Ensemble Wind Power Prediction Interval with Optimal Reserve Requirement  

作　　者：Hamid Rezaie Cheuk Hei Chung Nima Safari 

机构地区：[1]the Saskatchewan Power Corporation(SaskPower),Regina,Saskatchewan,Canada [2]the Department of Electrical and Computer Engineering and Department of Human Biology,University of Toronto,Toronto,ON M5S 3G4,Canada [3]the Alberta Electric System Operator(AESO),Calgary,Alberta,Canada

出　　处：《Journal of Modern Power Systems and Clean Energy》2024年第1期65-76,共12页现代电力系统与清洁能源学报（英文）

基　　金：supported in part by the Natural Sciences and Engineering Research Council(NSERC)of Canada and the Saskatchewan Power Corporation(SaskPower).

摘　　要：Wind power prediction interval(WPPI)models in the literature have predominantly been developed for and tested on specific case studies.However,wind behavior and characteristics can vary significantly across regions.Thus,a prediction model that performs well in one case might underperform in another.To address this shortcoming,this paper proposes an ensemble WPPI framework that integrates multiple WPPI models with distinct characteristics to improve robustness.Another important and often overlooked factor is the role of probabilistic wind power prediction(WPP)in quantifying wind power uncertainty,which should be handled by operating reserve.Operating reserve in WPPI frameworks enhances the efficacy of WPP.In this regard,the proposed framework employs a novel bi-layer optimization approach that takes both WPPI quality and reserve requirements into account.Comprehensive analysis with different real-world datasets and various benchmark models validates the quality of the obtained WPPIs while resulting in more optimal reserve requirements.

关 键 词：Ensemble model linear programming operating reserve optimal reserve requirement prediction interval probabilistic prediction renewable integration uncertainty representation wind power prediction(WPP) 

分 类 号：TM614[电气工程—电力系统及自动化]