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The role of public policy in optimizing renewable energy development in the greater southern Appalachian mountains

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  • Arnette, Andrew N.
  • Zobel, Christopher W.

Abstract

This research presents a third component of a comprehensive decision support system for energy planning that allows for combining existing electricity generating capabilities with increased use of renewable energy sources. It focuses on energy planning at the regional level, and concentrates specifically on the greater southern Appalachian mountains of the eastern United States: a region that was chosen for analysis not only due to its heavy dependence on coal for electricity, but also because of its potential for increased use of wind and solar power. Previous research used a geographic information system (GIS) model for identifying renewable energy potential to provide input data for a multi-objective linear programming (MOLP) model to determine the optimal constrained mix of renewable energy sources and existing fossil fuel facilities by balancing annual generation costs against the corresponding greenhouse gas emissions. This new component of the system analyzes three potential public policies—renewable portfolio standard, carbon tax, and renewable energy production tax credit—that have been used to foster increased renewable energy usage. These policies require minor modifications to the MOLP model for implementation. The results of these policy cases were then analyzed to determine the impact that these policies have on generation cost and pollution emissions within the region.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:15:y:2011:i:8:p:3690-3702
    DOI: 10.1016/j.rser.2011.07.012
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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. Arnette, Andrew N., 2013. "Integrating rooftop solar into a multi-source energy planning optimization model," Applied Energy, Elsevier, vol. 111(C), pages 456-467.
    3. 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.
    4. Naief A. Aldossary & Abdulaziz A. Alzahrani & Jamal K. Alghamdi & Ali Alqahtany & Rehan Jamil & Saleh H. Alyami, 2022. "A Procedural Framework to Identify Critical Indicators for the Protection of Environment and Ecosystem during Sustainable Urban Development in South-Western Saudi Arabia," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    5. Sung-Lin Hsueh, 2012. "A Fuzzy Utility-Based Multi-Criteria Model for Evaluating Households’ Energy Conservation Performance: A Taiwanese Case Study," Energies, MDPI, vol. 5(8), pages 1-17, August.
    6. Katsoulakos, Nikolas M. & Kaliampakos, Dimitris C., 2016. "Mountainous areas and decentralized energy planning: Insights from Greece," Energy Policy, Elsevier, vol. 91(C), pages 174-188.
    7. Cristofari, C. & Storai, C. & Canaletti, J.L., 2014. "Development policy to increase the competitiveness of renewable energy-sector companies in a territory like Corsica (France)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 61-66.

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