Derivation of Optimal Operating Rules of Hydropower Reservoir Systems Using a Hybrid Optimization method (Case study: Karoon-Dez basin)

Background and objectives:
Optimal operation of dams' reservoirs, as one of the most important water resource systems, has a high complexity. This complexity is due to the stochastic nature of the river discharge, the high dimensionality, and conflicting objectives of reservoir operation problems. Increasing the number of dams, placing dams relative to each other and having different objectives will significantly increase the dimensions of the problem, which can complicate and non-linearize the structure of these problems. In this research, with respect to the unique nature of evolutionary algorithms (EAs) in the evaluation of objective functions and the probability of low localization in the local optimum solutions, a hybrid of differential evolution (DE) and particle swarm optimization (PSO) with multi-strategy (DEPSO) is used to optimize operation of a system with three reservoirs of Karoon1, Godar, and Dez with the purpose of hydropower generation.
Materials and methods:
In this research, by modifying the parameters and factors affecting both algorithms of DE and PSO, a new hybrid algorithm is presented. The proposed algorithm (DEPSO) promotes the local and global search capability of the basic DE algorithm to obtain optimal operating policies. Firstly, the efficiency and accuracy of the proposed algorithm are evaluated using the Ackley and Griewank mathematical functions. After that, the results of the DEPSO were compared to the DE, PSO, and ABC algorithms. Finally, the proposed algorithm is applied to optimally solve a three-reservoir system in Iran to generate hydropower energy. It is worth mentioning that the results are presented in ten different runs for all problems to evaluate the reliability and accuracy of the contestant algorithms.
Results:
The obtained results by the proposed hybrid algorithm (DEPSO) indicated that the average of objective function value for 10 runs and during 15-year operation period was 14.33, 10.00, and 38.50 percent better than those form the DE, ABC, and PSO algorithms, respectively. And also, by increasing the number of operation period from 180 to 240 monthly periods, the average of objective function value calculated by the DEPSO for 10 runs was 14, 22, and 35 percent better than those from the DE, ABC, and PSO, respectively.
Conclusion:
Regarding the calculated results using the DEPSO, it can be clearly seen a significant improvement in the objective function value compared to the DE and PSO algorithms, and especially with the increase of decision variables from 180 to 240 the performance of the method was more suitable than the other algorithms. This indicates the superior performance of this method compared to the other algorithm for optimizing the hydropower energy generated from multi-reservoir systems.