A novel chaotic differential evolution algorithm for short-term cascaded hydroelectric system scheduling

Abstract A novel chaotic differential evolution (CDE) algorithm of optimal scheduling of short-term cascaded hydroelectric system based on improved logistic map is presented to maximize the expected generation benefit in a day, which uses the water discharge as the decision variables combined with the death penalty function. According to the principle of expected power generation, the proposed approach makes use of the ergodicity, symmetry and stochastic property of improved logistic chaotic map for enhancing the performance of differential evolution (DE) algorithm. The improved logistic map between (−1,1) is utilized to explore globally around the best individual until the lagged ones are close to best one. Meanwhile, the fitness value of objective function is handled by a piecewise linear interpolation function (PLIF). The new hybrid method has been examined and tested on a practical cascaded hydroelectric system. The experimental results show that the effectiveness and robustness of the proposed CDE algorithm are better than existing algorithms.

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