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 presents a novel flexibility management system to handle short-term power deviations generated by intrinsic variability in generation and demand, and behind-the-meter flexibility provision in a close-to-real-time framework (30-second intervals). The proposed system employs an adaptive autoregression algorithm as a short-term forecast of energy exchange to the grid and a cost-benefit analysis to adjust the power setpoints of the energy devices by means of redispatching and unit reassignment modules. Experimental case studies are conducted in the PVZEN microgrid laboratory to validate the proposed system, testing critical scenarios and diverse flexibility requests. The results confirm the optimal management of the power deviations and the techno-economic feasibility of flexibility supply with real-world equipment, reducing the total operation cost from 11% to 85% compared to the baseline scenario. Finally, findings of previous studies based on simulations are verified experimentally, validating the influence of variables such as sample frequency, aging cost models, and flexibility price.