PEV Charging Infrastructure Siting Based on Spatial-Temporal Traffic Flow Distribution

Plug-in electric vehicles (PEVs) offer a solution to reduce greenhouse gas emissions and decrease fossil fuel consumption. PEV charging infrastructure siting must ensure not only a satisfactory charging service for PEV users but also a high utilization and profitability for the chosen facility locat...

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Veröffentlicht in:IEEE transactions on smart grid Jg. 10; H. 6; S. 6115 - 6125
Hauptverfasser: Abdalrahman, Ahmed, Zhuang, Weihua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.11.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Batteries
Charging
charging infrastructure
Computer simulation
Electric vehicles
Estimation
Flow distribution
Fossil fuels
Gaussian mixture model
Greenhouse effect
Greenhouse gases
Infrastructure
Integer programming
Linear programming
Mixed integer
Optimization
Plug-in electric vehicle
Probabilistic models
Profitability
Road transportation
Roads
siting
Temporal distribution
Traffic assignment
Traffic flow
traffic flow capturing
Traffic models
Transportation models
Transportation networks
User satisfaction
Vehicle dynamics
ISSN:1949-3053, 1949-3061
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Zusammenfassung:Plug-in electric vehicles (PEVs) offer a solution to reduce greenhouse gas emissions and decrease fossil fuel consumption. PEV charging infrastructure siting must ensure not only a satisfactory charging service for PEV users but also a high utilization and profitability for the chosen facility locations. Thus, the various types of charging facilities should be located based on an accurate location estimation of the potential PEV charging demand. In this paper, we propose a spatial-temporal flow capturing location model. This model determines the locations of various types of charging facilities based on the spatial-temporal distribution of traffic flows. We utilize the dynamic traffic assignment model to estimate the time-varying traffic flows on the road transportation network. Then, we cluster the traffic flow dataset into distinct categories using the Gaussian mixture model and site each type of charging facilities to capture a specific traffic pattern. We formulate our siting model as an mixed integer linear programming optimization problem. The model is evaluated based on two benchmark transportation networks, and the simulation results demonstrate the effectiveness of the proposed model.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2019.2896697