Digital Twin-Based Predictive Control for Energy Flexibility in Smart Grids and Buildings

Irsyad N Haq, Edi Leksono, Justin Pradipta

Abstract


This paper investigates the integration of digital twin-based predictive control techniques to achieve energy flexibility in both smart grids and buildings. The study develops a comprehensive control framework that leverages digital twin models to optimize energy consumption and enable demand response at different scales - from individual buildings to smart grid networks. The proposed approach integrates real-time data from digital twins with predictive control algorithms to make informed decisions for energy optimization. Experimental results demonstrate the effectiveness of the framework in enhancing energy flexibility and enabling dynamic energy management in both building and grid contexts.

References


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Lu, Qiuchen, et al. ‘Developing a Digital Twin at Building and City Levels: Case Study of West Cambridge Campus’. Journal of Management in Engineering, vol. 36, no. 3, American Society of Civil Engineers, 2020, p. 05020004.


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