Optimum management of cyclic storage systems: A simulation–optimization approach

Abstract

A semidistributed system dynamics simulation model was coupled with a genetic algorithm to develop a novel simulation–optimization approach for conjunctive water use management. The proposed simulation–optimization method uses the concept of cyclic storage systems as a framework to solve conjunctive use problems. As a highly sophisticated conjunctive use template, a cyclic storage system includes two major subsystems: surface water and groundwater. In this research, the dynamic behavior of a cyclic storage system was simulated using system dynamics methodology. The real‐world case study chosen was Kineh Vars Reservoir and its irrigating area, located downstream of the Abhar Rud watershed in west‐central Iran. Operating rules for the system were optimized to satisfy water demands over a planning period of 40 seasons, minimizing the total costs of system construction, operation, maintenance, and elements replacement.

Cyclic storage approach for conjunctive operation of surface and groundwater systems (Case study: ZarrinehRoud catchment area)

“…At first, Holland (1975) proposed GA, based on natural evolution of alive creatures. By creating the specified numbers of chromosomes population representing the problem solutions, the current generation is formed.…”

“…By determining all individual fitness, the individuals are chosen, using their corresponding probability to their relative fitness, for mating and creating next generation. These processes should be continued until stop criterion is reached (Holland, 1975).…”

A web‐based application for optimization of single reservoir operation

“…In addition, there is no concern for numerical instabilities associated with matrix inversion that can occur in traditional optimization approaches such as linear programming. Furthermore, a holistic search pattern of metaheuristic algorithms reduces the probability to become entrapped in local optima compared with gradient‐based methods (Jahanpour et al, 2013a).…”

“…Similar to other metaheuristic search algorithms, a GA strives to locate feasible and near‐optimal solutions and/or global optimal solutions under a large number of function evaluations. It has been successfully used to optimize discrete or continuous variables while searching a broad area of the decision space (Jahanpour et al, 2013a; Afshar et al, 2010).…”

“…When Klemeš's definition of an LF is accepted, the loss can be assumed to be zero in the vicinity of the target, implying negligible losses for small deviations from this particular value and increasingly higher loss values for larger deviations (Datta & Burges, 1984). Various studies have considered more hydrological, economical, and even political measures in LF calculations (Jahanpour et al, 2013a; Afshar et al, 2010; Jenkins et al, 2003).…”