{"title":"Reliability assessment of power systems with wind power generation","authors":"S. Wang, M. Baran","doi":"10.1109/PES.2010.5590079","DOIUrl":null,"url":null,"abstract":"The paper focuses on reliability assessment of power systems with wind power generation. A Monte Carlo based production cost simulation model is introduced in the paper. The model closely simulates actual system operation processes and takes system random behaviors into account. A simplified unit commitment method is created to fit the simulation for reliability evaluation purpose. The effects of wind forecast error is addressed in the model by applying forecasted value in day-ahead unit commitment and actual value in real-time operation. An Auto-Regressive Moving Average (ARMA) based process is designed to automatically perform day-ahead hourly wind generation forecasting through the simulation period. Numerical results of a 50-unit system case study are presented.","PeriodicalId":177545,"journal":{"name":"IEEE PES General Meeting","volume":"1 10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE PES General Meeting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PES.2010.5590079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
The paper focuses on reliability assessment of power systems with wind power generation. A Monte Carlo based production cost simulation model is introduced in the paper. The model closely simulates actual system operation processes and takes system random behaviors into account. A simplified unit commitment method is created to fit the simulation for reliability evaluation purpose. The effects of wind forecast error is addressed in the model by applying forecasted value in day-ahead unit commitment and actual value in real-time operation. An Auto-Regressive Moving Average (ARMA) based process is designed to automatically perform day-ahead hourly wind generation forecasting through the simulation period. Numerical results of a 50-unit system case study are presented.
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