A Hybrid Optimization Framework for Emergency Dispatch of Natural Gas Pipeline Networks Under Abnormal Conditions

This study investigates emergency scheduling optimization for natural gas networks under unexpected abnormal conditions, such as unplanned valve closures or equipment failures, which may trigger pressure alarms. Unlike normal planned operations, emergency scheduling requires rapid response and accounts for the dynamic lag of gas flow. A mixed-integer programming model is formulated to ensure system safety, satisfy pressure constraints, and minimize compressor energy consumption. To solve the high-dimensional nonlinear problem, a variable neighborhood search algorithm is proposed, which constructs initial feasible solutions via a greedy proximity-based approach and iteratively improves them using four “delete-compensate” neighborhood operators with the Metropolis acceptance criterion. Validation on a real natural gas network containing 645 stations, 69 pipelines, and 26 compressor stations, together with repeated computational experiments under two representative abnormal scenarios, demonstrates the effectiveness and applicability of the proposed method.