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Title: Optimal charging scheduling and management for a fast-charging battery electric bus system

Abstract

Herein we discuss how battery electric buses (BEBs) are rapidly being embraced by public transit agencies because of their environmental and economic benefits. To address the problems of limited driving range and time-consuming charging for BEBs, manufacturers have developed rapid on-route charging technology that utilizes typical layovers at terminals to charge buses in operation using high power. With on-route fast-charging, BEBs are as capable as their diesel counterparts in terms of range and operating time. However, on-route fast-charging makes it more challenging to schedule and manage charging events for a BEB system. First, on-route fast-charging may lead to high electricity power demand charges. Second, it may increase electricity energy charges because of charging that occurs during on-peak hours. Without careful charging scheduling and management, on-route fast-charging may significantly increase fuel costs and reduce the economic attractiveness of BEBs. The present study proposes a network modeling framework to optimize the charging scheduling and management for a fast-charging BEB system, effectively minimizing total charging costs. The charging schedule determines when to charge a BEB, while the charging management strategically controls the actual charging power. Charging costs include both electricity demand charges and energy charges. The charging scheduling and management problem is firstmore » formulated as a nonlinear nonconvex program with time-continuous variables. A discretizing method and a linear reformulation technique are then adopted to reformulate the model as a linear program, which can be easily solved using off-the-shelf solvers, even for large-scale problems. Finally, the model is demonstrated with extensive numerical studies based on two real-world bus networks. The results demonstrate that the proposed model can effectively determine the optimal charging scheduling and management for a fast-charging BEB system, which carries the potential for use in large-scale real-world bus networks.« less

Authors:
 [1];  [1];  [1]
  1. Utah State Univ., Logan, UT (United States). Dept. of Civil and Environmental Engineering
Publication Date:
Research Org.:
PacifiCorp, Portland, OR (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1848663
Alternate Identifier(s):
OSTI ID: 1647394
Grant/Contract Number:  
EE0007997
Resource Type:
Accepted Manuscript
Journal Name:
Transportation Research Part E: Logistics and Transportation Review
Additional Journal Information:
Journal Volume: 142; Journal Issue: C; Journal ID: ISSN 1366-5545
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; Business & Economics; Engineering; Operations Research & Management Science; Transportation; Battery electric buses; Charging scheduling and management; Fast-charging; Demand charges; Time-of-use rate structure

Citation Formats

He, Yi, Liu, Zhaocai, and Song, Ziqi. Optimal charging scheduling and management for a fast-charging battery electric bus system. United States: N. p., 2020. Web. doi:10.1016/j.tre.2020.102056.
He, Yi, Liu, Zhaocai, & Song, Ziqi. Optimal charging scheduling and management for a fast-charging battery electric bus system. United States. https://doi.org/10.1016/j.tre.2020.102056
He, Yi, Liu, Zhaocai, and Song, Ziqi. Thu . "Optimal charging scheduling and management for a fast-charging battery electric bus system". United States. https://doi.org/10.1016/j.tre.2020.102056. https://www.osti.gov/servlets/purl/1848663.
@article{osti_1848663,
title = {Optimal charging scheduling and management for a fast-charging battery electric bus system},
author = {He, Yi and Liu, Zhaocai and Song, Ziqi},
abstractNote = {Herein we discuss how battery electric buses (BEBs) are rapidly being embraced by public transit agencies because of their environmental and economic benefits. To address the problems of limited driving range and time-consuming charging for BEBs, manufacturers have developed rapid on-route charging technology that utilizes typical layovers at terminals to charge buses in operation using high power. With on-route fast-charging, BEBs are as capable as their diesel counterparts in terms of range and operating time. However, on-route fast-charging makes it more challenging to schedule and manage charging events for a BEB system. First, on-route fast-charging may lead to high electricity power demand charges. Second, it may increase electricity energy charges because of charging that occurs during on-peak hours. Without careful charging scheduling and management, on-route fast-charging may significantly increase fuel costs and reduce the economic attractiveness of BEBs. The present study proposes a network modeling framework to optimize the charging scheduling and management for a fast-charging BEB system, effectively minimizing total charging costs. The charging schedule determines when to charge a BEB, while the charging management strategically controls the actual charging power. Charging costs include both electricity demand charges and energy charges. The charging scheduling and management problem is first formulated as a nonlinear nonconvex program with time-continuous variables. A discretizing method and a linear reformulation technique are then adopted to reformulate the model as a linear program, which can be easily solved using off-the-shelf solvers, even for large-scale problems. Finally, the model is demonstrated with extensive numerical studies based on two real-world bus networks. The results demonstrate that the proposed model can effectively determine the optimal charging scheduling and management for a fast-charging BEB system, which carries the potential for use in large-scale real-world bus networks.},
doi = {10.1016/j.tre.2020.102056},
journal = {Transportation Research Part E: Logistics and Transportation Review},
number = C,
volume = 142,
place = {United States},
year = {Thu Aug 13 00:00:00 EDT 2020},
month = {Thu Aug 13 00:00:00 EDT 2020}
}

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