Analysis of the discrete choice model representing the electric vehicle owners’ behavior in Slovakia

Kajanová, Martina; Bracinik, Peter; Belány, Pavol

doi:10.1007/s00202-021-01255-z

Cited by 6 publications

(32 citation statements)

References 14 publications

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“…If the respondent does not want to choose any of the defined alternatives, there is also a possibility to opt out. The list of the alternatives is presented in Table 1 [17]. Within each hypothetical scenario presented to the respondents, they were reminded about the battery capacity of their hypothetical EV and its driving range with a fully charged battery.…”

Section: The Sp Surveymentioning

confidence: 99%

“…The authors assumed that these two parameters affect the utility of each alternative. Therefore, they are defined as generic attributes [17].…”

Section: The Sp Surveymentioning

confidence: 99%

“…In our previous paper [17], we defined a design of the SP survey created and carried out from July to October 2020 with a sample of 289 residents of Slovakia. The survey was designed to obtain data on the decisions of future EV owners.…”

Section: Introductionmentioning

confidence: 99%

“…There was also a possibility to opt out and not choose any of the alternatives. Based on the survey data, the prediction model with its estimated parameters has been determined and validated [17].…”

Section: Introductionmentioning

confidence: 99%

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Definition of Discrete Choice Models of EV Owners Based on Different Socio-Demographic Aspects

Kajanová

Bracinik

2021

Applied Sciences

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With an increasing number of electric vehicles (EVs), their owners’ involvement in the control of electric power systems and their market seems to be the only option for stable operation of future power networks. However, these people usually have little knowledge about power systems’ operation and follow just their interests. Therefore, this paper deals with the decision-making process of EV drivers at the charging station. The paper presents the stated preference survey used to collect the responses to hypothetical scenarios, where respondents chose between three alternatives, namely slow charging, fast charging, and vehicle-to-grid services. The survey also contained questions about respondents’ socio-demographic characteristics, as gender, age, etc. The decision-making prediction models for each socio-demographic characteristic were created using the acquired data. The paper presents the estimated parameters of the attributes affecting the respondents’ choices for the models that allow models’ simple implementation. Knowing these models and the customers’ composition, the operators of the charging stations or the distribution networks could better estimate EV owners’ behavior and so their expected power demand. Moreover, operators could more effectively implement incentives for their customers and affect the customers’ behavior in a way that is suitable for better operation of their power systems.

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Section: The Sp Surveymentioning

confidence: 99%

“…The authors assumed that these two parameters affect the utility of each alternative. Therefore, they are defined as generic attributes [17].…”

Section: The Sp Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Definition of Discrete Choice Models of EV Owners Based on Different Socio-Demographic Aspects

Kajanová

Bracinik

2021

Applied Sciences

Self Cite

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“…Due to the increase in the global usage of EVs, charging behaviors have been widely studied in recent years. [ 7–10 ] Khoo et al [ 11 ] used the charging behavior characteristics, including the duration, daily frequency, and starting time of charging, to analyze the charging patterns of household and fleet vehicles. Triebke et al [ 12 ] used the loaded energy amount, starting time of charging, duration, and frequency of charging as characteristics to conduct a descriptive statistical analysis of charging behaviors.…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven Temporal Charging Patterns of Electric Vehicles in China

Zhang

Chibane

et al. 2021

Energy Tech

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Users of electric vehicles (EVs) have different preferences for the temporal charging patterns. The analysis of temporal charging patterns can aid in the decision‐making of EV‐related stakeholders, including policymakers, grid operators, and automobile companies. Herein, a recognition and factor analysis framework of temporal charging patterns for EVs’ usage improvement is proposed. A Gaussian mixture model (GMM) is used to obtain temporal charging patterns by considering characteristics related to the charging session and time of parking period. A multinomial logit model is used for the factor analysis of the temporal charging patterns. An experiment is conducted with the proposed framework using data of private electric vehicles (PEVs) and carsharing electric vehicles (CSEVs) collected between 2018 and 2019 in China. The data are statistically analyzed, and then six types of temporal charging patterns are obtained by the GMM. A comparison of the pattern distributions over time shows that they are obviously different between PEVs and CSEVs. The analysis of the patterns’ influencing factors of PEVs and CSEVs shows that the PEVs have more influencing factors than the CSEVs.

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Probability modelling of storage based‐smart energy hub considering electric vehicles charging stations performance and demand side management

Javid,

Derakhshan,

Hakimi

2023

IET Renewable Power Gen

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Nowadays, researchers are studying the energy discharging of electric vehicles (EVs) in charging stations. Furthermore, these stations can help maintain the balance of electric energy and reduce costs in the energy hub in the discharging mode. In this regard, this article proposes a probability and possibility approach by the Z‐number method to investigate the uncertainty of the EV owners’ behaviour at charging stations during the discharge of electric energy to the energy hub. Also, here, the uncertainty of electric energy demand, thermal energy, drinking water, and the price of electric energy, thermal energy, drinking water, and electric energy production from a solar power plant has been done by the scenario‐base method. The seawater desalination unit has been modelled to meet the drinking water needs. To check the final results of the proposed method, the mixed integer linear programming (MILP) model has been selected for energy management in the energy hub in the CPLEX optimization solver. The final results show that modelling the behaviour of EV owners in the charging stations using the Z‐number method leads to reduced consumption of some energy carriers in comparison with the fuzzy method in the proposed energy hub.

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Analysis of the discrete choice model representing the electric vehicle owners’ behavior in Slovakia

Cited by 6 publications

References 14 publications

Definition of Discrete Choice Models of EV Owners Based on Different Socio-Demographic Aspects

Definition of Discrete Choice Models of EV Owners Based on Different Socio-Demographic Aspects

Data‐Driven Temporal Charging Patterns of Electric Vehicles in China

Probability modelling of storage based‐smart energy hub considering electric vehicles charging stations performance and demand side management

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