{"title":"Real-time optimal power flow under wind energy penetration-Part II: Implementation","authors":"E. Mohagheghi, A. Gabash, Pu Li","doi":"10.1109/EEEIC.2016.7555465","DOIUrl":null,"url":null,"abstract":"In this paper (Part II) we implement the prediction-updating approach developed in Part I to address fast changes in wind power generation when solving a complex realtime optimal power flow (RT-OPF) problem. The approach considers essential scenarios around forecasted wind power values in a moving prediction horizon (120 seconds). The individual optimal power flow problems corresponding to these scenarios are solved in parallel using a multi-processor server. Then the operation strategy is updated in a short sampling time (every 20 seconds) considering real wind power values. The RT-OPF problem is formulated considering both technical and economic aspects simultaneously. The RT-OPF is implemented on a 41-bus medium-voltage distribution network with two wind stations. The results show the benefits of the proposed approach and highlight further challenges of RT-OPF.","PeriodicalId":246856,"journal":{"name":"2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EEEIC.2016.7555465","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
In this paper (Part II) we implement the prediction-updating approach developed in Part I to address fast changes in wind power generation when solving a complex realtime optimal power flow (RT-OPF) problem. The approach considers essential scenarios around forecasted wind power values in a moving prediction horizon (120 seconds). The individual optimal power flow problems corresponding to these scenarios are solved in parallel using a multi-processor server. Then the operation strategy is updated in a short sampling time (every 20 seconds) considering real wind power values. The RT-OPF problem is formulated considering both technical and economic aspects simultaneously. The RT-OPF is implemented on a 41-bus medium-voltage distribution network with two wind stations. The results show the benefits of the proposed approach and highlight further challenges of RT-OPF.