摘要：

在新型能源“质能网”背景下，多能系统耦合不断加深，天然气管网作为连接油气供给端与电力系统的关键枢纽起着灵活转运与跨域调峰的作用。随着上游可再生能源并网规模扩大、下游用气负荷波动加剧，天然气管网的调度运行问题已由稳态分析与日级调整，变为面向精细时间尺度的动态控制问题。围绕这一转变，相关研究形成了两条相互耦合的主线：一是面向控制的模型构建，即在保留压力传播、管存变化、压缩机作用和边界扰动等关键物理特征的基础上，将高维非线性瞬态模型转化为适于在线反馈控制的低维动态模型；二是基于模型的控制设计，即如何在设备操作限制和高度不确定性源荷波动下，利用模型开展系统状态预测与控制约束表达，实现系统灵活运行。本文从模型构建与控制设计两条主线出发，对该领域的代表性研究进行了系统梳理。在建模方面，重点总结了集总参数形式下传递函数模型、状态空间模型及人工智能技术推动下数据驱动模型的发展，分析不同模型在物理可解释性、瞬态精度、在线计算效率与控制适用性之间的权衡；在控制方面，归纳由开环运行优化向预测反馈控制和直接反馈控制演进的研究脉络。在此基础上，进一步指出现有理论与工程部署中的关键障碍包括传播时滞的建模与补偿，以及实际执行器的非理想特性。最后总结了在模型精确性与控制适配性之间的矛盾、单一算法优势与多目标追求之间的矛盾以及理论前提假设与实际工程偏差之间的矛盾，并展望了多工况自适应低维建模、机理与数据驱动深度融合、智能控制方法以及复合型控制体系等未来研究方向。本文可为后续天然气管网系统级智能调控提供参考，助力构建质智共生新生态。

关键词: 天然气管网, 控制建模, 模型降阶, 模型控制, 智能调控

Abstract:

Against the backdrop of the emerging “substance-energy network”, the coupling among multi-energy systems is becoming increasingly strong. As a key hub linking the oil and gas supply side with the power system, natural gas pipeline networks play an important role in flexible energy transfer and cross-domain peak shaving. With the growing penetration of upstream renewable energy and the increasing fluctuations of downstream gas demand, the dispatch and operation of natural gas pipeline networks have shifted from steady-state analysis and day-level adjustment to dynamic control over refined temporal scales. Around this transition, related studies have developed along two closely coupled lines. The first is control-oriented model construction, which transforms high-dimensional nonlinear transient models into low-dimensional dynamic models suitable for online feedback control while preserving key physical characteristics such as pressure-wave propagation, linepack variation, compressor effects, and boundary disturbances. The second is model-based control design, which involves using models to predict system states and express control constraints under equipment operation limitations and highly uncertain source-load fluctuations, so as to achieve flexible system operation. This paper systematically reviews representative studies in this field from the perspectives of model construction and control design. On the modeling side, it focuses on the development of transfer-function models and state-space models in lumped-parameter form, as well as AI-enabled data-driven models, and analyzes the trade-offs among physical interpretability, transient accuracy, online computational efficiency, and control applicability. On the control side, it summarizes the research trajectory from open-loop operational optimization to predictive feedback control and direct feedback control. On this basis, the paper further identifies key obstacles in existing theoretical studies and engineering deployment, including the modeling and compensation of propagation delays and the non-ideal characteristics of practical actuators. Finally, it summarizes three major tensions: the tension between model accuracy and control applicability, the tension between the strengths of individual algorithms and the pursuit of multiple objectives, and the tension between theoretical assumptions and practical engineering deviations. Future research directions are then discussed, including adaptive low-dimensional modeling for multiple operating conditions, deep integration of mechanism-based and data-driven methods, intelligent control methods, and composite control architectures. This paper is expected to provide useful references for future system-level intelligent control of natural gas pipeline networks and to support the construction of a new ecosystem characterized by the symbiotic integration of substance and intelligence.

Key words: natural gas pipeline network, control-oriented modelling, model order reduction, model-based control, intelligent dispatch and control

中图分类号: 

• TE832

• TE973