University of Mississippi eGrove Electronic Theses and Dissertations Graduate School 2018 Multiobjective Reservoir Optimization Using Genetic Algorithm Seyma Tiryakioglu University of Mississippi Follow this and additional works at: https://egrove.olemiss.edu/etd Part of the Engineering Commons Recommended Citation Tiryakioglu, Seyma, "Multiobjective Reservoir Optimization Using Genetic Algorithm" (2018). Electronic Theses and Dissertations. 435. https://egrove.olemiss.edu/etd/435 This Thesis is brought to you for free and open access by the Graduate School at eGrove. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of eGrove. For more information, please contact
[email protected]. MULTIOBJECTIVE RESERVOIR OPTIMIZATION USING GENETIC ALGORITHM A Thesis presented in partial fulfillment of requirements for the degree of Master of Science National Center for Computational Hydroscience and Engineering The University Of Mississippi by SEYMA SEHRIBAN TIRYAKIOGLU May 2018 Copyright © 2018 Seyma S Tiryakioglu All rights reserved ABSTRACT This thesis investigates the optimization of energy generation in a multipurpose reservoir. The objective functions and the constraints for the multiobjective optimization of reservoir operation for maximization of energy production involve the solution of a set of nonlinear equations governing pressure flow in penstock and turbine system and the hydropower generation. The hydropower generation also requires operational rules that define how the reservoir storage volume must be used at different storage levels. For these reasons, the present thesis uses genetic algorithm (GA) functions available in Matlab to perform the multiobjective optimization of hydropower energy production. Multiobjective optimization is based on two objective functions: maximization of total energy production over a specified number of years for which observed data is available, and maximization of annual firm energy production for individual years.