Multistage Linearization Probabilistic Power Flow Calculation Based on Piecewise Copula and Gaussian Mixture Model

doi:10.3969/j.issn.1000－7229.2018.09.015

Abstract

Abstract: Large-scale integration of wind power into grid and load stochastic volatility increase the uncertainty in power system operation, in order to effectively analyze the system operation features in the new environment, a calculating method based on wind power piecewise Copula and load Gaussian mixture model for multistage linearization probabilistic power flow is proposed. Piecewise Copula is used to establish the spatial correlation model among wind farms on the time dimension considering seasonal variation. For non-normal and multimodal load, expectation maximization （EM） algorithm is used to establish load Gaussian mixture model, and an improved K-means clustering is proposed to optimize EM algorithm, which can simplify the modeling process. On the premise of these models, calculating probabilistic power flow in the method of multistage linearization cumulant method, fully considering the impact of wind power and load fluctuation on the system operation. The accuracy and efficiency of the proposed probabilistic power flow calculation process is verified through the test on modified IEEE 14-bus system.

Key words: probabilistic power flow, piecewise Copula, seasonal correlation, Gaussian mixture model, multistage linearization, cumulant

CLC Number:

TM 74

JIANG Xuechen, YUAN Yue, WU Han, XU Yundai, HUANG Ruanming, WANG Yuefeng. Multistage Linearization Probabilistic Power Flow Calculation Based on Piecewise Copula and Gaussian Mixture Model[J]. Electric Power Construction, 2018, 39(9): 120-128.

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