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.