Multi-timescale distributed control for multi-energy virtual power plant clusters via cloud-edge collaboration

The proliferation of distributed energy resources and multi-energy coupling technologies has transformed virtual power plants into complex MEVPP clusters. However, coordinating these heterogeneous clusters poses multifaceted challenges, particularly due to the deep stochastic uncertainties of renewables, the complex coupling of energy vectors with distinct dynamic inertias, and the severe computational bottlenecks inherent in traditional centralized control across multiple timescales. This paper proposes a novel hierarchical control strategy that leverages cloud-edge collaborative architecture and digital twin technology to optimize MEVPP clusters across electricity, heat, and gas networks. The framework employs a three-layer temporal decomposition: cloud-based day-ahead scheduling using mixed-integer linear programming for 24-h economic dispatch; edge-based intra-day rolling optimization with 15-min intervals for uncertainty mitigation; and real-time DMPC with 5-min resolution for dynamic balancing. A comprehensive digital twin framework integrates physics-based multi-energy flow models with data-driven techniques to enhance state estimation and prediction accuracy while maintaining computational efficiency for edge deployment. The DMPC algorithm coordinates multiple MEVPPs via decomposition and coordination, managing cross-coupling constraints and optimizing power distribution via tie-line sharing. Case studies on three heterogeneous MEVPPs demonstrate that the proposed strategy achieves a 1.07% reduction in total system costs through coordinated operation compared to independent optimization, with individual MEVPP cost reductions ranging from 8.77% to 28.84% during intra-day operation under renewable forecast uncertainties, while maintaining system stability and inter-MEVPP power exchange balance.