A novel informer-time-series generative adversarial networks for day-ahead scenario generation of wind power.

With the rapid growth of wind power penetration, its inherent stochasticity and uncertainty will seriously affect the stable operation of power systems. How to effectively characterize the uncertainty of wind power is a great challenge for day-ahead power system dispatching, scenario generation is an important method to describe the uncertainty of wind power. Currently, most of the wind power scenarios are generated using a generative adversarial network with two-dimensional convolution as the main structure, which may make it difficult to adequately characterize the temporal features, the day-ahead mode properties, and seasonality of wind power. In this paper, we first establish an auxiliary classification time-series generation adversarial network based on error stratification, construct the numerical characteristic conditional labels that can reflect the fluctuation characteristics of day-ahead wind power and power output level, and design the temporal embedding function that captures the seasonal characteristics of wind power. On this basis, to fully extract the dynamic variation characteristics and global effective information of wind power prediction error sequences, Informer is combined with a time-series generative adversarial network, and a joint loss function incorporating supervised learning and unsupervised learning is constructed. Subsequently, the generated set of prediction error sequences is superimposed with the day-ahead predicted value of wind power to obtain the day-ahead wind power scenario set. Finally, to verify the effectiveness of the proposed method, two datasets from different geographic locations are used to comprehensively evaluate the generated day-ahead wind power scenario set in terms of three aspects: temporal correlation characteristics, fluctuation characteristics, and accuracy. The experimental results indicate that the scenario generation method proposed can improve the quality of the day-ahead wind power scenario set and has an excellent performance in describing wind power uncertainty compared with other methods. • A novel generation model for wind power based on Probspare self-attention and TimeGANs is developed. • A method for generating day-ahead wind power scenarios considering numerical characteristics of prediction errors is proposed. • A joint loss function is designed by combining auxiliary classification supervised training and unsupervised adversarial training. [ABSTRACT FROM AUTHOR]