Adaptive Hybrid Chance‐Constrained SOCP‐Based ACOPF for Coordinated Optimisation of Integrated Energy Systems

ABSTRACT Integrated source–grid–load–storage (SGLS) systems with high renewable energy penetration face dispatch challenges, including multi‐factor coordination, renewable uncertainties and multiple policy constraints. Traditional deterministic optimisation struggles to handle the uncertainty of wind and solar power, whereas complex chance‐constrained programming often suffers from low computational efficiency and poor practicality. To address these challenges, this paper proposes an adaptive chance‐constrained hybrid second‐order cone programming (SOCP)‐based AC optimal power flow (ACOPF), designed for the coordinated optimisation of SGLS systems. The model incorporates a simplified chance‐constrained method that exploits the structural characteristics of SGLS systems, substantially lowering computational complexity. An iterative mechanism combining external power flow verification and dynamic constraint augmentation ensures solution robustness while avoiding the dimensionality curse caused by embedding numerous scenarios. Additionally, the algorithm features self‐adaptability. When the original problem is infeasible due to constraint conflicts, it automatically relaxes chance constraints into the objective function to ensure solvability. The main contribution lies in establishing a coordinated dispatch framework that balances computational efficiency, reliability and practicality while incorporating industrial load demand response characteristics and relevant policy constraints. Simulations demonstrate that the model rapidly achieves cost‐effective and robust scheduling by converging to exact global optima.