[CCoE Notice] PhD Defense Announcement Form for Arun Venkatesh Ramesh (1854611)

[ccoecomm at Central.UH.EDU]

[Dissertation Defense Announcement at the Cullen College of Engineering]

System Flexibility and AI Computational Enhancement for Power System Day-Ahead Operations

Arun Venkatesh Ramesh

December 1, 2021; 1:00 PM - 3:00 PM (CST)
Zoom: https://urldefense.com/v3/__https://uh-edu-cougarnet.zoom.us/j/97669241989__;!!LkSTlj0I!FiMb7GRbkhhM2R2K06W7bP1MkirTVj5PqdxeMmw1JQ4TUGwMJ9Tmf9MKGsrB-EGze_fhwV2CZS9bQjyomu-OnpqRd58$ 

Committee Chair:
Xingpeng Li, Ph.D.

Committee Members:
Kaushik Rajashekara, Ph.D. | Harish Krishnamoorthy, Ph.D. | Lei Fan, Ph.D. | Jian Shi, Ph.D.

Abstract

The power system day-ahead operation involves a complex and computationally intensive optimization process to determine the least-cost and reliable solution which identifies the generator commitment schedule to meet the system electrical demand. The optimization process is a mixed-integer linear program (MILP) also known as security-constrained unit commitment (SCUC). In USA, Independent system operators (ISO’s) run SCUC daily and require state-of-the-art algorithms to introduce new technologies while also providing computational enhancements. Current SCUC model does not capitalize on existing system flexibility, for example it uses a static network to deliver power and meet demand optimally. A dynamic network can provide a lower optimal system cost and alleviate network congestion. However, due to the computational complexity network reconfiguration has not been included in the SCUC model. This talk first introduces system flexibility and its benefits in day-ahead operations. Following this, scalability to large power system networks are addressed through two computational enhancements methods for SCUC. In the first method, an optimization-based approach is considered to decompose the SCUC as smaller problems and then iteratively solved. In the second approach, supervised and semi-supervised machine learning algorithms are effectively utilized and post-processed to reduce the model size of SCUC.

[Engineered For What's Next]




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