Perception Method of Voltage Spatial-Temporal Distribution of Distribution Network with High Penetration of Renewable Energy

被引：0

作者：

Zhang T. ^{[1
]}

Wang J. ^{[1
]}

Li G. ^{[1
]}

Zhou M. ^{[1
]}

Wang X. ^{[2
]}

Liu Z. ^{[2
]}

机构：

[1] State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing

[2] State Grid Jibei Electric Power Co., Ltd., Beijing

来源：

Wang, Jianxiao (wangjx@ncepu.edu.cn) | 1600年 / Automation of Electric Power Systems Press卷 / 45期

基金：

中国国家自然科学基金;

关键词：

Distribution network; Generalized regression neural network; High penetration of renewable energy; Kernel density estimation; Numerical weather prediction; Voltage awareness;

D O I：

10.7500/AEPS20200430026

中图分类号：

学科分类号：

摘要：

With the increasing penetration of renewable energy in distribution networks, a series of reliability issues, e.g., voltage over-limit and power flow overloading, have become severe threats. Therefore, an perception method of voltage spatial-temporal distribution with high penetration of renewable energy is proposed. Due to the absence of power flow models of the distribution network in practice, a data-driven method is designed to make accurate short-term prediction for nodal voltage perception. The proposed method is composed of three sectors: numerical weather prediction (NWP) based distributed wind power and photovoltaic forecasting, in which the relationship between meteorological data and distributed energy output is developed; generalized regression neural network (GRNN) based learning mechanism for constructing voltage sensitivity matrix, in which data-driven nodal power-voltage mapping is developed without a power flow model of the distribution network; and the kernel density estimation (KDE) based GRNN sample amendment method for avoiding the forecasting errors caused by the local density deviation of the original sample. Case studies based on IEEE 33-bus and Venezuela 141-bus distribution systems demonstrate the effectiveness of the proposed method. Compared with similar methods, the proposed KDE-GRNN has a significant advantage in forecast precision and rate of convergence. © 2021 Automation of Electric Power Systems Press.

引用

页码：37 / 45

页数：8

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