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Abstract

The increasing integration of renewable energy sources into power systems is intensifying the demand for greater flexibility among industrial electricity consumers. However, operational constraints, production requirements, and market dynamics pose significant challenges to achieving optimal flexibility. This paper presents an enhanced mixed integer linear programming (MILP) model that directly optimizes electricity consumption flexibility in manufacturing plants. Unlike previous approaches, the proposed model determines optimal transactions with both day-ahead and intraday continuous electricity markets, while ensuring production continuity and adhering to plant-specific operational constraints. The methodology is validated through annual simulations of two real world industrial configurations, cement manufacturing and steel production, using 2023 market data. Comparative results highlight that the steel plant achieved average electricity cost savings through flexibility of 0.41 euro/MWh, whereas the cement plant achieved 0.24 euro/MWh, reflecting differences in storage capacities, production rates, and operational flexibility. A comprehensive sensitivity analysis further identifies key parameters affecting flexibility potential, such as the production to demand ratio, storage capacity, and minimum operation periods. The findings offer valuable insights for industrial operators aiming to reduce energy costs, enhance operational flexibility, and support the decarbonization of electricity systems.