Distributed Model Predictive Control Strategy for Multi-energy Virtual Power Plant Based on Digital Twin

Multi-energy virtual power plant (MEVPP) faces significant challenges stemming from the inherent intermittency of renewable energy, the underutilized flexibility of distributed load-side resources, and the intricate complexities associated with optimizing multi-energy coupling and dynamic carbon emission management. This paper proposes a multi-timescale digital twin rolling optimization strategy incorporating vehicle-to-grid (V2G) interaction and stepwise dynamic carbon trading to address these issues. First, a digital twin dynamic aggregation modeling approach is developed by combining deep neural networks with an enhanced k-means spectral clustering algorithm. This enables accurate dynamic characterization of wind and solar generation uncertainties and equivalent aggregation of distributed resources, thereby unlocking greater flexibility on the demand side. Second, considering electricity-carbon coupling and the unique operational characteristics of electric vehicle charging and discharging, a real-time pricing strategy based on time-of-use tariffs and incentive/penalty mechanisms is introduced. Simultaneously, a stepwise dynamic carbon trading mechanism is proposed to facilitate more precise measurement of carbon emissions from various devices and enable coordinated optimization of electricity and carbon flows. Finally, a multi-stage rolling optimization model is constructed within a distributed model predictive control framework to mitigate deviations between day-ahead scheduling and intra-day operation caused by prediction errors and changing weather conditions. Multi-scenario comparative analyses demonstrate that the proposed strategy can effectively reduce the peak-to-valley difference of the electric load, lower the total system economic cost, and decrease carbon emissions while maintaining operational cost variance within 1.2%. These findings offer theoretical and practical guidance for the efficient and sustainable operation of MEVPP within next-generation power systems, particularly under evolving market conditions and increasing renewable penetration.