نوع مقاله : پژوهشی
نویسندگان
1 دانشکده عمران و حمل و نقل - دانشگاه اصفهان
2 دانشکده عمران و حمل و نقل، دانشگاه اصفهان
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
In this paper, the optimal solution of a single reservoir system operation optimization problem is determined using new Meta heuristic algorithm. Generally, various methods have been proposed to solve this problem. These methods are classified as: 1) Linear Programming (LP) 2) Non-Linear Programming (NLP) 3) Dynamic Programming (DP) and 4) Meta heuristic algorithms. Most recently, Meta heuristic algorithms, because of intelligent performance of them, are more useful method to solve optimization problem. Meta heuristic algorithms such as Genetic Algorithm (GA), Honey Bee Mating algorithm (HBMO), Ant Colony Optimization algorithm (ACO) and Particle Swarm Optimization algorithm (PSO) are new classification of optimization methods in which they are usually proposed based on the swarm behavior of social insects and real phenomena. Gravitational search algorithm is one of these newest algorithms that is based on the Newton's law of gravity. In the Gravitational search algorithm, a collection of masses is considered as searcher agents, in which these masses interact with each other based on the Newton's law of gravity and motion. In this paper, the simple and hydropower reservoir operation optimization problems of Dez dam have been solved for 5 and 20 operation periods proposing two different formulations. In the first formulation, the water releases from the reservoir and in the second formulation the reservoir storage volumes are taken as decision variables of the problem. The results are presented and compared with each other and with other available results. Comparison of the result with other existing results indicates better
performance of the gravitational search algorithm to solve reservoir operation optimization problem. Furthermore, while both proposed formulations show good performance to solve this problem, the first formulation is shown to produce better results with the same computational effort and to be less sensitive to the randomly generated initial guess presented by the scaled standard.
کلیدواژهها [English]
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