Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Network
This work addresses the need for efficient wind power scenario generation for grid impact studies, though it is incremental as it builds on existing GAN approaches with specific architectural improvements.
The authors tackled the problem of generating realistic wind power scenarios for interconnected wind farms by developing a graph convolutional generative adversarial network (GCGAN) that embeds spatial and temporal characteristics, resulting in scenarios with more realistic statistics than other GAN-based methods as demonstrated on real Australian data.
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 scenarios without using statistical modeling. Unlike existing GAN-based wind power data generation approaches, we design GAN's hidden layers to match the underlying spatial and temporal characteristics. We advocate the use of graph filters to embed the spatial correlation among multiple wind farms, and a one-dimensional (1D) convolutional layer to represent the temporal feature filters. The proposed graph and feature filter design significantly reduce the GAN model complexity, leading to improvements in training efficiency and computation complexity. Numerical results using real wind power data from Australia demonstrate that the scenarios generated by the proposed GCGAN exhibit more realistic spatial and temporal statistics than other GAN-based outputs.