The integration of renewable energy sources, battery energy storage, and electric vehicles into industrial systems unlocks new opportunities for reducing emissions and improving sustainability. However, the coordination and management of these new technologies also pose new challenges due to complex interactions. This paper proposes a methodology for designing a holistic energy management system, based on advanced digital twins and optimization techniques, to minimize the cost of supplying industry loads and electric vehicles using local renewable energy sources, second-life battery energy storage systems, and grid power. The digital twins represent and forecast the principal energy assets, providing variables necessary for optimizers, such as photovoltaic generation, the state of charge and state of health of electric vehicles and stationary batteries, and industry power demand. Furthermore, a two-layer optimization framework based on mixed-integer linear programming is proposed. The optimization aims to minimize the cost of purchased energy from the grid, local second-life battery operation, and electric vehicle fleet charging. The paper details the mathematical fundamentals behind digital twins and optimizers. Finally, a real-world case study is used to demonstrate the operation of the proposed approach within the context of the waste collection and management industry. The study confirms the effectiveness of digital twins for forecasting and performance analysis in complex energy systems. Furthermore, the optimization strategies reduce the operational costs by 1.3%, compared to the actual industry procedure, resulting in daily savings of EUR 24.2 through the efficient scheduling of electric vehicle fleet charging.

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.