Enhanced skill optimization algorithm: Solution to the stochastic reactive power dispatch framework with optimal inclusion of renewable resources using large‐scale network
Abstract Optimal reactive power dispatch (ORPD) is taken as a vital problem related to electric power networks for economic and control operations. Nowadays, thermal generators are no longer utilized and renewable resources (RERs) have been integrated owing to their marvellous benefits. The integrat...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.13167 |
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| author | Noor Habib Khan Yong Wang Raheela Jamal Sheeraz Iqbal Mohamed Ebeed Yazeed Yasin Ghadi Z. M. S. Elbarbary |
| author_facet | Noor Habib Khan Yong Wang Raheela Jamal Sheeraz Iqbal Mohamed Ebeed Yazeed Yasin Ghadi Z. M. S. Elbarbary |
| author_sort | Noor Habib Khan |
| collection | DOAJ |
| description | Abstract Optimal reactive power dispatch (ORPD) is taken as a vital problem related to electric power networks for economic and control operations. Nowadays, thermal generators are no longer utilized and renewable resources (RERs) have been integrated owing to their marvellous benefits. The integration of RERs into power networks is considered as a strenuous imposition due to their uncertainties. The objective is to determine the placement of four wind and four PV units into large‐scale 118‐bus network to reduce expected power losses. The normal, lognormal, and Weibull distributions are utilized to model system uncertainties, while Monte‐Carlo simulation and reduction‐based approaches are utilized to generate the novel set of optimal scenarios. To avoid stagnation problems in skilled optimization algorithm (SOA), three strategies such as fitness‐distance balance selection, mutation, and gorilla troops‐based approaches are utilized to improve overall strength of SOA. Effectiveness of ESOA is proved via statistical and non‐parametric analysis using benchmark functions, the results are further compared with other optimization techniques. The proposed ESOA is also used to resolve the deterministic and stochastic ORPD frameworks to reduce power losses and expected power losses. By incorporation of RERs into the stochastic ORPD framework can saved the expected power losses around 24.01%. |
| format | Article |
| id | doaj-art-0748bb0c0c7a49f79a93bf1967543858 |
| institution | OA Journals |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-0748bb0c0c7a49f79a93bf19675438582025-08-20T02:32:05ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118S14565458310.1049/rpg2.13167Enhanced skill optimization algorithm: Solution to the stochastic reactive power dispatch framework with optimal inclusion of renewable resources using large‐scale networkNoor Habib Khan0Yong Wang1Raheela Jamal2Sheeraz Iqbal3Mohamed Ebeed4Yazeed Yasin Ghadi5Z. M. S. Elbarbary6Department of New Energy North China Electric Power University Beijing ChinaDepartment of New Energy North China Electric Power University Beijing ChinaDepartment of New Energy North China Electric Power University Beijing ChinaDepartment of Electrical Engineering University of Azad Jammu and Kashmir Muzaffarabad, AJK PakistanElectrical Department, Faculty of Electrical Engineering Sohag University Sohag EgyptDepartment of Software Engineering, Computer Science Al Ain University Abu Dhabi UAEDepartment of Industrial Engineering King Khalid University Abha Saudi ArabiaAbstract Optimal reactive power dispatch (ORPD) is taken as a vital problem related to electric power networks for economic and control operations. Nowadays, thermal generators are no longer utilized and renewable resources (RERs) have been integrated owing to their marvellous benefits. The integration of RERs into power networks is considered as a strenuous imposition due to their uncertainties. The objective is to determine the placement of four wind and four PV units into large‐scale 118‐bus network to reduce expected power losses. The normal, lognormal, and Weibull distributions are utilized to model system uncertainties, while Monte‐Carlo simulation and reduction‐based approaches are utilized to generate the novel set of optimal scenarios. To avoid stagnation problems in skilled optimization algorithm (SOA), three strategies such as fitness‐distance balance selection, mutation, and gorilla troops‐based approaches are utilized to improve overall strength of SOA. Effectiveness of ESOA is proved via statistical and non‐parametric analysis using benchmark functions, the results are further compared with other optimization techniques. The proposed ESOA is also used to resolve the deterministic and stochastic ORPD frameworks to reduce power losses and expected power losses. By incorporation of RERs into the stochastic ORPD framework can saved the expected power losses around 24.01%.https://doi.org/10.1049/rpg2.13167renewable energy sourcessolar powertransmission networkswind power |
| spellingShingle | Noor Habib Khan Yong Wang Raheela Jamal Sheeraz Iqbal Mohamed Ebeed Yazeed Yasin Ghadi Z. M. S. Elbarbary Enhanced skill optimization algorithm: Solution to the stochastic reactive power dispatch framework with optimal inclusion of renewable resources using large‐scale network IET Renewable Power Generation renewable energy sources solar power transmission networks wind power |
| title | Enhanced skill optimization algorithm: Solution to the stochastic reactive power dispatch framework with optimal inclusion of renewable resources using large‐scale network |
| title_full | Enhanced skill optimization algorithm: Solution to the stochastic reactive power dispatch framework with optimal inclusion of renewable resources using large‐scale network |
| title_fullStr | Enhanced skill optimization algorithm: Solution to the stochastic reactive power dispatch framework with optimal inclusion of renewable resources using large‐scale network |
| title_full_unstemmed | Enhanced skill optimization algorithm: Solution to the stochastic reactive power dispatch framework with optimal inclusion of renewable resources using large‐scale network |
| title_short | Enhanced skill optimization algorithm: Solution to the stochastic reactive power dispatch framework with optimal inclusion of renewable resources using large‐scale network |
| title_sort | enhanced skill optimization algorithm solution to the stochastic reactive power dispatch framework with optimal inclusion of renewable resources using large scale network |
| topic | renewable energy sources solar power transmission networks wind power |
| url | https://doi.org/10.1049/rpg2.13167 |
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