The location optimization of electric vehicle charging stations considering charging behavior

Tian, Zhen; Hou, Wenbin; Gu, Xiaoning; Feng, Gu; Yao, Baozhen

doi:10.1177/0037549717743807

Cited by 44 publications

(16 citation statements)

References 24 publications

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“…Installation and electricity costs of charging stations were added to the estimated cost of land based on Smith and Castellano [33] who estimated annual electricity and installation costs to be $686/yr and $1270/yr for Level 2, respectively, and $1128 and $5100 for Level 3, respectively. Finally, the arrival rate is estimated to be proportional to the Annual Average Daily Traffic (AADT) in D.C. [36]. Figure 2 illustrates demand and cost distribution based on the available data.…”

Section: Resultsmentioning

confidence: 99%

Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

2021

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Optimal placement of Charging stations (CSs) and infrastructure planning are one of the most critical challenges that face the Electric Vehicles (EV) industry nowadays. A variety of approaches have been proposed to address the problem of demand uncertainty versus the optimal number of CSs required to build the EV infrastructure. In this paper, a Markov-chain network model is designed to study the estimated demand on a CS by using the birth and death process model. An investigation on the desired number of electric sockets in each CS and the average number of electric vehicles in both queue and waiting times is presented. Furthermore, a CS allocation algorithm based on the Markov-chain model is proposed. Grey Wolf Optimization (GWO) algorithm is used to select the best CS locations with the objective of maximizing the net profit under both budget and routing constraints. Additionally, the model was applied to Washington D.C. transportation network. Experimental results have shown that to achieve the highest net profit, Level 2 chargers need to be installed in low demand areas of infrastructure implementation. On the other hand, Level 3 chargers attain higher net profit when the number of EVs increases in the transportation network or/and in locations with high charging demands.

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Section: Resultsmentioning

confidence: 99%

Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

2021

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“…where the first M stands for EV arrivals following a Gaussian distribution with mean arrival time at each station λ S i , the second M represents the distribution of the charging times with µ S i mean-service time and N S i c is the maximum number of car slots available at each station (for both waiting and charging). For this paper, we follow the queue modelling procedures from [14], [19] which are using the Markov Chain theory for probability calculation of charging at specific plugs of a station, with the assumption that the number of waiting cars inside the stations are limited to a maximum threshold.…”

Section: B Queue Modelling Of Ev Chargingmentioning

confidence: 99%

How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach

Grigorev¹,

Mao²,

Berry³

et al. 2021

Preprint

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This paper explores the impact of electric vehicles (EVs) on traffic congestion and energy consumption by proposing an integrated bi-level framework comprising of: a) a dynamic micro-scale traffic simulation suitable for modelling current and hypothetical traffic and charging demand scenarios and b) a queue model for capturing the impact of fast charging station use, informed by traffic flows, travel distances, availability of charging infrastructure and estimated vehicle battery state of charge. To the best of our knowledge, this paper represents the first integrated analysis of potential traffic congestion and energy infrastructure impacts linked to EV uptake, based on real traffic flows and the placement and design of existing fast-charging infrastructure. Results showcase that the integrated queue-energy-transport modelling framework can predict correctly the limitations of the EV infrastructure as well as the traffic congestion evolution. The modelling approach identifies concrete pain points to be addressed in both traffic and energy management and planning. The code for this project can be found at : https://github.com/ Future-Mobility-Lab/EV-charging-impact Index Terms-electric vehicles, traffic simulation modelling, queue modelling, recharging scenario evaluation, fast charge impact modelling I. MOTIVATIONThe adoption of electric vehicles (EVs) at large scale around the globe is largely dependent on two key factors: charging accessibility for its consumers and availability of sufficient electrical grid capacity to support vehicle charging, particularly where fast charging infrastructure is necessary. Under ideal conditions for the consumer, access to an EV charging station should be just as convenient as current access to a petrol station. Several studies have been conducted in recent years to model and identify suitable locations for such charging stations [2], [3]. The majority of these studies are based on the behavioural pattern of combustion engine (CE) drivers. However, the behaviour of electric vehicle (EV) drivers is significantly different in terms of location selection and their waiting time to charge [2], [4]. Multiple factors can influence the decision of EV drivers to choose a specific location to charge, among which we cite: the travel time

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“…Chakraborty et al examine the influence of electricity costs on charging behavior and thereby on infrastructure needs [12]. Tian et al similarly use analysis of charging behavior to optimize the location of charging stations in China [13]. As a final example, Hardman et al conduct a review of charging preferences in relation to the build-up charging infrastructure [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling

et al. 2020

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Battery electric vehicles (BEV) provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. Assumptions on fleet battery availability and a detailed representation of fast charging are found to have a strong impact on wind curtailment, renewable energy feed-in, and required gas power plant flexibility. A representation of fleet connection to the grid in high temporal detail is less important. Peak load can be reduced by 5% and 3% in both scenarios, respectively. Shifted load is robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

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The location optimization of electric vehicle charging stations considering charging behavior

Cited by 44 publications

References 24 publications

Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach

Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling

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