基于改进麻雀搜索算法的微网容量优化配置

Microgrid capacity optimization based on improved sparrow search algorithm

为得到微电网中各微电源的最佳容量配比,满足负荷的出力需求,本文针对含风光柴储的并网型微电网,以综合运行成本最低为目标函数,以分布式电源出力及污染物排放量为约束条件,建立容量优化配置模型。采用折射反向学习机制、差分变异交叉选择策略及动态步长因子改进标准麻雀搜索算法对模型进行求解,并与鲸鱼优化算法、差分算法、灰狼算法、麻雀搜索算法进行对比。选取宁夏地区两个典型日进行算例分析,所求成本较其他4种算法分别降低3.05%、4.12%、8.46%及1.13%。仿真结果表明所提模型具有合理性,且改进的SSA具有良好的寻优能力。

In order to obtain the optimal capacity ratio of the micro power sources in the microgrid, and satisfy the output demand of the load, this paper establishes a capacity optimization configuration model for the grid-connected microgrid with wind, solar, diesel and battery, and takes the lowest comprehensive operating cost as the objective function, and the distributed power output and pollutant emissions as constraints. Using refracted opposition-based learning strategy, differential mutation, cross selection strategies and the dynamic step factor to improve the standard sparrow search algorithm to solve the model, and comparing with whale optimization algorithm, differential evolution, gray wolf optimizer, and sparrow search algorithm. Two typical days in Ningxia are selected for analysising of calculation examples, the required cost is 3.05%, 4.12%, 8.46% and 1.13% lower than the other four algorithms respectively. The simulation results show that the proposed model is reasonable, and the improved sparrow search algorithm has better optimization ability.