A Comprehensive Review of Digital Twin Technology for Grid-Connected Microgrid Systems: State of the Art, Potential and Challenges Faced

Abstract

The concept of the digital twin has been adopted as an important aspect in digital transformation of power systems. Although the notion of the digital twin is not new, its adoption into the energy sector has been recent and has targeted increased operational efficiency. This paper is focused on addressing an important gap in the research literature reviewing the state of the art in utilization of digital twin technology in microgrids, an important component of power systems. A microgrid is a local power network that acts as a dependable island within bigger regional and national electricity networks, providing power without interruption even when the main grid is down. Microgrids are essential components of smart cities that are both resilient and sustainable, providing smart cities the opportunity to develop sustainable energy delivery systems. Due to the complexity of design, development and maintenance of a microgrid, an efficient simulation model with ability to handle the complexity and spatio-temporal nature is important. The digital twin technologies have the potential to address the above-mentioned requirements, providing an exact virtual model of the physical entity of the power system. The paper reviews the application of digital twins in a microgrid at electrical points where the microgrid connects or disconnects from the main distribution grid, that is, points of common coupling. Furthermore, potential applications of the digital twin in microgrids for better control, security and resilient operation and challenges faced are also discussed.

Sustainable Electrification—Advances and Challenges in Electrical-Distribution Networks: A Review

“…The discussion extends to acknowledge the existing gaps, particularly in addressing DC microgrids and power electronics structures. Energy-management systems for microgrids take center stage in the study conducted by [98], where the challenges associated with the rising electricalpower demand are addressed. This research reviews various strategies for load-demand, energy-generation, and energy-storage-system management, pointing towards a future dominated by metaheuristic optimization approaches, including artificial intelligence.…”