A cyber–physical digital twin for secure and optimal operation of smart energy grids under deep uncertainty

Smart energy networks, as critical cyber-physical infrastructures, face simultaneous challenges such as conflicting operational objectives, data uncertainty, and increasing cyber threats. Decision-making in such environments requires frameworks that can manage system dynamics, information ambiguity, and security risks in an integrated manner. In this study, an innovative decision-making framework is presented that enables adaptive and resilient optimization of energy networks by combining digital twin and fuzzy goal modeling. As an active component, the digital twin dynamically updates the decision-making process and reflects the impact of different cyber-attack scenarios on decisions. The results of various analyses show that the proposed framework is able to significantly improve the quality of decisions, performance stability, and system resilience under uncertain conditions. This study provides a new path for the development of smart and secure decision support systems in the energy sector.