Distributed Intraday Energy Management for 16.7-Hz Railway Power Systems---Part I: Theoretical Foundations
Abstract
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.
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BibTeX
@misc{noroozi2026distributed,
title = {{Distributed Intraday Energy Management for 16.7-Hz Railway Power Systems---Part I: Theoretical Foundations}},
author = {Navid Noroozi},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.21808},
eprint = {2609.21808},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.21808},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Distributed Intraday Energy Management for 16.7-Hz Railway Power Systems---Part I: Theoretical Foundations
AU - Navid Noroozi
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2609.21808
UR - https://arxiv.org/abs/2609.21808
AB - 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.
ER -