Operation Policies through Dynamic Programming and Genetic Algorithms, for a Reservoir with Irrigation and Water Supply Uses

Rosalva Mendoza Ramírez (), Maritza Liliana Arganis Juárez (), Ramón Domínguez Mora (), Luis Daniel Padilla Morales (), Óscar Arturo Fuentes Mariles (), Alejandro Mendoza Reséndiz (), Eliseo Carrizosa Elizondo () and Rafael Bernardo Carmona Paredes ()
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Rosalva Mendoza Ramírez: Universidad Nacional Autónoma de México, Instituto de Ingeniería, Campus Morelia
Maritza Liliana Arganis Juárez: Universidad Nacional Autónoma de México, Instituto de Ingeniería
Ramón Domínguez Mora: Universidad Nacional Autónoma de México, Instituto de Ingeniería
Luis Daniel Padilla Morales: Universidad Nacional Autónoma de México, Instituto de Ingeniería
Óscar Arturo Fuentes Mariles: Universidad Nacional Autónoma de México, Instituto de Ingeniería
Alejandro Mendoza Reséndiz: Universidad Nacional Autónoma de México, Instituto de Ingeniería
Eliseo Carrizosa Elizondo: Universidad Nacional Autónoma de México, Instituto de Ingeniería
Rafael Bernardo Carmona Paredes: Universidad Nacional Autónoma de México, Instituto de Ingeniería

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 5, No 11, 1573-1586

Abstract: Abstract In this study, operation policies were obtained for a reservoir in Michoacán, Mexico, used for irrigation and domestic water supplies. The main purpose of these policies is to optimize the uses of the water, an increasingly scarce resource everywhere. Two optimization methodologies were used; stochastic dynamic programming, that provides release decisions for each stage, and genetic algorithms coupled with a reservoir operation simulation program, to achieve annual release curves. The operation of the reservoir was evaluated using historical inflow records. Monthly requirements for crop cycles, as well as the volumes of spills and deficits were examined. Both methodologies gave inverse relationships between deficits and spilled volumes. While both methodologies proved efficient in achieving the objectives, the results of the stochastic dynamic programming showed a better performance for this system.

Keywords: Stochastic dynamic programming; Genetic algorithms; Optimal management; Cointzio reservoir (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11269-021-02802-w

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