低压柔性互联将同一区域配电网的低压台区间形成互联互供，可提高配电系统的最大供电能力（total supply capability，TSC），且低压台区接入的分布式能源具有时序性和不确定性，影响TSC值的变动。为此，提出了一种基于时序卷积神经网络（temporal convolutional network，TCN）的低压柔性互联配电网短期TSC评估方法，以兼具快速性和准确性。首先，进行低压柔性互联配电网短期TSC分析，建立基于数据驱动的短期TSC评估架构；然后，采用模型驱动的短期TSC评估方法，得到短期TSC值以获得训练样本，再离线训练TCN模型，得到短期TSC值与具有一定时序性的影响因素间非线性映射的TCN模型，实现由TCN在线评估系统短期TSC。最后，低压柔性互联配电网算例系统测试验证了所提方法的有效性。

Low-voltage flexible interconnection forms interconnection and mutual power supply between low-voltage substation areas in the same regional distribution network, which can improve the total supply capacity (TSC) of the distribution system. Moreover, the distributed energy connected to the low-voltage substation area has temporal and uncertain characteristics, which affect the variation of TSC value. Therefore, a short-term TSC evaluation method for low-voltage flexible interconnected distribution networks based on temporal convolutional network (TCN) is proposed, which combines speed and accuracy. Firstly, short-term TSC analysis of low-voltage flexible interconnected distribution networks is conducted and establish a data-driven short-term TSC evaluation architecture is established. Then, a model-driven short-term TSC evaluation method is adopted to obtain short-term TSC values to obtain training samples. The TCN model is then trained offline to obtain a nonlinear mapping between short-term TSC values and influencing factors with certain temporal characteristics. This enables the online evaluation of short-term TSC by the TCN system. Finally, the effectiveness of the proposed method is verified through system testing of low-voltage flexible interconnected distribution networks.