Urban charging station location model based on multi-objective programming

Lou, Junhong; Qiu, Zheyong; Yu, Jiachen; Guo, Chen

doi:10.1088/1742-6596/1053/1/012028

Cited by 2 publications

(4 citation statements)

References 5 publications

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“…Erbaş et al [32] optimized the location of EV charging stations in Istanbul, Turkey, using nearby facilities, land value, number of EVs, and population. Lou et al [33] used the number of EVs, construction cost, and population to optimize the location of EV charging stations in Seoul, Republic of Korea. Zhang and Iman [34] optimized the location of EV charging stations in the Wasatch Front of Utah, USA, based on sustainability.…”

Section: B Previous Studiesmentioning

confidence: 99%

“…Using population ( [30], [32][33][34][35], [37]) Using number of registered EVs ( [32,33], [36]) Using traffic volume ( [30], [31], [37]) Using land value or income level ( [30], [32], [36,37]). Purpose of the facility or land ([31, 34, 35]) Construction expense ( [33], [37])…”

Section: Using Various Factorsmentioning

confidence: 99%

“…Many socioeconomic factors play a role, so it is important to determine which ones should be analyzed to prioritize the location of EV charging stations. This study examined the key factors, including the population (population within a radius of approximately 914 m) ( [18,22,23], [30], [32][33][34][35], [37]), the number of registered EVs ( [18,22], [19,20], [32,33], [36]), traffic volume ( [23], [30], [31], [37]), and land value, which can approximate income level (land value within a radius of approximately 914 m) ( [18], [19][20][21], [30], [32], [37]).…”

Section: A Key Factorsmentioning

confidence: 99%

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Optimization and Observation of EV Charging Station Deployment in the Republic of Korea: An Analysis of the Charging History and Correlation With Socioeconomic Factors

Gong,

Kim

2024

IEEE Access

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The demand for electric vehicles is increasing due to the recent focus on reducing greenhouse gas emissions. Hence, the number of electric-vehicle charging stations must be increased to meet the growing demand for electric vehicles. To this end, supportive policies are being established to increase the deployment of electric vehicle charging stations worldwide. On the other hand, it is not possible to subsidize all facilities simultaneously. This study developed a model to prioritize the deployment of electric vehicle charging stations. The Republic of Korea was analyzed as a case study region to develop this model, and the correlations between the amount of charging energy of electric vehicle charging stations and socioeconomic factors (traffic volume, population, number of electric vehicles, and land value) were analyzed. The correlations were analyzed differently depending on the purpose (e.g., residential or commercial) of the facilities where electric vehicle charging stations were installed. Correlation analysis was conducted to determine the factors that affect the amount of charging energy, and a model was developed to prioritize the deployment of electric vehicle charging stations through a genetic algorithm. A model with a correlation of more than 0.2 was developed, except for residential facilities with slow chargers and public institutions with slow chargers. The results of this paper can help identify the key factors to be analyzed by facility use when installing electric vehicle charging stations and determine the priorities for subsidizing the deployment of electric vehicle charging stations.

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Section: B Previous Studiesmentioning

confidence: 99%

Section: Using Various Factorsmentioning

confidence: 99%

Section: A Key Factorsmentioning

confidence: 99%

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Optimization and Observation of EV Charging Station Deployment in the Republic of Korea: An Analysis of the Charging History and Correlation With Socioeconomic Factors

Gong,

Kim

2024

IEEE Access

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“…S Sharma et al 3 established an integrated TOU (time of use) -PBDR model for grid to vehicle charging scheduling based on price demand response (PBDR) to solve the problem of grid instability and peak, and the results verified the feasibility. J H Lou et al 4 established the urban charging station planning model based on nonlinear integer programming with the goal of minimizing the construction cost and service cost of service facilities, and verified it with Seoul as an example, which had certain reference value for the later construction of charging stations. T P Zhou 5 established a multi-objective charging scheduling optimization model for electric vehicles based on the characteristics of great randomness of charging stations for electric vehicles, and adopted an improved particle swarm optimization algorithm based on bacterial chemotaxis to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

Research on orderly charging scheduling of new energy vehicles in airfield

ZhuGe,

Zhang,

Kang

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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In view of the charging scheduling problem of new energy vehicles in the airfield area, the traditional disordered charging phenomenon causes the unbalanced utilization of charging piles, which makes some charging piles overloaded and cannot be applied to the safe operation of the distribution network in the airfield area. By analyzing the vehicle charging law of a domestic hub airport, with the goal of minimizing the deviation of the comprehensive utilization rate of charging piles and the minimum vehicle queuing time, a new energy vehicle charging scheduling model was established, and the particle swarm optimization algorithm was used to solve the model, and the actual charging data of the airport was compared and verified. The results showed that the algorithm could reduce the variance of the comprehensive utilization rate of charging piles by 70.4%, reduce the queuing time of vehicle charging by 59.6%, and effectively improved the safety of the airport distribution network and the operating efficiency of new energy vehicles.

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Urban charging station location model based on multi-objective programming

Cited by 2 publications

References 5 publications

Optimization and Observation of EV Charging Station Deployment in the Republic of Korea: An Analysis of the Charging History and Correlation With Socioeconomic Factors

Optimization and Observation of EV Charging Station Deployment in the Republic of Korea: An Analysis of the Charging History and Correlation With Socioeconomic Factors

Research on orderly charging scheduling of new energy vehicles in airfield

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