A Real-Time Blockchain Enabled Digital Twin Framework With Energy Trading Estimation for Optimized Management in a Virtual Power Plant

This work presents a novel digital twin (DT) framework integrated with blockchain to ensure real-time optimized energy management of virtual power plants (VPPs). The framework design leverages energy trading forecasts for both day-ahead scheduling and real-time operation in a VPP consisting of solar generation, battery storage, and grid supply. Key inputs include forecasted solar irradiance, load patterns, real time sensor readings, and historical data, while the twin outputs span optimized energy dispatch, battery State of Charge (SoC), grid voltage stability, energy transactions, and load profiles for all participants. All critical sensor data and energy trades are securely recorded on the blockchain ledger, enabling transparent verification of operations rather than directly implementing the model on the DT making the overall design lightweight and fast responding. The proposed framework has been implemented and tested in MATLAB-Simulink, with validation against physical system performance to evaluate its effectiveness in efficient energy management. Experimental results show high fidelity between the twin and the physical VPP: real-time measurement discrepancies are essentially zero, with variances in RMS voltage close to 1 V and energy transactions remain below 2 W.