Multi-Time Scale Scenario Generation for Source–Load Modeling Through Temporal Generative Adversarial Networks

With the large-scale integration of distributed power sources, distribution network planning is undergoing significant transformations. To further enhance the efficiency and practicality of distribution network planning, it is essential to model the uncertainties in source–load dynamic scenarios. However, traditional scenario generation methods struggle with high-dimensional variables and complex spatiotemporal characteristics, posing severe challenges for distribution network planning. To address these issues, this paper proposes a multi-time scale source–load scenario generation method based on temporal convolutional networks and multi-head attention mechanisms within a temporal generative adversarial network framework. This algorithm not only enhances the richness and robustness of source–load scenarios in distribution networks but also serves as a valuable reference for medium-long-term analysis and planning. Finally, the results present a set of daily, weekly, and monthly multi-time scale source–load scenarios, and multiple evaluation indicators are utilized to evaluate the quality of the generated scenarios; the accuracy of the generated scenarios is increased by about 2%.