Computational cost reduction in electric traction network modelling and simulation

This article presents a comparative study between a newly developed electric traction grid modelling methodology and an earlier modular approach verified in the literature. A statistical analysis of simulation times from 44,000 runs demonstrates the significant computational cost reduction achieved with the proposed advanced circuit-based modelling method. In combination with its proven spatial precision, this efficiency makes the methodology particularly suitable for real-time applications. The enhanced computational performance also supports the model's potential for future integration of energy storage systems and other sustainable technologies into electric traction grids. • Study of novel traction grid models reveals key sources of computational effort. • Optimised method shows notable reduction in execution time for large, meshed grids. • Optimised method's real-time potential suits monitoring for sustainable grids.

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Publication Details

Journal
Results in Engineering
Published
2025-10-15
DOI
https://doi.org/10.1016/j.rineng.2025.107702
Primary Topic
Railway Systems and Energy Efficiency
Type
article
Field-Weighted Citation Impact
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article

Computational cost reduction in electric traction network modelling and simulation

Riccardo Mandrioli, Vincenzo Cirimele, Mattia Ricco, Riccardo Barbone
Results in Engineering
Railway Systems and Energy Efficiency
article

Computational cost reduction in electric traction network modelling and simulation

Riccardo Mandrioli, Vincenzo Cirimele, Mattia Ricco, Riccardo Barbone
article en

Abstract

This article presents a comparative study between a newly developed electric traction grid modelling methodology and an earlier modular approach verified in the literature. A statistical analysis of simulation times from 44,000 runs demonstrates the significant computational cost reduction achieved with the proposed advanced circuit-based modelling method. In combination with its proven spatial precision, this efficiency makes the methodology particularly suitable for real-time applications. The enhanced computational performance also supports the model's potential for future integration of energy storage systems and other sustainable technologies into electric traction grids. • Study of novel traction grid models reveals key sources of computational effort. • Optimised method shows notable reduction in execution time for large, meshed grids. • Optimised method's real-time potential suits monitoring for sustainable grids.

Results in EngineeringVol. 28
University of Bologna (IT)
Openalex Percentile: Top 58%
Railway Systems and Energy Efficiency
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