摘要
针对风能及负荷具有时序性的特点,本文提出了一种考虑分布式风电源和负荷年时序特性的选址定容优化模型,用以降低分布式风电源接入后给配电网网损、电压等带来的不利影响,从而实现配电网的经济化运行。首先,分析分布式风机发电时序特性,并建立其模型。其次,基于蒙特卡洛模拟全年风速的时序水平,并采用多场景分析法构建风机与负荷时序场景,利用改进K-means聚类法对划分的场景进行聚类;最后,以最小年费用成本为目标函数,利用改进的粒子群算法对IEEE33节点算例进行仿真,仿真结果验证了本文分布式风电源选址定容模型及方法的有效性。
Aiming at the time-series characteristics of wind energy and load, a site selection and capacitance optimization model that takes into account the annual time-series characteristics of distributed wind power supply and load is proposed to reduce the adverse effects of distributed network loss and voltage, which realizes the economic operation of distribution network.Firstly, the timing characteristics of distributed wind turbine generation is analyzed and the model is established.Then, based on the Monte Carlo method, the annual wind speed timing level is simulated, fan and load scene is built on the basis of multi-scene analysis method, and the improved K-means clustering method is used to cluster the scenes.Finally, taking the minimum annual cost as the objective function, the improved particle swarm optimization algorithm is used to simulate the IEEE33 node example.The simulation results verify the effectiveness of the model and method.
作者
陈浩
马平
CHEN Hao;MA Ping(College of Automation and Electrical Engineering,Qingdao University,Qingdao266071,China)
出处
《青岛农业大学学报（自然科学版）》
2019年第3期224-229,共6页
Journal of Qingdao Agricultural University(Natural Science)
基金
国家自然科学基金项目(51477078)
关键词
分布式风电源
配电网
选址定容
K-means聚类法
粒子群算法
distributed wind power supply
distribution network
location and volume
K-means clustering
particle swarm optimization
