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The economics of electricity generation from Gulf Stream currents

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  • Li, Binghui
  • de Queiroz, Anderson Rodrigo
  • DeCarolis, Joseph F.
  • Bane, John
  • He, Ruoying
  • Keeler, Andrew G.
  • Neary, Vincent S.

Abstract

Hydrokinetic turbines harnessing energy from ocean currents represent a potential low carbon electricity source. This study provides a detailed techno-economic assessment of ocean turbines operating in the Gulf Stream off the North Carolina coast. Using hindcast data from a high-resolution ocean circulation model in conjunction with the US Department of Energy's reference model 4 (RM4) for ocean turbines, we examine resource quality and apply portfolio optimization to identify the best candidate sites for ocean turbine deployment. We find that the lowest average levelized cost of electricity (LCOE) from a single site can reach 400 $/MWh. By optimally selecting geographically dispersed sites and taking advantage of economies of scale, the variations in total energy output can be reduced by an order of magnitude while keeping the LCOE below 300 $/MWh. Power take-off and transmission infrastructure are the largest cost drivers, and variation in resource quality can have a significant influence on the project LCOE. While this study focuses on a limited spatial domain, it provides a framework to assess the techno-economic feasibility of ocean current energy in other western boundary currents.

Suggested Citation

  • Li, Binghui & de Queiroz, Anderson Rodrigo & DeCarolis, Joseph F. & Bane, John & He, Ruoying & Keeler, Andrew G. & Neary, Vincent S., 2017. "The economics of electricity generation from Gulf Stream currents," Energy, Elsevier, vol. 134(C), pages 649-658.
  • Handle: RePEc:eee:energy:v:134:y:2017:i:c:p:649-658
    DOI: 10.1016/j.energy.2017.06.048
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    Cited by:

    1. Razi, P. & Ramaprabhu, P. & Tarey, P. & Muglia, M. & Vermillion, C., 2022. "A low-order wake interaction modeling framework for the performance of ocean current turbines under turbulent conditions," Renewable Energy, Elsevier, vol. 200(C), pages 1602-1617.
    2. de Faria, Victor A.D. & de Queiroz, Anderson R. & DeCarolis, Joseph F., 2022. "Optimizing offshore renewable portfolios under resource variability," Applied Energy, Elsevier, vol. 326(C).
    3. Okampo, Ewaoche John & Nwulu, Nnamdi, 2021. "Optimisation of renewable energy powered reverse osmosis desalination systems: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    4. Faria, Victor A.D. & Rodrigo de Queiroz, Anderson & DeCarolis, Joseph F., 2023. "Scenario generation and risk-averse stochastic portfolio optimization applied to offshore renewable energy technologies," Energy, Elsevier, vol. 270(C).
    5. Li, Zhenyu & Siddiqi, Afreen & Anadon, Laura Diaz & Narayanamurti, Venkatesh, 2018. "Towards sustainability in water-energy nexus: Ocean energy for seawater desalination," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3833-3847.

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