{"title":"基于遗传算法的配电网扩展。第2部分。案例研究:IEEE-30测试系统","authors":"S. Kilyeni, C. Barbulescu, C. Oros, A. Deacu","doi":"10.1109/ICATE.2014.6972610","DOIUrl":null,"url":null,"abstract":"The renewable sources' influence regarding the distribution network expansion planning is tackled. The network expansion is discussed in two cases: with and without renewable sources. The obtained expansion solutions are analyzed and a final one is proposed by the authors. To achieve this goal network reconfiguration and N-1 contingecies are performed. The goal is to supply all the consumers for each operating condition. Also, the technical losses have to be minimized. The power flow is computed using conventional methods, but the optimal power flow and distribution network expansion are performed using genetic algorithms (GA). Thus, the authors have developed an own software tool in Matlab environment.","PeriodicalId":327050,"journal":{"name":"2014 International Conference on Applied and Theoretical Electricity (ICATE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GA based distribution network expansion. Part 2. Case study: IEEE-30 test system\",\"authors\":\"S. Kilyeni, C. Barbulescu, C. Oros, A. Deacu\",\"doi\":\"10.1109/ICATE.2014.6972610\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The renewable sources' influence regarding the distribution network expansion planning is tackled. The network expansion is discussed in two cases: with and without renewable sources. The obtained expansion solutions are analyzed and a final one is proposed by the authors. To achieve this goal network reconfiguration and N-1 contingecies are performed. The goal is to supply all the consumers for each operating condition. Also, the technical losses have to be minimized. The power flow is computed using conventional methods, but the optimal power flow and distribution network expansion are performed using genetic algorithms (GA). Thus, the authors have developed an own software tool in Matlab environment.\",\"PeriodicalId\":327050,\"journal\":{\"name\":\"2014 International Conference on Applied and Theoretical Electricity (ICATE)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Applied and Theoretical Electricity (ICATE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICATE.2014.6972610\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Applied and Theoretical Electricity (ICATE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICATE.2014.6972610","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
GA based distribution network expansion. Part 2. Case study: IEEE-30 test system
The renewable sources' influence regarding the distribution network expansion planning is tackled. The network expansion is discussed in two cases: with and without renewable sources. The obtained expansion solutions are analyzed and a final one is proposed by the authors. To achieve this goal network reconfiguration and N-1 contingecies are performed. The goal is to supply all the consumers for each operating condition. Also, the technical losses have to be minimized. The power flow is computed using conventional methods, but the optimal power flow and distribution network expansion are performed using genetic algorithms (GA). Thus, the authors have developed an own software tool in Matlab environment.