{"title":"基于粒子群优化的非管制市场竞价策略研究","authors":"J. Kumar, Shaik Jameer pasha, D. Kumar","doi":"10.1109/INDCON.2010.5712648","DOIUrl":null,"url":null,"abstract":"In this paper, particle swarm optimization method is proposed to determine the optimal bidding strategy in competitive electricity market. The market includes Generating companies (Genco's), large consumers who participate in demand side bidding, and small consumers whose demand is present in aggregate form. The effectiveness of the proposed method is tested with IEEE-30 bus system in which six generators and two large consumers are considered. Results are compared with the solutions obtained using the Genetic algorithm and Monte Carlo method.","PeriodicalId":109071,"journal":{"name":"2010 Annual IEEE India Conference (INDICON)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Strategic bidding in deregulated market using particle swarm optimization\",\"authors\":\"J. Kumar, Shaik Jameer pasha, D. Kumar\",\"doi\":\"10.1109/INDCON.2010.5712648\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, particle swarm optimization method is proposed to determine the optimal bidding strategy in competitive electricity market. The market includes Generating companies (Genco's), large consumers who participate in demand side bidding, and small consumers whose demand is present in aggregate form. The effectiveness of the proposed method is tested with IEEE-30 bus system in which six generators and two large consumers are considered. Results are compared with the solutions obtained using the Genetic algorithm and Monte Carlo method.\",\"PeriodicalId\":109071,\"journal\":{\"name\":\"2010 Annual IEEE India Conference (INDICON)\",\"volume\":\"150 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Annual IEEE India Conference (INDICON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDCON.2010.5712648\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Annual IEEE India Conference (INDICON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDCON.2010.5712648","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Strategic bidding in deregulated market using particle swarm optimization
In this paper, particle swarm optimization method is proposed to determine the optimal bidding strategy in competitive electricity market. The market includes Generating companies (Genco's), large consumers who participate in demand side bidding, and small consumers whose demand is present in aggregate form. The effectiveness of the proposed method is tested with IEEE-30 bus system in which six generators and two large consumers are considered. Results are compared with the solutions obtained using the Genetic algorithm and Monte Carlo method.