Diverse and controllable scenario generation (e.g., wind, solar, load, etc.) is critical for robust power system planning and operation. As AI-based scenario generation methods are becoming the mainstream, existing methods (e.g., Conditional Generative Adversarial Nets) mainly rely on a fixed-length numerical conditioning vector to control the generation results, facing challenges in user conveniency and generation flexibility. In this paper, a natural-language-guided scenario generation framework, named LLM-enabled Frequency-aware Flow Diffusion (LFFD), is proposed to enable users to generate desired scenarios using plain human language. First, a pretrained LLM module is introduced to convert generation requests described by unstructured natural languages into ordered semantic space. Second, instead of using standard diffusion models, a flow diffusion model employing a rectified flow matching objective is introduced to achieve efficient and high-quality scenario generation, taking the LLM output as the model input. During the model training process, a frequency-aware multi-objective optimization algorithm is introduced to mitigate the frequency-bias issue. Meanwhile, a dual-agent framework is designed to create text-scenario training sample pairs as well as to standardize semantic evaluation. Experiments based on large-scale photovoltaic and load datasets demonstrate the effectiveness of the proposed method.

翻译：多样且可控的场景生成（如风电、光伏、负荷等）对于电力系统的鲁棒规划与运行至关重要。随着基于人工智能的场景生成方法逐渐成为主流，现有方法（如条件生成对抗网络）主要依赖固定长度的数值条件向量来控制生成结果，在用户便利性与生成灵活性方面面临挑战。本文提出一种自然语言引导的场景生成框架，称为基于大型语言模型的频域感知流扩散模型，使用户能够通过自然语言描述生成所需场景。首先，引入预训练的大型语言模型模块，将非结构化自然语言描述的生成请求转换为有序语义空间。其次，采用基于修正流匹配目标的流扩散模型替代标准扩散模型，以大型语言模型的输出作为模型输入，实现高效且高质量的场景生成。在模型训练过程中，引入频域感知多目标优化算法以缓解频率偏差问题。同时，设计双智能体框架用于创建文本-场景训练样本对并实现语义评估标准化。基于大规模光伏与负荷数据集的实验验证了所提方法的有效性。