IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i9p3790-d1135804.html
   My bibliography  Save this article

Maximising Distribution Grid Utilisation by Optimising E-Car Charging Using Smart Meter Gateway Data

Author

Listed:
  • André Ulrich

    (Cologne Institute for Renewable Energies (CIRE), TH Köln, 51519 Cologne, Germany)

  • Sergej Baum

    (Cologne Institute for Renewable Energies (CIRE), TH Köln, 51519 Cologne, Germany)

  • Ingo Stadler

    (Cologne Institute for Renewable Energies (CIRE), TH Köln, 51519 Cologne, Germany)

  • Christian Hotz

    (Cologne Institute for Renewable Energies (CIRE), TH Köln, 51519 Cologne, Germany)

  • Eberhard Waffenschmidt

    (Cologne Institute for Renewable Energies (CIRE), TH Köln, 51519 Cologne, Germany)

Abstract

The transition towards climate neutrality will result in an increase in electrical vehicles, as well as other electric loads, leading to higher loads on electrical distribution grids. This paper presents an optimisation algorithm that enables the integration of more loads into distribution grid infrastructure using information from smart meters and/or smart meter gateways. To achieve this, a mathematical programming formulation was developed and implemented. The algorithm determines the optimal charging schedule for all electric vehicles connected to the distribution grid, taking into account various criteria to avoid violating physical grid limitations and ensuring non-discriminatory charging of all electric vehicles on the grid while also optimising grid operation. Additionally, the expandability of the infrastructure and fail-safe operation are considered through the decentralisation of all components. Various scenarios are modelled and evaluated in a simulation environment. The results demonstrate that the developed optimisation algorithm allows for higher transformer loads compared to a P(U) control approach, without causing grid overload as observed in scenarios without optimisation or P(U) control.

Suggested Citation

  • André Ulrich & Sergej Baum & Ingo Stadler & Christian Hotz & Eberhard Waffenschmidt, 2023. "Maximising Distribution Grid Utilisation by Optimising E-Car Charging Using Smart Meter Gateway Data," Energies, MDPI, vol. 16(9), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3790-:d:1135804
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/9/3790/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/9/3790/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xuanhu He & Wei Wang & Jiuchun Jiang & Lijie Xu, 2015. "An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow," Energies, MDPI, vol. 8(4), pages 1-26, March.
    2. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
    3. Kanwar, Neeraj & Gupta, Nikhil & Niazi, K.R. & Swarnkar, Anil & Bansal, R.C., 2017. "Simultaneous allocation of distributed energy resource using improved particle swarm optimization," Applied Energy, Elsevier, vol. 185(P2), pages 1684-1693.
    4. Elattar, Ehab E. & ElSayed, Salah K., 2019. "Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement," Energy, Elsevier, vol. 178(C), pages 598-609.
    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. Sherif S. M. Ghoneim & Mohamed F. Kotb & Hany M. Hasanien & Mosleh M. Alharthi & Attia A. El-Fergany, 2021. "Cost Minimizations and Performance Enhancements of Power Systems Using Spherical Prune Differential Evolution Algorithm Including Modal Analysis," Sustainability, MDPI, vol. 13(14), pages 1-15, July.
    2. Good, Nicholas & Martínez Ceseña, Eduardo A. & Zhang, Lingxi & Mancarella, Pierluigi, 2016. "Techno-economic and business case assessment of low carbon technologies in distributed multi-energy systems," Applied Energy, Elsevier, vol. 167(C), pages 158-172.
    3. Abdullah Khan & Hashim Hizam & Noor Izzri bin Abdul Wahab & Mohammad Lutfi Othman, 2020. "Optimal power flow using hybrid firefly and particle swarm optimization algorithm," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-21, August.
    4. Md Shafiul Alam & Fahad Saleh Al-Ismail & Mohammad Ali Abido, 2021. "PV/Wind-Integrated Low-Inertia System Frequency Control: PSO-Optimized Fractional-Order PI-Based SMES Approach," Sustainability, MDPI, vol. 13(14), pages 1-21, July.
    5. Elsied, Moataz & Oukaour, Amrane & Youssef, Tarek & Gualous, Hamid & Mohammed, Osama, 2016. "An advanced real time energy management system for microgrids," Energy, Elsevier, vol. 114(C), pages 742-752.
    6. Singh, Pushpendra & Meena, Nand K. & Yang, Jin & Vega-Fuentes, Eduardo & Bishnoi, Shree Krishna, 2020. "Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks," Applied Energy, Elsevier, vol. 278(C).
    7. Zhu, Jiawei & Lin, Yishuai & Lei, Weidong & Liu, Youquan & Tao, Mengling, 2019. "Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm," Energy, Elsevier, vol. 171(C), pages 944-955.
    8. Brandon Cortés-Caicedo & Luis Fernando Grisales-Noreña & Oscar Danilo Montoya & Miguel Angel Rodriguez-Cabal & Javier Alveiro Rosero, 2022. "Energy Management System for the Optimal Operation of PV Generators in Distribution Systems Using the Antlion Optimizer: A Colombian Urban and Rural Case Study," Sustainability, MDPI, vol. 14(23), pages 1-35, December.
    9. Kamel, Rashad M. & Nagasaka, Ken, 2015. "Effect of load type on standalone micro grid fault performance," Applied Energy, Elsevier, vol. 160(C), pages 532-540.
    10. Yang, Fei-fei & Zhao, Xin-gang, 2018. "Policies and economic efficiency of China's distributed photovoltaic and energy storage industry," Energy, Elsevier, vol. 154(C), pages 221-230.
    11. Sahoo, Subham & Pullaguram, Deepak & Mishra, Sukumar & Wu, Jianzhong & Senroy, Nilanjan, 2018. "A containment based distributed finite-time controller for bounded voltage regulation & proportionate current sharing in DC microgrids," Applied Energy, Elsevier, vol. 228(C), pages 2526-2538.
    12. Velik, Rosemarie & Nicolay, Pascal, 2014. "Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer," Applied Energy, Elsevier, vol. 130(C), pages 384-395.
    13. Li, Rui & Wang, Wei & Wu, Xuezhi & Tang, Fen & Chen, Zhe, 2019. "Cooperative planning model of renewable energy sources and energy storage units in active distribution systems: A bi-level model and Pareto analysis," Energy, Elsevier, vol. 168(C), pages 30-42.
    14. Xuejie Wang & Yanchao Ji & Jianze Wang & Yuanjun Wang & Lei Qi, 2020. "Optimal energy management of microgrid based on multi-parameter dynamic programming," International Journal of Distributed Sensor Networks, , vol. 16(6), pages 15501477209, June.
    15. Zhao, Bo & Zhang, Xuesong & Li, Peng & Wang, Ke & Xue, Meidong & Wang, Caisheng, 2014. "Optimal sizing, operating strategy and operational experience of a stand-alone microgrid on Dongfushan Island," Applied Energy, Elsevier, vol. 113(C), pages 1656-1666.
    16. Veerasamy, Veerapandiyan & Abdul Wahab, Noor Izzri & Ramachandran, Rajeswari & Othman, Mohammad Lutfi & Hizam, Hashim & Devendran, Vidhya Sagar & Irudayaraj, Andrew Xavier Raj & Vinayagam, Arangarajan, 2021. "Recurrent network based power flow solution for voltage stability assessment and improvement with distributed energy sources," Applied Energy, Elsevier, vol. 302(C).
    17. Mohammed Hamouda Ali & Ali M. El-Rifaie & Ahmed A. F. Youssef & Vladimir N. Tulsky & Mohamed A. Tolba, 2023. "Techno-Economic Strategy for the Load Dispatch and Power Flow in Power Grids Using Peafowl Optimization Algorithm," Energies, MDPI, vol. 16(2), pages 1-29, January.
    18. Salah K. ElSayed & Ehab E. Elattar, 2021. "Slime Mold Algorithm for Optimal Reactive Power Dispatch Combining with Renewable Energy Sources," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    19. Parisio, Alessandra & Rikos, Evangelos & Tzamalis, George & Glielmo, Luigi, 2014. "Use of model predictive control for experimental microgrid optimization," Applied Energy, Elsevier, vol. 115(C), pages 37-46.
    20. Mohamed A. M. Shaheen & Hany M. Hasanien & Said F. Mekhamer & Mohammed H. Qais & Saad Alghuwainem & Zia Ullah & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado & Mohamed R. Elkadeem, 2022. "Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-23, August.

    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:9:p:3790-:d:1135804. 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.