{"title":"基于NSGA-II遗传算法的实际径向配电网分布式发电(DG)和静态无功补偿器(SVC)定位的技术经济优化","authors":"Oloulade Arouna, Moukengue Imano Adolphe, Adekambi O. Robert, Amoussou Zinsou Kenneth, Vianou Antoine, Badarou Ramanou, Tamadaho Herman, Dangbedji Celestin","doi":"10.1109/PowerAfrica.2019.8928631","DOIUrl":null,"url":null,"abstract":"Network manager overuse their energy system considering the competition linked to energy sectors economic organization. That induces excessive losses and consequently the degradation of the quality of the power supply. This work consists of optimizing a distribution network of SBEE through the optimal positioning of distributed generator (PV) and SVC in a 138 node HTA departure. Optimization criteria such as losses, installation costs and voltage deviation have been considered and integrated in the NSGA-II algorithm. The algorithms have led to an optimal positioning of a PV system with 1.03MW of power at node 69 and two SVCs of respective powers of 2.07 MVar at node 58 and 2.05 MVar at node 82 of the network. The optimal cost obtained from the simulation is 2,729,000(USD). The NSGA-II algorithm is then a very robust optimization tool, efficient and can be used to optimize electrical grid.","PeriodicalId":308661,"journal":{"name":"2019 IEEE PES/IAS PowerAfrica","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Technico-economic optimization of Distributed Generation (DG) and Static Var Compensator (SVC) positioning in a real radial distribution network using the NSGA-II genetic algorithm\",\"authors\":\"Oloulade Arouna, Moukengue Imano Adolphe, Adekambi O. Robert, Amoussou Zinsou Kenneth, Vianou Antoine, Badarou Ramanou, Tamadaho Herman, Dangbedji Celestin\",\"doi\":\"10.1109/PowerAfrica.2019.8928631\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Network manager overuse their energy system considering the competition linked to energy sectors economic organization. That induces excessive losses and consequently the degradation of the quality of the power supply. This work consists of optimizing a distribution network of SBEE through the optimal positioning of distributed generator (PV) and SVC in a 138 node HTA departure. Optimization criteria such as losses, installation costs and voltage deviation have been considered and integrated in the NSGA-II algorithm. The algorithms have led to an optimal positioning of a PV system with 1.03MW of power at node 69 and two SVCs of respective powers of 2.07 MVar at node 58 and 2.05 MVar at node 82 of the network. The optimal cost obtained from the simulation is 2,729,000(USD). The NSGA-II algorithm is then a very robust optimization tool, efficient and can be used to optimize electrical grid.\",\"PeriodicalId\":308661,\"journal\":{\"name\":\"2019 IEEE PES/IAS PowerAfrica\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE PES/IAS PowerAfrica\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PowerAfrica.2019.8928631\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE PES/IAS PowerAfrica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PowerAfrica.2019.8928631","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Technico-economic optimization of Distributed Generation (DG) and Static Var Compensator (SVC) positioning in a real radial distribution network using the NSGA-II genetic algorithm
Network manager overuse their energy system considering the competition linked to energy sectors economic organization. That induces excessive losses and consequently the degradation of the quality of the power supply. This work consists of optimizing a distribution network of SBEE through the optimal positioning of distributed generator (PV) and SVC in a 138 node HTA departure. Optimization criteria such as losses, installation costs and voltage deviation have been considered and integrated in the NSGA-II algorithm. The algorithms have led to an optimal positioning of a PV system with 1.03MW of power at node 69 and two SVCs of respective powers of 2.07 MVar at node 58 and 2.05 MVar at node 82 of the network. The optimal cost obtained from the simulation is 2,729,000(USD). The NSGA-II algorithm is then a very robust optimization tool, efficient and can be used to optimize electrical grid.