Abstract：A method for generating source and load scenarios of novel distribution networks based on

conditional Wasserstein generative adversarial network with gradient penalty（CWGAN-GP）was proposed to

address the issues of training instability，mode collapse，and insufficient controllability in traditional generative

adversarial networks（GANs）in source and load scenario generation for novel power system. The theoretical

foundation of CWGAN-GP was first discussed，including the advantages of Wasserstein distance and the gradient

penalty mechanism，as well as the conditional control capabilities of conditional generative adversarial networks.

By optimizing the network structure and training strategy，a CWGAN-GP model for source and load scenario

generation was constructed. To comprehensively evaluate the quality of the generated scenarios，a multidimensional evaluation system was developed，which includes the cumulative distribution function（CDF），Fréchet inception distance（FID），and mean absolute percentage error（MAPE）. The results of the case study demonstrate that the proposed method significantly outperforms traditional methods in terms of FID and MAPE. The generated load and photovoltaic output time-series curves are highly consistent with real data in terms of periodicity and fluctuation characteristics，validating the effectiveness and robustness of the model.

        Key words：source and load scenario generation；conditional Wasserstein generative adversarial network with

gradient penalty（CWGAN-GP）；Wasserstein distance；gradient penalty（GP）；conditional generative adversarial

network（GAN）；data-driven

     Format Citation:吴霜，刘汇川，翟晓萌,等. 基于CWGAN⁃GP的新型配网源荷场景生成方法[J].电气传动,2026,56(09):56-63. WU Shuang，LIU Huichuan，ZHAI Xiaomeng,et al. Source and load scenario generation for novel distribution networks based on CWGAN-GP [J].Electric Drive, 2026,56(09):56-63