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

arXiv: https://arxiv.org/abs/2607.05830 · HTML: https://mlsanity.com/paper/2607.05830/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes](https://mlsanity.com/paper/1910.03002/) (2019)
- [LfEdNet: A Task-based Day-ahead Load Forecasting Model for Stochastic Economic Dispatch](https://mlsanity.com/paper/2008.07025/) (2020)
- [Optimal Scheduling of Integrated Demand Response-Enabled Community Integrated Energy Systems in Uncertain Environments](https://mlsanity.com/paper/2108.08055/) (2021)
- [Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2209.00654/) (2022)
- [Toward Value-oriented Renewable Energy Forecasting: An Iterative Learning Approach](https://mlsanity.com/paper/2309.00803/) (2023)
- [Robust Generation Dispatch with Strategic Renewable Power Curtailment and Decision-Dependent Uncertainty](https://mlsanity.com/paper/2203.16251/) (2022)
- [A Weighted Predict-and-Optimize Framework for Power System Operation Considering Varying Impacts of Uncertainty](https://mlsanity.com/paper/2503.11001/) (2025)
- [A quantitative analysis of the effect of flexible loads on reserve markets](https://mlsanity.com/paper/1402.0362/) (2014)
- [FICA: Faster Inner Convex Approximation of Chance Constrained Grid Dispatch with Decision-Coupled Uncertainty](https://mlsanity.com/paper/2506.18806/) (2025)
- [An Optimal Solution is Not Enough: Alternative Solutions and Optimal Power Systems](https://mlsanity.com/paper/2511.08805/) (2025)

## Built on by (most cited first, 1 of 1)

- [The Operational Value of Spatial Dependence in Renewable Forecast Scenarios for Single-Period Economic Dispatch: A Controlled Ablation Study](https://mlsanity.com/paper/2609.23988/) (2026)

## Abstract (arXiv)

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.