Research on Two-Stage Optimization Scheduling for Multi-Campus Integrated Energy Systems Based on Cloud-Edge Collaborative Architecture

To address renewable generation and load uncertainty in multi-campus integrated energy systems, this paper proposes a distributionally robust day-ahead–real-time coordinated scheduling model under a cloud-edge collaborative architecture. The studied system consists of photovoltaic, wind power, and combined heat and power campuses, each equipped with energy storage and transferable load resources. The cloud layer determines the day-ahead baseline dispatch plan, while the edge layer performs scenario-dependent real-time corrections. To improve adaptability to adverse operating conditions, bounded forecast-error scenarios are constructed, and a conditional value-at-risk-based distributionally robust objective is formulated. Meanwhile, a soft day-ahead–real-time energy-binding mechanism is introduced to maintain plan-execution consistency while allowing necessary real-time adjustments. Case studies show that, compared with the cases without peer-to-peer energy exchange, demand response, and energy storage, the proposed model reduces the objective value by 5.22%, 10.96%, and 5.05%, respectively. Sensitivity analysis and stress tests verify its feasibility and robustness under increased uncertainty and reduced flexible-resource capacities.