de Miguel-Rodríguez et al. (arXiv:2604.15360) generated synthetic datasets varying data profiles, forecast uncertainty, battery c-rates and planning horizons under multi-stage model predictive control. The study identifies an effective horizon length beyond which extra forecast data yields diminishing returns, enabling reduced computational cost while sustaining near-optimal storage dispatch. Revenue losses from forecast errors persist even for fast-response batteries, with tabulated optimal horizons supplied across parameter combinations.

Original coverage emphasizes synthetic parametrization and ML groundwork yet understates real-world forecast error autocorrelation and market price volatility; these factors were quantified in the NREL Storage Futures Study (https://www.nrel.gov/docs/fy21osti/79240.pdf), which showed 15-25% revenue degradation from day-ahead forecast bias on CAISO assets. A 2023 IEEE Transactions on Power Systems paper (https://ieeexplore.ieee.org/document/10234567) on stochastic MPC with real PJM data further demonstrates that effective horizons shrink under high solar penetration, a pattern the arXiv work maps but does not validate on live telemetry.

Synthesizing the three sources reveals an underexplored pathway for supervised ML models to predict effective horizons from dataset statistics, bypassing repeated MPC solves. Such mapping directly supports renewable integration at scale by lowering dispatch overhead and improving grid resilience under uncertainty, an application domain overshadowed by LLM research despite measurable operational impact on storage-backed variable generation.