Power system hybrid dynamic economic emission dispatch with wind energy based on improved sailfish algorithm - Li, Ling-Ling; Shen, Qiang; Tseng, Ming-Lang; Luo, Shifan (Journal of cleaner production, 2021)
This study proposes an improved sailfish optimization algorithm to deal with the problems of high operation cost and large pollution emission in hybrid dynamic economic emission dispatch (HDEED) of the power system. The algorithm also solves the problem of power dispatching difficulty caused by the randomness of wind energy when considering wind energy. The randomness of wind energy is modeled by Weibull distribution and combined with the dynamic economic emission dispatch; hence, the proposed model needs to be established. The traditional sailfish optimization (SFO) algorithm is improved by introducing weight inertia, global search formula, and Levy flight strategy to improve the search performance and solution speed. The improved sailfish optimization (ISFO) algorithm is to deal with the constraint conditions such as valve point effect, power balance constraint, and slope constraint of thermal power unit involved. Two test systems test the proposed ISFO algorithm performance. The ISFO algorithm operation cost is 9% and 6% lower than other optimization algorithms and the pollution emission is reduced by 30% and 4%. The proposed algorithm provides a competitive scheduling scheme on the premise of ensuring the flexibility of power system scheduling and effectively deals with the randomness of wind energy while reducing the operation cost and pollution emissions of the power system. This study has a positive impact on improving power supply reliability and reducing environmental pollution. [Display omitted] •This study proposes a dynamic economic dispatch model involving wind.•This study argues that the randomness of wind power/energy generation is caused by the uncertainties.•The sailfish optimization algorithm is improved to solve the model constraints.•The test system verifies the effectiveness of the algorithm and model.