Darbandikhan reservoir operation optimization using ant colony optimization algorithm

Abstract EN

The importance of water resources management has unlimited scope, and from that importance comes the need for optimizing the operation of water reservoirs in terms of achieving hydropower generation demands, irrigation demands, and avoiding flood risks.

there were many optimization techniques or methods have been conducted for that purpose.

in this paper we try to bring the operation of darbandikhan reservoir (located 60 km southeast of sulaimaniya city) to an optimum level using ant colony optimization (ACO) technique.

thus, the objective is to find a monthly water release plan with least difference from the amount of water demand for that month.

this study covers the operation of the reservoir for one year, sampled into 12 monthly periods.

two methods for pheromone trail update - iteration best path (IBP) and iteration all path (IAP) - have been used and tested in the ACO algorithm to find out how fit they are with the reservoir operation problem.

also two levels of reservoir storage discretization have been applied to the problem; 100 and 200 intervals.

generally, the ACO algorithm showed a high performance in exploring the optimum solutions for the operation of darbandikhan reservoir.

the obtained results of the tests revealed that the IAP outperforms the IBP in finding the optimum solutions.

while the tests of the two discretization resolutions showed that the 100 intervals is more efficient than the 200 intervals in getting better results with a certain number of iterations and artificial ants.