{"title":"混合整数规划公式技术及其在机组承诺问题中的应用","authors":"A. Viswanath, L. Goel, Peng Wang","doi":"10.1109/ASSCC.2012.6523233","DOIUrl":null,"url":null,"abstract":"Unit Commitment (UC) problem with non-linear functions and probabilistic constraints are difficult to solve by standard optimization methods. This paper provides an introduction to mixed integer programming problem formulation techniques and its applications to UC problem. Use of binary variables to convert nonlinear functions and probabilistic constraints to a Mixed Integer linear programming (MILP) form are presented. This conversion helps in solving the UC problem by using commercially available MILP solvers.","PeriodicalId":341348,"journal":{"name":"2012 10th International Power & Energy Conference (IPEC)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Mixed integer programming formulation techniques and applications to Unit Commitment problem\",\"authors\":\"A. Viswanath, L. Goel, Peng Wang\",\"doi\":\"10.1109/ASSCC.2012.6523233\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unit Commitment (UC) problem with non-linear functions and probabilistic constraints are difficult to solve by standard optimization methods. This paper provides an introduction to mixed integer programming problem formulation techniques and its applications to UC problem. Use of binary variables to convert nonlinear functions and probabilistic constraints to a Mixed Integer linear programming (MILP) form are presented. This conversion helps in solving the UC problem by using commercially available MILP solvers.\",\"PeriodicalId\":341348,\"journal\":{\"name\":\"2012 10th International Power & Energy Conference (IPEC)\",\"volume\":\"85 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 10th International Power & Energy Conference (IPEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASSCC.2012.6523233\",\"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 10th International Power & Energy Conference (IPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASSCC.2012.6523233","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mixed integer programming formulation techniques and applications to Unit Commitment problem
Unit Commitment (UC) problem with non-linear functions and probabilistic constraints are difficult to solve by standard optimization methods. This paper provides an introduction to mixed integer programming problem formulation techniques and its applications to UC problem. Use of binary variables to convert nonlinear functions and probabilistic constraints to a Mixed Integer linear programming (MILP) form are presented. This conversion helps in solving the UC problem by using commercially available MILP solvers.
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