Electricity-intensive industries are highly impacted by electricity prices and their fluctuations. While these industries can change their consumption profiles to avoid peak price periods, they must also adhere to production schedules dictated by their unique manufacturing processes and related constraints. Balance Responsible Parties (BRPs), seek to optimize the electricity costs of their portfolio of customers and have limited access to real-time industrial flexibility. So, the concept of Daily Industrial Flexibility (DIF) is introduced as a trade-off between real-time flexibility and predetermined industrial schedules. Each factory can propose different fixed day-ahead consumption profiles to the BRP, each with a specific activation cost. In this context, this paper proposes a day-ahead flexibility service to minimize the BRP’s costs of electricity purchase on the day-ahead market, activating DIFs within the portfolio of industrial customers. A forecasting model for the electricity price is implemented and an optimization model is formulated to minimize day-ahead portfolio costs. Finally, the case study of a Catalan BRP is presented, considering DIF offers from its largest customers. Results demonstrate that the proposed approach enables accurate predictions of the BRP’s customers operation before DAM closure, successfully identifying alternative schedules to reduce portfolio costs.

This paper shows an impact assessment of second-life batteries (SLBs) and local photovoltaics (PV) for decarbonizing enterprises through system digitalization and energy management. SLBs from electric vehicles offer a cost-effective and environmentally sustainable energy storage solution for enterprises. These systems can significantly reduce fossil fuel dependence coupled with local PV installations. This paper proposes a methodology for developing the complete digital twin of an enterprise in combination with an optimization algorithm for energy management. This methodology can be applied to a wide range of enterprises across different sectors, both industrial and non-industrial, with diverse consumption patterns. A sensitivity analysis has been carried out to evaluate the potential of this methodology for enterprises in different contexts, where different battery sizes, PV installations, consumption types, and environmental prioritization policies are encountered. Findings indicate that combining SLBs and PV installation, supported by digital energy management, can substantially reduce carbon footprints and operational costs.

This paper proposes a two-stage framework for industrial cluster flexibility that separates day-ahead internal coordination from downstream balancing market assessment. In Step 1, a MILP based internal market optimization selects the combination of baseline and Alternative Production Plans (APPs) across industrial sites while coordinating shared PV and ESS resources to minimize cluster operating cost and emissions. In Step 2, the cluster wide schedule obtained in Step 1 is used as the basis for a deterministic external market assessment that checks which machine level flexibility can actually be delivered, determines when the aggregated response is large enough for mFRR participation, forms feasible shutdown sets, and estimates the corresponding revenue potential under market activation. The framework is evaluated on a synthetic industrial cluster derived by the FLEX4FACT project. Results show that cooperative internal scheduling creates substantial value, while the downstream Step 2 assessment identifies the bid-feasible flexibility that can be offered once machine-level constraints are enforced. The framework provides a practical and operationally grounded pathway for translating production constrained industrial flexibility into coordinated market participation, while highlighting the complementary roles of internal optimization and external market assessment in industrial flexibility aggregation.