AI 中文总结
研究针对大规模燃气网络情景评估的计算瓶颈问题，开发物理信息图神经网络代理，用于稳态燃气网络模拟和可行性筛选，经实验验证其在精度和速度上表现出色，能为高负荷情景筛选提供物理约束规划加速。
AI 中文摘要
大规模燃气网络情景评估是综合能源系统规划中的计算瓶颈，尤其是燃气基础设施与电力、热力、氢能及部门耦合路径相互作用时。传统非线性水力求解器能提供可靠可行性评估，但随机筛选成本高，基于无约束学习的代理可能产生水力不可行状态。本研究开发了用于稳态燃气网络模拟和可行性筛选的物理信息图神经网络代理。该模型采用以边为中心的架构预测管道级平方压差和流量，用可微投影层强制预测流量的节点质量守恒，用拉普拉斯重建将边压差映射到拓扑一致的节点压力。在GasLib - 134、GasLib - 135和GasLib - 582上使用随机生成的运行情景对框架进行评估。在582节点基准测试中，代理实现压力平均绝对误差1.05 bar，占实际压力范围的1.3%，\(R^2 = 0.981\)；预测流量\(R^2 = 0.972\)，质量平衡残差降至数值精度，约\(10^{-5}\) - \(10^{-4}\) Nm³/s。与MYNTS参考求解器相比，推理从秒级降至毫秒级，最大基准测试评估时间不到40 ms。负荷能力和分布外压力测试评估表明在高负荷条件下具有强大的可行性筛选能力，同时识别出强局部需求集中情况需在可行性极限附近基于求解器进行验证。该框架为大量情景筛选和优先级排序提供了物理约束规划加速器。
英文摘要
Large-scale gas-network scenario evaluation is a computational bottleneck in integrated energy-system planning, particularly when gas infrastructure interacts with power, heat, hydrogen, and sector-coupling pathways. Conventional nonlinear hydraulic solvers provide reliable feasibility assessment but are costly for stochastic screening, whereas unconstrained learning-based surrogates may produce hydraulically infeasible states. This study develops a physics-informed graph neural network surrogate for steady-state gas-network simulation and feasibility screening. The model uses an edge-centric architecture to predict pipe-level squared-pressure differences and flows. A differentiable projection layer enforces nodal mass conservation on predicted flows, while a Laplacian reconstruction maps edge pressure differences to topologically consistent nodal pressures. The framework is evaluated on GasLib-134, GasLib-135, and GasLib-582 using stochastically generated operating scenarios. On the meshed 582-node benchmark trained with 5000 scenarios, the surrogate achieves a pressure mean absolute error of 1.05~bar, corresponding to 1.3\% of the realized pressure range, with $R^2 = 0.981$. Projected-flow predictions reach $R^2 = 0.972$, and mass-balance residuals are reduced to numerical precision, on the order of $10^{-5}$--$10^{-4}$~Nm$^3$/s. Compared with the MYNTS reference solver, inference is reduced from seconds to milliseconds, with the largest benchmark evaluated in less than 40~ms. Loadability and out-of-distribution stress-test evaluations demonstrate robust feasibility screening under high-load conditions, while strongly localized demand concentrations are identified as cases requiring solver-based verification near feasibility limits. The framework provides a physically constrained planning accelerator for high-volume scenario screening and prioritization.
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