Typical wind power scenario generation for multiple wind farms using conditional improved Wasserstein generative adversarial network

Because of environmental benefits, wind power is taking an increasing role meeting electricity demand. However, wind power tends to exhibit large uncertainty and is largely influenced by meteorological conditions. Apart from the variability, when multiple wind farms have geographical adjacency, their power generation also displays strong correlation. Thus, scenario generation considering the spatiotemporal relationships is a useful tool to model the stochastic process. In this work, we propose a wind power scenario generation framework based on the conditional improved Wasserstein generative adversarial network (WGAN). The framework includes a cluster analysis to establish the classification rule, a support vector classifier (SVC) to predict labels, a conditional scenario generation process based on improved WGAN, and finally a scenario reduction procedure. We demonstrate that the clustering analysis and SVC based labeling model can provide accurate classification results and the scenarios conditioned on input labels can not only follow the marginal distribution of each category but also capture the spatiotemporal relationships. We also illustrate that by adding a gradient penalty term to the discriminator's loss function to enforce the Lipschitz constraint, the quality of scenarios is better than that of the existing method.