Comparative Analysis of Three Soft Computing Techniques for Optimizing Reservoir Rule Curves

ABSTRACT Reservoir rule curves (RCs) are crucial for guiding operators on the optimal water release based on the available water at the start of each month. In the absence of RCs, simulation and optimization techniques can be effectively employed to develop these curves. This study evaluates the performance of various optimization techniques for deriving optimal reservoir RCs for the Zarrineh Rud reservoir using soft computing (SC) algorithms. The algorithms investigated include the genetic algorithm (GA), particle swarm optimization (PSO), and gravitational search algorithm (GSA). To this end, monthly demand and discharge data from 1987 to 2018 were collected. Historical RCs were first simulated using the sequent peak algorithm (SPA), and optimal RCs were subsequently derived through the GA–SPA, PSO–SPA, and GSA–SPA algorithms to minimize water shortages. The results indicated that the GSA–SPA generally improved the time-based (αt) and volume-based (αv) reliability indices by 3 and 2%, respectively, compared to the historical SPA (SPA-Hist). Additionally, simulations with the GSA–SPA significantly reduced the mean annual shortage and total shortage by approximately 8% compared to SPA-Hist. The PSO–SPA ranked second, with a 7.4 and 6.8% reduction in mean annual shortage and total shortage, respectively.