Optimizing Electric Truck Routing and Charging with Soft Time Windows Using Vehicle-to-Grid Technology

Due to the rapid increase in the use of electric vehicles and instability in energy supply, the application of vehicle-to-grid (V2G) technology has gained attention in the freight transportation sector. V2G has the potential to increase the efficiency of power grid and make additional profits by uti...

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Bibliographic Details
Published inJournal of advanced transportation Vol. 2023; pp. 1 - 10
Main Authors Ryu, Jong-Hyun, Hwang, Seong Wook
Format Journal Article
LanguageEnglish
Published London Hindawi 05.12.2023
John Wiley & Sons, Inc
Hindawi Limited
Hindawi-Wiley
Subjects
Alternative energy sources
Automobiles, Electric
Cost analysis
Customers
Decision making
Electric vehicle charging
Electric vehicles
Electricity distribution
Emissions
Freight transportation
Greenhouse gases
Integer programming
Logistics
Machine learning
Optimization models
Renewable resources
Sensitivity analysis
Shipment of goods
Systems stability
Transportation industry
Traveling salesman problem
Trucks
Vehicle routing
Vehicle-to-grid
Windows (intervals)
United States > US
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Summary:Due to the rapid increase in the use of electric vehicles and instability in energy supply, the application of vehicle-to-grid (V2G) technology has gained attention in the freight transportation sector. V2G has the potential to increase the efficiency of power grid and make additional profits by utilizing surplus power from electric vehicle batteries. This paper proposes an optimization model for electric trucks (ETs) to provide operational decision-making support for the freight transportation sector. The objective of the model is to minimize the total net cost, which includes charging cost, discharging reward, and time penalties, while considering changes in ET charging cost and the system marginal price. Furthermore, we conduct sensitivity analysis in the vehicle routing problem with soft time windows using ETs in the V2G system.
ISSN:0197-6729
2042-3195
DOI:10.1155/2023/6674586