Data-Driven Scenarios Generation for Wind Power Profiles Using Implicit Maximum Likelihood Estimation

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Abstract

The scenario generation of wind power profiles is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a novel generative network is proposed to model wind power profiles based on implicit maximum likelihood estimation (IMLE). Firstly, the fake sample closest to each real sample is found to calculate the loss function used for updating weights. After training the model, the new wind power profiles are generated by feeding some Gaussian noises to the generator of the IMLE model. Compared with explicit density models, the IMLE model does not need to artificially assume the probability distribution of wind power profiles. The simulation results show that the proposed approach not only fits the probability distribution of wind power profiles well, but also accurately captures the shape, temporal correlation,and fluctuation of wind power profiles.
Original languageEnglish
Title of host publicationProceedings of 12th International Conference on Applied Energy (ICAE2020)
Number of pages5
PublisherElsevier
Publication date2020
Article number0428
Publication statusPublished - 2020
EventInternational Conference on Applied Energy 2020 - Bangkok, Thailand
Duration: 1 Dec 202010 Dec 2020
https://applied-energy.org/icae2020_cfp

Conference

ConferenceInternational Conference on Applied Energy 2020
CountryThailand
CityBangkok
Period01/12/202010/12/2020
Internet address

Keywords

  • wind power, scenario generation, deep learning, generative network

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