{"title":"A Stochastic Approach to Generate Short-Term Feed-in Profiles of Wind Power Plants","authors":"Sirkka Porada, Leonard Schulte, A. Moser","doi":"10.1109/SEGE52446.2021.9535108","DOIUrl":null,"url":null,"abstract":"The integration of wind turbines into the European power system poses new challenges for grid operations. One reason for this is the volatile feed-in behavior of wind turbines. Due to various meteorological influencing factors, feed-in profiles of wind turbines show not solely fluctuations in a hourly range, but also significant gradients in the timeframe of seconds to a few minutes. These short-term fluctuations of the power feed-in can cause local problems in the power system. Most studies address the generation of synthetic feed-in profiles with of temporal resolution of 15 till 60 minutes. To assess the impact of fluctuations in shorter timeframe, this paper focus on this paper focus on the generation of feed-in profiles with a resolution of 10 seconds. For this purpose, a stochastic method is developed generating feed-in profiles for wind turbines based on a Markov Chain Monte Carlo simulation. The generated feed-in profiles suitably represent the influence of meteorological phenomena in the seconds as well as in the hourly range.","PeriodicalId":438266,"journal":{"name":"2021 IEEE 9th International Conference on Smart Energy Grid Engineering (SEGE)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 9th International Conference on Smart Energy Grid Engineering (SEGE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SEGE52446.2021.9535108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The integration of wind turbines into the European power system poses new challenges for grid operations. One reason for this is the volatile feed-in behavior of wind turbines. Due to various meteorological influencing factors, feed-in profiles of wind turbines show not solely fluctuations in a hourly range, but also significant gradients in the timeframe of seconds to a few minutes. These short-term fluctuations of the power feed-in can cause local problems in the power system. Most studies address the generation of synthetic feed-in profiles with of temporal resolution of 15 till 60 minutes. To assess the impact of fluctuations in shorter timeframe, this paper focus on this paper focus on the generation of feed-in profiles with a resolution of 10 seconds. For this purpose, a stochastic method is developed generating feed-in profiles for wind turbines based on a Markov Chain Monte Carlo simulation. The generated feed-in profiles suitably represent the influence of meteorological phenomena in the seconds as well as in the hourly range.