Multi-Objective Optimal Charging Control of Plug-In Hybrid Electric Vehicles in Power Distribution Systems

Li, Wei; Lin, Zhiyun; Cai, Kai; Zhou, Hanyun; Yan, Gangfeng

doi:10.3390/en12132563

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…Energy storage is one the most promising proposed techniques that is mostly combined nowadays to AC/DC microgrids because offers several alternatives ranging from simple energy storage services to voltage stability [39] and energy management solutions [40]. In addition, optimal charging controllers for coordinated charging of EVs [41], decentralized control schemes in order to enhance AC/DC microgrids stability [42], as well as artificial intelligent methods (AI) such as machine learning (ML) open new horizons for the power systems management [43].…”

Section: Discussionmentioning

confidence: 99%

Investigation of the Impact of Large-Scale Integration of Electric Vehicles for a Swedish Distribution Network

Johansson¹,

Persson

Lazarou

et al. 2019

Energies

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Social considerations for a sustainable future lead to market demands for electromobility. Hence, electrical power distribution operators are concerned about the real ongoing problem of the electrification of the transport sector. In this regard, the paper aims to investigate the large-scale integration of electric vehicles in a Swedish distribution network. To this end, the integration pattern is taken into consideration as appears in the literature for other countries and applies to the Swedish culture. Moreover, different charging power levels including smart charging techniques are examined for several percentages of electric vehicles penetration. Industrial simulation tools proven for their accuracy are used for the study. The results indicate that the grid can manage about 50% electric vehicles penetration at its current capacity. This percentage decreases when higher charging power levels apply, while the transformers appear overloaded in many cases. The investigation of alternatives to increase the grid's capabilities reveal that smart techniques are comparable to the conventional re-dimension of the grid. At present, the increased integration of electric vehicles is manageable by implementing a combination of smart gird and upgrade investments in comparison to technically expensive alternatives based on grid digitalization and algorithms that need to be further confirmed for their reliability for power sharing and energy management.All indications show that the distributed generation (DG) will relieve the transmission and sub-transmission networks from the large amount of power demand needed by the end-user customers. However, it is expected that with an increased electrified vehicles fleet increased power demand by the electric vehicles (EVs) owners will emerge, presenting strong geographical variations and time fluctuations as its main characteristics. As such, in addition to the challenge of integrating DG units in the primary and secondary distribution grids, mostly based on renewable energy sources (RES), significant challenges are expected for the development, expansion and investments of the distribution networks [6] aiming to absorb the large-scale integration of vehicles that use non-fossil fuels. In Sweden, given the constraint of an equipment life span of 40 years, the system operator shall take all appropriate measures to achieve such a target [7] in combination to the duty to supply electricity to customers within its geographical concession area [8]. It is already recognized that the planning of investments and grid reinforcement processes became more complex because of the almost countless social and technological variables involved. The network companies have to design reasonable investments based on their experience from customers' and markets' behavior. However, nowadays the traditional role of the end-user customer as consumer is considered behind the times and has been replaced by the more active role of the prosumer. Therefore, a future investment should be based on predictions of fu...

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Section: Discussionmentioning

confidence: 99%

Investigation of the Impact of Large-Scale Integration of Electric Vehicles for a Swedish Distribution Network

Johansson¹,

Persson

Lazarou

et al. 2019

Energies

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“…Sustainable transportation is defined as a transportation facility with nondeclining capital, where the capital includes human capital, monetary capital, and natural capital [ 3 , 4 ]. Using electric vehicles (EVs) while enhancing the generation of renewable energies can help reduce the carbon footprint and prevent the depletion of fossil resources [ 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Multicriteria-Based Comparison of Electric Vehicles Using q-Rung Orthopair Fuzzy Numbers

Biswas

Sanyal

Božanić

et al. 2023

Entropy

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The subject of this research is the evaluation of electric cars and the choice of car that best meets the set research criteria. To this end, the criteria weights were determined using the entropy method with two-step normalization and a full consistency check. In addition, the entropy method was extended further with q-rung orthopair fuzzy (qROF) information and Einstein aggregation for carrying out decision making under uncertainty with imprecise information. Sustainable transportation was selected as the area of application. The current work compared a set of 20 leading EVs in India using the proposed decision-making model. The comparison was designed to cover two aspects: technical attributes and user opinions. For the ranking of the EVs, a recently developed multicriteria decision-making (MCDM) model, the alternative ranking order method with two-step normalization (AROMAN), was used. The present work is a novel hybridization of the entropy method, full consistency method (FUCOM), and AROMAN in an uncertain environment. The results show that the electricity consumption criterion (w = 0.0944) received the greatest weight, while the best ranked alternative was A7. The results also show robustness and stability, as revealed through a comparison with the other MCDM models and a sensitivity analysis. The present work is different from the past studies, as it provides a robust hybrid decision-making model that uses both objective and subjective information.

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“…This is due to the persistent need for longer drive ranges to be covered, battery charging time to be reduced, and energy efficiency to be improved. These operations are focused on smart charging control systems, reduction in manufacturing costs, and new safety and security technologies for EV owners [4].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Electromagnetic Disturbances from an Electric Vehicle Charging Station

Mazurek

Chudy

2021

Energies

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The electric vehicles (EVs) could potentially have a significant impact on power quality parameters and distribution networks as they are non-linear loads and their charging might result in tremendous power demand. When connected to the utility grid, a large number of EV charging stations from different manufacturers might create significant harmonic current emissions, impact the voltage profile, and eventually affect the power quality. Nevertheless, practical examples of disturbances from charging stations have not been made public. This paper aims to clarify the characteristics of conductive disturbances and levels of current harmonics generated by charging station and their severity on the quality of electric energy. The analysis was based on tests of a prototype station of an EV charging station integrated with a LED street light. The tests concern the determination of current harmonics and the values of conductive electromagnetic disturbances in the 150 kHz–30 MHz range. The test results of the prototype charger with observed exceedances of current harmonics (25th–39th range) and conducted interference exceedances are comprehensively described. After applying filtering circuits to the final version of the station, retesting in an accredited laboratory showed qualitative compliance.

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Multi-Objective Optimal Charging Control of Plug-In Hybrid Electric Vehicles in Power Distribution Systems

Cited by 9 publications

References 22 publications

Investigation of the Impact of Large-Scale Integration of Electric Vehicles for a Swedish Distribution Network

Investigation of the Impact of Large-Scale Integration of Electric Vehicles for a Swedish Distribution Network

A Multicriteria-Based Comparison of Electric Vehicles Using q-Rung Orthopair Fuzzy Numbers

An Analysis of Electromagnetic Disturbances from an Electric Vehicle Charging Station

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