying wind speeds and solar irradiation, complicate their integration into power systems. This paper proposes a solution to the ORPD problem in systems with RE-DG integration using the Archimedes Optimization Algorithm (AOA). The uncertainties of wind and solar power generation were modelled using Weibull and lognormal probability density functions (PDFs), respectively, and the optimization model was tested using a scenario-based method. The AOA was applied to the IEEE 57 bus system to minimize power loss, voltage deviation, and voltage stability index (VSI). The results demonstrated that AOA contributed to a 15.7% reduction in power loss, and an 83.9% enhancement in VSI compared to the base case. In the multi-objective optimization scenario, AOA achieved a 7.1% reduction in power loss, with an additional 11.6% improvement upon the integration of DGs. The performance of AOA was also compared with other metaheuristic algorithms, demonstrating superior results in terms of tracking accuracy and convergence speed. AOA outperformed the multi-objective ant lion optimization (MOALO) and the Levy-based Interior Search Algorithm (LISA) in terms of power loss reduction and voltage stability. AOA achieved a 1.83% lower power loss and a 29.67% lower VSI compared to MOALO. When compared to LISA, AOA achieved a 1.68% lower power loss, demonstrating its superior optimization capabilities. These findings confirm that AOA is a highly effective method for solving the ORPD problem, accounting for renewable energy uncertainties and improving overall system performance.
期刊介绍:
The journal covers theoretical developments in electrical power and energy systems and their applications. The coverage embraces: generation and network planning; reliability; long and short term operation; expert systems; neural networks; object oriented systems; system control centres; database and information systems; stock and parameter estimation; system security and adequacy; network theory, modelling and computation; small and large system dynamics; dynamic model identification; on-line control including load and switching control; protection; distribution systems; energy economics; impact of non-conventional systems; and man-machine interfaces.
As well as original research papers, the journal publishes short contributions, book reviews and conference reports. All papers are peer-reviewed by at least two referees.