Key result
Digital Twins applied in demand response predominantly operate at a maturity level of 3 (50% of studies), providing real-time data integration but lacking bidirectional automated control.
Why the study?
There are currently limited numbers of reviews exploring the concept and application of Digital Twins in the domain of demand response.
Systematic Review (n=27)
Answers are grounded in this paper and cite their sources.
Digital Twins show promise as an enabling tool for demand response in energy management, though most current applications remain at a maturity level that lacks bidirectional control.
Digital Twins (DT), as one of the emerging trends in digitalisation, have attracted a large amount of research in the past few years due to its ability to provide more accurate models and better predictions for various energy-related applications. In the context of demand response (DR), the real-time bidirectional communication characteristic of DT allows for functions such as real-time energy forecast and decision-making aid, which provided high potential in DR applications due to the nature of DR being highly time-sensitive. The objective of this paper is to explore the concept and application of DT in the domain of DR, as there are currently limited numbers of review on this topic. It was identified that most work in this area shows promising results but is still exploratory. The main application of DT in the domain of DR is using DT as an enabling tool by leveraging the real-time and data processing function of DT for monitoring and decision-making, and providing a medium for better visualisation. Future studies can experiment with applying DT frameworks to larger case studies and apply more elaborate DT with higher maturity levels.
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Xie et al. (2024) conducted a systematic review in Demand response in building energy management (n=27). Digital Twins was evaluated on Maturity level of Digital Twins applied in demand response. Digital Twins applied in demand response predominantly operate at a maturity level of 3 (50% of studies), providing real-time data integration but lacking bidirectional automated control.
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