Adaptive digital twin for time-varying energy flexibility dynamics

This paper introduces an adaptive digital twin framework that represents the time-varying energy flexibility dynamics within energy systems. The proposed digital twin captures the real-time dynamic variations in demand response, price sensitivity, and energy consumption patterns by adapting to evolving system behaviors and external influences. By utilizing recursive modeling techniques, the digital twin dynamically calibrates its parameters to reflect current system conditions, enabling accurate and responsive representation of price-demand interactions. This tool can then be utilized by aggregators and flexibility management systems to effectively and accurately predict the demand and manage demand-response in Smart Energy Operating Systems. Simulation results demonstrate the ability to adaptively maintain accuracy under varying demand profiles and price fluctuations, highlighting its potential as a valuable asset for modern, responsive energy systems.