{"title":"改进遗传算法优化DG分配和分级","authors":"U. R. Babu, V. K. Reddy, S. TaraKalyani","doi":"10.1109/EESCO.2015.7253775","DOIUrl":null,"url":null,"abstract":"This Paper presents a methodology for optimal distributed generation (DG) allocation and sizing in distribution systems, in order to reduce the electrical distribution power losses and to guarantee acceptable savings and voltage improvements. The optimization process is evaluated by the combination of genetic algorithm (GA) techniques with optimal power flow to evaluate DG impacts in savings, losses and voltage profile. The fitness evaluation function that drives the GA to the solution is the Network Performance Enhancement Index (NPEI). The objective is to maximize the DG capacity with the reduction in the system loss, by knowing the total number of DG units that the user is interested to connect. The new and fast algorithm is developed for solving the power flow for radial distribution feeders taking into account embedded distribution generations. And also, new approximation formulas are proposed to reduce the number of iterations. The economic constraints are also considered by accounting for the savings obtained when the DG is inserted into the system.","PeriodicalId":305584,"journal":{"name":"2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Modified GA for optimal DG allocation and sizing\",\"authors\":\"U. R. Babu, V. K. Reddy, S. TaraKalyani\",\"doi\":\"10.1109/EESCO.2015.7253775\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This Paper presents a methodology for optimal distributed generation (DG) allocation and sizing in distribution systems, in order to reduce the electrical distribution power losses and to guarantee acceptable savings and voltage improvements. The optimization process is evaluated by the combination of genetic algorithm (GA) techniques with optimal power flow to evaluate DG impacts in savings, losses and voltage profile. The fitness evaluation function that drives the GA to the solution is the Network Performance Enhancement Index (NPEI). The objective is to maximize the DG capacity with the reduction in the system loss, by knowing the total number of DG units that the user is interested to connect. The new and fast algorithm is developed for solving the power flow for radial distribution feeders taking into account embedded distribution generations. And also, new approximation formulas are proposed to reduce the number of iterations. The economic constraints are also considered by accounting for the savings obtained when the DG is inserted into the system.\",\"PeriodicalId\":305584,\"journal\":{\"name\":\"2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EESCO.2015.7253775\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EESCO.2015.7253775","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This Paper presents a methodology for optimal distributed generation (DG) allocation and sizing in distribution systems, in order to reduce the electrical distribution power losses and to guarantee acceptable savings and voltage improvements. The optimization process is evaluated by the combination of genetic algorithm (GA) techniques with optimal power flow to evaluate DG impacts in savings, losses and voltage profile. The fitness evaluation function that drives the GA to the solution is the Network Performance Enhancement Index (NPEI). The objective is to maximize the DG capacity with the reduction in the system loss, by knowing the total number of DG units that the user is interested to connect. The new and fast algorithm is developed for solving the power flow for radial distribution feeders taking into account embedded distribution generations. And also, new approximation formulas are proposed to reduce the number of iterations. The economic constraints are also considered by accounting for the savings obtained when the DG is inserted into the system.