电气与电子信息学院学术报告
报告主题：Learning Smart Meter Data for Distribution Grid Modeling and Observability Enhancement
报 告 人：Prof. Zhaoyu Wang
会议地点:腾讯会议189 151 507
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:
Zhaoyu Wang received the B.S. and M.S. degrees in electrical engineering from Shanghai Jiaotong University, and the M.S. and Ph.D. degrees in electrical and computer engineering from Georgia Institute of Technology.He is the Northrop Grumman Endowed Associate Professor with Iowa State University.His research interests include optimization and data analytics in power distribution systems and microgrids.He was the recipient of the National Science Foundation CAREER Award, the Society-Level Outstanding Young Engineer Award from IEEE Power and Energy Society (PES), the Northrop Grumman Endowment, College of Engineering's Early Achievement in Research Award and the Harpole-Pentair Young Faculty Award Endowment. He is the principal investigator for a multitude of projects funded by the National Science Foundation, the Department of Energy， National Laboratories,PSERC and Iowa Economic Development Authority. He is the Chair of IEEE PES PSOPE Award Subcommittee, the Co-Vice Chair of PES Distribution System Operation and Planning Subcommittee, and the Vice Chair of PES Task Force on Advances in Natural Disaster Mitigation Methods.He is an Editor of IEEE Transactions on Power Systems,IEEE Transactions on Smart Grid,IEEE Open Access Journal of Power and Energy, IEEE Power Engineering Letters and IET Smart Grid.
Abstract:
Missing or incorrect network models pose a significant challenge in distribution system planning and operation with high penetration of renewables. Although distribution grid modeling has been studied by many researchers using high-resolution and synchronized phasor data, the required high-quality sensors are costly and may not be available in practice. The increasing deployment of smart meters extends monitoring capability to grid edges and provides unprecedented amounts of data.However,most utilities use smart meters for billing purposes only without exploring insights or gaining actionable information from them because these data are limited to low-resolution power and voltage magnitude measurements. This talk will introduce robust, data-driven optimization methods that enable using only smart meter measurements for real-time identification of topology and line parameters with minimum prior information.For topology identification, the new method is to design a Laplacian-like matrix that can capture the physical network feature and leverage its inherent sparse structure to discover nodal connectivity even from low:quality smart meter data.For online parameter identification, the talk will introduce a bottom-up optimization algorithm that uses only smart meter data and conductor types in the network. In addition, the talk will briefly introduce other smart meter applications to enhance distribution grid observability, including outage detection and customer peak demand estimation.
