Hierarchical Operation Optimization for Regenerative Braking Energy Utilizing in Urban Rail Traction Power Supply System

Zhang, Hao; Zhang, Jian; Zhou, Linjie; Xiong, Peng; Zhao, Zhuofan

doi:10.3390/en16217268

Cited by 1 publication

(1 citation statement)

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“…Reference [2] explored an energy storage system for the efficient recovery of regenerative braking energy (RBE) and the improvement of power quality in AC-fed railway power supply systems characterised by fault-tolerance capabilities. Examples of such studies also include references [3][4][5]. Reference [6] presents research on railway power supply systems, with particular emphasis on analysing the power factor in the context of safe and reliable operation of traction railway systems.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Energy Consumption in Railway Signal Boxes

Kampik,

Bodzek,

Piaskowy

et al. 2023

Energies

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This study assessed hourly electricity consumption profiles in railway signal boxes located in Poland. The analyses carried out consisted of assessing the correlation among the hourly demand profile, weather indicators, and calendar indicators, e.g., temperature, cloud cover, day of the week, and month. The analysis allowed us to assess which indicator impacts the energy consumption profile and would be useful when forecasting energy demand. In total, 15 railway signal boxes were selected for analysis and grouped according to three characteristic repeatability profiles. On this basis, six of the signal boxes and one that did not fit into any of the groups were selected for further analysis. Four correlation research methods were selected for analysis: Pearson’s method, Spearman’s method, scatter plots, and distance covariance. The possibility of forecasting electricity consumption based on previously aggregated profiles and determining correlations with indicators was presented. The given indicators vary depending on the facility. Analyses showed different dependencies of the electricity demand profile. The ambient temperature and time of day have the greatest impact on the profile. Regarding the correlation with temperature, the results of the Pearson’s and Spearman’s coefficients ranged from approximately −0.4 to more than −0.8. The highest correlation coefficients were obtained when comparing the demand profile with the previous day. In this case, the Pearson’s and Spearman’s coefficients for all analysed objects range from approximately 0.7 to over 0.9.

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