考虑风电与负荷时序性的分布式风电源选址定容
发布时间：2018-08-22 16:25
【摘要】：配电网中分布式风电源选址定容时,计及风电机组出力和节点负荷的时序性特征。利用蒙特卡洛模拟MCS(Monte Carlo simulation)对一年内每小时风速进行抽样,并求出对应的风机出力。综合考虑每小时风机出力效率以及对应的节点小时负荷负载率,构建小时场景,利用改进K-means聚类法进行场景聚类。根据聚类后每个场景的风机出力效率均值、负荷负载率均值以及对应场景的概率,以配电公司最小年费用成本为目标函数,利用改进遗传算法对分布式风电源进行选址定容。对33节点算例的仿真分析结果表明,风机出力与节点负荷的时序特性对分布式风电源的选址定容有重大影响,同时也验证了所提模型及方法的有效性。
[Abstract]:When the distributed wind power source is selected for fixed capacity in distribution network, the timing characteristics of wind turbine output and node load are taken into account. Monte-Carlo simulation MCS (Monte Carlo simulation) is used to sample the wind speed per hour in one year, and the corresponding fan output force is calculated. Considering the output efficiency of blower per hour and the load rate of node hourly load, the hourly scene is constructed and the scene clustering is carried out by using improved K-means clustering method. According to the mean of fan output efficiency, the average load rate and the probability of the corresponding scenario, the minimum annual cost of the distribution company is taken as the objective function, and the improved genetic algorithm is used to locate the distributed wind power source. The simulation results of 33 node examples show that the timing characteristics of fan output and node load have great influence on the location and capacity of distributed wind power supply, and the validity of the proposed model and method is also verified.
【作者单位】： 东北电力大学电气工程学院;
【分类号】：TM715
本文编号：2197664
[Abstract]:When the distributed wind power source is selected for fixed capacity in distribution network, the timing characteristics of wind turbine output and node load are taken into account. Monte-Carlo simulation MCS (Monte Carlo simulation) is used to sample the wind speed per hour in one year, and the corresponding fan output force is calculated. Considering the output efficiency of blower per hour and the load rate of node hourly load, the hourly scene is constructed and the scene clustering is carried out by using improved K-means clustering method. According to the mean of fan output efficiency, the average load rate and the probability of the corresponding scenario, the minimum annual cost of the distribution company is taken as the objective function, and the improved genetic algorithm is used to locate the distributed wind power source. The simulation results of 33 node examples show that the timing characteristics of fan output and node load have great influence on the location and capacity of distributed wind power supply, and the validity of the proposed model and method is also verified.
【作者单位】： 东北电力大学电气工程学院;
【分类号】：TM715
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