Scenario Reduction of Power Systems with Renewable Generations Using Improved Time-GAN

Abstract

To investigate the uncertainties and spatiotemporal complexities of renewable energy represented by wind and photovoltaic, a scenario reduction of power systems with renewable generations uses improved time series generative adversarial networks (Time GAN). The long short-term memory neural network is used to construct the generative adversarial networks, and the time-series supervision loss function and generative adversarial loss function are introduced to jointly optimize the generator network for better generating the daily renewable energy power scenarios. Based on the results of scenario generation, the silhouette coefficient method is used to improve K-means for constructing a scenario reduction model. Finally, the case analysis shows that the proposed method can obtain typical renewable energy power scenarios with spatiotemporal correlation and provide a reference for the analysis of power system operation scenarios.