Under the background of increasing renewable energy penetration and rigid demand for midterm generation planning, the accurate expression of wind power uncertainty becomes more and more important. Firstly, this paper analyses the problem of scenario generation models based on traditional Generative Adversarial Networks(GAN), point that the fluctuation of scenarios that it generated usually deviates greatly from the real one. And further proposes a convolutional structure, that called Temporal-correlation Enhancement block (TE block), which can solve the aforementioned problem by enhance the temporal correlation perception ability of convolutional layers. Then, the problems of traditional conditional Wasserstein GAN-Gradient Penalty(WGAN-GP) in mid-scale scenario generation are discussed, and a conditional WGAN-GP scenario generation model that is suitable for mid-term scenario generation is presented. This model-free non-parametric model can generate a large number of realistic wind scenarios efficiently according to the given conditions. In order to validation, we use the data collected in a province in northern China to train the model in the Case Study part, and compare it with the traditional model. Result shows the fluctuation distribution deviation problem is improved obviously on the model with TE block, and in the comparison of auto-correlation coefficient, the proposed model also outperform the traditional model. This verifies the superiority of the proposed model in temporal expression ability compared with the traditional model, as well as the feasibility of the mid-term wind scenario generation.
Model-Free Renewable Scenario Generation Using Generative Adversarial Networks