Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Sebastián Rojas-Innocenti, Enrique Baeyens, Alejandro Martín-Crespo, Sergio Saludes-Rodil, Fernando Frechoso Escudero

TL;DR

The paper tackles uncertainty in short-term energy scheduling for electricity-intensive industries by introducing a scenario-based robust two-stage MILP. It combines a hybrid ARIMA-residual-simulation and clustering approach to generate representative price scenarios and couples this with flexible assets like BESS and SIDC participation to create risk-aware, feasible schedules. A tunable risk aversion parameter α balances worst-case protection and expected cost, and the method is validated on a real cement plant, showing reduced cost variability and improved resilience. The framework is scalable, modular, and applicable to industrial flexibility planning under uncertainty, with clear guidance for extending to multi-site, multi-market, and degradation-aware settings.

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.

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

TL;DR

The paper tackles uncertainty in short-term energy scheduling for electricity-intensive industries by introducing a scenario-based robust two-stage MILP. It combines a hybrid ARIMA-residual-simulation and clustering approach to generate representative price scenarios and couples this with flexible assets like BESS and SIDC participation to create risk-aware, feasible schedules. A tunable risk aversion parameter α balances worst-case protection and expected cost, and the method is validated on a real cement plant, showing reduced cost variability and improved resilience. The framework is scalable, modular, and applicable to industrial flexibility planning under uncertainty, with clear guidance for extending to multi-site, multi-market, and degradation-aware settings.

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.

Paper Structure

This paper contains 52 sections, 18 equations, 8 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. Methodological Overview
  5. Hybrid Scenario Generation for Robust Optimization
  6. Theoretical Justification.
  7. Deterministic Two-Stage MILP Framework (Reference Model)
  8. Stage 1: Baseline Production Scheduling
  9. Problem Structure
  10. Decision Variables
  11. Objective Function.
  12. System Constraints.
  13. Stage 2 – Flexibility Activation via SIDC Participation
  14. Conceptual overview.
  15. New decision variable.
  16. ...and 37 more sections

Figures (8)

  • Figure 1: Selection of the number of clusters. Top: Elbow method identifies diminishing returns beyond $k = 8$. Bottom: Silhouette scores indicate reduced clustering quality after $k = 8$.
  • Figure 2: Top: Full ensemble of 500 stochastic price trajectories and baseline ARIMA forecast. Bottom: Reduced set of 8 representative scenarios. The expected price profile is shown in dashed orange. Trajectory opacity reflects scenario probability.
  • Figure 3: Trade-off between expected cost and worst-case cost across values of $\alpha$. Results shown for Stage 1 (baseline scheduling) and Stage 2 (flexibility activation).
  • Figure 4: Top: Hourly BESS SoC profiles in Stage 1 under worst-case scenario. Bottom: Cumulative battery throughput across $\alpha$ values.
  • Figure 5: Top: Hourly BESS SoC profiles in Stage 2 (with SIDC) under worst-case scenario. Bottom: Cumulative battery throughput across $\alpha$ values.
  • ...and 3 more figures