IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v75y2017icp1-10.html
   My bibliography  Save this article

Regenerative practice of using photovoltaic solar systems for residential dwellings: An empirical study in Australia

Author

Listed:
  • Tam, Vivian W.Y.
  • Le, Khoa N.
  • Zeng, S.X.
  • Wang, Xiangyu
  • Illankoon, I.M. Chethana S.

Abstract

Solar electricity that is produced from photovoltaic solar systems has the potential to deliver clean sustainable energy. Positive steps are being undertaken to minimise greenhouse gas emissions in Australia and photovoltaic solar systems are contributing towards sustainability. The current amount of installed photovoltaic solar systems cannot address the global warming issues in whole, however renewable energy production is contributing towards minimising carbon emissions. One of the main concerns for the residential householders is the economic issue on the use of photovoltaic solar systems. This paper examines life cycle cost effectiveness in using photovoltaic solar systems with capacities ranging from 1.5kW to 5kW in relation to the number of occupants and consumption for residential dwellings over a 25-year period. Eight major cities in Australia, including Sydney, Canberra, Melbourne, Brisbane, Hobart, Adelaide, Darwin and Perth, are investigated. Life cycle cost comparisons among different types of electricity grid connected systems, including a gross-feed-in-tariff (GFIT) scheme, a net-feed-in-tariff (NFIT) scheme and a buy-back scheme, are also explored. It is found that all major cities can receive life cycle cost saving in installing photovoltaic solar systems in their residential dwellings. The life cycle cost saving is between $273 and $53,021 and the percentage of cost saving is between 0.35% and 123.83% in a 15-year period. It appears that the GFIT and NFIT schemes offer better benefits than the buy-back scheme in installing photovoltaic solar systems. It is also found that the higher the capacity of the photovoltaic solar systems, the higher the life cycle cost saving can be received. This paper contributes to prove the cost effectiveness of using photovoltaic solar systems with the example from Australian residential dwellings.

Suggested Citation

  • Tam, Vivian W.Y. & Le, Khoa N. & Zeng, S.X. & Wang, Xiangyu & Illankoon, I.M. Chethana S., 2017. "Regenerative practice of using photovoltaic solar systems for residential dwellings: An empirical study in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1-10.
  • Handle: RePEc:eee:rensus:v:75:y:2017:i:c:p:1-10
    DOI: 10.1016/j.rser.2016.10.040
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032116306931
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2016.10.040?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. García, Javier Ordóñez & Gago, Eulalia Jadraque & Bayo, Javier Alegre & Montes, Germán Martínez, 2007. "The use of solar energy in the buildings construction sector in Spain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(9), pages 2166-2178, December.
    2. Wu, Peng & Xia, Bo & Wang, Xiangyu, 2015. "The contribution of ISO 14067 to the evolution of global greenhouse gas standards—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 142-150.
    3. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    4. Ordóñez, J. & Jadraque, E. & Alegre, J. & Martínez, G., 2010. "Analysis of the photovoltaic solar energy capacity of residential rooftops in Andalusia (Spain)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 2122-2130, September.
    5. Evans, John Martin & de Schiller, Silvia & Garreta, Fabián, 1998. "Renewable energy and wild life conservation: Design and construction of a solar incubator," Renewable Energy, Elsevier, vol. 15(1), pages 364-367.
    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. Dinçer, Furkan, 2011. "The analysis on photovoltaic electricity generation status, potential and policies of the leading countries in solar energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 713-720, January.
    2. Liu, Shih-Yuan & Perng, Yeng-Horng & Ho, Yu-Feng, 2013. "The effect of renewable energy application on Taiwan buildings: What are the challenges and strategies for solar energy exploitation?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 92-106.
    3. Shen, Yuxuan & Pan, Yue, 2023. "BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, Elsevier, vol. 333(C).
    4. Carolina Rodriguez & María Coronado & Marta D’Alessandro & Juan Medina, 2019. "The Importance of Standardised Data-Collection Methods in the Improvement of Thermal Comfort Assessment Models for Developing Countries in the Tropics," Sustainability, MDPI, vol. 11(15), pages 1-22, August.
    5. Miguel Ángel Rodríguez López & Diego Rodríguez Rodríguez, 2024. "La aplicación de datos masivos en economía de la energía: una revisión," Working Papers 2024-08, FEDEA.
    6. Re Cecconi, F. & Moretti, N. & Tagliabue, L.C., 2019. "Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 266-277.
    7. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    8. Liangwen Yan & Fengfeng Qian & Wei Li, 2018. "Research on Key Parameters Operation Range of Central Air Conditioning Based on Binary K-Means and Apriori Algorithm," Energies, MDPI, vol. 12(1), pages 1-13, December.
    9. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    10. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    11. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    12. Kutlu, Elif Ceren & Durusoy, Beyza & Ozden, Talat & Akinoglu, Bulent G., 2022. "Technical potential of rooftop solar photovoltaic for Ankara," Renewable Energy, Elsevier, vol. 185(C), pages 779-789.
    13. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    14. Wang, Manyu & Wei, Chu, 2024. "Toward sustainable heating: Assessment of the carbon mitigation potential from residential heating in northern rural China," Energy Policy, Elsevier, vol. 190(C).
    15. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    16. Abu Bakar, Nur Najihah & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah & Bandi, Masilah, 2015. "Energy efficiency index as an indicator for measuring building energy performance: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 1-11.
    17. Fuster-Palop, Enrique & Prades-Gil, Carlos & Masip, X. & Viana-Fons, Joan D. & Payá, Jorge, 2021. "Innovative regression-based methodology to assess the techno-economic performance of photovoltaic installations in urban areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    18. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    19. D'Amico, A. & Ciulla, G. & Panno, D. & Ferrari, S., 2019. "Building energy demand assessment through heating degree days: The importance of a climatic dataset," Applied Energy, Elsevier, vol. 242(C), pages 1285-1306.
    20. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(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:eee:rensus:v:75:y:2017:i:c:p:1-10. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.