IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipds0306261924021287.html
   My bibliography  Save this article

Efficient allocation of capacitors and vehicle-to-grid integration with electric vehicle charging stations in radial distribution networks

Author

Listed:
  • Soliman, Ismail A.
  • Tulsky, Vladimir
  • Abd el-Ghany, Hossam A.
  • ElGebaly, Ahmed E.

Abstract

Electric vehicles offer zero tailpipe emissions and effectively reduce the release of harmful pollutants, which are significant contributors to air pollution and climate changes. However, the increased adoption of electric vehicles presents challenges to the power grid and could create a surge in demand characterized by fast-absorbing electrical energy. This surge can affect voltage profiles and escalate energy losses within distribution lines. This study introduces an optimization framework leveraging parallel search real-coded genetic algorithms (PSRCGA) for the efficient allocation and sizing of fast charging stations, vehicle-to-grid integration, and capacitors utilization. The optimization aims to enhance grid profitability by minimizing capacitor costs and optimizing power quality metrics through a multi-objective function. Various constraints such as power balance, bus voltage, current flow limits, and capacitor bank specifications are considered in the optimization process. By employing the PSRCGA, the study proposes a fitness function that effectively incorporates the objective function with the constraints. The proposed framework is validated through diverse scenarios applied to IEEE 33-bus and 69-bus systems, showcasing its efficacy in optimizing system performance.

Suggested Citation

  • Soliman, Ismail A. & Tulsky, Vladimir & Abd el-Ghany, Hossam A. & ElGebaly, Ahmed E., 2025. "Efficient allocation of capacitors and vehicle-to-grid integration with electric vehicle charging stations in radial distribution networks," Applied Energy, Elsevier, vol. 377(PD).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pd:s0306261924021287
    DOI: 10.1016/j.apenergy.2024.124745
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924021287
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124745?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. Shariatio, O. & Coker, P.J. & Smith, S.T. & Potter, B. & Holderbaum, W., 2024. "An integrated techno-economic approach for design and energy management of heavy goods electric vehicle charging station with energy storage systems," Applied Energy, Elsevier, vol. 369(C).
    2. Guo, Sen & Zhao, Huiru, 2015. "Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective," Applied Energy, Elsevier, vol. 158(C), pages 390-402.
    3. Algafri, Mohammed & Alghazi, Anas & Almoghathawi, Yasser & Saleh, Haitham & Al-Shareef, Khaled, 2024. "Smart City Charging Station allocation for electric vehicles using analytic hierarchy process and multiobjective goal-programming," Applied Energy, Elsevier, vol. 372(C).
    4. Xiang, Yue & Liu, Junyong & Li, Ran & Li, Furong & Gu, Chenghong & Tang, Shuoya, 2016. "Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates," Applied Energy, Elsevier, vol. 178(C), pages 647-659.
    5. Sun, Chuyu & Zhao, Xiaoli & Qi, Binbin & Xiao, Weihao & Zhang, Hongjun, 2022. "Economic and environmental analysis of coupled PV-energy storage-charging station considering location and scale," Applied Energy, Elsevier, vol. 328(C).
    6. Wang, Zhaoqi & Zhang, Lu & Tang, Wei & Ma, Ziyao & Huang, Jiajin, 2024. "Equilibrium configuration strategy of vehicle-to-grid-based electric vehicle charging stations in low-carbon resilient distribution networks," Applied Energy, Elsevier, vol. 361(C).
    7. Matteo Muratori, 2018. "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," Nature Energy, Nature, vol. 3(3), pages 193-201, March.
    8. Jin, Ruiyang & Zhou, Yuke & Lu, Chao & Song, Jie, 2022. "Deep reinforcement learning-based strategy for charging station participating in demand response," Applied Energy, Elsevier, vol. 328(C).
    9. Adetunji, Kayode E. & Hofsajer, Ivan W. & Abu-Mahfouz, Adnan M. & Cheng, Ling, 2022. "An optimization planning framework for allocating multiple distributed energy resources and electric vehicle charging stations in distribution networks," Applied Energy, Elsevier, vol. 322(C).
    10. Zhou, Siyu & Han, Yang & Mahmoud, Karar & Darwish, Mohamed M.F. & Lehtonen, Matti & Yang, Ping & Zalhaf, Amr S., 2023. "A novel unified planning model for distributed generation and electric vehicle charging station considering multi-uncertainties and battery degradation," Applied Energy, Elsevier, vol. 348(C).
    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. Muratori, Matteo & Kontou, Eleftheria & Eichman, Joshua, 2019. "Electricity rates for electric vehicle direct current fast charging in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    2. Zhou, Guangyou & Zhu, Zhiwei & Luo, Sumei, 2022. "Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm," Energy, Elsevier, vol. 247(C).
    3. Micari, Salvatore & Polimeni, Antonio & Napoli, Giuseppe & Andaloro, Laura & Antonucci, Vincenzo, 2017. "Electric vehicle charging infrastructure planning in a road network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 98-108.
    4. Miao, Hongzhi & Jia, Hongfei & Li, Jiangchen & Qiu, Tony Z., 2019. "Autonomous connected electric vehicle (ACEV)-based car-sharing system modeling and optimal planning: A unified two-stage multi-objective optimization methodology," Energy, Elsevier, vol. 169(C), pages 797-818.
    5. Andrenacci, N. & Genovese, A. & Ragona, R., 2017. "Determination of the level of service and customer crowding for electric charging stations through fuzzy models and simulation techniques," Applied Energy, Elsevier, vol. 208(C), pages 97-107.
    6. Milan Straka & Rui Carvalho & Gijs van der Poel & v{L}ubov{s} Buzna, 2020. "Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data," Papers 2006.01672, arXiv.org, revised Jun 2020.
    7. Qian, Tao & Liang, Zeyu & Shao, Chengcheng & Guo, Zishan & Hu, Qinran & Wu, Zaijun, 2025. "Unsupervised learning for efficiently distributing EVs charging loads and traffic flows in coupled power and transportation systems," Applied Energy, Elsevier, vol. 377(PB).
    8. Metais, M.O. & Jouini, O. & Perez, Y. & Berrada, J. & Suomalainen, E., 2022. "Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    9. Schmidt, Marc & Staudt, Philipp & Weinhardt, Christof, 2020. "Evaluating the importance and impact of user behavior on public destination charging of electric vehicles," Applied Energy, Elsevier, vol. 258(C).
    10. Shi, Haojie & Xiong, Houbo & Gan, Wei & Lin, Yumian & Guo, Chuangxin, 2025. "Fully distributed planning method for coordinated distribution and urban transportation networks considering three-phase unbalance mitigation," Applied Energy, Elsevier, vol. 377(PA).
    11. Yue Xiang & Wei Yang & Junyong Liu & Furong Li, 2016. "Multi-Objective Distribution Network Expansion Incorporating Electric Vehicle Charging Stations," Energies, MDPI, vol. 9(11), pages 1-17, November.
    12. Li, Shunxi & Su, Bowen & St-Pierre, David L. & Sui, Pang-Chieh & Zhang, Guofang & Xiao, Jinsheng, 2017. "Decision-making of compressed natural gas station siting for public transportation: Integration of multi-objective optimization, fuzzy evaluating, and radar charting," Energy, Elsevier, vol. 140(P1), pages 11-17.
    13. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.
    14. Reza Fachrizal & Joakim Munkhammar, 2020. "Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles," Energies, MDPI, vol. 13(5), pages 1-19, March.
    15. Nock, Destenie & Baker, Erin, 2019. "Holistic multi-criteria decision analysis evaluation of sustainable electric generation portfolios: New England case study," Applied Energy, Elsevier, vol. 242(C), pages 655-673.
    16. Danijela Tuljak-Suban & Patricija Bajec, 2022. "A Hybrid DEA Approach for the Upgrade of an Existing Bike-Sharing System with Electric Bikes," Energies, MDPI, vol. 15(21), pages 1-23, October.
    17. Lizana, Jesus & Friedrich, Daniel & Renaldi, Renaldi & Chacartegui, Ricardo, 2018. "Energy flexible building through smart demand-side management and latent heat storage," Applied Energy, Elsevier, vol. 230(C), pages 471-485.
    18. Yang, Jun & Guo, Fang & Zhang, Min, 2017. "Optimal planning of swapping/charging station network with customer satisfaction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 174-197.
    19. Abdel-Mohsen O. Mohamed & Dina Mohamed & Adham Fayad & Moza T. Al Nahyan, 2024. "Enhancing Decision Making and Decarbonation in Environmental Management: A Review on the Role of Digital Technologies," Sustainability, MDPI, vol. 16(16), pages 1-34, August.
    20. Ahmadian, Amirhossein & Ghodrati, Vahid & Gadh, Rajit, 2023. "Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework," Applied Energy, Elsevier, vol. 352(C).

    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:appene:v:377:y:2025:i:pd:s0306261924021287. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.