{"title":"Optimization based bidding strategies in the deregulated market","authors":"Daoyuan Zhang, Yajun Wang, P. Luh","doi":"10.1109/PICA.1999.779386","DOIUrl":null,"url":null,"abstract":"With the deregulation of electric power systems, market participants are facing an important task of bidding energy to an independent system operator (ISO). This paper presents a model and a method for optimization-based bidding and self-scheduling where a utility bids part of its energy and self-schedules the rest as in New England. The model considers ISO bid selections and uncertain bidding information of other market participants. With appropriately simplified bidding and ISO models, closed-form ISO solutions are first obtained. These solutions are then plugged into the utility's bidding and self-scheduling model which is solved by using Lagrangian relaxation. Testing results show that the method effectively solves the problem with reasonable amount of CPU time.","PeriodicalId":113146,"journal":{"name":"Proceedings of the 21st International Conference on Power Industry Computer Applications. Connecting Utilities. PICA 99. To the Millennium and Beyond (Cat. No.99CH36351)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"205","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 21st International Conference on Power Industry Computer Applications. Connecting Utilities. PICA 99. To the Millennium and Beyond (Cat. No.99CH36351)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PICA.1999.779386","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 205
Abstract
With the deregulation of electric power systems, market participants are facing an important task of bidding energy to an independent system operator (ISO). This paper presents a model and a method for optimization-based bidding and self-scheduling where a utility bids part of its energy and self-schedules the rest as in New England. The model considers ISO bid selections and uncertain bidding information of other market participants. With appropriately simplified bidding and ISO models, closed-form ISO solutions are first obtained. These solutions are then plugged into the utility's bidding and self-scheduling model which is solved by using Lagrangian relaxation. Testing results show that the method effectively solves the problem with reasonable amount of CPU time.