IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i7p1728-d155686.html
   My bibliography  Save this article

A Robust Optimization for Designing a Charging Station Based on Solar and Wind Energy for Electric Vehicles of a Smart Home in Small Villages

Author

Listed:
  • Amir Ahadi

    (Department of Mechanical Engineering, Graduate School of Kunsan National University, Gunsan 573-701, Korea)

  • Shrutidhara Sarma

    (School of Mechanical & Automotive Engineering, Kunsan National University, Gunsan 573-701, Korea)

  • Jae Sang Moon

    (School of Mechanical & Automotive Engineering, Kunsan National University, Gunsan 573-701, Korea)

  • Sangkyun Kang

    (School of Mechanical & Automotive Engineering, Kunsan National University, Gunsan 573-701, Korea)

  • Jang-Ho Lee

    (School of Mechanical & Automotive Engineering, Kunsan National University, Gunsan 573-701, Korea)

Abstract

In recent years, integration of electric vehicles (EVs) has increased dramatically due to their lower carbon emissions and reduced fossil fuel dependency. However, charging EVs could have significant impacts on the electrical grid. One promising method for mitigating these impacts is the use of renewable energy systems. Renewable energy systems can also be useful for charging EVs where there is no local grid. This paper proposes a new strategy for designing a renewable energy charging station consisting of wind turbines, a photovoltaic system, and an energy storage system to avoid the use of diesel generators in remote communities. The objective function is considered to be the minimization of the total net present cost, including energy production, components setup, and financial viability. The proposed approach, using stochastic modeling, can also guarantee profitable operation of EVs and reasonable effects on renewable energy sizing, narrowing the gap between real-life daily operation patterns and the design stage. The proposed strategy should enhance the efficiency of conventional EV charging stations. The key point of this study is the efficient use of excess electricity. The infrastructure of the charging station is optimized and modeled.

Suggested Citation

  • Amir Ahadi & Shrutidhara Sarma & Jae Sang Moon & Sangkyun Kang & Jang-Ho Lee, 2018. "A Robust Optimization for Designing a Charging Station Based on Solar and Wind Energy for Electric Vehicles of a Smart Home in Small Villages," Energies, MDPI, vol. 11(7), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1728-:d:155686
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/7/1728/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/7/1728/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Schuller, Alexander & Flath, Christoph M. & Gottwalt, Sebastian, 2015. "Quantifying load flexibility of electric vehicles for renewable energy integration," Applied Energy, Elsevier, vol. 151(C), pages 335-344.
    2. Jochem, Patrick & Babrowski, Sonja & Fichtner, Wolf, 2015. "Assessing CO2 emissions of electric vehicles in Germany in 2030," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 68-83.
    3. Bin Ye & Jingjing Jiang & Lixin Miao & Peng Yang & Ji Li & Bo Shen, 2015. "Feasibility Study of a Solar-Powered Electric Vehicle Charging Station Model," Energies, MDPI, vol. 8(11), pages 1-19, November.
    4. Romo, R. & Micheloud, O., 2015. "Power quality of actual grids with plug-in electric vehicles in presence of renewables and micro-grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 189-200.
    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. Atia, Raji & Yamada, Noboru, 2015. "More accurate sizing of renewable energy sources under high levels of electric vehicle integration," Renewable Energy, Elsevier, vol. 81(C), pages 918-925.
    7. Verma, Aman & Raj, Ratan & Kumar, Mayank & Ghandehariun, Samane & Kumar, Amit, 2015. "Assessment of renewable energy technologies for charging electric vehicles in Canada," Energy, Elsevier, vol. 86(C), pages 548-559.
    8. Ahadi, Amir & Kang, Sang-Kyun & Lee, Jang-Ho, 2016. "A novel approach for optimal combinations of wind, PV, and energy storage system in diesel-free isolated communities," Applied Energy, Elsevier, vol. 170(C), pages 101-115.
    9. Hardman, Scott & Chandan, Amrit & Tal, Gil & Turrentine, Tom, 2017. "The effectiveness of financial purchase incentives for battery electric vehicles – A review of the evidence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1100-1111.
    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. Corneliu Marinescu, 2022. "Progress in the Development and Implementation of Residential EV Charging Stations Based on Renewable Energy Sources," Energies, MDPI, vol. 16(1), pages 1-31, December.

    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. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
    2. 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.
    3. Li, Wei & Jia, Zhijie & Zhang, Hongzhi, 2017. "The impact of electric vehicles and CCS in the context of emission trading scheme in China: A CGE-based analysis," Energy, Elsevier, vol. 119(C), pages 800-816.
    4. Schwab, Julia & Sölch, Christian & Zöttl, Gregor, 2022. "Electric Vehicle Cost in 2035: The impact of market penetration and charging strategies," Energy Economics, Elsevier, vol. 114(C).
    5. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M., 2016. "A multi-agent based scheduling algorithm for adaptive electric vehicles charging," Applied Energy, Elsevier, vol. 177(C), pages 354-365.
    6. Schücking, Maximilian & Jochem, Patrick & Fichtner, Wolf & Wollersheim, Olaf & Stella, Kevin, 2017. "Charging strategies for economic operations of electric vehicles in commercial applications," MPRA Paper 91599, University Library of Munich, Germany.
    7. Requia, Weeberb J. & Adams, Matthew D. & Arain, Altaf & Koutrakis, Petros & Ferguson, Mark, 2017. "Carbon dioxide emissions of plug-in hybrid electric vehicles: A life-cycle analysis in eight Canadian cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1390-1396.
    8. Seddig, Katrin & Jochem, Patrick & Fichtner, Wolf, 2017. "Integrating renewable energy sources by electric vehicle fleets under uncertainty," Energy, Elsevier, vol. 141(C), pages 2145-2153.
    9. Bahamonde-Birke, Francisco J., 2020. "Who will bell the cat? On the environmental and sustainability risks of electric vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 79-81.
    10. Antti Lajunen & Klaus Kivekäs & Jari Vepsäläinen & Kari Tammi, 2020. "Influence of Increasing Electrification of Passenger Vehicle Fleet on Carbon Dioxide Emissions in Finland," Sustainability, MDPI, vol. 12(12), pages 1-13, June.
    11. Taljegard, M. & Göransson, L. & Odenberger, M. & Johnsson, F., 2019. "Impacts of electric vehicles on the electricity generation portfolio – A Scandinavian-German case study," Applied Energy, Elsevier, vol. 235(C), pages 1637-1650.
    12. Zongfei Wang & Patrick Jochem & Hasan Ümitcan Yilmaz & Lei Xu, 2022. "Integrating vehicle‐to‐grid technology into energy system models: Novel methods and their impact on greenhouse gas emissions," Journal of Industrial Ecology, Yale University, vol. 26(2), pages 392-405, April.
    13. Paterakis, Nikolaos G. & Gibescu, Madeleine, 2016. "A methodology to generate power profiles of electric vehicle parking lots under different operational strategies," Applied Energy, Elsevier, vol. 173(C), pages 111-123.
    14. Falcão, Eduardo Aparecido Moreira & Teixeira, Ana Carolina Rodrigues & Sodré, José Ricardo, 2017. "Analysis of CO2 emissions and techno-economic feasibility of an electric commercial vehicle," Applied Energy, Elsevier, vol. 193(C), pages 297-307.
    15. Lauvergne, Rémi & Perez, Yannick & Françon, Mathilde & Tejeda De La Cruz, Alberto, 2022. "Integration of electric vehicles into transmission grids: A case study on generation adequacy in Europe in 2040," Applied Energy, Elsevier, vol. 326(C).
    16. Nienhueser, Ian Andrew & Qiu, Yueming, 2016. "Economic and environmental impacts of providing renewable energy for electric vehicle charging – A choice experiment study," Applied Energy, Elsevier, vol. 180(C), pages 256-268.
    17. Funke, Simon Árpád & Plötz, Patrick & Wietschel, Martin, 2019. "Invest in fast-charging infrastructure or in longer battery ranges? A cost-efficiency comparison for Germany," Applied Energy, Elsevier, vol. 235(C), pages 888-899.
    18. Riesz, Jenny & Sotiriadis, Claire & Ambach, Daisy & Donovan, Stuart, 2016. "Quantifying the costs of a rapid transition to electric vehicles," Applied Energy, Elsevier, vol. 180(C), pages 287-300.
    19. Will, Christian & Lehmann, Nico & Baumgartner, Nora & Feurer, Sven & Jochem, Patrick & Fichtner, Wolf, 2022. "Consumer understanding and evaluation of carbon-neutral electric vehicle charging services," Applied Energy, Elsevier, vol. 313(C).
    20. Zhang, Tianyang & Pota, Himanshu & Chu, Chi-Cheng & Gadh, Rajit, 2018. "Real-time renewable energy incentive system for electric vehicles using prioritization and cryptocurrency," Applied Energy, Elsevier, vol. 226(C), pages 582-594.

    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:11:y:2018:i:7:p:1728-:d:155686. 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.