This paper presents a prototype of a digital twin (DT) for an automated control system (ACS) of a smart power grid, developed for the analysis of cybersecurity threats. The proposed architecture replicates the behavior of the control layers of the energy system and incorporates modules for synthetic data generation, attack simulation, anomaly detection, and threat assessment. Experimental validation was carried out in a laboratory environment through the execution of typical cyberattack scenarios (DoS, malware injection, control signal compromise). A comparative evaluation of two configurations – one based solely on real data and the other incorporating synthetic data – demonstrated an increase in F1-score metrics when using extended datasets. The study discusses the limitations of the prototype, including simplified modeling of physical processes and the need for manual verification of generated data. The results suggest the applicability of the proposed approach for testing threat detection mechanisms in smart grid environments.