发表机构
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 中文总结
本文实现16.7-Hz铁路电力系统两层能量管理软件，涵盖日前与日内优化、场景预测控制及可审计配置，支持弃权（不执行）决策。
AI 中文摘要
本文介绍了第一部分中数学建模的两层能量管理系统在Python中的可执行实现。如有需要，软件首先将每小时铁路运行数据转换为15分钟时刻表，并通过时刻表处理层以15分钟分辨率计算总牵引功率和可用再生制动功率。在通过价格处理层从缓存或向ENTSO-E透明度平台发送请求获取能源价格后，软件构建确定性的每小时日前计划，可采用所谓的必须运行变流器承诺模式或亚小时可行性证书模式，后者需要更高的计算复杂度。基于时刻表、日前计划层的输入以及日内分辨率网络物理量的初始测量值，日内处理层通过区域二次规划和共识ADMM执行风险中性的日内场景模型预测控制器。保留的S1预测层将一周季节性朴素点预测与因果残差路径重采样相结合，而当前测量阶段在所有场景中保持一致。我们详细说明了日前和日内数据契约、绝对UTC耦合、局部QP组装、热启动、残差检验、求解器状态解释、物理验证的恢复路径、状态和关键绩效指标更新，以及铁路自有发电和抽水增量。Python包版本、依赖锁定、冻结计划清单、确定性测试夹具、运行日志和哈希清单使软件配置可执行且可审计。本文描述了软件行为和数值契约；比较性能和压力测试声明保留至第三部分。
英文摘要
This paper presents the executable Python implementation~\cite{noroozi2026bahnstromems} of the two-layer energy-management system developed mathematically in Part~I~\cite{norooziP1I}. If needed, the software first converts hourly railway operation data into a 15-minute timetable and calculates the amount of gross motoring and available regenerative powers in a 15-minute resolution using the timetable handling layer. Given the energy prices are available either cached or by sending request to ENTSO-E Transparency Platform through the price handling layer, the software constructs a deterministic hourly day-ahead plan with either in the so-called must-run converter commitment mode or in a sub-hourly feasibility certificate mode, where the latter demands more computational complexity. Given the inputs from the timetable, day-ahead planning layers and initial measurements of the intraday-resolutions network's physical quantities, the intraday handling layer executes a risk-neutral intraday scenario model predictive controller by area-wise quadratic programming and consensus ADMM. The retained S1 forecast layer combines a one-week seasonal-naive point forecast with causal residual-path resampling, while the current measured stage is identical in all scenarios. We specify the day-ahead and intraday data contracts, absolute-UTC coupling, local-QP assembly, warm starts, residual tests, solver-status interpretation, physically validated recovery paths, state and key-performance-index updates, and the railway-owned generation and pumping increment. The Python package version, dependency lock, frozen-plan manifests, deterministic fixtures, run logs, and hash manifests make the software configuration executable and auditable. The paper describes software behavior and numerical contracts; comparative performance and stress-test claims are reserved for Part~III~\cite{norooziP1III}.
