A Bilevel EV Charging Station and DC Fast Charger Planning Model for Highway Network Considering Dynamic Traffic Demand and User Equilibrium

Weilun Wang; Yikui Liu; Wei Wei; Lei Wu

doi:10.1109/TSG.2023.3275013

A Bilevel EV Charging Station and DC Fast Charger Planning Model for Highway Network Considering Dynamic Traffic Demand and User Equilibrium

Weilun Wang, Yikui Liu, Wei Wei, Lei Wu

Department of Electrical and Computer Engineering

Stevens Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

The rapid electric vehicle (EV) adoption and relatively short EV driving range urge city planners to expand charging infrastructure (i.e., EV charging stations (EVCSs) and direct current fast chargers (DCFCs)) in the highway network for supporting long-distance intercity EV travels. To properly address distinct roles as well as interactions of the EVCS planner, traffic network, power distribution networks (PDNs), and drivers, this paper explores a bilevel planning model in which the upper level determines EVCS and DCFC construction plans and the two lower-level subproblems describe the user equilibrium-based traffic assignment model of the traffic network and the operation model of PDNs. Moreover, the statistical charging time is modeled in the traffic network subproblem as a function of statistical charging demand and the number of DCFCs, leveraging the macro traffic pattern and micro charging behavior of individual drivers with balanced accuracy and computational complexity. The proposed model also captures the impacts of social factors, charging infrastructure, and driver behaviors on dynamic EV traffic demands. Reformulation and linearization techniques are applied to convert the proposed bilevel problem into a tractable single-level mixed-integer linear programming model for effective computation. The effectiveness of the proposed approach is tested on an intercity highway network with multiple PDNs. The findings from numerical case studies highlight the important interplay among the construction plans of EVCSs and DCFCs, the PDN constraints, the user equilibrium traffic network, and the EV drivers. Case studies illustrate the feasibility and necessity of considering the number of DCFCs to calculate the macro-perspective charging time in the user equilibrium traffic network model. The dynamic traffic demand, induced by social factors and traffic demand elasticity, was also found to be a crucial factor in reshaping EV traffic. Thus, properly considering these important factors would lead to a more accurate quantification of traffic demands and ultimately result in a more reasonable charging facility construction plan.

Original language	English
Pages (from-to)	714-728
Number of pages	15
Journal	IEEE Transactions on Smart Grid
Volume	15
Issue number	1
DOIs	https://doi.org/10.1109/TSG.2023.3275013
State	Published - Jan 1 2024

All Science Journal Classification (ASJC) codes

General Computer Science

Keywords

EVCS planning
bilevel optimization
dynamic traffic demand
social factors
user equilibrium

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10.1109/TSG.2023.3275013

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title = "A Bilevel EV Charging Station and DC Fast Charger Planning Model for Highway Network Considering Dynamic Traffic Demand and User Equilibrium",

abstract = "The rapid electric vehicle (EV) adoption and relatively short EV driving range urge city planners to expand charging infrastructure (i.e., EV charging stations (EVCSs) and direct current fast chargers (DCFCs)) in the highway network for supporting long-distance intercity EV travels. To properly address distinct roles as well as interactions of the EVCS planner, traffic network, power distribution networks (PDNs), and drivers, this paper explores a bilevel planning model in which the upper level determines EVCS and DCFC construction plans and the two lower-level subproblems describe the user equilibrium-based traffic assignment model of the traffic network and the operation model of PDNs. Moreover, the statistical charging time is modeled in the traffic network subproblem as a function of statistical charging demand and the number of DCFCs, leveraging the macro traffic pattern and micro charging behavior of individual drivers with balanced accuracy and computational complexity. The proposed model also captures the impacts of social factors, charging infrastructure, and driver behaviors on dynamic EV traffic demands. Reformulation and linearization techniques are applied to convert the proposed bilevel problem into a tractable single-level mixed-integer linear programming model for effective computation. The effectiveness of the proposed approach is tested on an intercity highway network with multiple PDNs. The findings from numerical case studies highlight the important interplay among the construction plans of EVCSs and DCFCs, the PDN constraints, the user equilibrium traffic network, and the EV drivers. Case studies illustrate the feasibility and necessity of considering the number of DCFCs to calculate the macro-perspective charging time in the user equilibrium traffic network model. The dynamic traffic demand, induced by social factors and traffic demand elasticity, was also found to be a crucial factor in reshaping EV traffic. Thus, properly considering these important factors would lead to a more accurate quantification of traffic demands and ultimately result in a more reasonable charging facility construction plan.",

keywords = "EVCS planning, bilevel optimization, dynamic traffic demand, social factors, user equilibrium",

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A Bilevel EV Charging Station and DC Fast Charger Planning Model for Highway Network Considering Dynamic Traffic Demand and User Equilibrium

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