基于改进蝙蝠算法的微电网优化调度

Optimal Power Flow of Microgrid Based on Improved Bat Algorithm

在分析微电网中相关电源发电技术的基础上,建立以发电成本、污染物排放费用、甩负荷补偿费用和微电网网损补偿费用最小的多目标数学优化模型,应用超效率数据包分析评价方法,将多目标优化转换为单目标规划问题。鉴于传统蝙蝠群体易聚集于局部极值,导致早熟,将混沌序列以及自适应调整策略融入到蝙蝠优化算法,提出一种改进型多目标蝙蝠优化算法,为克服算法本身对缺乏变异机制的缺陷,利用混沌理论以及动态自适应调整机制的特性,对蝙蝠算法参数进行调整。最后通过算例验证所提算法具有良好的实用性和适应性,同时也验证了所提模型的实际意义。

Based on the analysis on power generation technologies related to microgrid, this paper established a multi- objective optimization model, aiming at minimizing the generation cost, pollutant emission cost, the compensation cost of load shedding and microgrid network loss, and used super efficiency data packet analysis evaluation methods to transform multi-objective optimization problem into a single-objective programming one. The traditional bat group easily gathered in local minima, which would lead to premature. So the chaotic sequence and adaptive adjustment strategy were applied into bat optimization algorithm, and a improved multi-objective bat optimization algorithm was proposed, in which the characteristics of chaos theory and dynamic adaptive adjustment mechanism were used to adjust the parameters of the bat algorithm, in order to overcome the defect that the algorithm itself was lack of variation mechanism. Finally, the good practicability and adaptability of proposed algorithm were verified through numerical example, as well as the practical significance of the proposed model.