Reducing variability in the cost of energy of ocean energy arrays

Mathew B.R. Topper, Vincenzo Nava, Adam J. Collin, David Bould, Francesco Ferri, Sterling S. Olson, Ann R. Dallman, Jesse D. Roberts, Pablo Ruiz-Minguela and Henry F. Jeffrey

Renewable and Sustainable Energy Reviews, 2019, vol. 112, issue C, 263-279

Abstract: Variability in the predicted cost of energy of an ocean energy converter array is more substantial than for other forms of energy generation, due to the combined stochastic action of weather conditions and failures. If the variability is great enough, then this may influence future financial decisions. This paper provides the unique contribution of quantifying variability in the predicted cost of energy and introduces a framework for investigating reduction of variability through investment in components. Following review of existing methodologies for parametric analysis of ocean energy array design, the development of the DTOcean software tool is presented. DTOcean can quantify variability by simulating the design, deployment and operation of arrays with higher complexity than previous models, designing sub-systems at component level. A case study of a theoretical floating wave energy converter array is used to demonstrate that the variability in levelised cost of energy (LCOE) can be greatest for the smallest arrays and that investment in improved component reliability can reduce both the variability and most likely value of LCOE. A hypothetical study of improved electrical cables and connectors shows reductions in LCOE up to 2.51% and reductions in the variability of LCOE of over 50%; these minima occur for different combinations of components.

Keywords: Financial; Variability; Energy; Ocean; Wave; Tidal; Arrays (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.rser.2019.05.032

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