As global energy demand continues to rise, natural gas pipeline systems face significant challenges, including congestion during peak demand periods and the complexities of system expansion. This paper introduces an integrated congestion identification and expansion planning framework (ICIEPF) designed to enhance the hydraulic reliability of pipeline networks. The framework integrates two key models: a physics-based congestion location model (CL-Model) and a multi-component expansion optimization model (MCEO-Model). The CL-Model employs a flow relaxation variable to precisely identify congestion points, quantifying the severity of bottlenecks and providing a deterministic foundation for targeted investments. The MCEO-Model utilizes these insights to optimize expansion strategies—considering pipeline loops, new pipelines, and compressor station upgrades—while strictly enforcing equipment operational safety. The framework utilizes a two-stage sequential optimization strategy, incorporating convex relaxation and linearization techniques to enable a high-quality warm start, thereby accelerating solution speed and minimizing costs. Case studies demonstrate that, compared with the direct solving method, the ICIEPF achieves an average computational speedup of 12.37 times. Furthermore, it reduces expansion costs by 54.8% compared to single-component expansion strategies and significantly improves the compressor safety margins. These results demonstrate the framework's potential to address complex pipeline expansion challenges while improving system reliability and operational efficiency.

As the proportion of natural gas consumption in the energy market gradually increases, optimizing the design of gas storage surface system (GSSS) has become a current research focus. Existing studies on the two independent injection pipeline network (InNET) and production pipeline network (ProNET) for underground natural gas storage (UNGS) are scarce, and no optimization methods have been proposed yet. Therefore, this paper focuses on the flow and pressure boundary characteristics of the GSSS. It constructs systematic models, including the injection multi-condition coupled model (INM model), production multi-condition coupled model (PRM model), injection single condition model (INS model) and production single condition model (PRS model) to optimize the design parameters. Additionally, this paper proposes a hybrid genetic algorithm based on generalized reduced gradient (HGA-GRG) for solving the models. The models and algorithm are applied to a case study with the objective of minimizing the cost of the pipeline network. For the GSSS, nine different condition scenarios are considered, and iterative process analysis and sensitivity analysis of these scenarios are conducted. Moreover, simulation scenarios are set up to verify the applicability of different scenarios to the boundaries. The research results show that the cost of the InNET considering the coupled pressure boundary is 64.4890 × 10⁴ CNY, and the cost of the ProNET considering coupled flow and pressure boundaries is 87.7655 × 10⁴ CNY, demonstrating greater applicability and economy than those considering only one or two types of conditions. The algorithms and models proposed in this paper provide an effective means for the design of parameters for GSSS.