摘要
为计及同一区域光伏输出功率的强相关性对配电网动态无功优化的影响,简化传统随机潮流相关性控制方法,提出一种结合模拟退火算法和拉丁超立方采样的相关性控制方法。针对离散设备动作造成的动态无功优化时空耦合问题,采用混沌粒子群算法逐时段进行寻优,并由随机潮流计算结果对输出随机变量进行机会约束。在Matlab中进行模拟测试,并与静态无功优化结果进行对比。测试结果表明：此方法可有效控制输入随机变量相关性,在动、静态无功优化系统有功损耗相近的前提下满足离散设备动作次数约束,抑制系统的不确定性。
In order to consider the influence of the strong correlation of the PV output power in the same area on the dynamic reactive power optimization of distribution network, and simplify the traditional input variable correlation control method of the stochastic flow. A Correlation control method combining simulated annealing algorithm and latin hypercube sampling is proposed. Chaotic particle swarm optimization （CPSO） is adopted to optimize in each period according to the spatiotemporal coupling problem of dynamic reactive power optimization caused by discrete device actions, and the chance constrain of output stochastic variables is set by the stochastic power flow calculation results. Performing simulation is tested in Matlab and then compared with the static reactive power optimization. The simulation results show that the correlation of input variables for the improved control method can be effectively controlled. The dynamic reactive power optimization based on that can under the condition that the system loss is similar to the can be suppressed. satisfy the constraint of the number of discrete device actions static reactive power optimization, and the system uncertainty
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2018年第1期101-109,共9页
Acta Energiae Solaris Sinica
基金
甘肃省科技支撑计划(1204GKCA038)
关键词
动态无功优化
随机潮流计算
拉丁超立方采样
模拟退火算法
粒子群算法
dynamic reactive power optimization
stochastic power flow calculation
Latin hypercube sampling
simulated annealing
particle swarm optimization algorithm
