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

Leijiao Ge, Wenlong Liao*, Shouxiang Wang, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai

*Corresponding author

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

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.
Original languageEnglish
Article number9075210
JournalIEEE Access
Volume8
Pages (from-to)77587-77597
Number of pages11
ISSN2169-3536
DOIs
Publication statusPublished - Apr 2020

Keywords

  • Daily load profiles
  • Distribution network
  • generative network

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