考虑相关性的广义负荷联合概率建模及应用

大规模风电并网带来广义负荷节点功率流向不确定性问题,对广义负荷建模提出新的要求。如何全面考虑随机变量所具有的波动性以及地域的相关性特点,准确进行广义负荷建模,成为亟待解决的问题。为此,提出一种带有概率标识的计及节点空间地域相关性的节点特性建模学习方法。首先,将与各风电场相连的根母线节点据其各自的功率流向,分为电源特性与负荷特性;其次,对各根母线节点分别依照有功功率进行区间细化,统计其概率信息。针对节点地域的相关性,采用空间相关性方法计算相邻节点功率区间内相关特征参数并纳入节点的特性学习中,采用径向基函数(RBF)神经网络学习训练并提取区间集的节点特性;以风险分析为例验证所提方法的有效性和实用性。仿真结果表明,通过将节点空间相关性纳入广义负荷建模范畴,建模因素更为全面并细化了整个系统空间形成风险场景集,风险分析结果指出系统高风险场景,为系统决策提供参考依据。

Generalized Probability Load Modeling and Application Considering Spatial Correlation of Power System Nodes

Large-scale integration of wind power into grid leads to uncertainty of generalized load flow, making new requirements on generalized load modeling. How to accurately build a generalized load model considering the stochastic variation and spatial correlation of uncertainties has become an urgent problem to solve. A novel method considering spatial correlation based on probability statistics is proposed to learn and abstract steady-state generalized load characteristics. According to the power direction of bus nodes connecting wind farms, the node characteristics are divided into source characteristics and load characteristics; then, the measured active power is used to represent characteristics and segment. The range of the segments is determined adaptively and the probability distribution is got by probability statistics. The radial basis function (RBF) neural network is used to abstract the node characteristics considering relevant characteristic parameters between segments divided by different nodes based on the spatial correlation method. Then the generalized model is applied to the actual case about the risk analysis. It is verified by simulation results that the new method can not only build a model accurately and comprehensively, but also refine system space risk forming scenarios by introducing spatial correlation to generalized load modeling, which is useful for dispatching and controlling with wind power integration taken into account. This work is supported by National Natural Science Foundation of China (No. 51177091).