# The Operational Value of Spatial Dependence in Renewable Forecast Scenarios for Single-Period Economic Dispatch: A Controlled Ablation Study

arXiv: https://arxiv.org/abs/2609.23988 · HTML: https://mlsanity.com/paper/2609.23988/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 21 Sep 2026.

## Builds on (most cited first, 13 of 13)

- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization](https://mlsanity.com/paper/1809.05504/) (2018)
- [End-to-End Constrained Optimization Learning: A Survey](https://mlsanity.com/paper/2103.16378/) (2021)
- [Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities](https://mlsanity.com/paper/2307.13565/) (2023)
- [Model-Free Renewable Scenario Generation Using Generative Adversarial Networks](https://mlsanity.com/paper/1707.09676/) (2017)
- [Open Power System Data - Frictionless data for electricity system modelling](https://mlsanity.com/paper/1812.10405/) (2018)
- [Toward Value-oriented Renewable Energy Forecasting: An Iterative Learning Approach](https://mlsanity.com/paper/2309.00803/) (2023)
- [Forecast Skill Is Not Decision Skill: Evidence from Weather-Dependent Decision Tasks](https://mlsanity.com/paper/2512.14779/) (2025)
- [Decision-Focused Forecasting: A Differentiable Multistage Optimisation Architecture](https://mlsanity.com/paper/2405.14719/) (2024)
- [Economic Dispatch Considering Spatial and Temporal Correlations of Multiple Renewable Power Plants](https://mlsanity.com/paper/1707.00237/) (2017)
- [Value-oriented forecast reconciliation for renewables in electricity markets](https://mlsanity.com/paper/2501.16086/) (2025)
- [Decision-Focused Scenario Generation and Selection for Efficient and Robust Grid Dispatch](https://mlsanity.com/paper/2607.05830/) (2026)

## Abstract (arXiv)

Renewable forecasts are evaluated by statistical skill (e.g., CRPS), but grid operators pay for realized dispatch cost. We diagnose what drives dispatch value in a single-period newsvendor-style economic dispatch using real public data from two European transmission systems (CWE, DE-4TSO). Spatial coherence across forecast sites falls below the pre-specified 1% practical-significance threshold: a controlled ablation holding per-zone marginal forecasts bit-identical and varying only cross-zone dependence (10 configurations, 3 seeds, paired-bootstrap confidence intervals) shows a coherence gain of at most 0.64% of dispatch cost, indistinguishable from zero in 3 of 10 configurations, reached only under an unrealistic 8-fold forecast-error stress test. Decision-focused training, an established paradigm in this venue, delivers a robust 2.82-5.19% gain. A parametric Gaussian-copula approximation matches the empirical copula at realistic error magnitudes but performs worse than no dependence under extreme stress. A single-seed sweep shows that a 12% energy-score gain changes cost by less than 0.1%. Results characterize this single-period dispatch class; a lightweight four-period extension supports the same conclusion. For this dispatch class, spatially-correlated scenario generation provides limited operational value on its own; grid operators and forecast vendors should instead evaluate dependence models by downstream decision value and prioritize decision-focused training.