IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i6p4853-4870d50148.html
   My bibliography  Save this article

The Optimum PV Plant for a Given Solar DC/AC Converter

Author

Listed:
  • Roberto S. Faranda

    (Politecnico Di Milano Department of Energy, Via la Masa 34, 20156 Milano, Italy)

  • Hossein Hafezi

    (Politecnico Di Milano Department of Energy, Via la Masa 34, 20156 Milano, Italy)

  • Sonia Leva

    (Politecnico Di Milano Department of Energy, Via la Masa 34, 20156 Milano, Italy)

  • Marco Mussetta

    (Politecnico Di Milano Department of Energy, Via la Masa 34, 20156 Milano, Italy)

  • Emanuele Ogliari

    (Politecnico Di Milano Department of Energy, Via la Masa 34, 20156 Milano, Italy)

Abstract

In recent years, energy production by renewable sources is becoming very important, and photovoltaic (PV) energy has became one of the main renewable sources that is widely available and easily exploitable. In this context, it is necessary to find correct tools to optimize the energy production by PV plants. In this paper, by analyzing available solar irradiance data, an analytical expression for annual DC power production for some selected places is introduced. A general efficiency curve is extracted for different solar inverter types, and by applying approximated function, a new analytical method is proposed to estimate the optimal size of a grid-connected PV plant linked up to a specific inverter from the energetic point of view. An exploitable energy objective function is derived, and several simulations for different locations have been provided. The derived analytical expression contains only the available data of the inverter (such as efficiency, nominal power, etc .) and the PV plant characteristics (such as location and PV nominal power).

Suggested Citation

  • Roberto S. Faranda & Hossein Hafezi & Sonia Leva & Marco Mussetta & Emanuele Ogliari, 2015. "The Optimum PV Plant for a Given Solar DC/AC Converter," Energies, MDPI, vol. 8(6), pages 1-18, May.
  • Handle: RePEc:gam:jeners:v:8:y:2015:i:6:p:4853-4870:d:50148
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/6/4853/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/8/6/4853/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Emanuele Ogliari & Francesco Grimaccia & Sonia Leva & Marco Mussetta, 2013. "Hybrid Predictive Models for Accurate Forecasting in PV Systems," Energies, MDPI, vol. 6(4), pages 1-12, April.
    2. Alberto Dolara & Francesco Grimaccia & Sonia Leva & Marco Mussetta & Emanuele Ogliari, 2015. "A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output," Energies, MDPI, vol. 8(2), pages 1-16, February.
    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. Balfour, John & Hill, Roger & Walker, Andy & Robinson, Gerald & Gunda, Thushara & Desai, Jal, 2021. "Masking of photovoltaic system performance problems by inverter clipping and other design and operational practices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    2. Antonio Ocana-Miguel & Jose R. Andres-Diaz & Enrique Navarrete-de Galvez & Alfonso Gago-Calderon, 2021. "Adaptation of an Insulated Centralized Photovoltaic Outdoor Lighting Installation with Electronic Control System to Improve Service Guarantee in Tropical Latitudes," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    3. Zhun Meng & Yi-Feng Wang & Liang Yang & Wei Li, 2017. "Analysis of Power Loss and Improved Simulation Method of a High Frequency Dual-Buck Full-Bridge Inverter," Energies, MDPI, vol. 10(3), pages 1-18, March.
    4. Silvestro Cossu & Roberto Baccoli & Emilio Ghiani, 2021. "Utility Scale Ground Mounted Photovoltaic Plants with Gable Structure and Inverter Oversizing for Land-Use Optimization," Energies, MDPI, vol. 14(11), pages 1-16, May.
    5. Rae-Kyun Kim & Mark B. Glick & Keith R. Olson & Yun-Su Kim, 2020. "MILP-PSO Combined Optimization Algorithm for an Islanded Microgrid Scheduling with Detailed Battery ESS Efficiency Model and Policy Considerations," Energies, MDPI, vol. 13(8), pages 1-17, April.
    6. Ferdinando Chiacchio & Fabio Famoso & Diego D’Urso & Sebastian Brusca & Jose Ignacio Aizpurua & Luca Cedola, 2018. "Dynamic Performance Evaluation of Photovoltaic Power Plant by Stochastic Hybrid Fault Tree Automaton Model," Energies, MDPI, vol. 11(2), pages 1-22, January.
    7. Yilmaz, Saban & Dincer, Furkan, 2017. "Impact of inverter capacity on the performance in large-scale photovoltaic power plants – A case study for Gainesville, Florida," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 15-23.
    8. Good, Jeremy & Johnson, Jeremiah X., 2016. "Impact of inverter loading ratio on solar photovoltaic system performance," Applied Energy, Elsevier, vol. 177(C), pages 475-486.

    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. Grzegorz Dec & Grzegorz Drałus & Damian Mazur & Bogdan Kwiatkowski, 2021. "Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic," Energies, MDPI, vol. 14(6), pages 1-16, March.
    2. Orest Lozynskyy & Damian Mazur & Yaroslav Marushchak & Bogdan Kwiatkowski & Andriy Lozynskyy & Tadeusz Kwater & Bohdan Kopchak & Przemysław Hawro & Lidiia Kasha & Robert Pękala & Robert Ziemba & Bogus, 2021. "Formation of Characteristic Polynomials on the Basis of Fractional Powers j of Dynamic Systems and Stability Problems of Such Systems," Energies, MDPI, vol. 14(21), pages 1-35, November.
    3. Athanasios I. Salamanis & Georgia Xanthopoulou & Napoleon Bezas & Christos Timplalexis & Angelina D. Bintoudi & Lampros Zyglakis & Apostolos C. Tsolakis & Dimosthenis Ioannidis & Dionysios Kehagias & , 2020. "Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting," Energies, MDPI, vol. 13(22), pages 1-31, November.
    4. Ogliari, Emanuele & Dolara, Alberto & Manzolini, Giampaolo & Leva, Sonia, 2017. "Physical and hybrid methods comparison for the day ahead PV output power forecast," Renewable Energy, Elsevier, vol. 113(C), pages 11-21.
    5. Alberto Dolara & Francesco Grimaccia & Giulia Magistrati & Gabriele Marchegiani, 2017. "Optimization Models for Islanded Micro-Grids: A Comparative Analysis between Linear Programming and Mixed Integer Programming," Energies, MDPI, vol. 10(2), pages 1-20, February.
    6. Ümmühan Başaran Filik & Tansu Filik & Ömer Nezih Gerek, 2018. "A Hysteresis Model for Fixed and Sun Tracking Solar PV Power Generation Systems," Energies, MDPI, vol. 11(3), pages 1-15, March.
    7. Leva, S. & Dolara, A. & Grimaccia, F. & Mussetta, M. & Ogliari, E., 2017. "Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 88-100.
    8. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
    9. Federica Cucchiella & Idiano D’Adamo & Paolo Rosa, 2015. "Industrial Photovoltaic Systems: An Economic Analysis in Non-Subsidized Electricity Markets," Energies, MDPI, vol. 8(11), pages 1-16, November.
    10. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
    11. 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.
    12. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    13. Alessandro Niccolai & Alberto Dolara & Emanuele Ogliari, 2021. "Hybrid PV Power Forecasting Methods: A Comparison of Different Approaches," Energies, MDPI, vol. 14(2), pages 1-18, January.
    14. Seyed Mahdi Miraftabzadeh & Cristian Giovanni Colombo & Michela Longo & Federica Foiadelli, 2023. "A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks," Forecasting, MDPI, vol. 5(1), pages 1-16, February.
    15. Le Cam, M. & Daoud, A. & Zmeureanu, R., 2016. "Forecasting electric demand of supply fan using data mining techniques," Energy, Elsevier, vol. 101(C), pages 541-557.
    16. Wahiba Yaïci & Michela Longo & Evgueniy Entchev & Federica Foiadelli, 2017. "Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System," Sustainability, MDPI, vol. 9(8), pages 1-14, August.
    17. Honglu Zhu & Weiwei Lian & Lingxing Lu & Songyuan Dai & Yang Hu, 2017. "An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window," Energies, MDPI, vol. 10(10), pages 1-18, October.
    18. Gabriel Mendonça de Paiva & Sergio Pires Pimentel & Bernardo Pinheiro Alvarenga & Enes Gonçalves Marra & Marco Mussetta & Sonia Leva, 2020. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks," Energies, MDPI, vol. 13(11), pages 1-28, June.
    19. Honglu Zhu & Xu Li & Qiao Sun & Ling Nie & Jianxi Yao & Gang Zhao, 2015. "A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks," Energies, MDPI, vol. 9(1), pages 1-15, December.
    20. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.

    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:8:y:2015:i:6:p:4853-4870:d:50148. 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.