Applications of Polynomial Chaos Expansion-based Methods in Power System Probabilistic Security Assessments


报告主题:Applications of Polynomial Chaos Expansion-based Methods in Power System Probabilistic Security Assessments

报 告 人:Prof. Xiaozhe Wang


会议地点:Tencent会议588 475 202



Personal Profile:

Xiaozhe Wang is currently an Assistant Professor in the Department of Electrical and Computer Engineering at McGill UniversityMontreal, QC, Canada. Before joining McGill in 2016, she received her Ph.D. degree in the School of Electrical and Computer Engineering from Cornell UniversityIthacaNY, USA, in 2015and her B.Eng. degree in Information Science &Electronic Engineering from Zhejiang UniversityZhejiangChinain 2010. Her research interests are in the general areas of power system stability and control, uncertainty quantification and management in power system security and stability, and wide-area measurement system (WAMS)-based detection, estimation, and control. She is serving on the editorial boards of IEEE Transactions on Power Systems, Power Engineering Letters, IET Generation, Transmission and Distributionand IEEE Transactions on Circuits and Systems—II: Express Briefs. She is an IEEE senior member. Her two papers co-authored with her students have been selected as Best Papers at the 2019 1EEEPower & Energy Society General Meeting and the 2018 IEEE Canadian Conference on Electrical & Computer Engineering, respectively.


The ever-increasing integration of renewable energy sources and new forms of load demand introduces a growing uncertainty level to power systems, which greatly affect various security properties of a system. In this talk, I will present some recent works of my group in utilizing polynomial chaos expansion (PCE)-based methods in power system probabilistic security assessments including probabilistic power flow solutions, available transfer capability assessments and economic dispatch. In contrast to Monte Carlo-based simulations that require a large number of scenarios and model evaluations, the polynomial chaos expansion method can build a surrogate model for assessing the model response (e.g., probabilistic power flow solution) from a small number of scenarios and model evaluationswhich thus saves huge computational efforts. l will also introduce the efforts to relax the assumption of knowing marginal distributions of random variables required in PCE. Insights for decision-making to reduce the negative impacts of uncertainty on power system security will also be discussed.

地址:中国·四川·成都市 澳门十大正规平台-yabo娱乐(610039)

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