IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i19p5100-d422176.html
   My bibliography  Save this article

Computationally Efficient Modeling of DC-DC Converters for PV Applications

Author

Listed:
  • Fabio Corti

    (Department of Information Engineering, University of Florence, Via di S.Marta 3, 50139 Florence, Italy)

  • Antonino Laudani

    (Engineering Department, Roma Tre University, Via Vito Volterra 62b, 00146 Roma, Italy)

  • Gabriele Maria Lozito

    (Engineering Department, Roma Tre University, Via Vito Volterra 62b, 00146 Roma, Italy)

  • Alberto Reatti

    (Department of Information Engineering, University of Florence, Via di S.Marta 3, 50139 Florence, Italy)

Abstract

In this work, a computationally efficient approach for the simulation of a DC-DC converter connected to a photovoltaic device is proposed. The methodology is based on a combination of a highly efficient formulation of the one-diode model for photovoltaic (PV) devices and a state-space formulation of the converter as well as an accurate steady-state detection methodology. The approach was experimentally validated to assess its accuracy. The model is accurate both in its dynamic response (tested in full linearity and with a simulated PV device as the input) and in its steady-state response (tested with an outdoor experimental measurement setup). The model detects automatically the reaching of a steady state, thus resulting in lowered computational costs. The approach is presented as a mathematical model that can be efficiently included in a large simulation system or statistical analysis.

Suggested Citation

  • Fabio Corti & Antonino Laudani & Gabriele Maria Lozito & Alberto Reatti, 2020. "Computationally Efficient Modeling of DC-DC Converters for PV Applications," Energies, MDPI, vol. 13(19), pages 1-18, September.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5100-:d:422176
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/19/5100/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/19/5100/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. González, Ander & Goikolea, Eider & Barrena, Jon Andoni & Mysyk, Roman, 2016. "Review on supercapacitors: Technologies and materials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1189-1206.
    2. Teuvo Suntio & Tuomas Messo & Aapo Aapro & Jyri Kivimäki & Alon Kuperman, 2017. "Review of PV Generator as an Input Source for Power Electronic Converters," Energies, MDPI, vol. 10(8), pages 1-25, July.
    3. Díaz-González, Francisco & Sumper, Andreas & Gomis-Bellmunt, Oriol & Villafáfila-Robles, Roberto, 2012. "A review of energy storage technologies for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2154-2171.
    4. Yang Du & Ke Yan & Zixiao Ren & Weidong Xiao, 2018. "Designing Localized MPPT for PV Systems Using Fuzzy-Weighted Extreme Learning Machine," Energies, MDPI, vol. 11(10), pages 1-10, October.
    5. Maen Takruri & Maissa Farhat & Oscar Barambones & José Antonio Ramos-Hernanz & Mohammed Jawdat Turkieh & Mohammed Badawi & Hanin AlZoubi & Maswood Abdus Sakur, 2020. "Maximum Power Point Tracking of PV System Based on Machine Learning," Energies, MDPI, vol. 13(3), pages 1-14, February.
    6. Jaw-Kuen Shiau & Chien-Wei Ma, 2013. "Li-Ion Battery Charging with a Buck-Boost Power Converter for a Solar Powered Battery Management System," Energies, MDPI, vol. 6(3), pages 1-31, March.
    7. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2013. "Short-term solar irradiance forecasting using exponential smoothing state space model," Energy, Elsevier, vol. 55(C), pages 1104-1113.
    8. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Lu, Xinhui, 2019. "Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting," Energy, Elsevier, vol. 171(C), pages 1053-1065.
    9. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    10. Humada, Ali M. & Hojabri, Mojgan & Mekhilef, Saad & Hamada, Hussein M., 2016. "Solar cell parameters extraction based on single and double-diode models: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 494-509.
    11. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
    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. Martina Radicioni & Valentina Lucaferri & Francesco De Lia & Antonino Laudani & Roberto Lo Presti & Gabriele Maria Lozito & Francesco Riganti Fulginei & Riccardo Schioppo & Mario Tucci, 2021. "Power Forecasting of a Photovoltaic Plant Located in ENEA Casaccia Research Center," Energies, MDPI, vol. 14(3), pages 1-22, January.
    2. Mohammed Kharrich & Salah Kamel & Ali S. Alghamdi & Ahmad Eid & Mohamed I. Mosaad & Mohammed Akherraz & Mamdouh Abdel-Akher, 2021. "Optimal Design of an Isolated Hybrid Microgrid for Enhanced Deployment of Renewable Energy Sources in Saudi Arabia," Sustainability, MDPI, vol. 13(9), pages 1-26, April.
    3. Salvatore Musumeci, 2022. "Special Issue “Advanced DC-DC Power Converters and Switching Converters”," Energies, MDPI, vol. 15(4), pages 1-5, February.
    4. Syed Muhammad Ahsan & Hassan Abbas Khan & Akhtar Hussain & Sarmad Tariq & Nauman Ahmad Zaffar, 2021. "Harmonic Analysis of Grid-Connected Solar PV Systems with Nonlinear Household Loads in Low-Voltage Distribution Networks," Sustainability, MDPI, vol. 13(7), pages 1-23, March.
    5. Fabio Corti & Antonino Laudani & Gabriele Maria Lozito & Martina Palermo & Michele Quercio & Francesco Pattini & Stefano Rampino, 2023. "Dynamic Analysis of a Supercapacitor DC-Link in Photovoltaic Conversion Applications," Energies, MDPI, vol. 16(16), pages 1-19, August.
    6. Cheng-En Ye & Cheng-Chi Tai & Yu-Pei Huang, 2023. "Disperse Partial Shading Effect of Photovoltaic Array by Means of the Modified Complementary SuDoKu Puzzle Topology," Energies, MDPI, vol. 16(13), pages 1-16, June.

    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. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    2. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    3. Trapero, Juan R., 2016. "Calculation of solar irradiation prediction intervals combining volatility and kernel density estimates," Energy, Elsevier, vol. 114(C), pages 266-274.
    4. Akarslan, Emre & Hocaoglu, Fatih Onur & Edizkan, Rifat, 2018. "Novel short term solar irradiance forecasting models," Renewable Energy, Elsevier, vol. 123(C), pages 58-66.
    5. Chao-Rong Chen & Unit Three Kartini, 2017. "k-Nearest Neighbor Neural Network Models for Very Short-Term Global Solar Irradiance Forecasting Based on Meteorological Data," Energies, MDPI, vol. 10(2), pages 1-18, February.
    6. Reikard, Gordon & Hansen, Clifford, 2019. "Forecasting solar irradiance at short horizons: Frequency and time domain models," Renewable Energy, Elsevier, vol. 135(C), pages 1270-1290.
    7. Trapero, Juan R. & Kourentzes, Nikolaos & Martin, A., 2015. "Short-term solar irradiation forecasting based on Dynamic Harmonic Regression," Energy, Elsevier, vol. 84(C), pages 289-295.
    8. Dehghani-Sanij, A.R. & Tharumalingam, E. & Dusseault, M.B. & Fraser, R., 2019. "Study of energy storage systems and environmental challenges of batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 192-208.
    9. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren, 2017. "Overview of wind power intermittency: Impacts, measurements, and mitigation solutions," Applied Energy, Elsevier, vol. 204(C), pages 47-65.
    10. Argyrou, Maria C. & Christodoulides, Paul & Kalogirou, Soteris A., 2018. "Energy storage for electricity generation and related processes: Technologies appraisal and grid scale applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 804-821.
    11. Majid Hosseini & Satya Katragadda & Jessica Wojtkiewicz & Raju Gottumukkala & Anthony Maida & Terrence Lynn Chambers, 2020. "Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units," Energies, MDPI, vol. 13(15), pages 1-15, July.
    12. Ren, Xiaoying & Zhang, Fei & Zhu, Honglu & Liu, Yongqian, 2022. "Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting," Applied Energy, Elsevier, vol. 323(C).
    13. Reddi Khasim, Shaik & Dhanamjayulu, C., 2021. "Selection parameters and synthesis of multi-input converters for electric vehicles: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    14. Boland, John, 2015. "Spatial-temporal forecasting of solar radiation," Renewable Energy, Elsevier, vol. 75(C), pages 607-616.
    15. Md Jamal Ahmed Shohan & Md Omar Faruque & Simon Y. Foo, 2022. "Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model," Energies, MDPI, vol. 15(6), pages 1-18, March.
    16. Jessica Wojtkiewicz & Matin Hosseini & Raju Gottumukkala & Terrence Lynn Chambers, 2019. "Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units," Energies, MDPI, vol. 12(21), pages 1-13, October.
    17. John Boland, 2020. "Characterising Seasonality of Solar Radiation and Solar Farm Output," Energies, MDPI, vol. 13(2), pages 1-15, January.
    18. Gallo, A.B. & Simões-Moreira, J.R. & Costa, H.K.M. & Santos, M.M. & Moutinho dos Santos, E., 2016. "Energy storage in the energy transition context: A technology review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 800-822.
    19. Zhang, Lei & Hu, Xiaosong & Wang, Zhenpo & Sun, Fengchun & Dorrell, David G., 2018. "A review of supercapacitor modeling, estimation, and applications: A control/management perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1868-1878.
    20. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(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:gam:jeners:v:13:y:2020:i:19:p:5100-:d:422176. 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.