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Renewable energy and carbon capture and sequestration for a reduced carbon energy plan: An optimization model

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  • Arnette, Andrew N.

Abstract

Built on a framework that combines geographic analysis and a multi-objective optimization model used to analyze costs and benefits of renewable energy sources (wind farms, solar farms, biomass co-fire, rooftop solar), this research introduces the potential for carbon capture and sequestration (CCS) in the model as a tool for carbon emissions reduction. The carbon capture process is available for retrofit at existing coal plants and the sequestration of carbon is allowed in underground saline aquifers. The aim of this research is to provide a model that can compare renewable energy and CCS to determine the optimal combination of these resources. Over the course of 47 model iterations, CCS is implemented five times, with a maximum of 1.71% of a required 30% decrease in carbon emissions. Renewable energy options were more cost-effective means of achieving environmental goals. With respect to public policy and planning, expanding the potential role of rooftop solar generation is more cost-effective than implementing CCS. Finally, the introduction of a $30/ton carbon tax was not always sufficient to encourage investment in CCS, and through the use of tax incentives for renewable energy combined with a carbon tax, the greatest reduction in emissions were found.

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  • 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.
    • Handle: RePEc:eee:rensus:v:70:y:2017:i:c:p:254-265
    DOI: 10.1016/j.rser.2016.11.218
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    References listed on IDEAS

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    1. Arnette, Andrew N. & Zobel, Christopher W., 2011. "Spatial analysis of renewable energy potential in the greater southern Appalachian mountains," Renewable Energy, Elsevier, vol. 36(11), pages 2785-2798.
    2. Nakata, Toshihiko & Kubo, Kazuo & Lamont, Alan, 2005. "Design for renewable energy systems with application to rural areas in Japan," Energy Policy, Elsevier, vol. 33(2), pages 209-219, January.
    3. Arnette, Andrew N., 2013. "Integrating rooftop solar into a multi-source energy planning optimization model," Applied Energy, Elsevier, vol. 111(C), pages 456-467.
    4. Matthias Finkenrath, 2011. "Cost and Performance of Carbon Dioxide Capture from Power Generation," IEA Energy Papers 2011/5, OECD Publishing.
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    6. Arnette, Andrew & Zobel, Christopher W., 2012. "An optimization model for regional renewable energy development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4606-4615.
    7. Bazmi, Aqeel Ahmed & Zahedi, Gholamreza, 2011. "Sustainable energy systems: Role of optimization modeling techniques in power generation and supply—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3480-3500.
    8. Arnette, Andrew N. & Zobel, Christopher W., 2011. "The role of public policy in optimizing renewable energy development in the greater southern Appalachian mountains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3690-3702.
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    Cited by:

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    5. Czuma, Natalia & Samojeden, Bogdan & Zarębska, Katarzyna & Motak, Monika & Da Costa, Patrick, 2022. "Modified fly ash, a waste material from the energy industry, as a catalyst for the CO2 reduction to methane," Energy, Elsevier, vol. 243(C).
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    7. Huang, Qian & Xu, Jiuping, 2023. "Carbon tax revenue recycling for biomass/coal co-firing using Stackelberg game: A case study of Jiangsu province, China," Energy, Elsevier, vol. 272(C).

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