Advanced Prediction Techniques Applied to Smart Grids

#technical #development #energy #planning #professional #grid #smart #power

IEEE PES - Schenectady Chapter is excited to host a virtual webinar/Distinguished Lecture on the topic of "Advanced Prediction Techniques Applied to Smart Grids". This technical presentation will also offer one (1) continuing education credits (PDH).  The PES chapter would like to invite you to participate and learn from a well know power system modeling researcher and expert.

Speaker: Prof. C.Y. Chung form The Hong Kong Polytechnic University.

Cost: 

• $10 for IEEE Members (Includes 1 PDH credit)
• $15 for Non-IEEE Members (Includes 1 PDH credit)
• Free for General Admission (No PDH credit)
• Free for Student and Graduate Student Members (No PDH credit)

(All payments must be received by August 23, 2023).

Time: Thursday, August 24, 2023 at 12 PM, EDT

Reserve your seat by clicking on the below Register Now Tab.

Abstract:
The power system industry is shifting towards a new digitalization era to better manage risk within volatile energy commodities, increase customer engagement, and enhance efficiency via grid optimization. Data analytics play a vital role in this transformation and, as such, different measurement architectures have been used and implemented to facilitate data capturing process and supervisory control at the generation, transmission, and distribution levels. This seminar will briefly review the recent outcomes of some smart grid challenges addressed by novel prediction techniques. At the generation level, decomposition techniques have been applied to handle the inherent uncertainty in short-term wind power prediction. At the transmission level, dynamic thermal line rating prediction has been studied as a viable solution to reduce congestion and utilize the actual capacity of the line. Considering the high inclusion of phasor measurement units at the transmission level, cutting-edge methods have been proposed to address stability status prediction of the grid following a contingency. Finally, at the distribution level, real-life data obtained from advanced metering infrastructure have been used for load prediction and customer segmentation.