Gradient Evolution Optimization Algorithm to Optimize Reservoir Operation Systems

Abstract

Population growth, environmental destruction, and climate change have all led to water scarcity on the available water resources. In this regard, reservoir systems have an important role to manage water resources. Thus, it is essential to optimize the management of water resources. Optimizing reservoir systems involves complications such as nonlinear functions, large number of sizing variables and numerous constraints. To solve complicated optimization problems, meta-heuristic optimization algorithms are reliable and powerful methods. Hence, the present paper applies gradient evolution (GE) algorithm to optimize reservoir operation systems. This algorithm is extracted from a gradient-based optimizer. In fact, the main novelty of this study is the application of GE algorithm to optimize single- and multi-reservoir systems. Accordingly, the GE is employed to optimize a four-reservoir system, the Khersan-1 reservoir and the Dez reservoir in Iran. The results confirm the high capacity of the GE to optimize the single and multi-reservoir systems as it can obtain solutions 99.99, 96 and 94% of global optimum for the four-reservoir, Khersan-1 reservoir and Dez reservoir operation problems respectively. The results of the GE are compared with those solutions calculated with linear programming (LP), non-linear programming (NLP) and genetic algorithm (GA), which corroborate the superior ability of GE to reach global optimum solution of reservoir operation systems.