A comparative analysis of electricity consumption flexibility in different industrial plant configurations

The increasing integration of renewable energy intensifies the demand for flexibility among industrial electricity consumers. However, operational constraints, production requirements, and market dynamics pose significant challenges. This paper presents an enhanced mixed-integer linear programming (MILP) model that directly optimizes electricity consumption flexibility in manufacturing plants. Unlike previous approaches, the model determines optimal transactions across both day-ahead and intraday continuous electricity markets, ensuring production continuity and adherence to plant-specific operational constraints. The methodology is validated through annual simulations of two real-world industrial configurations—cement and steel production—using 2023 market data. Results highlight average electricity cost savings through flexibility of 0.41 €/MWh for the steel plant and 0.24 €/MWh for the cement plant. These variations reflect differences in storage capacities, production rates, and operational flexibility. Although modest per unit, these savings translate into substantial annual economic benefits at an industrial scale. Beyond these specific case studies, the process-agnostic formulation is readily transferable to other energy-intensive industries. Additionally, a comprehensive sensitivity analysis identifies the production-to-demand ratio, storage capacity, and minimum operation periods as key parameters affecting flexibility potential, providing valuable insights for operators aiming to reduce costs and support power system decarbonization.