发表机构
University of Houston; Los Alamos National Laboratory（休斯顿大学; 洛斯阿拉莫斯国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对动态天然气网络最优控制问题，提出结合机会约束重构的高斯过程与富集循环等方法，实现了稳态和瞬态下的压力风险控制，提升了调度可靠性且计算效率高。
AI 中文摘要
天然气网络必须在维持管道压力的同时适应可变的供需，管道中存储的气体通过时间耦合了运行决策。概率代理模型可简化压缩机调度，但优化过程中使用其预测不确定性时必须保证可靠性。我们结合最小压力裕度的直接学习与优化器选定调度的评估，使用扭曲高斯过程（warped Gaussian process）将后验压力需求表示为其预测均值与标准差的一个不等式。随后我们对块调度和傅里叶调度进行参数化，使标量预测和解析导数替代全天优化中的轨迹状态与时间积分。稳态下，一个通过保留数据的精度和覆盖率检验的代理模型，在名义1%容忍度下，其自身调度中有71%的情况低于最小压力，原因是优化器被吸引到裕度被高估的调度。该诊断促使我们构建一个富集循环，该循环对照物理规则审计选定调度，并通过精确条件更新代理模型，同时保持拟合量固定。该循环在所有研究的容忍度下均恢复了稳态最小压力风险控制，且条件更新比重拟合快约四个数量级。瞬态运行中，我们通过经验阈值校准补充该富集过程，以解决剩余的小时级乐观偏差，在压缩机工作开销很小的情况下，使小时级违规率更接近其声明的容忍度。
英文摘要
Natural gas networks must accommodate variable demand and supply while maintaining pressure across pipelines whose stored gas couples operating decisions through time. Probabilistic surrogates can simplify compressor scheduling, but their predictive uncertainty must remain reliable where optimization uses it. We combine direct learning of the minimum pressure margin with assessment at optimizer-selected schedules. Using a warped Gaussian process, we express the posterior pressure requirement as one inequality in its predictive mean and standard deviation. We then parameterize block and Fourier schedules so scalar predictions and analytic derivatives replace trajectory states and time integration in whole-day optimization. In steady state, a surrogate that passes held-out accuracy and coverage checks nevertheless falls below the minimum pressure on 71% of its own dispatches at a nominal 1% tolerance, as the optimizer is drawn to schedules where the margin is overestimated. This diagnostic motivates an enrichment loop that audits selected dispatches against the physics and updates the surrogate by exact conditioning, with fitted quantities held fixed. The loop restores steady-state minimum-pressure risk control at every studied tolerance, with conditioning about four orders of magnitude faster than refitting. In transient operation, we complement this enrichment with empirical threshold calibration to address residual hourly optimism, bringing hourly violation rates closer to their declared tolerances with little compressor work overhead.
Comments9 pages, 6 figures, 4 tables
