{"title":"基于需求响应的智能电网拥塞管理","authors":"J. Hazra, K. Das, D. Seetharam","doi":"10.1109/SmartGridComm.2012.6485968","DOIUrl":null,"url":null,"abstract":"This paper proposes a novel cost-effective congestion management (CM) scheme for smart grids through demand response (DR). In this congestion management, two objectives i.e. acceptable congestion and congestion cost including DR are optimized by choosing optimal mix of generation rescheduling and DR of participating buses by minimizing the impact on revenues and customer satisfaction. Participating generators for rescheduling and loads for DR are selected using an sensitivity index which combines both biding cost and sensitivity to alleviate the congestion. The scheme employs a meta-heuristic optimization technique called Ant Colony Optimization to optimize the individual options and uses a fuzzy satisfying technique to choose the best compromise solution from the set of Pareto optimal solutions. The proposed system has been evaluated on benchmark IEEE 30 bus test systems and the results of this evaluation are presented in this paper.","PeriodicalId":143915,"journal":{"name":"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Smart grid congestion management through demand response\",\"authors\":\"J. Hazra, K. Das, D. Seetharam\",\"doi\":\"10.1109/SmartGridComm.2012.6485968\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a novel cost-effective congestion management (CM) scheme for smart grids through demand response (DR). In this congestion management, two objectives i.e. acceptable congestion and congestion cost including DR are optimized by choosing optimal mix of generation rescheduling and DR of participating buses by minimizing the impact on revenues and customer satisfaction. Participating generators for rescheduling and loads for DR are selected using an sensitivity index which combines both biding cost and sensitivity to alleviate the congestion. The scheme employs a meta-heuristic optimization technique called Ant Colony Optimization to optimize the individual options and uses a fuzzy satisfying technique to choose the best compromise solution from the set of Pareto optimal solutions. The proposed system has been evaluated on benchmark IEEE 30 bus test systems and the results of this evaluation are presented in this paper.\",\"PeriodicalId\":143915,\"journal\":{\"name\":\"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)\",\"volume\":\"117 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SmartGridComm.2012.6485968\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SmartGridComm.2012.6485968","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Smart grid congestion management through demand response
This paper proposes a novel cost-effective congestion management (CM) scheme for smart grids through demand response (DR). In this congestion management, two objectives i.e. acceptable congestion and congestion cost including DR are optimized by choosing optimal mix of generation rescheduling and DR of participating buses by minimizing the impact on revenues and customer satisfaction. Participating generators for rescheduling and loads for DR are selected using an sensitivity index which combines both biding cost and sensitivity to alleviate the congestion. The scheme employs a meta-heuristic optimization technique called Ant Colony Optimization to optimize the individual options and uses a fuzzy satisfying technique to choose the best compromise solution from the set of Pareto optimal solutions. The proposed system has been evaluated on benchmark IEEE 30 bus test systems and the results of this evaluation are presented in this paper.