{"title":"Optimal Bidding Strategies for Load Server Entities in Electric Power Markets","authors":"G. Ghanavati, S. Esmaeili","doi":"10.1109/ICCEE.2008.152","DOIUrl":null,"url":null,"abstract":"In this paper, a method for developing optimal bidding strategies for load server entities (LSE) is presented. The market structure consists of a day-ahead market and a real time market. The LSE's objective is to minimize the total cost of purchasing power from two markets. Given the expected demand and the two market price forecasts, the quantity which should be purchased in each market and demand bid curve are derived. The problem is formulated as a stochastic optimization problem because demand of load server entities and prices of electricity markets are uncertain. The problem is solved by Monte-carlo simulation, and a numerical simulation is performed using California power market data.","PeriodicalId":365473,"journal":{"name":"2008 International Conference on Computer and Electrical Engineering","volume":"53 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Computer and Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCEE.2008.152","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, a method for developing optimal bidding strategies for load server entities (LSE) is presented. The market structure consists of a day-ahead market and a real time market. The LSE's objective is to minimize the total cost of purchasing power from two markets. Given the expected demand and the two market price forecasts, the quantity which should be purchased in each market and demand bid curve are derived. The problem is formulated as a stochastic optimization problem because demand of load server entities and prices of electricity markets are uncertain. The problem is solved by Monte-carlo simulation, and a numerical simulation is performed using California power market data.
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