Model Analysis of Electric Vehicle Charging Infrastructure Development on Highways—An Approximation of the Required Scale of Electric Vehicle Charging Facilities—

Honma, Yudai; Toriumi, Shigeki

doi:10.5047/forma.2014.007

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…An urban transportation network is adopted as an example, and the charging load of an EV in various zones is simulated. The various zones in the transportation network correspond to different nodes, such as RZ (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16), CZ (24)(25)(26)(27)(28)(29)(30), and WZ (17)(18)(19)(20)(21)(22)(23), respectively. The transportation network contains 30 nodes and 52 routes corresponding to the main routes.…”

Section: Parameter Settingsmentioning

confidence: 99%

“…Nevertheless, traffic scenarios' impact on charging load is underestimated. High congestion has a significant effect on charging performance, causing an additional burden during peak hours [16,17]. A spatial-temporal distribution strategy was presented to effectively capture the electric vehicle charging load mobility in coupled power-traffic networks.…”

Section: Introductionmentioning

confidence: 99%

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Electrical Vehicle Charging Load Mobility Analysis Based on a Spatial–Temporal Method in Urban Electrified-Transportation Networks

Jawad

Liu

2023

Energies

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Charging load mobility evaluation becomes one of the main concerns for charging services and power system stability due to the stochastic nature of electrical vehicles (EVs) and is critical for the robust scheduling of economic operations at different intervals. Therefore, the EV spatial–temporal approach for load mobility forecasting is presented in this article. Furthermore, the reliability indicators of large-scale EV distribution network penetration are analyzed. The Markov decision process (MDP) theory and Monte Carlo simulation are applied to efficiently forecast the charging load and stochastic path planning. A spatial–temporal model is established to robustly forecast the load demand, stochastic path planning, traffic conditions, and temperatures under different scenarios to evaluate the charging load mobility and EV drivers’ behavior. In addition, the distribution network performance indicators are explicitly evaluated. A Monte Carlo simulation is adopted to examine system stability considering various charging scenarios. Urban coupled traffic-distribution networks comprising 30-node transportation and 33-bus distribution networks are considered as a test case to illustrate the proposed study. The results analysis reveals that the proposed method can robustly estimate the charging load mobility. Furthermore, significant EV penetrations, weather, and traffic congestion further adversely affect the performance of the power system.

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Section: Parameter Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electrical Vehicle Charging Load Mobility Analysis Based on a Spatial–Temporal Method in Urban Electrified-Transportation Networks

Jawad

Liu

2023

Energies

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“…The model on a line gives an insight into analysis on road networks. 13) An efficient location of alternative fuel stations has also been studied. Kuby and Lim 14) developed the flow refueling location model (FRLM) for optimally locating refueling facilities.…”

Section: Introductionmentioning

confidence: 99%

Trip Length and Sufficient Number of Alternative Fuel Stations

Miyagawa

2016

URPR

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This paper develops a model for determining the sufficient number of stations for alternative fuel vehicles. The model extends a previous model to incorporate both flow demand and the trip length distribution, thereby providing a more appropriate framework for estimating the sufficient number of stations. The service level is represented as the probability that the vehicle can make the repeated round trip between randomly selected origin and destination. The probability is obtained for the random pattern of stations for three cases: fuel is available at both origin and destination, fuel is available at either origin or destination, and fuel is available at neither origin nor destination. The analytical expressions for the probability demonstrate how the number of stations, the trip length, the vehicle range, and the refueling availability at origin and destination affect the service level of stations. The number of stations required to achieve a certain level of service is then calculated.

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A Method to Evaluate an Urban Area by Using the Model That Calculates a Number of Facilities from an Area and a Population

Sakamoto¹

2016

CUS

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This paper presents 1) a new mathematical model improved from a conventional model that calculates a number of facility from an area and a population in a town and, 2) by using this model, a method to evaluate the enrichment (in other words, convenience or enough) of facilities in the town where many high-rise apartments have been built (the area is hereinafter referred to as the high-rise apartment town). The conventional model uses only a nighttime population, but this paper considers a daytime population in addition to the nighttime population. Also, there has not been the method to evaluate the enrichment of facilities in a town, but we can evaluate it using this model. We apply this method to 23 special wards in Tokyo and the highrise apartment towns, the results prove the effectiveness of the proposed method.

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Model Analysis of Electric Vehicle Charging Infrastructure Development on Highways—An Approximation of the Required Scale of Electric Vehicle Charging Facilities—

Cited by 3 publications

References 9 publications

Electrical Vehicle Charging Load Mobility Analysis Based on a Spatial–Temporal Method in Urban Electrified-Transportation Networks

Electrical Vehicle Charging Load Mobility Analysis Based on a Spatial–Temporal Method in Urban Electrified-Transportation Networks

Trip Length and Sufficient Number of Alternative Fuel Stations

A Method to Evaluate an Urban Area by Using the Model That Calculates a Number of Facilities from an Area and a Population

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