Ensemble Wind Power Prediction Interval with Optimal Reserve Requirement

扫码查看

原文链接

NETL
NSTL
万方数据

外文摘要：Wind power prediction interval(WPPI)models in the literature have predominantly been developed for and test-ed on specific case studies.However,wind behavior and charac-teristics 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 en-semble WPPI framework that integrates multiple WPPI models with distinct characteristics to improve robustness.Another im-portant and often overlooked factor is the role of probabilistic wind power prediction(WPP)in quantifying wind power uncer-tainty,which should be handled by operating reserve.Operat-ing reserve in WPPI frameworks enhances the efficacy of WPP.In this regard,the proposed framework employs a novel bi-lay-er optimization approach that takes both WPPI quality and re-serve 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.

外文关键词：

Ensemble modellinear programmingoperat-ing reserveoptimal reserve requirementprediction intervalprobabilistic predictionrenewable integrationuncertainty rep-resentationwind power prediction(WPP)

作者：

Hamid Rezaie、Cheuk Hei Chung、Nima Safari

展开 >

作者单位：

Saskatchewan Power Corpora-tion(SaskPower),Regina,Saskatchewan,Canada

Department of Electrical and Computer Engineering and Department of Human Biology,University of Toronto,Toronto,ON M5S 3G4,Canada

Alberta Electric System Operator(AESO),Calgary,Al-berta,Canada

基金：

Natural Sciences and Engineering Research Council(NSERC)of Canada and the Saskatchewan Power Corporation(SaskPower)

项目编号：

出版年：

2024

DOI：

10.35833/MPCE.2023.000464

现代电力系统与清洁能源学报(英文版)

ISSN：

年,卷(期)：2024.12(1)

参考文献量29