Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy systems. This paper proposes a deep generative network based method to model time-series curves, e.g., power generation curves and load curves, of renew...

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Bibliographic Details
Main Authors: Wenlong Liao, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Zhe Yang, Yusen Wang, Kuangpu Liu
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:Journal of Modern Power Systems and Clean Energy
Subjects:
Renewable energy source
scenario generation
implicit maximum likelihood estimation (IMLE)
deep learning
generative network
Online Access:https://ieeexplore.ieee.org/document/9841522/

Internet

https://ieeexplore.ieee.org/document/9841522/

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