考虑负荷与DG随机性特征的配电网多目标动态重构

Multi-objective dynamic reconstruction of distribution network considering load and DG randomness characteristics

传统确定性潮流和静态重构方法无法计及电网中不确定性因素的影响,为此文中提出了一种基于概率潮流考虑负荷与DG随机性特征的配电网多目标动态重构方法。建立了分布式电源(DG)出力与负荷的概率模型,并采用半不变量与Gram-Charlier级数进行概率潮流计算。提出了一种模拟退火-中心聚类(SA-PAM)算法,将退火机制引入PAM算中进行负荷聚类实现重构时段划分。随后,建立了以网损期望、电压越限概率、电压偏移为目标的配电网重构模型。为提升模型求解速度和效率,提出了一种多种群并行免疫算法,其中优势子种群采用高斯变异算子进行局部搜索实现快速收敛,普通子种群采取抗体交叉、超变异保证种群多样性实现全局搜索。通过IEEE33节点系统进行仿真验证了所提方法的有效性。

Deterministic trend currents and static reconstruction methods cannot account for the influence of uncertainties in the power grid.For this reason,a multi-objective dynamic reconstruction method based on load and DG randomness is proposed in this paper.Firstly,the probability model of DG and load is established,and the semi-invariant and Gram-Charlier series are used to calculate the probabilistic load flow.Then,a SA-PAM algorithm is proposed to introduce annealing mechanism to PAM to perform load clustering to realize the reconstruction period division.Subsequently,a distribution network reconstruction model with network loss expectation,voltage over-limit probability and voltage offset is established.In order to improve the speed and efficiency of the model,a multi-group parallel immune algorithm is proposed,in which the dominant subpopulation USES Gaussian mutation operator for local search to achieve rapid convergence,while the common subpopulation adopts antibody crossover and super-mutation to ensure population diversity to achieve global search.Finally,the effectiveness of the proposed method is verified by simulation of IEEE33-node system.