16.7-Hz铁路电力系统分布式日内能量管理——第一部分：理论基础
Distributed Intraday Energy Management for 16.7-Hz Railway Power Systems---Part I: Theoretical Foundations
- SIGNON Deutschland GmbH (DB InfraGO AG)（SIGNON德国有限公司（德铁基础设施股份公司））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文为16.7-Hz铁路电力系统提出分布式日内能量管理框架，建立两层系统数学模型，将规划转化为稀疏凸二次规划，并采用分布式情景MPC求解，同时提出保护隐私的微观功率曲线估计方法。
AI中文摘要：
在一系列三篇论文中，我们提出了一种用于16.7-Hz单相铁路电力系统能量管理的分布式日内情景规划器，该规划器已作为开源Python包\ exttt{bahnstrom\_ems v0.9.1}实现并测试。我们工作的第一部分为16.7-Hz单相铁路电力网络的两层能量管理系统奠定了数学基础，其中该网络是多个控制区域的互联。给定日前计划、路侧电池存储能量的最新测量值以及牵引能量、可用再生能量和可再生能源的预测，我们提供了16.7-Hz单相铁路电力网络在日内时间尺度上的数学建模。然后，日内能量规划被表述为一个风险中性的凸随机优化问题。整体日内规划被证明是一个稀疏凸二次规划问题。因此，该问题通过采用分布式情景模型预测控制（MPC）设置递归求解。相应的区域问题、协调更新、收敛条件以及闭环关键性能指标累积均被推导得出。在实践中，详细的列车运动以及微观牵引和再生功率曲线通常对于精确模拟和数值验证是必需的。由于数据隐私和安全问题，真实电力网络中的详细列车运动以及微观牵引和再生功率曲线可能无法公开获取。因此，我们还提出了一种数学上可靠的方法来估计此类微观信息，该方法将因果时刻表和列车运动信息转换为每刻钟的牵引和再生功率曲线，同时精确保持每个小时的能量目标。
英文摘要：
In a series of three papers, we propose a distributed intraday scenario planner for energy management in 16.7-Hz single-phase railway power systems, which is realized and tested as open-source Python package \texttt{bahnstrom\_ems v0.9.1}~\cite{noroozi2026bahnstromems}. The first part of our work develops the mathematical foundation of a two-layer energy management system for a 16.7-Hz single-phase railway power network, where the network is an interconnection of several control areas. Given a day-ahead plan, last measurement of stored energy in wayside batteries, and forecasts of the tractive, available regenerative and renewable energy, we provide a mathematical modeling of the 16.7-Hz single-phase railway power network in intraday time-scale. Then the intraday energy planning is formulated as a risk-neutral convex stochastic optimization problem. The overall intraday planning is shown to be a sparse convex quadratic program. Hence, this problem is solved recursively by adopting a distributed scenario model predictive control (MPC) setting. The corresponding area problems, coordination updates, convergence conditions, and closed-loop key-performance-index accumulation are derived. In practice, detailed train-motion and microscopic motoring and regenerative-power profiles are usually needed for accurate simulations and numerical validation purposes. Due to data privacy and security concerns, detailed train-motion and microscopic motoring and regenerative-power profiles in a real power network may not be publicly available. Therefore, we also propose a mathematically-solid approach to estimate such microscopic information by converting causally timetable and train-motion information into quarter-hour motoring and regenerative-power profiles while exactly preserving each hourly energy target.
