Rail transport of liquefied natural gas (LNG), as an essential means of transmitting energy across regions, has been widely implemented and promoted. The significant growth of gas-fired power and power-to-gas units has intensified the interaction and interdependency between wind power penetrated transmission system and LNG transported railway network. Hence, a robust and hierarchical scheduling approach to coordinate the transmission system and LNG railway transportation is innovatively proposed. First, an advanced time-space network (TSN) model is established with additional security constraints to exactly simulate the railway traffic. Then, a two-stage robust scheduling model is formulated with an uncertainty set incorporating random components failures of the transmission system and the railway network as well as variability of wind power and loads. Subsequently, the two-stage robust scheduling problem is decomposed into two interactive levels according to the analytical target cascading (ATC) technique, thus ensuring the information privacy and decision independency of the power and railway systems. Finally, an ATC-based hierarchical solution framework embedded with the column-and-constraint generation (C&CG) algorithm is presented to solve the proposed model. Case studies demonstrate the effectiveness and benefit of collaboratively managing the power and railway systems.

中文翻译：

---

协调风电直输系统与LNG铁路运输的鲁棒分层调度方法

液化天然气（LNG）铁路运输作为能源跨区域传输的重要手段，得到广泛实施和推广。燃气发电和电转气机组的显着增长，加剧了风电穿透传输系统与液化天然气运输铁路网络之间的相互作用和相互依存。因此，创新性地提出了协调传输系统和 LNG 铁路运输的鲁棒和分层调度方法。首先，建立了具有附加安全约束的高级时空网络 (TSN) 模型，以准确模拟铁路交通。然后，一个两阶段的鲁棒调度模型被制定为一个不确定集，包括传输系统和铁路网络的随机组件故障以及风力发电和负载的可变性。随后，根据分析目标级联（ATC）技术将两阶段鲁棒调度问题分解为两个交互层次，从而确保电力和铁路系统的信息隐私和决策独立性。最后，提出了一种基于 ATC 的分层解决方案框架，该框架嵌入了列和约束生成 (C&CG) 算法，用于求解所提出的模型。案例研究证明了协同管理电力和铁路系统的有效性和好处。根据分析目标级联（ATC）技术将两阶段鲁棒调度问题分解为两个交互层次，从而保证了电力和铁路系统的信息隐私和决策独立性。最后，提出了一种基于 ATC 的分层解决方案框架，该框架嵌入了列和约束生成 (C&CG) 算法，用于求解所提出的模型。案例研究证明了协同管理电力和铁路系统的有效性和好处。根据分析目标级联（ATC）技术将两阶段鲁棒调度问题分解为两个交互层次，从而保证了电力和铁路系统的信息隐私和决策独立性。最后，提出了一种基于 ATC 的分层解决方案框架，该框架嵌入了列和约束生成 (C&CG) 算法，用于求解所提出的模型。案例研究证明了协同管理电力和铁路系统的有效性和好处。