Multi-scenario renewable energy absorption capacity assessment method based on the attention-enhanced time convolutional network - Wu, Yang; Zhou, Han; Zhang, Congtong; Liu, Shuangquan; Chen, Zongyuan (Frontiers in Energy Research, 2024)
As the penetration rate of renewable energy in modern power grids continues to increase, the assessment of renewable energy absorption capacity plays an increasingly important role in the planning and operation of power and energy systems. However, traditional renewable energy absorption capacity assessment methods rely on complex mathematical modeling, resulting in low assessment efficiency. Assessment in a single scenario determined by the source-load curve is difficult to reflect the random fluctuation characteristics of the source-load, resulting in inaccurate assessment results. To this end, this paper proposes a multi-scenario renewable energy absorption capacity assessment method based on attention-enhanced time convolutional network to solve the above challenges. First, a source-load scene set is generated based on a generative adversarial network to accurately characterize the uncertainty on both sides of the source and the load. Then, the dependence of historical time series information in multiple scenarios is fully mined through the attention mechanism and temporal convolution network. Finally, simulation and experimental verification are carried out based on a provincial power grid in southwest China. The results show that the method proposed in this article has higher evaluation accuracy and speed than the traditional model.