IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v268y2023ics0360544223000075.html
   My bibliography  Save this article

Optimal management of parking lots as a big data for electric vehicles using internet of things and Long–Short term Memory

Author

Listed:
  • Ding, Xuefeng
  • Gan, Qihong
  • Shaker, Mir Pasha

Abstract

In recent years, many people have been moving to urban life. It has been estimated that more than 60% of the population will live in urban environments in 2030. Some systems that can face the challenges of increasing population will help develop the smart city. Today, the relationship between various urban systems, such as transportation, communication and business networks, is very complicated. These complexities have greatly enhanced the importance of intelligent and quick coordination of cities with modern technologies. Internet of Things (IoT) one of the new technologies in the present era, and its application in cities can be considered as a major innovation for sustainable urban development. A smart city is a city that has the ability to effectively provide and present data in a meaningful way. On the other hand, today, with the expansion of the influence of Electric Vehicles (EV) in the transportation industry and the need for these EV to charge their battery, it is impossible to ignore the impact of these EV on the smart grid. In this thesis, the presence of EV in the context of urban and IoT is examined. As we use the intelligent city and the IoT, the historical data can be collected from the behavior of the owners of EV and the Deep learning method, one of the methods of machine learning; can be used to predict the level of charging of EV when entering the parking lot, EV location and parking lot connection periods. This will make us more successful in optimizing energy management, which is designed to minimize operational costs. The proposed model has been implemented on the IEEE 33-bus distribution system to evaluate the performance of the proposed optimization method for scheduling of distribution network in the presence of EV in the GAMS software environment. The results showed that the net profit is increased under the proposed plan.

Suggested Citation

  • Ding, Xuefeng & Gan, Qihong & Shaker, Mir Pasha, 2023. "Optimal management of parking lots as a big data for electric vehicles using internet of things and Long–Short term Memory," Energy, Elsevier, vol. 268(C).
    • Handle: RePEc:eee:energy:v:268:y:2023:i:c:s0360544223000075
    DOI: 10.1016/j.energy.2023.126613
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223000075
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.126613?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lund, Henrik & Kempton, Willett, 2008. "Integration of renewable energy into the transport and electricity sectors through V2G," Energy Policy, Elsevier, vol. 36(9), pages 3578-3587, September.
    2. Kiviluoma, Juha & Meibom, Peter, 2011. "Methodology for modelling plug-in electric vehicles in the power system and cost estimates for a system with either smart or dumb electric vehicles," Energy, Elsevier, vol. 36(3), pages 1758-1767.
    3. Englberger, Stefan & Abo Gamra, Kareem & Tepe, Benedikt & Schreiber, Michael & Jossen, Andreas & Hesse, Holger, 2021. "Electric vehicle multi-use: Optimizing multiple value streams using mobile storage systems in a vehicle-to-grid context," Applied Energy, Elsevier, vol. 304(C).
    4. Soares, João & Borges, Nuno & Fotouhi Ghazvini, Mohammad Ali & Vale, Zita & de Moura Oliveira, P.B., 2016. "Scenario generation for electric vehicles' uncertain behavior in a smart city environment," Energy, Elsevier, vol. 111(C), pages 664-675.
    5. Ryosuke Kataoka & Kazuhiko Ogimoto & Yumiko Iwafune, 2021. "Marginal Value of Vehicle-to-Grid Ancillary Service in a Power System with Variable Renewable Energy Penetration and Grid Side Flexibility," Energies, MDPI, vol. 14(22), pages 1-21, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Faustino, Fausta J. & Lopes, José Calixto & Melo, Joel D. & Sousa, Thales & Padilha-Feltrin, Antonio & Brito, José A.S. & Garcia, Claudio O., 2023. "Identifying charging zones to allocate public charging stations for electric vehicles," Energy, Elsevier, vol. 283(C).
    2. Junkai Zhang & Jun Wang & Haoyu Zang & Ning Ma & Martin Skitmore & Ziyi Qu & Greg Skulmoski & Jianli Chen, 2024. "The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends," Sustainability, MDPI, vol. 16(14), pages 1-34, July.
    3. Hosseini Dehshiri, Seyyed Jalaladdin & Amiri, Maghsoud, 2023. "Evaluating the risks of the internet of things in renewable energy systems using a hybrid fuzzy decision approach," Energy, Elsevier, vol. 285(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bogdanov, Dmitrii & Breyer, Christian, 2024. "Role of smart charging of electric vehicles and vehicle-to-grid in integrated renewables-based energy systems on country level," Energy, Elsevier, vol. 301(C).
    2. Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Dai, Guyu & Chai, Jianxue, 2022. "Cost compensation method for PEVs participating in dynamic economic dispatch based on carbon trading mechanism," Energy, Elsevier, vol. 239(PA).
    3. Szinai, Julia K. & Sheppard, Colin J.R. & Abhyankar, Nikit & Gopal, Anand R., 2020. "Reduced grid operating costs and renewable energy curtailment with electric vehicle charge management," Energy Policy, Elsevier, vol. 136(C).
    4. Soares M.C. Borba, Bruno & Szklo, Alexandre & Schaeffer, Roberto, 2012. "Plug-in hybrid electric vehicles as a way to maximize the integration of variable renewable energy in power systems: The case of wind generation in northeastern Brazil," Energy, Elsevier, vol. 37(1), pages 469-481.
    5. Schill, Wolf-Peter & Gerbaulet, Clemens, 2015. "Power System Impacts of Electric Vehicles in Germany: Charging with Coal or Renewables," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 156, pages 185-196.
    6. Oshiro, Ken & Masui, Toshihiko, 2015. "Diffusion of low emission vehicles and their impact on CO2 emission reduction in Japan," Energy Policy, Elsevier, vol. 81(C), pages 215-225.
    7. Hedegaard, Karsten & Ravn, Hans & Juul, Nina & Meibom, Peter, 2012. "Effects of electric vehicles on power systems in Northern Europe," Energy, Elsevier, vol. 48(1), pages 356-368.
    8. Shareef, Hussain & Islam, Md. Mainul & Mohamed, Azah, 2016. "A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 403-420.
    9. Brown, T. & Schlachtberger, D. & Kies, A. & Schramm, S. & Greiner, M., 2018. "Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system," Energy, Elsevier, vol. 160(C), pages 720-739.
    10. Richardson, David B., 2013. "Electric vehicles and the electric grid: A review of modeling approaches, Impacts, and renewable energy integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 247-254.
    11. Božič, Dušan & Pantoš, Miloš, 2015. "Impact of electric-drive vehicles on power system reliability," Energy, Elsevier, vol. 83(C), pages 511-520.
    12. Mubbashir Ali & Jussi Ekström & Matti Lehtonen, 2018. "Sizing Hydrogen Energy Storage in Consideration of Demand Response in Highly Renewable Generation Power Systems," Energies, MDPI, vol. 11(5), pages 1-11, May.
    13. Kriegler, Elmar, 2011. "Comment," Energy Economics, Elsevier, vol. 33(4), pages 594-596, July.
    14. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    15. Maria Taljegard & Lisa Göransson & Mikael Odenberger & Filip Johnsson, 2021. "To Represent Electric Vehicles in Electricity Systems Modelling—Aggregated Vehicle Representation vs. Individual Driving Profiles," Energies, MDPI, vol. 14(3), pages 1-25, January.
    16. Zhang, Qi & Mclellan, Benjamin C. & Tezuka, Tetsuo & Ishihara, Keiichi N., 2013. "A methodology for economic and environmental analysis of electric vehicles with different operational conditions," Energy, Elsevier, vol. 61(C), pages 118-127.
    17. Varga, Bogdan Ovidiu, 2013. "Electric vehicles, primary energy sources and CO2 emissions: Romanian case study," Energy, Elsevier, vol. 49(C), pages 61-70.
    18. Alqahtani, Mohammed & Hu, Mengqi, 2022. "Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning," Energy, Elsevier, vol. 244(PA).
    19. Abolhosseini, Shahrouz & Heshmati, Almas & Altmann, Jörn, 2014. "A Review of Renewable Energy Supply and Energy Efficiency Technologies," IZA Discussion Papers 8145, Institute of Labor Economics (IZA).
    20. Tan, Kang Miao & Padmanaban, Sanjeevikumar & Yong, Jia Ying & Ramachandaramurthy, Vigna K., 2019. "A multi-control vehicle-to-grid charger with bi-directional active and reactive power capabilities for power grid support," Energy, Elsevier, vol. 171(C), pages 1150-1163.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:268:y:2023:i:c:s0360544223000075. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.