Abstract
In order to analyze the randomness of wind power in dynamic economic dispatch (DED) with wind power, based on non-parametric kernel density estimation (KDE) technology, the probability distribution of wind power output and wind power forecast error is accurately modeled. A segmented statistical method on wind power forecast data is adopted to construct the confidence interval of the wind power output, the upper and lower bounds of the forecast errors. According to the established wind power output probability model, forecast confidence interval and forecast error upper and lower bounds, a DED model with wind power is formulated in this paper. A hybrid algorithm combining the evolutionary advantages of bat algorithm (BA) and particle swarm optimization (PSO) algorithm is designed to solve the proposed model. A crossover mechanism, which can solve the problem of falling into local optimum easily existed in BA and PSO, is introduced in the evolution of the algorithm. Finally, the effectiveness of the proposed model and algorithm is verified by simulation examples.
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