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.

中文翻译：

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基于非参数核密度估计的风电渗透动态经济调度

摘要 为了分析风电动态经济调度（DED）中风电的随机性，基于非参数核密度估计（KDE）技术，对风电出力概率分布和风电功率预测误差进行精确建模。采用对风电功率预测数据进行分段统计的方法构建风电出力的置信区间、预测误差的上下限。根据建立的风电出力概率模型、预测置信区间和预测误差上下界，建立了含风电的DED模型。结合蝙蝠算法（BA）和粒子群优化（PSO）算法的进化优势，设计了一种混合算法来求解所提出的模型。交叉机制，在算法的演化中引入了可以解决BA和PSO中容易陷入局部最优的问题。最后，通过仿真算例验证了所提模型和算法的有效性。