On future power system digital twins: A vision towards a standard architecture

TL;DR

This paper envisions a standardized architecture for digital twins in power systems, integrating AI and ML to enhance system lifecycle management and address current challenges in measurement, modeling, and uncertainty.

Contribution

It introduces a tailored definition of power system digital twins and proposes a comprehensive, standard ecosystem architecture for their integration and application.

Findings

Proposes a standard digital twin ecosystem architecture for power systems.

Highlights integration of AI/ML to improve digital twin functionalities.

Discusses challenges like measurement errors and uncertainty management.

Abstract

The energy sector's digital transformation brings mutually dependent communication and energy infrastructure, tightening the relationship between the physical and the digital world. Digital twins (DT) are the key concept for this. This paper initially discusses the evolution of the DT concept across various engineering applications before narrowing its focus to the power systems domain. By reviewing different definitions and applications, the authors present a new definition of DTs specifically tailored to power systems. Based on the proposed definition and extensive deliberations and consultations with distribution system operators, energy traders, and municipalities, the authors introduce a vision of a standard DT ecosystem architecture that offers services beyond real-time updates and can seamlessly integrate with existing transmission and distribution system operators' processes while reconciling with concepts such as microgrids and local energy communities based on a system-of-systems view. The authors also discuss their vision related to the integration of power system DTs into various phases of the system's life cycle, such as long-term planning, emphasising challenges that remain to be addressed, such as managing measurement and model errors, and uncertainty propagation. Finally, the authors present their vision of how artificial intelligence and machine learning can enhance several power systems DT modules established in the proposed architecture.