This paper presents a scenario based robust optimization framework for short term energy scheduling in electricity intensive industrial plants, explicitly addressing uncertainty in planning decisions. The model is formulated as a two-stage Mixed Integer Linear Program (MILP) and integrates a hybrid scenario generation method capable of representing uncertain inputs such as electricity prices, renewable generation, and internal demand. A convex objective function combining expected and worst case operational costs allows for tunable risk aversion, enabling planners to balance economic performance and robustness. The resulting schedule ensures feasibility across all scenarios and supports coordinated use of industrial flexibility assets, including battery energy storage and shiftable production. To isolate the effects of market volatility, the framework is applied to a real world cement manufacturing case study considering only day-ahead electricity price uncertainty, with all other inputs treated deterministically. Results show improved resilience to forecast deviations, reduced cost variability, and more consistent operations. The proposed method offers a scalable and risk-aware approach for industrial flexibility planning under uncertainty.
@article{arxiv.2506.10824,
title = {A Robust Optimization Framework for Flexible Industrial Energy Scheduling: Application to a Cement Plant with Market Participation},
author = {Sebastián Rojas-Innocenti and Enrique Baeyens and Alejandro Martín-Crespo and Sergio Saludes-Rodil and Fernando Frechoso Escudero},
journal= {arXiv preprint arXiv:2506.10824},
year = {2025}
}