A novel scenario generation method of renewable energy using improved VAEGAN with controllable interpretable features

Zilu Li, Xiangang Peng, Wenbo Cui, Yilin Xu, Jianan Liu, Haoliang Yuan, Chun Sing Lai and Loi Lei Lai

Applied Energy, 2024, vol. 363, issue C, No S0306261924002885

Abstract: With the high penetration of renewable generation systems in the power grid, the accurate simulation of the uncertainty in renewable energy generation is vital to the safe operation of the power system This paper proposes a novel controllable method for renewable scenario generation based on the improved VAEGAN model. The standard VAEGAN model is first improved using spectral normalization technique and the generator of GAN is trained using VAE. Then, the external and internal interpretable features in the latent space are learned as the controllable vector utilizing the principle of mutual information maximization. Finally, the renewable energy scenarios with overall features are generated using the external universal meteorological features, and renewable energy scenarios with specific features are generated by tuning along the internal interpretable feature of the controllable vector in the latent space. The proposed approach is used to produce real-time series data for renewable energy including wind and solar power. Experiments demonstrate that our method has a better performance in terms of controllable generation and enables the generation of preference patterns covering various statistical features.

Keywords: Renewable scenario generation; Generative adversarial networks; Variational autoencoders; Mutual information; Interpretable feature (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2024.122905

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