Digital twin applications and case studies in modern power grid management

This research presents a comprehensive overview of case studies and applications of digital twin DT technology in modern power grids. The analysis incorporates insights from diverse pilot projects, including a remote microgrid deployment in Alaska, a national-scale transmission network initiative in Singapore and a community-wide decarbonization program in the United Kingdom. Collectively, these projects illustrate the capability of DTs to generate virtual counterparts of power systems enabling real-time simulation, predictive maintenance and resilience planning. The theoretical foundation emphasizes the role of DTs in constructing dynamic digital representations of power systems facilitating enhanced system monitoring and control. The case studies examined reveal several key benefits like improved situational awareness, deferral of capital-intensive infrastructure investments, increased operational efficiency and the capacity to evaluate complex scenarios such as high renewable energy penetration, grid faults and novel load integrations within a risk-free virtual environment. In addition to highlighting benefits, this work also addresses the technical challenges associated with DT implementation. These include the integration of heterogeneous data sources such as SCADA, advanced metering infrastructure and internet of things IoT devices. The incorporation of machine learning-based modeling approaches and concerns related to data security and governance. In conclusion, the examined scenarios demonstrate that digital twin technology offers a robust framework for modernizing power systems, facilitating the integration of distributed energy resources, and preparing electrical grids to accommodate future operational and structural transformations. • This paper presents a comprehensive multi-scale synthesis of digital twins in cyber-physical energy systems. • By consolidating quantitative performance indicators such as cost savings, outage reduction, forecasting accuracy and efficiency gains • This work proposes actionable recommendations for sustainable deployment, hybrid physics-AI digital twins and standards-aware system design. • Collectively, these contributions position digital twins as a foundation enabler for resilient, intelligent and sustainable power grid optimization