چكيده به لاتين
Cellular automata (CA) was used as a simulation method in different branches of science and recently it is used as an optimization method. In this study, the abilities of CA are developed for solving reliability based reservoir operation problems. For this, the single reservoir hydropower operation problem is considered and different is proposed for solving the problem. As the first method relaxed CA is proposed for solving reliability based hydropower operation of a single reservoir, in which operational and reliability constraints are dealt differently. In this method, the reliability constraint is relaxed and the optimal solution is achieved after doing some CA procedure. The hybrid genetic algorithm-cellular automata (GA-CA) method is then developed in which reliability constraints are handled by GA while CA is used to solve an operation problem. After that, an adaptive GA-CA (AGA-CA) is introduced with two versions in which the reliability constraints are adaptively satisfied during GA evolution. At the end, an adaptive relaxed cellular automata (ARCA) method is developed in which the reliability constraints are handled during CA iterations.
For solving muli-hydropower reservoir operation problems, two methods are developed disregarding reliability constraints. A hybrid cellular automata-simulated annealing (CA-SA) is introduced in which the main problem is broken down to some sub-problems and each sub-problem is solved by SA. In order to increase the computational efficiency, a modified CA-SA method is developed in which the cooling procedure of SA is handled during CA iterations. Using findings of these method, an adaptive cellular automata-simulated annealing (ARCA-SA) method is developed for solving reliability based muli-hydropower reservoir operation problems in which the reliability constraints are adaptively satisfied during CA iterations.
In order to test the performance of the proposed methods, one single reservoir and some multi reservoir systems considered and the proposed methods are applied to solve them. The results are also compared with existing results obtained by other methods showing the superiority of the proposed methods to those of existing methods in both efficiency and effectiveness.
Keywords: cellular automata, optimization, hybrid method, optimal operation.
