A modified water cycle algorithm for long-term multi-reservoir optimization

Abstract

Hydropower generation is a common consideration for reservoir operations. Due to the characteristic of high dimensionality, multi-reservoir optimization for hydropower generation is challenging. Although the original water cycle algorithm (WCA) outperforms other metaheuristic optimization algorithms in solving constrained and unconstrained problems, it still gets trapped in the local optima in the case of some high-dimensional problems. For multi-reservoir optimization problems, this paper provides a modified WCA based on the diversity evaluation and chaos theory (DC-WCA) to avoid premature convergence. Six benchmark problems are examined using DC-WCA, WCA, improved chaos genetic algorithm (ICGA) and chaotic particle swarm optimization algorithm (CPSO) to evaluate the efficacy of DC-WCA. To investigate the application of DC-WCA for multi-reservoir optimization, a serially linked reservoir system located in the upper reach of the Yangtze river of China is used as a case study. After sensitivity analyses of the parameters, the four algorithms are used for long-term multi-reservoir optimization for hydropower generation. The optimization results show that 1) the annual hydropower generation optimized using DC-WCA is superior to those optimized using the other reported optimizers, and 2) the convergence rate of DC-WCA is faster than those of the other reported optimizers. This case study indicates that the application of DC-WCA for multi-reservoir optimization not only improves the calculation accuracy but also reduces computation time.