IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v215y2023ics0960148123009047.html
   My bibliography  Save this article

Energy yield estimation of on-vehicle photovoltaic systems in urban environments

Author

Listed:
  • Rigogiannis, Nick
  • Perpinias, Ioannis
  • Bogatsis, Ioannis
  • Roidos, Ioannis
  • Vagiannis, Nick
  • Zournatzis, Athanasios
  • Kyritsis, Anastasios
  • Papanikolaou, Nick
  • Kalogirou, Soteris

Abstract

Greenhouse gases from the propulsion systems of road transportations constitute a significant obstacle to achieve the Paris Agreement objectives. Nowadays, the substitution of conventional internal combustion engines with electric motors, along with electrochemical storage systems are the leading efforts to reduce the use of fossil fuels in road transportations. However, their limited driving range and the long charging times are the main technical factors that hinder the development of electromobility. Thus, energy harvesters and regeneration systems are increasingly incorporated in road vehicles, in order to increase their driving range. In this context, Vehicle Integrated and Applied Photovoltaics (VIAPVs) constitute an attractive prospect. The electricity yield for VIAPVs depends strongly on the route, the shadings due to the urban environment, the applied Maximum Power Point (MPPT) algorithm and the traffic conditions. In this paper, four commonly used commercial MPPT algorithms are experimentally evaluated, regarding their ability to extract the maximum available power simulating realistic city routes. The results show notable discrepancies in the performance of the studied algorithms, between terrestrial and VIAPV applications, highlighting the impact of poor MPPT performance in terms of power generation in moving vehicles.

Suggested Citation

  • Rigogiannis, Nick & Perpinias, Ioannis & Bogatsis, Ioannis & Roidos, Ioannis & Vagiannis, Nick & Zournatzis, Athanasios & Kyritsis, Anastasios & Papanikolaou, Nick & Kalogirou, Soteris, 2023. "Energy yield estimation of on-vehicle photovoltaic systems in urban environments," Renewable Energy, Elsevier, vol. 215(C).
  • Handle: RePEc:eee:renene:v:215:y:2023:i:c:s0960148123009047
    DOI: 10.1016/j.renene.2023.118998
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123009047
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2023.118998?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. Youssef, Ayman & El-Telbany, Mohammed & Zekry, Abdelhalim, 2017. "The role of artificial intelligence in photo-voltaic systems design and control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 72-79.
    2. Oh, Myeongchan & Kim, Sung-Min & Park, Hyeong-Dong, 2020. "Estimation of photovoltaic potential of solar bus in an urban area: Case study in Gwanak, Seoul, Korea," Renewable Energy, Elsevier, vol. 160(C), pages 1335-1348.
    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. Ahmad, Muhammad Waseem & Mourshed, Monjur & Rezgui, Yacine, 2018. "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, Elsevier, vol. 164(C), pages 465-474.
    2. Tamer Khatib & Dhiaa Halboot Muhsen, 2020. "Optimal Sizing of Standalone Photovoltaic System Using Improved Performance Model and Optimization Algorithm," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    3. Collin Barker & Sam Cipkar & Tyler Lavigne & Cameron Watson & Maher Azzouz, 2021. "Real-Time Nuisance Fault Detection in Photovoltaic Generation Systems Using a Fine Tree Classifier," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    4. Akhter, Muhammad Naveed & Mekhilef, Saad & Mokhlis, Hazlie & Ali, Raza & Usama, Muhammad & Muhammad, Munir Azam & Khairuddin, Anis Salwa Mohd, 2022. "A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems," Applied Energy, Elsevier, vol. 307(C).
    5. Tian, B. & Loonen, R.C.G.M. & Bognár, Á. & Hensen, J.L.M., 2022. "Impacts of surface model generation approaches on raytracing-based solar potential estimation in urban areas," Renewable Energy, Elsevier, vol. 198(C), pages 804-824.
    6. Mosa Machesa & Lagouge Tartibu & Modestus Okwu, 2021. "Prediction of the Oscillatory Heat Transfer Coefficient in Thermoacoustic Refrigerators," Sustainability, MDPI, vol. 13(17), pages 1-17, August.
    7. Baek, Jieun & Choi, Yosoon, 2023. "Optimal installation and operation planning of parking spaces for solar-powered electric vehicles using hemispherical images," Renewable Energy, Elsevier, vol. 219(P1).
    8. Meneghetti, Antonella & Dal Magro, Fabio & Romagnoli, Alessandro, 2021. "Renewable energy penetration in food delivery: Coupling photovoltaics with transport refrigerated units," Energy, Elsevier, vol. 232(C).
    9. Xu, Yingying & Shao, Xuefeng & Tanasescu, Cristina, 2024. "How are artificial intelligence, carbon market, and energy sector connected? A systematic analysis of time-frequency spillovers," Energy Economics, Elsevier, vol. 132(C).
    10. Joshuva Arockia Dhanraj & Ali Mostafaeipour & Karthikeyan Velmurugan & Kuaanan Techato & Prem Kumar Chaurasiya & Jenoris Muthiya Solomon & Anitha Gopalan & Khamphe Phoungthong, 2021. "An Effective Evaluation on Fault Detection in Solar Panels," Energies, MDPI, vol. 14(22), pages 1-14, November.
    11. Sun, Lejia & Jia, Jingqi & Wang, QuanLi & Zhang, Yimeng, 2024. "A novel multiphase DC/DC boost converter for interaction of solar energy and hydrogen fuel cell in hybrid electric vehicles," Renewable Energy, Elsevier, vol. 229(C).
    12. Li, Ping & Zhou, Ying & Huang, Sijie, 2023. "Role of information technology in the development of e-tourism marketing: A contextual suggestion," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 307-318.
    13. Wendeker, Mirosław & Gęca, Michał Jan & Grabowski, Łukasz & Pietrykowski, Konrad & Kasianantham, Nanthagopal, 2022. "Measurements and analysis of a solar-assisted city bus with a diesel engine," Applied Energy, Elsevier, vol. 309(C).
    14. Liu, Zhengguang & Guo, Zhiling & Chen, Qi & Song, Chenchen & Shang, Wenlong & Yuan, Meng & Zhang, Haoran, 2023. "A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives," Energy, Elsevier, vol. 263(PE).
    15. Chen, Haoqian & Sui, Yi & Shang, Wen-long & Sun, Rencheng & Chen, Zhiheng & Wang, Changying & Han, Chunjia & Zhang, Yuqian & Zhang, Haoran, 2022. "Towards renewable public transport: Mining the performance of electric buses using solar-radiation as an auxiliary power source," Applied Energy, Elsevier, vol. 325(C).
    16. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
    17. Temitayo O. Olowu & Aditya Sundararajan & Masood Moghaddami & Arif I. Sarwat, 2018. "Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey," Energies, MDPI, vol. 11(7), pages 1-32, July.
    18. Ridha, Hussein Mohammed & Gomes, Chandima & Hizam, Hashim & Ahmadipour, Masoud & Heidari, Ali Asghar & Chen, Huiling, 2021. "Multi-objective optimization and multi-criteria decision-making methods for optimal design of standalone photovoltaic system: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    19. Li, B. & Delpha, C. & Diallo, D. & Migan-Dubois, A., 2021. "Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    20. Qamar Navid & Ahmed Hassan & Abbas Ahmad Fardoun & Rashad Ramzan & Abdulrahman Alraeesi, 2021. "Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review," Sustainability, MDPI, vol. 13(4), pages 1-22, February.

    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:renene:v:215:y:2023:i:c:s0960148123009047. 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.journals.elsevier.com/renewable-energy .

    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.