{"title":"具有多种燃料成本函数的统一裸骨粒子群经济调度","authors":"Chang-Huang Chen, J. Sheu","doi":"10.1109/APL.2011.6111106","DOIUrl":null,"url":null,"abstract":"Economic generating electric power is a very important issue for power utilities, especially in current state of fuel cost booming. In this paper, the unified bare bone particle swarm algorithm (UBPSO), which integrates local and global learning strategies, is proposed to solve economic dispatch problems with multiple fuel options. Tested on three systems with different number of units has verified that the proposed method can obtain better solution compared with other methods found in literature.","PeriodicalId":105667,"journal":{"name":"2011 7th Asia-Pacific International Conference on Lightning","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Unified bare bone particle swarm for economic dispatch with multiple fuel cost functions\",\"authors\":\"Chang-Huang Chen, J. Sheu\",\"doi\":\"10.1109/APL.2011.6111106\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Economic generating electric power is a very important issue for power utilities, especially in current state of fuel cost booming. In this paper, the unified bare bone particle swarm algorithm (UBPSO), which integrates local and global learning strategies, is proposed to solve economic dispatch problems with multiple fuel options. Tested on three systems with different number of units has verified that the proposed method can obtain better solution compared with other methods found in literature.\",\"PeriodicalId\":105667,\"journal\":{\"name\":\"2011 7th Asia-Pacific International Conference on Lightning\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 7th Asia-Pacific International Conference on Lightning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APL.2011.6111106\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 7th Asia-Pacific International Conference on Lightning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APL.2011.6111106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Unified bare bone particle swarm for economic dispatch with multiple fuel cost functions
Economic generating electric power is a very important issue for power utilities, especially in current state of fuel cost booming. In this paper, the unified bare bone particle swarm algorithm (UBPSO), which integrates local and global learning strategies, is proposed to solve economic dispatch problems with multiple fuel options. Tested on three systems with different number of units has verified that the proposed method can obtain better solution compared with other methods found in literature.