Electric Factor—A Comparison of Car Usage Profiles of Electric and Conventional Vehicles by a Probabilistic Approach

Niklas, Ulrich; Behren, Sascha von; Chlond, Bastian; Vortisch, Peter

doi:10.3390/wevj11020036

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…The model assumes that human behavior on the use of EV is the same as in ICEs, however studies have shown that it is not the same in daily mobility [65]. If the pattern of use is lower than the current one, it can be very beneficial from the environmental point of view, the emissions avoided would be higher.…”

Section: Co 2 Emission Targetsmentioning

confidence: 99%

Electric Vehicle and Renewable Energy Sources: Motor Fusion in the Energy Transition from a Multi-Indicator Perspective

et al. 2021

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Energy transition requires actions from different sectors and levels, mainly focused on achieving a low-carbon and high-renewable integration society. Among the different sectors, the transport sector is responsible for more than 20% of global greenhouse gas emissions, mostly emitted in cities. Therefore, initiatives and analysis focused on electric vehicles integration powered by renewables is currently a desirable solution to mitigate climate change and promote energy transition. Under this framework, this paper proposes a multi-indicator analysis for the estimation of CO2 emissions combining renewable integration targets, reduction emission targets and realistic renewable resource potentials. Four scenarios are identified and analyzed: (i) current situation with conventional vehicles, (ii) replacement of such conventional by electric vehicles without renewable integration, (iii) and (iv) integration of renewables to fulfill emission reduction targets for 2030 and 2050 respectively. The analysis is evaluated in the state of Maine (United States). From the results, a minimum renewable penetration of 39% and 82%, respectively, is needed to fulfill the emission reduction targets for 2030 and 2050 by considering 100% conventional vehicle replacement. Different combinations of available renewable resources can reduce emissions by more than 35%.

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Section: Co 2 Emission Targetsmentioning

confidence: 99%

Electric Vehicle and Renewable Energy Sources: Motor Fusion in the Energy Transition from a Multi-Indicator Perspective

et al. 2021

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“…However, the null hypothesis that the parameters ASC 1,0 , ASC 3,2 , β SoC,2 , β C€ , and β R€,2 are equal to zero cannot be rejected with a significance level equal to or lower than 5%. The estimated parameters of the model in (17)(18)(19)(20), assuming that these parameters are irrelevant (ASC 1,0 The results in Table 6 showed that in the case of respondents aged over 60 years, the SEM of the estimated parameters is usually high. Therefore, the parameters cannot be assumed to affect their decision.…”

Section: Agementioning

confidence: 99%

“…Moreover, the usage of EVs differs from vehicles with internal combustion engines. For example, the EVs are primarily used for short-distance commuting currently, and so they are usually charged at home or work, where the time of charging is not so significant [18]. Therefore, the paper compares the results between the respondents with and without experience of driving an EV, where the effect of this different behavior can be present.…”

Section: Introductionmentioning

confidence: 99%

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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“…The twenty-first century saw the rapid development of EV technology, where some EVs were shown to have a better performance than regular internal combustion engine vehicles (ICEVs) in terms of energy conversion efficiency, maximum torque and distance covered per full charge [3]. Hence, the projection of EV to replace the ICEV in the nearest future is quite inevitable [4]. In Malaysia, the National Automotive Policy had distributed soft loans amounting to RM 100 million per year to the EVs vendor for installing and developing advanced EV technology and machinery [5].…”

Section: Introductionmentioning

confidence: 99%

Design of a master power factor controller for an industrial plant with solar photovoltaic and electric vehicle chargers

2021

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The power factor of industrial facilities is typically inductive. The case study in this paper was based on a typical Malaysian 11-kV on-grid industrial system with renewable energy sources and electric vehicle charging station connected. The integration of renewable energy sources reduces energy consumption from the grid; it consecutively reduces greenhouse gas emissions. However, the integration of renewable energy sources such as solar photovoltaic operating at unity power factor results in a reduction of the industry’s power factor. According to the Malaysian Distribution Code, the power factor of a medium voltage industrial system should be more than 0.85 lagging. A long-term low power factor will reduce the related electrical equipment lifespan and increase the monthly electricity bills. A classic method to overcome this issue was by installing reactive power compensator devices, such as the synchronous condenser, static VAr compensator and static synchronous compensator. Studies had revealed that solar photovoltaic with appropriate control system design could perform short-term reactive power compensation. The control techniques used are either power factor control, active power control, reactive power control or any combination of them. However, neither the reactive power compensator devices nor the solar photovoltaic with a control system can regulate the industry’s power factor to an intended value throughout its operation. Thus, this paper presents a simple, relatively cost-effective design of a master power factor controller that is capable of regulating the industry’s power factor to an intended value throughout its operation with a single preset reference. In this research, an industry-grade system comprises an industrial load installed with a power factor-controlled capacitor bank, a power factor-controlled solar photovoltaic system, a bidirectional current-controlled electric vehicle charging system based on CHAdeMO 1.1 standard charging protocol and a master power factor controller was designed using the Matrix Laboratory/Simulink software. This paper has provided simulation results as proof that each of the designed equipment was functioning appropriately. The results also proved that the proposed master power factor controller was capable of regulating the power factor of the industrial system to above 0.85 lagging throughout its operation.

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Electric Factor—A Comparison of Car Usage Profiles of Electric and Conventional Vehicles by a Probabilistic Approach

Cited by 9 publications

References 16 publications

Electric Vehicle and Renewable Energy Sources: Motor Fusion in the Energy Transition from a Multi-Indicator Perspective

Electric Vehicle and Renewable Energy Sources: Motor Fusion in the Energy Transition from a Multi-Indicator Perspective

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

Design of a master power factor controller for an industrial plant with solar photovoltaic and electric vehicle chargers

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