Optimal allocation of electric vehicle charging stations in a highway network: Part 1. Methodology and test application

Napoli, G.; Polimeni, Antonio; Micari, Salvatore; Andaloro, L.

doi:10.1016/j.est.2019.101102

Cited by 59 publications

(34 citation statements)

References 27 publications

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“…Yan [56] also followed a flow-based approach and proposed a two-stage genetic algorithm with multi-objective formulation, which aims to minimize construction costs and drivers' charging costs. Napoli et al [57] developed an iterative allocation algorithm that correlates the distance between two charging stations along the network with the driving range of vehicles. Csiszár et al [58] designed a sequential selection of positions for charging infrastructure installation based on traffic volume and nearby settlement's population.…”

Section: Graph-based Charging Infrastructure Allocation Optimizationmentioning

confidence: 99%

Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network

2022

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Given the ongoing transformation of the transport sector toward electrification, expansion of the current charging infrastructure is essential to meet future charging demands. The lack of fast-charging infrastructure along highways and motorways is a particular obstacle for long-distance travel with battery electric vehicles (BEVs). In this context, we propose a charging infrastructure allocation model that allocates and sizes fast-charging stations along high-level road networks while minimizing the costs for infrastructure investment. The modeling framework is applied to the Austrian highway and motorway network, and the needed expansion of the current fast-charging infrastructure in place is modeled under different future scenarios for 2030. Within these, the share of BEVs in the car fleet, developments in BEV technology and road traffic load changing in the face of future modal shift effects are altered. In particular, we analyze the change in the requirements for fast-charging infrastructure in response to enhanced driving range and growing BEV fleets. The results indicate that improvements in the driving range of BEVs will have limited impact and hardly affect future costs of the expansion of the fast-charging infrastructure. On the contrary, the improvements in the charging power of BEVs have the potential to reduce future infrastructure costs.

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Section: Graph-based Charging Infrastructure Allocation Optimizationmentioning

confidence: 99%

Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network

2022

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“…The EVs promise many environmental benefits, including very low to zero tailpipe emissions, reduced noise, etc. [87]. However, its charging station requires land and energy in a very large amount.…”

Section: Impact Of Charging Station Infrastructure On the Environmentmentioning

confidence: 99%

A Comprehensive Review on Developments in Electric Vehicle Charging Station Infrastructure and Present Scenario of India

et al. 2021

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The transportation sector of the world is in the transformation stage, shifting from conventional fossil fuel-powered vehicles to zero or ultra-low tailpipe emission vehicles. To support this transformation, a proper charging station (CS) infrastructure in combination with information technology, smart distributed energy generating units, and favorable government policies are required. The motive of this paper is to address the key aspects to be taken care of while planning for the charging station infrastructure for electric vehicles. The paper also provides major indagation and developments in planning and technological aspects that are going on for the enhancement of the design and efficient management of charging station infrastructure. The paper addresses the present scenario of India related to electric vehicle charging station developments. The paper specially provides a critical review on the research and developments in the charging station infrastructure, the problems associated with it, and the efforts that are going on for its standardization to help the researchers address the problems.

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“…For example, the network seeks to reduce power losses or control voltage, and on the other hand, the EVCS owner aims to earn higher revenue regardless of the amount of its impact on the network. In this regard, several objective functions have been proposed in the references to establish a compromise between the benefits of both parties and to obtain the most optimal solution [7,20,21,25,42]. In the optimal EVCSs allocation problem of this paper, the following main functions have been defined as the objective function: are the benefits functions of the DSO and the EVCS owner, respectively.…”

Section: Evcs Allocationmentioning

confidence: 99%

“…It is also necessary to use stochastic algorithms, which accounts for uncertainties, in order to enhance the accuracy of modeling and achieve optimal solutions. In [20,21] a multiobjective optimization problem is solved with the aim of minimizing power loss, voltage deviation, and cost to optimally allocate the EVCS in candidate buses. In [22] bi-level optimal allocation model is presented for allocating an EVCS with the aim of maximizing the coordinated benefits of the EVCS investor and EV owners, but the benefits of DSO is not considered.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

et al. 2020

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Modeling and allocation of the Electric vehicles Charging Stations (EVCS) within the distribution network, as per the growing use of Electric vehicles (EVs) is a challenging task. In this paper, to manage the EVCS, first, with the aim of peak shaving, valley filling, and flattening the load curve of the network, the optimal planning of EV's charging/discharging is devised. In this regard, after modeling involving random variables, a novel hybrid method, based on the Multi Objective Particle Swarm Optimization (MOPSO) optimization algorithm and sequential Monte Carlo simulation is presented. The purpose of the presented optimal charge/discharge schedule is to control the rate and time of charging/ discharging of EVs. In the proposed model, various battery operation strategies, including Uncontrolled Charging Mode (UCM), Controlled Charging Mode (CCM), and Smart Charge/Discharge Mode (SCDM) are also considered. In the next step, in order to implement the charge/discharge schedule of the EVCS profitably, a new formulation is presented for the allocation of two EVCSs (administrative and residential EVCS). In the proposed formulation, various objective functions such as power loss reduction, reducing power purchases from the upstream network, reducing voltage deviation in buses, improving reliability is addressed. Moreover, to incentivize the owner to construct the EVCS with adopted charging/discharging schedule, the optimal profits sharing between Distribution System Operator (DSO) and EVCS owner is also performed. The proposed formulation is applied to a standard network (IEEE 69 buses) and encouraging results are achieved.

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Optimal allocation of electric vehicle charging stations in a highway network: Part 1. Methodology and test application

Cited by 59 publications

References 27 publications

Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network

Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network

A Comprehensive Review on Developments in Electric Vehicle Charging Station Infrastructure and Present Scenario of India

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

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