Optimizing operating rules for multi-reservoir hydropower generation systems: An adaptive hybrid differential evolution algorithm

Abstract

The optimization of hydropower multi-reservoir systems is one of the most challenging reservoir management problems due to the complex dynamic nonlinear influencing variables such as reservoir active storage volume, released water discharge rate, and water pressure head available for hydropower generation. To optimize such a complex problem in an efficient way, the development of a high-precision meta-heuristic algorithm are of essential significance. This paper develops an adaptive differential evolution with particle swarm optimization (A-DEPSO) algorithm to derive optimal operating rules for multi-reservoir systems with the purpose of hydropower production. A-DEPSO algorithm uses novel mutation and crossover mechanisms to improve global and local search capabilities, respectively. The application of the proposed method is verified to solve a complex four-reservoir hydropower generation system, located in the southwest of Iran. The results achieved indicate that the A-DEPSO reduced the value of the objective function by an average of 57% compared with other well-known optimizers in the literature. Furthermore, the total power generated from the four-reservoir system using the proposed method increased by an average of 11% compared to the other methods. Finally, the results obtained in this study confirm that the A-DEPSO can be used efficiently to solve complex multi-reservoir hydropower generation systems.