Optimization Scheduling of Integrated Energy Systems Considering Power Flow Constraints

To further investigate the complementary characteristics among subsystems of the combined electricity–gas–heat system (CEGHS) and to enhance the renewable energy accommodation capability, this study proposes a comprehensive optimization scheduling framework. First, an optimization model is developed with the objective of minimizing the total system cost, incorporating key coupling components such as combined heat and power units, gas turbines, and power-to-gas (P2G) facilities. Second, to address the limitations of traditional robust optimization in managing wind power uncertainty, a distributionally robust optimization scheduling model based on Hausdorff distance is constructed, employing a data-driven uncertainty set to accurately characterize wind power fluctuations. Furthermore, to tackle the computational challenges posed by complex nonlinear equations within the model, various linearization techniques are applied, and a two-stage distributionally robust optimization approach is introduced to enhance solution efficiency. Simulation studies on an improved CEGHS system validate the feasibility and effectiveness of the proposed model, demonstrating significant improvements in both economic performance and system robustness compared to conventional methods.