Industrial electric drives account for a dominant share of electricity consumption in manufacturing, making their optimal configuration a critical factor for both sustainability and cost reduction. Traditional design approaches based on prototyping and empirical testing are often costly and insufficient for systematically exploring alternative configurations. This study introduces an integrated computational framework that combines digital twin (DT) modeling and virtual commissioning (VC) to enable energy-aware configuration of industrial electric drive systems at early design stages. The methodology employs parameterized component models derived from manufacturer catalog data, implemented in a commercial simulation environment and integrated into an industrial-grade VC platform. Validation is performed on two conveyor-based testbeds, enabling systematic comparison of simulation outputs with physical measurements. The results demonstrate predictive accuracy sufficient to quantify trade-offs in energy consumption, losses, and efficiency across different vendor solutions. Case studies involving belt and strap conveyors highlighted how the framework supports vendor-neutral decision making, revealing nonintuitive optimization trade-offs between minimizing energy consumption and maximizing efficiency. The proposed framework advances sustainable automation by embedding energy analysis directly into commissioning workflows, offering reproducible, scalable, and cross-domain applicability. Its modular design supports transfer to sectors such as renewable energy, transportation, and biomedical mechatronics, where energy efficiency is equally decisive.
Bysko et al. (Mon,) studied this question.
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