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E-transit-bench: simulation platform for analyzing electric public transit bus fleet operations

Authors:

Alok Kumar Bharati,

Seyedmehdi Khaleghian,

Michael Wilbur,

Philip Pugliese,

Himanshu Neema,

Abhishek DubeyAuthors Info & Claims

e-Energy '22: Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

Pages 532 - 541

https://doi.org/10.1145/3538637.3539586

Published: 28 June 2022 Publication History

Abstract

When electrified transit systems make grid aware choices, improved social welfare is achieved by reducing grid stress, reducing system loss, and minimizing power quality issues. Electrifying transit fleet has numerous challenges like non availability of buses during charging, varying charging costs and so on, that are related the electric grid behavior. However, transit systems do not have access to the information about the co-evolution of the grid's power flow and therefore cannot account for the power grid's needs in its day-to-day operation. In this paper we propose a framework of transportation-grid co-simulation, analyzing the spatio-temporal interaction between the transit operations with electric buses and the power distribution grid. Real-world data for a day's traffic from Chattanooga city's transit system is simulated in SUMO and integrated with a realistic distribution grid simulation (using GridLAB-D) to understand the grid impact due to transit electrification. Charging information is obtained from the transportation simulation to feed into grid simulation to assess the impact of charging. We also discuss the impact to the grid with higher degree of transit electrification that further necessitates such an integrated transportation-grid co-simulation to operate the integrated system optimally. Our future work includes extending the platform for optimizing the charging and trip assignment operations.

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Cited By

Khaleghian STran TCho JHarris ASartipi M(2023)Electric Vehicle Identification in Low-Sampling Non-Intrusive Load Monitoring Systems Using Machine Learning2023 IEEE International Smart Cities Conference (ISC2)10.1109/ISC257844.2023.10293461(1-7)Online publication date: 24-Sep-2023
https://doi.org/10.1109/ISC257844.2023.10293461
Sen RTran TKhaleghian SPugliese PSartipi MNeema HDubey A(2022)BTE-Sim: Fast Simulation Environment For Public Transportation2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020973(2886-2894)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020973

Index Terms

E-transit-bench: simulation platform for analyzing electric public transit bus fleet operations

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cover image ACM Conferences

e-Energy '22: Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

June 2022

630 pages

ISBN:9781450393973

DOI:10.1145/3538637

General Chairs:
Sebastian Lehnhoff
University of Oldenburg, Germany
,
David Irwin
University of Massachusetts, Amherst
,
Dan Wang
The Hong Kong Polytechnic University, Hong Kong

Copyright © 2022 ACM.

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New York, NY, United States

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Published: 28 June 2022

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e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems

June 28 - July 1, 2022

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Overall Acceptance Rate 160 of 446 submissions, 36%

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Cited By

Khaleghian STran TCho JHarris ASartipi M(2023)Electric Vehicle Identification in Low-Sampling Non-Intrusive Load Monitoring Systems Using Machine Learning2023 IEEE International Smart Cities Conference (ISC2)10.1109/ISC257844.2023.10293461(1-7)Online publication date: 24-Sep-2023
https://doi.org/10.1109/ISC257844.2023.10293461
Sen RTran TKhaleghian SPugliese PSartipi MNeema HDubey A(2022)BTE-Sim: Fast Simulation Environment For Public Transportation2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020973(2886-2894)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020973

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