A Robust Optimization Framework for Flexible Industrial Energy Scheduling: Application to a Cement Plant with Market Participation

TL;DR

This paper introduces a robust optimization framework for industrial energy scheduling that accounts for uncertainties like electricity prices and renewable generation, improving resilience and cost stability in industrial operations.

Contribution

It develops a scenario-based robust optimization model with a hybrid scenario generation method and a tunable risk measure, specifically applied to cement plant energy scheduling under market uncertainty.

Findings

Enhanced resilience to forecast deviations

Reduced operational cost variability

More consistent industrial operations

Abstract

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.