CoChat
Sign Up
Cited by 15

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Impact Assessment of Electric Vehicle Charging in an AC and DC Microgrid: A Comparative Study

Cleenwerck, Rémy, Azaioud, Hakim, Vafaeipour, Majid, Coosemans, Thierry, Desmet, Jan·2023·Energies
DOI 10.3390/en16073205
Open this paper in CoChat — free account

Abstract

This paper presents an in-depth comparison of the benefits and limitations of using a low-voltage DC (LVDC) microgrid versus an AC microgrid with regard to the integration of low-carbon technologies. To this end, a novel approach for charging electric vehicles (EVs) on low-voltage distribution networks by utilizing an LVDC backbone is discussed. The global aim of the conducted study is to investigate the overall energy losses as well as voltage stability problems on DC and AC microgrids. Both architectures are assessed and compared to each other by performing a power flow analysis. Along this line, an actual low-voltage distribution network with various penetration levels of EVs, combined with photovoltaic (PV) systems and battery energy storage systems is considered. Obtained results indicate significant power quality improvements in voltage imbalances and conversion losses thanks to the proposed backbone. Moreover, the study concludes with a discussion of the impact level of EVs and PVs penetration degrees on energy efficiency, besides charging power levels’ impact on local self-consumption reduction of the studied system. The outcomes of the study can provide extensive insights for hybrid microgrid and EV charging infrastructure designers in a holistic manner in all aspects.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/en16073205

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Line diagram of the studied LV DN with a detailed view of the connection of the load and SDGEs, with ( a ) the conventional approach and ( b ) the proposed LVDC backbone.
Figure 1 Line diagram of the studied LV DN with a detailed view of the connection of the load and SDGEs, with ( a ) the conventional approach and ( b ) the proposed LVDC backbone.CC-BY · From the PDF
Figure 2 Probability densities and dynamic charging curves, with: ( a ) the distribution of arrival times for weekdays and weekends; ( b ) the SoC upon arrival at destination. Both right panels are representative of a randomly selected day with: ( c ) the charging voltage and current; ( d ) the respective SoC curve.
Figure 2 Probability densities and dynamic charging curves, with: ( a ) the distribution of arrival times for weekdays and weekends; ( b ) the SoC upon arrival at destination. Both right panels are representative of a randomly selected day with: ( c ) the charging voltage and current; ( d ) the respective SoC curve.CC-BY · From the PDF
Figure 3 Gaussian distribution of the voltage profiles for different EV and PV levels at various charging rates; † the grey shaded zone denotes the 0% EV and 0% PV case.
Figure 3 Gaussian distribution of the voltage profiles for different EV and PV levels at various charging rates; † the grey shaded zone denotes the 0% EV and 0% PV case.CC-BY · From the PDF
Figure 4 Voltage variation ( Vpv ) on the DC-branch and impact on the curtailed production ( Ppv ) as a function of the produced (positive) and consumed (negative) power.
Figure 4 Voltage variation ( Vpv ) on the DC-branch and impact on the curtailed production ( Ppv ) as a function of the produced (positive) and consumed (negative) power.CC-BY · From the PDF
Figure 5 Conversion and cable losses for different EV charging powers and PV penetration levels for a conventional LVAC grid and the proposed LVDC backbone.
Figure 5 Conversion and cable losses for different EV charging powers and PV penetration levels for a conventional LVAC grid and the proposed LVDC backbone.CC-BY · From the PDF
Figure 6 Comparison of the losses between the conventional approach and the proposed LVDC backbone for different charging rates, EV and PV penetration levels.
Figure 6 Comparison of the losses between the conventional approach and the proposed LVDC backbone for different charging rates, EV and PV penetration levels.CC-BY · From the PDF
Figure 7 Voltage unbalance as a function of the node losses for various EV penetrations without PV systems, with: ( a ) 0% EV penetration; ( b ) 50% EV penetration; ( c ) 100% EV penetration.
Figure 7 Voltage unbalance as a function of the node losses for various EV penetrations without PV systems, with: ( a ) 0% EV penetration; ( b ) 50% EV penetration; ( c ) 100% EV penetration.CC-BY · From the PDF

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Research works printed but not linked: 34

  1. Monteiro, V., Gonçalves, H., and Afonso, J.a.L. (2011, January 17–19). Impact of Electric Vehicles on power quality in a Smart Grid context. Proceedings of the 11th International Conference on Electrical Power Quality and Utilisation, Lisbon, Portugal. · DOI
  2. Lopes, 2011, Integration of Electric Vehicles in the Electric Power System, Proc. IEEE, 99, 168, 10.1109/JPROC.2010.2066250 · DOI · Paper page
  3. Codani, 2017, Measurement of power loss during electric vehicle charging and discharging, Energy, 127, 730, 10.1016/j.energy.2017.03.015 · DOI · Paper page
  4. Bollen, 2016, Power quality issues in the electric power system of the future, Electr. J., 29, 49, 10.1016/j.tej.2016.11.006 · DOI
  5. Leemput, 2014, Impact of Electric Vehicle On-Board Single-Phase Charging Strategies on a Flemish Residential Grid, IEEE Trans. Smart Grid, 5, 1815, 10.1109/TSG.2014.2307897 · DOI
  6. Ploumpidou, E. (2017). Supporting the Transition to DC Micro Grids in the Built Environment. [Ph.D. Thesis, Technische Universiteit Eindhoven].
  7. Yang, 2011, An industry-based survey of reliability in power electronic converters, IEEE Trans. Ind. Appl., 47, 1441, 10.1109/TIA.2011.2124436 · DOI
  8. Rekola, J. (2015). Factors Affecting Efficiency of LVDC Distribution Network–Power Electronics Perspective. [Ph.D. Thesis, Tampere University of Technology].
  9. Azaioud, H., Claeys, R., Knockaert, J., Vandevelde, L., and Desmet, J. (2021). A Low-Voltage DC Backbone with Aggregated RES and BESS: Benefits Compared to a Traditional Low-Voltage AC System. Energies, 14. · DOI · Paper page
  10. Kabus, M., Nolting, L., Mortimer, B.J., Koj, J.C., Kuckshinrichs, W., De Doncker, R.W., and Praktiknjo, A. (2020). Environmental impacts of charging concepts for battery electric vehicles: A comparison of on-board and off-board charging systems based on a life cycle assessment. Energies, 13. · DOI
  11. Huynh, W., Hoang, T., Abhisek, U., and Nirmal-Kumar, C.N. (December, January 29). Comparison of Low-Voltage AC and DC Distribution Networks for EV Charging. Proceedings of the 2022 7th IEEE Workshop on the Electronic Grid (eGRID), Auckland, New Zealand. · DOI
  12. Konstantinos, 2020, Electrical system architectures for building-integrated photovoltaics: A comparative analysis using a modelling framework in Modelica, Appl. Energy, 261, 114247, 10.1016/j.apenergy.2019.114247 · DOI
  13. Gerber, 2018, A simulation-based efficiency comparison of AC and DC power distribution networks in commercial buildings, Appl. Energy, 210, 1167, 10.1016/j.apenergy.2017.05.179 · DOI
  14. 2017, and Ruiz-Rodriguez, F.J. and Jurado, F.. Modelling and assessment of the combined technical impact of electric vehicles and photovoltaic generation in radial distribution systems, Energy, 141, 316, 10.1016/j.energy.2017.09.025 · DOI
  15. Fachrizal, 2021, Combined PV–EV hosting capacity assessment for a residential LV distribution grid with smart EV charging and PV curtailment, Sustain. Energy Grids Netw., 26, 100445, 10.1016/j.segan.2021.100445 · DOI · Paper page
  16. Masoum, M.A., Moses, P.S., and Hajforoosh, S. (2012, January 16–22). Distribution transformer stress in smart grid with coordinated charging of Plug-In Electric Vehicles. Proceedings of the 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, USA. · DOI
  17. Alpcan, 2015, Optimal Charging of Electric Vehicles Taking Distribution Network Constraints Into Account, IEEE Trans. Power Syst., 30, 365, 10.1109/TPWRS.2014.2318293 · DOI
  18. Gemassmer, J., Daam, C., and Reibsch, R. (2021). Challenges in Grid Integration of Electric Vehicles in Urban and Rural Areas. World Electr. Veh. J., 12. · DOI
  19. Bhattacharyya, S., Wijnand, M., and Slangen, T. (2022, January 2–3). Estimating the future impact of residential EV loads on low voltage distribution networks. Proceedings of the CIRED Porto Workshop 2022: E-mobility and power distribution systems, Porto, Portugal. · DOI
  20. Marah, B., Bhavanam, Y., Taylor, G., and Ekwue, A. (2016, January 6–9). Impact of electric vehicle charging systems on low voltage distribution networks. Proceedings of the 2016 51st International Universities Power Engineering Conference (UPEC), Coimbra, Portugal. · DOI
  21. Alame, D., Azzouz, M., and Kar, N. (2020). Assessing and mitigating impacts of electric vehicle harmonic currents on distribution systems. Energies, 13. · DOI
  22. Khaligh, 2019, Global Trends in High-Power On-Board Chargers for Electric Vehicles, IEEE Trans. Veh. Technol., 68, 3306, 10.1109/TVT.2019.2897050 · DOI · Paper page
  23. Claeys, R., Azaioud, H., Cleenwerck, R., Knockaert, J., and Desmet, J. (2021). A Novel Feature Set for Low-Voltage Consumers, Based on the Temporal Dependence of Consumption and Peak Demands. Energies, 14. · DOI
  24. Claeys, R., Delerue, T., and Desmet, J. (October, January 29). Assessing the influence of the aggregation level of residential consumers through load duration curves. Proceedings of the 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), Bucharest, Romania. · DOI
  25. Pelletier, 2017, Battery degradation and behaviour for electric vehicles: Review and numerical analyses of several models, Transp. Res. B: Methodol., 103, 158, 10.1016/j.trb.2017.01.020 · DOI
  26. Tremblay, O., Louis, -A.D., and Dekkiche, A.I. (2007, January 9–12). A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles. Proceedings of the 2007 IEEE Vehicle Power and Propulsion Conference, Arlington, TX, USA. · DOI
  27. Villalva, 2009, Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays, IEEE Trans. Power Electron., 24, 1198, 10.1109/TPEL.2009.2013862 · DOI
  28. Azaioud, H., Desmet, J., and Vandevelde, L. (2020). Benefit Evaluation of PV Orientation for Individual Residential Consumers. Energies, 13. · DOI
  29. Li, K., and Tseng, K.J. (2015, January 9–12). Energy efficiency of lithium-ion battery used as energy storage devices in micro-grid. Proceedings of the IECON 2015—41st Annual Conference of the IEEE Industrial Electronics Society, Yokohama, Japan. · DOI
  30. Trichakis, P., Taylor, P., Cipcigan, L., Lyons, P., Hair, R., and Ma, T. (2006, January 6–8). An Investigation of Voltage Unbalance in Low Voltage Distribution Networks with High Levels of SSEG. Proceedings of the 41st International Universities Power Engineering Conference, Newcastle Upon Tyne, UK. · DOI
  31. Hariri, 2017, Open-source python-OpenDSS interface for hybrid simulation of PV impact studies, IET Gener. Trans. Distrib., 11, 3125, 10.1049/iet-gtd.2016.1572 · DOI
  32. Cleenwerck, 2022, An approach to the impedance modelling of low-voltage cables in digital twins, Electr. Power Syst. Res., 210, 108075, 10.1016/j.epsr.2022.108075 · DOI
  33. Girigoudar, 2020, On the impact of different voltage unbalance metrics in distribution system optimization, Electr. Power Syst. Res., 189, 106656, 10.1016/j.epsr.2020.106656 · DOI · Paper page
  34. Azaioud, H., Farnam, A., Knockaert, J., Vandevelde, L., and Desmet, J. (2023). Efficiency optimisation and converterless PV integration by applying a dynamic voltage on an LVDC backbone, submitted. · DOI · Paper page

Cited by

Sorted by citations

Metadata

Authors
Cleenwerck, R., Azaioud, H., Vafaeipour, M., Coosemans, T., & Desmet, J.
Affiliations
  • EELab/Lemcko, Deptartment of Electromechanical, Systems and Metal Engineering, Ghent University, 8500 Kortrijk, Belgium
  • EVERGi, MOBI Research Centre & ETEC Deptartment, Vrije Universiteit Brussel, 1050 Brussel, Belgium
ORCID
Venue
Energies
Publisher
MDPI AG
Published
2023-04-02
Volume
16
Issue
7
Pages
3205
ISSN
1996-1073
DOI
10.3390/en16073205
Cited by
15
References
3
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review