IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i9p7078-d1131017.html
   My bibliography  Save this article

Effective Deterministic Methodology for Enhanced Distribution Network Performance and Plug-in Electric Vehicles

Author

Listed:
  • Zeeshan Anjum Memon

    (Centre of Electrical Energy Systems (CEES), Institute of Future Energy (IFE), Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia
    Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia
    Department of Electrical Engineering, Mehran University of Engineering and Technology (MUET), SZAB, Campus, Khairpur Mirs 66020, Sindh, Pakistan)

  • Dalila Mat Said

    (Centre of Electrical Energy Systems (CEES), Institute of Future Energy (IFE), Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia
    Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia)

  • Mohammad Yusri Hassan

    (Centre of Electrical Energy Systems (CEES), Institute of Future Energy (IFE), Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia
    Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia)

  • Hafiz Mudassir Munir

    (Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Sindh, Pakistan)

  • Faisal Alsaif

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Sager Alsulamy

    (Energy & Climate Change Division, Sustainable Energy Research Group, Faculty of Engineering & Physical Sciences, University of Southampton, Southampton SO16 7QF, UK)

Abstract

The rapid depletion of fossil fuel motivates researchers and policymakers to switch from the internal combustion engine (ICE) to plug-in electric vehicles (PEVs). However, the electric power distribution networks are congested, which lowers the accommodation of PEVs and produces higher power losses. Therefore, the study proposes an effective deterministic methodology to maximize the accommodation of PEVs and percentage power loss reduction (%PLR) in radial distribution networks (RDNs). In the first stage, the PEVs are allocated to the best bus, which is chosen based on the loading capacity to power loss index (LCPLI), and the accommodation profile of PEVs is developed based on varying states of charge (SoC) and battery capacities (BCs). In the second stage, the power losses are minimized in PEV integrated networks with the allocation of DG units using a recently developed parallel-operated arithmetic optimization algorithm salp swarm algorithm (AOASSA). In the third stage, the charging and discharging ratios of PEVs are optimized analytically to minimize power losses after planning PEVs and DGs. The outcomes reveal that bus-2 is the most optimal bus for accommodation of PEVs, as it has the highest level of LCPLI, which is 9.81 in the 33-bus system and 28.24 in the 69-bus system. The optimal bus can safely accommodate the largest number of electric vehicles, with a capacity of 31,988 units in the 33-bus system and 92,519 units in the 69-bus system. Additionally, the parallel-operated AOASSA mechanism leads to a reduction in power losses of at least 0.09% and 0.25% compared with other algorithms that have been previously applied to the 33-bus and 69-bus systems, respectively. Moreover, with an optimal charging and discharging ratio of PEVs in the IEEE-33-bus radial distribution network (RDN), the %PLR further improved by 3.08%, 4.19%, and 2.29% in the presence of the optimal allocation of one, two and three DG units, respectively. In the IEEE-69-bus RDN, the %PLR further improved by 0.09%, 0.09%, and 0.08% with optimal charge and discharge ratios in the presence of one, two, and three DG units, respectively. The proposed study intends to help the local power distribution companies to maximize accommodation of PEV units and minimize power losses in RDNs.

Suggested Citation

  • Zeeshan Anjum Memon & Dalila Mat Said & Mohammad Yusri Hassan & Hafiz Mudassir Munir & Faisal Alsaif & Sager Alsulamy, 2023. "Effective Deterministic Methodology for Enhanced Distribution Network Performance and Plug-in Electric Vehicles," Sustainability, MDPI, vol. 15(9), pages 1-37, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7078-:d:1131017
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/9/7078/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/9/7078/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pepermans, G. & Driesen, J. & Haeseldonckx, D. & Belmans, R. & D'haeseleer, W., 2005. "Distributed generation: definition, benefits and issues," Energy Policy, Elsevier, vol. 33(6), pages 787-798, April.
    2. Mahsa Z. Farahmand & Sara Javadi & Sayyed Muhammad Bagher Sadati & Hannu Laaksonen & Miadreza Shafie-khah, 2021. "Optimal Operation of Solar Powered Electric Vehicle Parking Lots Considering Different Photovoltaic Technologies," Clean Technol., MDPI, vol. 3(2), pages 1-16, June.
    3. Mr. Anil Ari & Mr. Nicolas Arregui & Mr. Simon Black & Oya Celasun & Ms. Dora M Iakova & Ms. Aiko Mineshima & Mr. Victor Mylonas & Ian W.H. Parry & Iulia Teodoru & Karlygash Zhunussova, 2022. "Surging Energy Prices in Europe in the Aftermath of the War: How to Support the Vulnerable and Speed up the Transition Away from Fossil Fuels," IMF Working Papers 2022/152, International Monetary Fund.
    4. Elio Chiodo & Maurizio Fantauzzi & Davide Lauria & Fabio Mottola, 2018. "A Probabilistic Approach for the Optimal Sizing of Storage Devices to Increase the Penetration of Plug-in Electric Vehicles in Direct Current Networks," Energies, MDPI, vol. 11(5), pages 1-20, May.
    5. Carlos D. Zuluaga-Ríos & Alejandro Villa-Jaramillo & Sergio D. Saldarriaga-Zuluaga, 2022. "Evaluation of Distributed Generation and Electric Vehicles Hosting Capacity in Islanded DC Grids Considering EV Uncertainty," Energies, MDPI, vol. 15(20), pages 1-17, October.
    6. Sultana, U. & Khairuddin, Azhar B. & Sultana, Beenish & Rasheed, Nadia & Qazi, Sajid Hussain & Malik, Nimra Riaz, 2018. "Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm," Energy, Elsevier, vol. 165(PA), pages 408-421.
    7. Qing Deng & Changsen Feng & Fushuan Wen & Chung-Li Tseng & Lei Wang & Bo Zou & Xizhu Zhang, 2019. "Evaluation of Accommodation Capability for Electric Vehicles of a Distribution System Considering Coordinated Charging Strategies," Energies, MDPI, vol. 12(16), pages 1-20, August.
    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. Pereira da Silva, Patrícia & Dantas, Guilherme & Pereira, Guillermo Ivan & Câmara, Lorrane & De Castro, Nivalde J., 2019. "Photovoltaic distributed generation – An international review on diffusion, support policies, and electricity sector regulatory adaptation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 30-39.
    2. Funcke, Simon & Bauknecht, Dierk, 2016. "Typology of centralised and decentralised visions for electricity infrastructure," Utilities Policy, Elsevier, vol. 40(C), pages 67-74.
    3. Yuru Guan & Jin Yan & Yuli Shan & Yannan Zhou & Ye Hang & Ruoqi Li & Yu Liu & Binyuan Liu & Qingyun Nie & Benedikt Bruckner & Kuishuang Feng & Klaus Hubacek, 2023. "Burden of the global energy price crisis on households," Nature Energy, Nature, vol. 8(3), pages 304-316, March.
    4. Paul Westacott & Chiara Candelise, 2016. "A Novel Geographical Information Systems Framework to Characterize Photovoltaic Deployment in the UK: Initial Evidence," Energies, MDPI, vol. 9(1), pages 1-20, January.
    5. Ozoegwu, C.G. & Eze, C. & Onwosi, C.O. & Mgbemene, C.A. & Ozor, P.A., 2017. "Biomass and bioenergy potential of cassava waste in Nigeria: Estimations based partly on rural-level garri processing case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 625-638.
    6. Tsikalakis, A.G. & Hatziargyriou, N.D., 2007. "Environmental benefits of distributed generation with and without emissions trading," Energy Policy, Elsevier, vol. 35(6), pages 3395-3409, June.
    7. Eksi, Guner & Karaosmanoglu, Filiz, 2017. "Combined bioheat and biopower: A technology review and an assessment for Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1313-1332.
    8. Bouzid, Allal M. & Guerrero, Josep M. & Cheriti, Ahmed & Bouhamida, Mohamed & Sicard, Pierre & Benghanem, Mustapha, 2015. "A survey on control of electric power distributed generation systems for microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 751-766.
    9. Szabolcs Nagy & Laszlo Molnar & Noemi Hajdu, 2023. "Understanding the Human Dimensions of the Intention to Use Renewable Energy in Hungary Applying an Extended Model of Theory of Planned Behaviour," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 25(64), pages 830-830, August.
    10. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    11. Haeseldonckx, Dries & D'haeseleer, William, 2008. "The environmental impact of decentralised generation in an overall system context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(2), pages 437-454, February.
    12. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    13. Abdmouleh, Zeineb & Gastli, Adel & Ben-Brahim, Lazhar & Haouari, Mohamed & Al-Emadi, Nasser Ahmed, 2017. "Review of optimization techniques applied for the integration of distributed generation from renewable energy sources," Renewable Energy, Elsevier, vol. 113(C), pages 266-280.
    14. Zangeneh, Ali & Jadid, Shahram & Rahimi-Kian, Ashkan, 2009. "A hierarchical decision making model for the prioritization of distributed generation technologies: A case study for Iran," Energy Policy, Elsevier, vol. 37(12), pages 5752-5763, December.
    15. Tóth, Tamás & Somossy, Éva Szabina & Horváth, Péter János, 2022. "A decentralizált villamosenergia-rendszerek fejlődésének nemzetközi és hazai szempontjai [International and domestic aspects of decentralized electricity system development]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 697-720.
    16. Vitor Fernão Pires & Armando Pires & Armando Cordeiro, 2023. "DC Microgrids: Benefits, Architectures, Perspectives and Challenges," Energies, MDPI, vol. 16(3), pages 1-20, January.
    17. Botelho, D.F. & de Oliveira, L.W. & Dias, B.H. & Soares, T.A. & Moraes, C.A., 2022. "Prosumer integration into the Brazilian energy sector: An overview of innovative business models and regulatory challenges," Energy Policy, Elsevier, vol. 161(C).
    18. Taylor, Josh A. & Dhople, Sairaj V. & Callaway, Duncan S., 2016. "Power systems without fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1322-1336.
    19. Singh, Bindeshwar & Mukherjee, V. & Tiwari, Prabhakar, 2015. "A survey on impact assessment of DG and FACTS controllers in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 846-882.
    20. Frilingou, Natasha & Koasidis, Konstantinos & Spyridaki, Niki-Artemis & Nikas, Alexandros & Marinakis, Vangelis & Doukas, Haris, 2024. "Is it feasible to implement minimum energy performance standards (MEPS) for existing buildings in Greece? A cost-benefit evaluation," Energy Policy, Elsevier, vol. 188(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jsusta:v:15:y:2023:i:9:p:7078-:d:1131017. 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.