Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Network

• Published in: 2023 IEEE Power & Energy Society General Meeting (PESGM) pp. 1 - 5
• Main Authors: Cho, Young-Ho, Liu, Shaohui, Zhu, Hao, Lee, Duehee
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.07.2023
• Subjects: Computational modeling; Correlation; Generative adversarial network; Generative adversarial networks; Graph Convolutional Network; Matched filters; Spatio-temporal data generation; Training; Wind farms; Wind power generation; Wind power scenario

Generating wind power scenarios is very important for studying the impacts of multiple wind farms that are interconnected to the grid. We develop a graph convolutional generative adversarial network (GCGAN) approach by leveraging GAN's capability in generating large number of realistic scenario...