电气与电子信息学院学术报告
报告主题：Applications of Polynomial Chaos Expansion-based Methods in Power System Probabilistic Security Assessments
报 告 人：Prof. Xiaozhe Wang
会议时间:12月11日（周六）9:00
会议地点:腾讯会议588 475 202
主办单位:重庆大学、输配电装备及系统安全与新技术国家重点实验室、重庆大学潥阳智慧城市研究院
协办单位:四川大学、电子科技大学，西南交通大学、成都理工大学、成都中医药大学、四川师范大学、西华大学、西南科技大学、西南大学、重庆邮电大学、重庆科技学院
Personal Profile:
Xiaozhe Wang is currently an Assistant Professor in the Department of Electrical and Computer Engineering at McGill University，Montreal, QC, Canada. Before joining McGill in 2016, she received her Ph.D. degree in the School of Electrical and Computer Engineering from Cornell University，Ithaca，NY, USA, in 2015， and her B.Eng. degree in Information Science &Electronic Engineering from Zhejiang University，Zhejiang，China，in 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 Distribution，and 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.
Abstract:
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 evaluations，which 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.
