RETRACTED ARTICLE: Novel gooseneck barnacle optimization a nature inspired technique for optimizing reactive power within systems of power with electric vehicles
Abstract The significance of ORPD (Optimal Reactive Power Dispatch) cannot be emphasized within the operation of power systems, particularly according to the growing use of electric vehicles (EVs). Electric vehicles (EVs) have the potential to influence the power grid via their ability to augment po...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-90178-x |
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| author | Juntao Zhuang Chengwei Wang Qiong Cheng Samad Nourmohammadi Khalid A. Alnowibet |
| author_facet | Juntao Zhuang Chengwei Wang Qiong Cheng Samad Nourmohammadi Khalid A. Alnowibet |
| author_sort | Juntao Zhuang |
| collection | DOAJ |
| description | Abstract The significance of ORPD (Optimal Reactive Power Dispatch) cannot be emphasized within the operation of power systems, particularly according to the growing use of electric vehicles (EVs). Electric vehicles (EVs) have the potential to influence the power grid via their ability to augment power demand and function as distributed energy resources. The effective administration of Optimal Renewable Power Dispatch (ORPD) in conjunction with Electric Vehicle (EV) integration necessitates meticulous examination of charging schedules, battery capacity, and the desired state of charge. In the current paper, a novel optimizer known as the Novel Gooseneck Barnacle Optimization (NGBO) algorithm is introduced to address the ORPD problem within the presence of Electric Vehicles (EVs). The NGBO algorithm draws inspiration from the regular mating behavior of gooseneck barnacles involving self-fertilization and casting sperm. To evaluate its performance, the NGBO algorithm is applied to two standard exam systems, including the IEEE 118- and IEEE 57-system of bus, considering various scenarios of EV penetration. The experimental outcomes demonstrate the NGBO effectively mitigates active power loss and voltage variation in power systems, surpassing several existing metaheuristic optimization techniques by reducing power loss by up to 15% and voltage deviation by up to 10% compared to traditional methods, demonstrating the effectiveness of the method in handling EV-related uncertainties. |
| format | Article |
| id | doaj-art-baad68b4bfed4f1bbbbcc699245de83a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-baad68b4bfed4f1bbbbcc699245de83a2025-08-20T04:02:46ZengNature PortfolioScientific Reports2045-23222025-02-0115112110.1038/s41598-025-90178-xRETRACTED ARTICLE: Novel gooseneck barnacle optimization a nature inspired technique for optimizing reactive power within systems of power with electric vehiclesJuntao Zhuang0Chengwei Wang1Qiong Cheng2Samad Nourmohammadi3Khalid A. Alnowibet4Geely Automotive Institute, Hangzhou Vocational & Technical CollegeSeres Automotive Co., Ltd.Geely Automotive Institute, Hangzhou Vocational & Technical CollegeAnkara Yildirim Beyazit UniversityStatistics and Operations Research Department, College of Science, King Saud UniversityAbstract The significance of ORPD (Optimal Reactive Power Dispatch) cannot be emphasized within the operation of power systems, particularly according to the growing use of electric vehicles (EVs). Electric vehicles (EVs) have the potential to influence the power grid via their ability to augment power demand and function as distributed energy resources. The effective administration of Optimal Renewable Power Dispatch (ORPD) in conjunction with Electric Vehicle (EV) integration necessitates meticulous examination of charging schedules, battery capacity, and the desired state of charge. In the current paper, a novel optimizer known as the Novel Gooseneck Barnacle Optimization (NGBO) algorithm is introduced to address the ORPD problem within the presence of Electric Vehicles (EVs). The NGBO algorithm draws inspiration from the regular mating behavior of gooseneck barnacles involving self-fertilization and casting sperm. To evaluate its performance, the NGBO algorithm is applied to two standard exam systems, including the IEEE 118- and IEEE 57-system of bus, considering various scenarios of EV penetration. The experimental outcomes demonstrate the NGBO effectively mitigates active power loss and voltage variation in power systems, surpassing several existing metaheuristic optimization techniques by reducing power loss by up to 15% and voltage deviation by up to 10% compared to traditional methods, demonstrating the effectiveness of the method in handling EV-related uncertainties.https://doi.org/10.1038/s41598-025-90178-xElectric vehiclesGooseneck barnacle optimizationMetaheuristic optimizationOptimal reactive power dispatchPower system operationPower loss reduction |
| spellingShingle | Juntao Zhuang Chengwei Wang Qiong Cheng Samad Nourmohammadi Khalid A. Alnowibet RETRACTED ARTICLE: Novel gooseneck barnacle optimization a nature inspired technique for optimizing reactive power within systems of power with electric vehicles Scientific Reports Electric vehicles Gooseneck barnacle optimization Metaheuristic optimization Optimal reactive power dispatch Power system operation Power loss reduction |
| title | RETRACTED ARTICLE: Novel gooseneck barnacle optimization a nature inspired technique for optimizing reactive power within systems of power with electric vehicles |
| title_full | RETRACTED ARTICLE: Novel gooseneck barnacle optimization a nature inspired technique for optimizing reactive power within systems of power with electric vehicles |
| title_fullStr | RETRACTED ARTICLE: Novel gooseneck barnacle optimization a nature inspired technique for optimizing reactive power within systems of power with electric vehicles |
| title_full_unstemmed | RETRACTED ARTICLE: Novel gooseneck barnacle optimization a nature inspired technique for optimizing reactive power within systems of power with electric vehicles |
| title_short | RETRACTED ARTICLE: Novel gooseneck barnacle optimization a nature inspired technique for optimizing reactive power within systems of power with electric vehicles |
| title_sort | retracted article novel gooseneck barnacle optimization a nature inspired technique for optimizing reactive power within systems of power with electric vehicles |
| topic | Electric vehicles Gooseneck barnacle optimization Metaheuristic optimization Optimal reactive power dispatch Power system operation Power loss reduction |
| url | https://doi.org/10.1038/s41598-025-90178-x |
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