Maximize the hydropower generation in multi-objective reservoir system (The 6 dam system of Karun)

Background and objectives: The multi-objective water resource reservoir systems are generally composed of conflict purposes. In this study, keeping the minimum water level at above elevations increases hydropower generation through the water effective head. However, this operating policy results in decreasing the potential of storage variation and active storage capacity, which may be caused some deficits for meeting downstream demands. Accordingly, one of the major aims in this research is to maximize the hydropower generation in complicated multiple and multi-objective reservoir systems in which the desired reliability is kept to meet downstream demands. To reach this aim, the optimal minimum water level is calculated. In this area of research, it can be pointed to the hybrid optimization model; classical mathematical models and evolutionary algorithms (1), hybrid evolutionary algorithms (4) and the multi-objective optimization model (14).
Materials and Methods: In this research, a simulation-optimization model is developed for the Karun basin included the 6 dams system of the current condition. In this hybrid model, maximizing of the total produced energy is defined as objective function constrained to water balance and reliability. This model is capable to investigate the water resource system in details with allocating priority to different demands. In this way, the hydropower generation is maximized using the genetic algorithm and via evolutionary process, in which desired reliability for meeting demands is kept using penalty in the objective function.
Results: The results indicate that the system reliability for meeting demands is kept in the level of 75% in which the annual average of hydropower energy produced by the system is 18193 GWH. The most portions is related to Karun 1 reservoir with 3483GWH and the less one is related to Karun 4 with 2007 GWH per year. Additionally, agriculture networks of Dez river and Gargar network on Gargar river, that is one of the Karun branches, are the boundary area of the optimization for satisfying minimum acceptable reliability. In other words, these networks have been identified as critical networks for meeting demands.
Conclusion: In the common states, evolutionary algorithms are unable to consider the constraint and should be found a remedy to impose constraints. However, this research showed that using penalty in objective function accordance with the violation values of target reliability makes desired performance in the complicated system. Moreover, applying of the simulation-optimization model helps significantly to input more details of the water resource systems in the simulation model. This is more efficient than applying the single optimization model made simplifying of the problems.
Key words: Hydropower, Genetic algorithm, Multi-objective, Optimization Reservoirs.