用于高效稳健电网调度的决策聚焦场景生成与选择
Decision-Focused Scenario Generation and Selection for Efficient and Robust Grid Dispatch
AI总结：
针对电网调度中分布鲁棒优化依赖预测场景构建模糊集存在的问题，提出基于决策聚焦的生成框架，依下游运行成本优化场景，适配多种生成模型，开发可微场景选择器，案例研究显示可有效降低运行成本。
摘要：
灵活需求和可再生能源发电带来的不确定性增加，使分布鲁棒优化（DRO）成为电力系统稳健调度的重要工具。DRO依赖预测场景构建模糊集，但传统场景生成管道常以精度为导向训练，可能忽略不确定性间的空间相关性，导致模糊集虽统计合理但对下游运行非最优。本文提出基于决策聚焦的生成框架用于DRO调度中的相关场景生成。该框架根据生成场景对下游运行成本的影响来优化，适用于多种主流生成模型，还开发了可微场景选择器。案例研究表明，与精度导向方法相比，该框架能有效降低不同生成模型下0.80%-2.02%的运行成本。
英文摘要：
The increasing uncertainty from flexible demand and renewable generation has made distributionally robust optimization (DRO) an important tool for robust power system dispatch. DRO relies on forecast scenarios to construct ambiguity sets, but conventional scenario generation pipelines are often trained in an accuracy-oriented manner and may neglect spatial correlations among uncertainties. This mismatch can produce ambiguity sets that are statistically plausible but suboptimal for downstream operation. This work proposes a decision-focused generative framework for correlated scenario generation in DRO-based dispatch. Instead of training generative models solely to fit the historical uncertainty distribution, the proposed framework optimizes generated scenarios according to their induced downstream operational cost. The proposed framework is tailored to mainstream generative models, including variational autoencoders, generative adversarial networks, and diffusion models, while capturing the joint distribution of uncertainties across buses. To improve computational tractability, we further develop a differentiable scenario selector that selects decision-relevant scenarios from a generated pool and can be trained within the same decision-focused pipeline. Case studies demonstrate that the proposed framework effectively reduces 0.80%-2.02% operational cost across different generative models compared to accuracy-oriented methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
