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Advanced Data Analytics for Probabilistic Security Assessment

* 21PESGM2620, From real-time monitoring to effective control of future power systems: V. TERZIJA , Skolkovo Institute of Science and Technology (Skoltech) * 21PESGM2621, Data Valuation for Decision-Making with Uncertainty in Energy Transactions: Q. GUO, Tsinghua University * 21PESGM2622, Data-driven power dispatch technology for improving renewable energy accommodation: Practice in Jibei gird of China, J. WANG, State Grid Jibei Electric Power Company Ltd. * 21PESGM2623, Data-driven analysis method for static power system analysis: J. YU, Chongqing University * 21PESGM2624, Probabilistic Short-term Voltage Forecasting of Distribution Grid, Y. WANG, ETH Zurich * 21PESGM2625, Hybrid data-driven method for fast approximation of practical dynamic security region boundary of power systems, Y. LIU, Tianjin University * 21PESGM2650, Data-driven approaches for enhancing resilience of multi-energy systems: J. WANG, Technical University of Denmark

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26 Jul 2021

Deep integration of huge amount of renewable generation (wind and solar) and electric vehicles make both the demand side and generation side much more uncertain. Probabilistic security assessment (PSA), which provides current/future security level evaluation considering uncertainties of the power system, plays more and more important role. Along with the smart grid development, modern power systems are also entering a “data-intensive” era, where a vast volume of data is collected through advanced sensing and communication technologies, such as SCADA measurement, smart metering data, synchrophasor measurements, meteorological data, social data, weather data, etc. Advance data analytics provides great chance to push forward many critical issues of PSA and some topics are the focus of this panel, including wind and load probabilistic forecast, data-driven electric vehicles behavior analysis, data-driven power system stability assessment, data-driven probabilistic power flow, data-driven probabilistic security assessment and so on.

Chairs:
Yanli Liu, Tianjin
Sponsor Committees:
(AMPS) Big Data Analytics

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