Decision-Focused Scenario Generation and Selection for Efficient and Robust Grid Dispatch

2026-07-07

AI Summary

This work addresses the suboptimality of ambiguity sets in distributionally robust optimization for power system dispatch, which arises from conventional scenario generation methods neglecting spatial correlations among uncertainties. To overcome this limitation, the authors propose a decision-oriented framework that aligns scenario generation directly with the dispatch objective. The framework incorporates a differentiable scenario selector, enabling end-to-end optimization and seamless integration with diverse generative architectures—including variational autoencoders, generative adversarial networks, and diffusion models. Experimental results demonstrate that the proposed approach consistently reduces operational costs by 0.80%–2.02% across multiple generative models compared to traditional methods, significantly enhancing both dispatch efficiency and robustness.

Abstract

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.

Problem

• scenario generation
• distributionally robust optimization
• power system dispatch
• spatial correlation
• decision-focused learning

Innovation

• decision-focused learning
• distributionally robust optimization
• correlated scenario generation
• differentiable scenario selection
• power system dispatch