IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1213-d1044111.html
   My bibliography  Save this article

Linear Programming-Based Power Management for a Multi-Feeder Ultra-Fast DC Charging Station

Author

Listed:
  • Luigi Rubino

    (Department of Engineering, University of Campania Luigi Vanvitelli, 81031 Aversa, CE, Italy
    These authors contributed equally to this work.)

  • Guido Rubino

    (Department of Electrical and Information Engineering (DIEI), University of Cassino and South Lazio, 03043 Cassino, FR, Italy
    These authors contributed equally to this work.)

  • Raffaele Esempio

    (Department of Engineering, University of Campania Luigi Vanvitelli, 81031 Aversa, CE, Italy
    These authors contributed equally to this work.)

Abstract

The growing number of electric vehicles (EVs) affects the national electricity system in terms of power demand and load variation. Turning our attention to Italy, the number of vehicles on the road is 39 million; this represents a major challenge, as they will need to be recharged constantly when the transition to electric technology is complete. If we consider that the average power is 55 GW and the installed system can produce 120 GW of peak power, we can calculate that with only 5% of vehicles in recharging mode, the power demand increases to 126 GW, which is approximately 140% of installed power. The integration of renewable energy sources will help the grid, but this solution is less useful for handling large load variations that negatively affect the grid. In addition, some vehicles committed to public utility must have a reduced stop time and can be considered to have higher priority. The introduction of priorities implies that the power absorption limit cannot be easily introduced by limiting the number of charging vehicles, but rather by computing the power flow that respects constraints and integrates renewable and local storage power contributions. The problem formulated in this manner does not have a unique solution; in this study, the linear programming method is used to optimise renewable resources, local storage, and EVs to mitigate their effects on the grid. Simulations are performed to verify the proposed method.

Suggested Citation

  • Luigi Rubino & Guido Rubino & Raffaele Esempio, 2023. "Linear Programming-Based Power Management for a Multi-Feeder Ultra-Fast DC Charging Station," Energies, MDPI, vol. 16(3), pages 1-17, January.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1213-:d:1044111
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1213/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/3/1213/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gabriel Antonio Salvatti & Emerson Giovani Carati & Rafael Cardoso & Jean Patric da Costa & Carlos Marcelo de Oliveira Stein, 2020. "Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids," Energies, MDPI, vol. 13(5), pages 1-22, March.
    2. Mohamed S. Abdalzaher & Mostafa M. Fouda & Mohamed I. Ibrahem, 2022. "Data Privacy Preservation and Security in Smart Metering Systems," Energies, MDPI, vol. 15(19), pages 1-19, October.
    3. Rubino, Luigi & Capasso, Clemente & Veneri, Ottorino, 2017. "Review on plug-in electric vehicle charging architectures integrated with distributed energy sources for sustainable mobility," Applied Energy, Elsevier, vol. 207(C), pages 438-464.
    4. Bachir Zine & Haithem Bia & Amel Benmouna & Mohamed Becherif & Mehroze Iqbal, 2022. "Experimentally Validated Coulomb Counting Method for Battery State-of-Charge Estimation under Variable Current Profiles," Energies, MDPI, vol. 15(21), pages 1-15, November.
    5. Aste, Niccolò & Del Pero, Claudio & Leonforte, Fabrizio & Manfren, Massimiliano, 2013. "A simplified model for the estimation of energy production of PV systems," Energy, Elsevier, vol. 59(C), pages 503-512.
    6. Gerardo J. Osório & Miadreza Shafie-khah & Pedro D. L. Coimbra & Mohamed Lotfi & João P. S. Catalão, 2018. "Distribution System Operation with Electric Vehicle Charging Schedules and Renewable Energy Resources," Energies, MDPI, vol. 11(11), pages 1-20, November.
    7. Almonacid, F. & Rus, C. & Pérez, P.J. & Hontoria, L., 2009. "Estimation of the energy of a PV generator using artificial neural network," Renewable Energy, Elsevier, vol. 34(12), pages 2743-2750.
    Full references (including those not matched with items on IDEAS)

    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. Woo-Gyun Shin & Ju-Young Shin & Hye-Mi Hwang & Chi-Hong Park & Suk-Whan Ko, 2022. "Power Generation Prediction of Building-Integrated Photovoltaic System with Colored Modules Using Machine Learning," Energies, MDPI, vol. 15(7), pages 1-17, April.
    2. Md. Mosaraf Hossain Khan & Amran Hossain & Aasim Ullah & Molla Shahadat Hossain Lipu & S. M. Shahnewaz Siddiquee & M. Shafiul Alam & Taskin Jamal & Hafiz Ahmed, 2021. "Integration of Large-Scale Electric Vehicles into Utility Grid: An Efficient Approach for Impact Analysis and Power Quality Assessment," Sustainability, MDPI, vol. 13(19), pages 1-18, October.
    3. Boud Verbrugge & Mohammed Mahedi Hasan & Haaris Rasool & Thomas Geury & Mohamed El Baghdadi & Omar Hegazy, 2021. "Smart Integration of Electric Buses in Cities: A Technological Review," Sustainability, MDPI, vol. 13(21), pages 1-23, November.
    4. Chankook Park & Wan Gyu Heo & Myung Eun Lee, 2024. "Study on Consumers’ Perceived Benefits and Risks of Smart Energy System," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 288-300, May.
    5. Mahmoud M. Badr & Mohamed I. Ibrahem & Hisham A. Kholidy & Mostafa M. Fouda & Muhammad Ismail, 2023. "Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems," Energies, MDPI, vol. 16(6), pages 1-18, March.
    6. Konstantinos Kotsalos & Ismael Miranda & Nuno Silva & Helder Leite, 2019. "A Horizon Optimization Control Framework for the Coordinated Operation of Multiple Distributed Energy Resources in Low Voltage Distribution Networks," Energies, MDPI, vol. 12(6), pages 1-27, March.
    7. Ouammi, Ahmed & Zejli, Driss & Dagdougui, Hanane & Benchrifa, Rachid, 2012. "Artificial neural network analysis of Moroccan solar potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4876-4889.
    8. Del Pero, Claudio & Aste, Niccolò & Leonforte, Fabrizio, 2021. "The effect of rain on photovoltaic systems," Renewable Energy, Elsevier, vol. 179(C), pages 1803-1814.
    9. Wu, Di & Radhakrishnan, Nikitha & Huang, Sen, 2019. "A hierarchical charging control of plug-in electric vehicles with simple flexibility model," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Almonacid, F. & Rus, C. & Pérez-Higueras, P. & Hontoria, L., 2011. "Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks," Energy, Elsevier, vol. 36(1), pages 375-384.
    11. Lucas V. Bellinaso & Edivan L. Carvalho & Rafael Cardoso & Leandro Michels, 2021. "Price-Response Matrices Design Methodology for Electrical Energy Management Systems Based on DC Bus Signalling," Energies, MDPI, vol. 14(6), pages 1-19, March.
    12. Achraf Saadaoui & Mohammed Ouassaid & Mohamed Maaroufi, 2023. "Overview of Integration of Power Electronic Topologies and Advanced Control Techniques of Ultra-Fast EV Charging Stations in Standalone Microgrids," Energies, MDPI, vol. 16(3), pages 1-21, January.
    13. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    14. Vavilapalli, Sridhar & Umashankar, S. & Sanjeevikumar, P. & Ramachandaramurthy, Vigna K. & Mihet-Popa, Lucian & Fedák, Viliam, 2018. "Three-stage control architecture for cascaded H-Bridge inverters in large-scale PV systems – Real time simulation validation," Applied Energy, Elsevier, vol. 229(C), pages 1111-1127.
    15. Francesco Mancini & Benedetto Nastasi, 2020. "Solar Energy Data Analytics: PV Deployment and Land Use," Energies, MDPI, vol. 13(2), pages 1-18, January.
    16. Mohammad Shadnam Zarbil & Abolfazl Vahedi & Hossein Azizi Moghaddam & Pavel Aleksandrovich Khlyupin, 2022. "Design and Sizing of Electric Bus Flash Charger Based on a Flywheel Energy Storage System: A Case Study," Energies, MDPI, vol. 15(21), pages 1-23, October.
    17. Lazrak, Amine & Boudehenn, François & Bonnot, Sylvain & Fraisse, Gilles & Leconte, Antoine & Papillon, Philippe & Souyri, Bernard, 2016. "Development of a dynamic artificial neural network model of an absorption chiller and its experimental validation," Renewable Energy, Elsevier, vol. 86(C), pages 1009-1022.
    18. Pradeep Vishnuram & Suresh Panchanathan & Narayanamoorthi Rajamanickam & Vijayakumar Krishnasamy & Mohit Bajaj & Marian Piecha & Vojtech Blazek & Lukas Prokop, 2023. "Review of Wireless Charging System: Magnetic Materials, Coil Configurations, Challenges, and Future Perspectives," Energies, MDPI, vol. 16(10), pages 1-31, May.
    19. Mohamed S. Abdalzaher & Moez Krichen & Derya Yiltas-Kaplan & Imed Ben Dhaou & Wilfried Yves Hamilton Adoni, 2023. "Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey," Sustainability, MDPI, vol. 15(15), pages 1-38, July.
    20. Mortaz, Ebrahim & Vinel, Alexander & Dvorkin, Yury, 2019. "An optimization model for siting and sizing of vehicle-to-grid facilities in a microgrid," Applied Energy, Elsevier, vol. 242(C), pages 1649-1660.

    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:gam:jeners:v:16:y:2023:i:3:p:1213-:d:1044111. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.