Modeling Daily Load Profiles of Distribution Network for Scenario Generation Using Flow-Based Generative Network

被引：48

作者：

Ge, Leijiao ^{[1
]}

Liao, Wenlong ^{[1
,2
]}

Wang, Shouxiang ^{[1
]}

Bak-Jensen, Birgitte ^{[3
]}

Pillai, Jayakrishnan Radhakrishna ^{[3
]}

机构：

[1] Tianjin Univ, Sch Elect & Informat Engn, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China

[2] Tianjin Xianghe Elect Co Ltd, Tianjin 300000, Peoples R China

[3] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark

来源：

IEEE ACCESS | 2020年 / 8卷

基金：

中国国家自然科学基金;

关键词：

Load modeling; Probability distribution; Generative adversarial networks; Gallium nitride; Autoregressive processes; Mathematical model; Training; Daily load profiles; distribution network; generative network; ADVERSARIAL; SYSTEM;

D O I：

10.1109/ACCESS.2020.2989350

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化];

摘要：

The daily load profiles modeling is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a flow-based generative network is proposed to model daily load profiles of the distribution network. Firstly, the real samples are used to train a series of reversible functions that map the probability distribution of real samples to the prior distribution. Then, the new daily load profiles are generated by taking the random number obeying the Gaussian distribution as the input data of these reversible functions. Compared with existing methods such as explicit density models, the proposed approach does not need to assume the probability distribution of real samples, and can be used to model different loads only by adjusting the structure and parameters. The simulation results show that the proposed approach not only fits the probability distribution of real samples well, but also accurately captures the spatial-temporal correlation of daily load profiles. The daily load profiles with specific characteristics can be obtained by simply classification.

引用

页码：77587 / 77597

页数：11

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