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Integrating rooftop solar into a multi-source energy planning optimization model

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  • Arnette, Andrew N.

Abstract

This research uses an optimization model to compare the role of rooftop solar generation versus large-scale solar and wind farm installations in renewable energy planning. The model consists of competing objectives, minimizing annual generation costs and minimizing annual greenhouse gas emissions. Rather than focus on the individual consumer’s investment decision, over 20 scenarios were developed which explored key input parameters such as the maximum penetration level of rooftop solar installations, pricing of equipment, tax credits, and net-metering policy to determine what role rooftop solar plays in renewable energy investment at an aggregate level. The research finds that at lower levels of penetration, such as those currently found in the United States, other renewable energy sources remain viable options, thus rooftop solar should be just one option considered when increasing development of renewable energy sources. The research also shows that a balanced approach taking into account both of the opposing objectives will lead to greater levels of rooftop solar generation than focusing solely on cost or emissions. Therefore, rooftop solar should be considered as part of an overall balanced approach to increasing renewable energy generation.

Suggested Citation

  • Arnette, Andrew N., 2013. "Integrating rooftop solar into a multi-source energy planning optimization model," Applied Energy, Elsevier, vol. 111(C), pages 456-467.
  • Handle: RePEc:eee:appene:v:111:y:2013:i:c:p:456-467
    DOI: 10.1016/j.apenergy.2013.05.003
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    References listed on IDEAS

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    Cited by:

    1. Arnette, Andrew N., 2017. "Renewable energy and carbon capture and sequestration for a reduced carbon energy plan: An optimization model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 254-265.
    2. Hong, Taehoon & Lee, Minhyun & Koo, Choongwan & Jeong, Kwangbok & Kim, Jimin, 2017. "Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis," Applied Energy, Elsevier, vol. 194(C), pages 320-332.
    3. Dadi Wang, 2017. "Benchmarking the Performance of Solar Installers and Rooftop Photovoltaic Installations in California," Sustainability, MDPI, vol. 9(8), pages 1-17, August.
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    6. Ramshani, Mohammad & Khojandi, Anahita & Li, Xueping & Omitaomu, Olufemi, 2020. "Optimal planning of the joint placement of photovoltaic panels and green roofs under climate change uncertainty," Omega, Elsevier, vol. 90(C).
    7. Cole, Wesley & Lewis, Haley & Sigrin, Ben & Margolis, Robert, 2016. "Interactions of rooftop PV deployment with the capacity expansion of the bulk power system," Applied Energy, Elsevier, vol. 168(C), pages 473-481.
    8. Savić, Aleksandar & Đurišić, Željko, 2014. "Optimal sizing and location of SVC devices for improvement of voltage profile in distribution network with dispersed photovoltaic and wind power plants," Applied Energy, Elsevier, vol. 134(C), pages 114-124.
    9. Luis Arribas & Yolanda Lechón & Alberto Perula & Javier Domínguez & Manuel Ferres & Jorge Navarro & Luis F. Zarzalejo & Carolina García Barquero & Ignacio Cruz, 2021. "Review of Data and Data Sources for the Assessment of the Potential of Utility-Scale Hybrid Wind–Solar PV Power Plants Deployment, under a Microgrid Scope," Energies, MDPI, vol. 14(21), pages 1-23, November.
    10. Hannes Koch & Stefan Lechner & Sebastian Erdmann & Martin Hofmann, 2022. "Assessing the Potential of Rooftop Photovoltaics by Processing High-Resolution Irradiation Data, as Applied to Giessen, Germany," Energies, MDPI, vol. 15(19), pages 1-17, September.

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