Data Science and Applications for Modern Power Systems

Inbunden, Engelska, 2023

Av Le Xie, Yang Weng, Ram Rajagopal

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This book offers a comprehensive collection of research articles that utilize data—in particular large data sets—in modern power systems operation and planning. As the power industry moves towards actively utilizing distributed resources with advanced technologies and incentives, it is becoming increasingly important to benefit from the available heterogeneous data sets for improved decision-making. The authors present a first-of-its-kind comprehensive review of big data opportunities and challenges in the smart grid industry. This book provides succinct and useful theory, practical algorithms, and case studies to improve power grid operations and planning utilizing big data, making it a useful graduate-level reference for students, faculty, and practitioners on the future grid.

Produktinformation

Utgivningsdatum2023-06-21
Mått155 x 235 x undefined mm
FormatInbunden
SpråkEngelska
SeriePower Electronics and Power Systems
Antal sidor436
FörlagSpringer International Publishing AG
ISBN9783031290992

Tillhör följande kategorier

Energiteknik inom Naturvetenskap och teknik
Databaser inom Data och it
Affärsförhandlingar inom Ekonomi och ledarskap

Le Xie is an Associate Professor and Eugene Webb Faculty Fellow in the Department of Electrical and Computer Engineering at Texas A&M University. He received B.E. in Electrical Engineering from Tsinghua University in 2004, S.M. in Engineering Sciences from Harvard in 2005, and Ph.D. in Electrical and Computer Engineering from Carnegie Mellon in 2009. His industry experience includes ISO-New England and Edison Mission Energy Marketing and Trading. His research interest includes modeling and control in data-rich large-scale systems, grid integration of clean energy resources, and electricity markets. Dr. Xie received the U.S. National Science Foundation CAREER Award, and DOE Oak Ridge Ralph E. Powe Junior Faculty Enhancement Award. He was awarded the 2017 IEEE PES Outstanding Young Engineer Award. He was recipient of Texas A&M Dean of Engineering Excellence Award, ECE Outstanding Professor Award, and TEES Select Young Fellow. He is an Editor of IEEE Transactions on SmartGrid, and the founding chair of IEEE Power and Energy Society Subcommittee on Big Data & Analytics for Grid Operations. He and his students received the Best Paper awards at North American Power Symposium and IEEE SmartGridComm. He is the founding faculty advisor of TAMU Energy Club. Dr. Ram Rajagopal is an Assistant Professor of Civil and Environmental Engineering at Stanford University, where he directs the Stanford Sustainable Systems Lab (S3L), focused on large scale monitoring, data analytics and stochastic control for infrastructure networks, in particular power networks. His current research interests in power systems are in integration of renewables, smart distribution systems and demand-side data analytics. Prior to his current position he was a DSP Research Engineer at National Instruments and a Visiting Research Scientist at IBM Research. He holds a Ph.D. in Electrical Engineering and Computer Sciences and an M.A. in Statistics, both from the Universityof California Berkeley, Masters in Electrical and Computer Engineering from University of Texas, Austin and Bachelors in Electrical Engineering from the Federal University of Rio de Janeiro. He is a recipient of the NSF CAREER Award, Powell Foundation Fellowship, Berkeley Regents Fellowship and the Makhoul Conjecture Challenge award. He holds more than 30 patents and several best paper awards from his work, and has advised or founded various companies in the fields of sensor networks, power systems and data analytics.

Big Data Challenges in Power Systems.- Challenges and Opportunities in Utility Data.- Wholesale Markets Data Deluge.- Distribution System Data Operation.- Synchrophasor Data Analytics.- Smart Meter and its Implications.- Deep Learning in Power Markets.- Data-driven Planning in Electric Energy Systems.- Common Information Model for Unifying Data Sets.- Inference and Business for Aggregators Non-intrusive Load Monitoring.- Utility Business Model in the Era of Big Data.- Data Security Services for Utilities.