Digital Twin Control Strategy for Multi-energy Virtual Power Plants Integrating Waste-to-energy Plants and Vehicle-to-grid Interaction

Introduction: Multi-Energy Virtual Power Plants (MEVPPs) integrating Waste-to-Energy Plants (WEPs), distributed renewables, and Electric Vehicles (EVs) offer low-carbon benefits, yet their uncertainties—fluctuating WEP output, stochastic wind/solar generation, and erratic EV charging— hinder accurate quantification of environmental performance. This study aims to precisely characterize these uncertainties and enhance MEVPP coordination via a combined physical-data modeling approach. Methods: A Distributed Model Predictive Control (DMPC) method based on digital twins is proposed. Specifically, a five-stage physical-digital hybrid digital twin model captures WEP uncertainty; dynamic adaptive spectral clustering and weighted capacity aggregation address spatial heterogeneity of distributed wind/solar; a truncated-gamma-distribution-based ordered charging model handles EV uncertainty. A multi-timescale DMPC framework (day-ahead planning, intraday rolling, real-time adjustment) is established. Results: The hybrid digital twin improved WEP output prediction accuracy by 41% over single-data models, and renewable prediction under sudden weather events by 42.3%. Compared with stochastic MPC, robust MPC, and a consensus-ADMM DMPC baseline, our scheme reduced operating cost by 5.2%–7.1% and computation time by 36.3%–82.5%, while maintaining constraint-violation rate below 0.21%. Discussion: These findings underscore that the hybrid physical-digital framework effectively mitigates multiple uncertainties, enabling more reliable scheduling and significant computational savings. The robustness is further validated across summer, winter, high-renewable, and stressed weather scenarios, alongside sensitivity analyses on EV penetration, wind-to-solar ratio, and carbon pricing, confirming broad applicability under diverse operational conditions. Conclusion: The proposed DMPC with digital-twin enhancement substantially improves scheduling accuracy, operational efficiency, and low-carbon performance of MEVPP, providing a practical pathway for promoting renewable integration and supporting the "dual carbon" goals.