Leveraging the Synergy of Digital Twins and Artificial Intelligence for Sustainable Power Grids: A Scoping Review

Ama Ranawaka, Damminda Alahakoon (), Yuan Sun and Kushan Hewapathirana
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Ama Ranawaka: Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia
Damminda Alahakoon: Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia
Yuan Sun: Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia
Kushan Hewapathirana: Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC 3086, Australia

Energies, 2024, vol. 17, issue 21, 1-52

Abstract: As outlined by the International Energy Agency, 44% of carbon emissions in 2021 were attributed to electricity and heat generation. Under this critical scenario, the power industry has adopted technologies promoting sustainability in the form of smart grids, microgrids, and renewable energy. To overcome the technical challenges associated with these emerging approaches and to preserve the stability and reliability of the power system, integrating advanced digital technologies such as Digital Twins (DTs) and Artificial Intelligence (AI) is crucial. While existing research has explored DTs and AI in power systems separately, an overarching review of their combined, synergetic application in sustainable power systems is lacking. Hence, in this work, a comprehensive scoping review is conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The main results of this review analysed the breadth and relationships among power systems, DTs, and AI dynamics and presented an evolutionary timeline with three distinct periods of maturity. The prominent utilisation of deep learning, supervised learning, reinforcement learning, and swarm intelligence techniques was identified as mainly constrained to power system operations and maintenance functions, along with the potential for more sophisticated AI techniques in computer vision, natural language processing, and smart robotics. This review also discovered sustainability-related objectives addressed by AI-powered DTs in power systems, encompassing renewable energy integration and energy efficiency, while encouraging the investigation of more direct efforts on sustainable power systems.

Keywords: electrical power grids; digital twins; artificial intelligence; sustainability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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