Location
Innovation Lab Atrium
Start Date
4-3-2025 2:00 PM
End Date
4-3-2025 3:30 PM
Presentation Date
3 April 2025, 2:00pm - 3:30pm
Biography
Ahmad Heidari is a PhD Graduate student, and a Kummer I&E Fellow. He is now seeking his third year in the Electrical Engineering department of Missouri University of Science and Technology, majoring in Power Systems. His advisor is Dr. Rui Bo, and his research interests are integration of renewable energies in power systems, application of optimization and machine learning in power system operation, and energy economics. His research project focuses on the application of cascaded hydropower plants including on-channel and off-channel reservoirs in the electricity market.
Meeting Name
2025 - Miners Solving for Tomorrow Research Conference
Department(s)
Electrical and Computer Engineering
Document Type
Poster
Document Version
Final Version
File Type
event
Language(s)
English
Rights
© 2025 The Authors, All rights reserved
Included in
Cascaded Hydropower Plant Optimization Using a Bi-level Mixed Integer Model
Innovation Lab Atrium
Comments
Advisor: Rui Bo
Abstract:
The Hydropower Operations Optimization (H2Os) Prize, initiated by the U.S. Department of Energy's Water-Power Technologies Office, encourages innovative solutions to enhance hydropower's grid contribution. This study addresses the critical need to improve the operational strategies of hydropower plants (HPPs) for increased profitability in modern electric power markets. We propose a mixed-integer bi-level optimization approach using the Column and Constraint Generation (CCG) technique to tackle this complex problem. Our contributions include the following. Framing the profit maximization problem of HPPs within a bi-level programming model, employing an advanced optimization technique to manage the mixed-integer nature of the optimization problem, validating our approach with a proof of concept and applying it to a modified PJM 5-bus system, and demonstrating the potential of advanced algorithms to enhance hydropower plant operations. Our findings highlight the efficacy of the proposed method in reducing computation time and improving practical applications in dynamic electric power markets.