Digital Twin Technology For Power Systems: Researching Virtual Models For Predicting Blackout Scenarios Under Climate Variability

This work explores how digital twin tech applies to electrical grids, particularly through simulated models designed to foresee blackouts caused by shifting weather patterns. Through a structured analysis of ten scholarly and industrial publications, it suggests these virtual replicas hold both conceptual depth and practical strength in forecasting grid behavior during unstable climate phases. Evidence shows such twins - which mirror actual infrastructure through live, two-way data flow - allow ongoing tracking, forward-looking modeling, and smart regulation, features highly relevant for spotting faults before they spread. It also becomes clear that changing climates bring layered pressures like intense temperatures, downpours, storms, and fire outbreaks, challenges traditional network designs struggle to handle. One path forward lies in thoughtfully built digital duplicates, combining internet-connected sensors, machine learning tools, and up-to-the-minute environmental inputs, forming a response to today's growing uncertainty. Still, major obstacles persist - lacking uniform frameworks, facing steep setup expenses, exposed to cyber threats, along with difficulties in simulating vast, diverse power systems. For digital replicas to function effectively across a country's electricity network, changes in organizational structure plus funding toward shared technological platforms become essential first steps.