Announced 10 days in advance due to Student Services Office oversight.

School of Civil and Environmental Engineering

Ph.D. Thesis Defense Announcement

State Form Dynamic Programming: A New Optimization Method for Large Reservoir Systems

By Lida Chen

Advisor:

Dr. Aris Georgakakos

Committee Members: Dr. Jian Luo (CEE), Dr. Jingfeng Wang (CEE), Dr. Husayn Sharif (CEE), Dr. Wei Zeng (Georgia Department of Natural Resources)

Date and Time: July 10, 2025.  9:00AM-12:00PM

Location: SEB 122

Microsoft Teams Meeting ID: 224 569 152 165 5; Passcode: CA6XS7wn

ABSTRACT
Numerous optimization methods have been proposed for reservoir management in the last several decades. Although these methods claim superior solutions compared to heuristic management procedures, their adoption in practice has been hindered due to their simplified system representations and excessive computational requirements. This thesis introduces and evaluates a new optimization approach (State Form Dynamic Programming, SFDP) designed to address these challenges and facilitate efficient management of large, real-world reservoir systems.
SFDP avoids full-space discretization by decomposing the optimization process into two iterative steps aiming to (1) optimize total system storage sequence and (2) distribute the system storage optimally among the individual reservoirs at any point in time. It is shown that this iterative strategy identifies optimal solutions while drastically reducing the required computational effort.
To validate its performance, SFDP is benchmarked against well-known optimization methods including Dynamic Programming (DP), Discrete Differential Dynamic Programming (DDDP), and Genetic Algorithms (GA) in systems of increasing size from 2 to 10 reservoirs. These systems are portions of the ACF and ACT Basins in the southeast U.S. The system performance is assessed across multiple realistic metrics reflecting water, energy, and environmental management objectives. SFDP is shown to outperform other optimization methods, especially for systems of more than 3-4 reservoirs, where it quickly becomes the only viable optimization option.
The SFDP practical potential is further demonstrated through comparison with ResSim, an industry-standard simulation tool developed by the U.S. Army Corps of Engineers. SFDP consistently identifies: (1) superior trade-offs between dependable capacity and prime energy generation during wet, normal, and dry periods, (2) substantially increases primary energy production and economic value, (3) maintains higher mean and minimum system storage, (4) achieves better drought management, and (5) protects aquatic systems with higher reliability.
Lastly, the SFDP applicability under uncertain hydrologic conditions is demonstrated and evaluated through a retrospective assessment of a coupled inflow forecasting - SFDP model. The inflow forecasting model utilizes a Markov Chain framework and the assessment is carried out for the ACF Basin. Notwithstanding forecast uncertainty, SFDP meets prime energy targets and sustains greater system storage with higher reliability than ResSim, confirming its high potential for use in the management of real‑world reservoir systems.