A Traction Substation State Estimator for Integrating Smart Loads in Transportation Grids Without the Need for Additional Sensors

Research output: Contribution to journalArticleScientificpeer-review

29 Downloads (Pure)

Abstract

Public electric transport grids tend to be oversized and underutilized. Therefore, they can become sustainable and multi-functional backbones to the city AC grid by integrating smart grid elements into their infrastructures. However, integrating smart grid loads and renewables requires a large array of wirelessly communicating sensors across the traction substations, the smart grid components, and each vehicle of the transport fleet. This can be both costly and technically complex. This paper proposes an analytical state estimator that can predict vehicle traffic count and spare power capacity under a traction substation without the use of any additional sensors. The estimator uses existing, locally available measurements at any power node on the traction section to inform the decision-making of the power management scheme at that node. Validating the results with up to 100000 stochastic test simulations of a verified traction grid model, up to 76% of the monitored conditions were detected, with no false positives, and without the need for additional sensors and wireless communication.
Original languageEnglish
Pages (from-to)2669-2680
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Volume25
Issue number3
DOIs
Publication statusPublished - 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Fingerprint

Dive into the research topics of 'A Traction Substation State Estimator for Integrating Smart Loads in Transportation Grids Without the Need for Additional Sensors'. Together they form a unique fingerprint.

Cite this