有效准确地刻画可再生能源短期出力不确定性是电力系统稳定运行的重要基础。近年来，科研人员对生成模型进行了大量研究，其中基于流的生成模型展现出了巨大的潜力。然而，流模型很少用于刻画可再生能源的不确定性。因此，提出了一种基于条件流模型的可再生能源短期出力场景生成方法。所提方法能够在已知功率预测的基础上从历史实测数据生成短期出力场景。首先使用实际出力数据训练一系列可逆函数将数据的概率分布映射到标准高斯分布，训练完成后将服从高斯分布的随机噪声输入经过训练的流模型生成新的出力曲线。该方法可以直接通过最大似然估计进行训练，并采用一系列特殊设计的可逆变换，能够高效地计算训练目标。此外，构建了生成场景评价指标体系对生成场景的质量进行评估，实现了人工智能黑盒模型生成结果的可解释性。算例验证了所提方法的有效性和先进性。

Effectively and accurately portraying the short-term output uncertainty of renewable energy is an important foundation for the stable operation of power systems. In recent years, researchers conduct a lot of studies on generative models, among which flow-based generative models show great potential. However, flow models are rarely used to portray the uncertainty of renewable energy. Therefore, a short-term output scenario generation method of renewable energy based on conditional flow model is proposed. Firstly, a series of invertible functions are trained using real output data to map the probability distribution of the data to a standard Gaussian distribution. After the training is completed, the new output curve can be generated by inputting random noise obeying Gaussian distribution into the trained flow model. The method can be trained directly by maximum likelihood estimation and employs a series of specially designed invertible transformations, which can efficiently compute the training target. In addition, a generated scene evaluation index system is constructed to assess the quality of generated scenes, achieving interpretability of the results generated by the artificial intelligence black box model. The examples verify the effectiveness and sophistication of the proposed method.