Elahe Fallah-Mehdipour - Ph D Candidate Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj,Tehran, Iran
Omid Bozorg Haddad - Assistant Professor, Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj

Reservoir is one of the water structures which can be operated with hydropower energy generation purpose. This structure stores water in the wet periods and release it to generate energy. The rule curves are some control strategies for reservoir that balance inflow, storage volume and release water to enhance the hydropower efficiency. There are some optimization tools such as genetic algorithm (GA) to find optimized strategy. GA finds constant in a predefine strategy which is specified by operator. This strategy commonly presents the inflow, storage volume and release water in each period in a linear pattern. Also, it presents inflow as a stochastic variable which needs a prediction model to apply in real-time operation. In this paper, genetic programming (GP) as a simulation-optimization tool has been applied to find appropriate operational rule curve which is function of deterministic variables and has no predefined pattern. In this process, the data set has been divided to the two train and test sets. The performance of extracted rule curves by GA and GP has been compared in Bazoft reservoir where is a hydropower reservoir. Results show that the obtained objective function value as a performance criterion is enhanced significantly for both train and test data using GP. These results indicate that the proposed rule, based on the GP, is effective in determining optimal rule curves for reservoirs.

Rule curves, Operation, Hydropower Reservoir, Genetic Programming, and Performance