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De-Risking Marine Energy Project Development Through Improved Financial Uncertainty Analysis

[+] Author Affiliations
Sunny Shah

Industrial Doctoral Centre for Offshore Renewable Energy (IDCORE), Edinburgh, UK

Hannah Buckland

Black & Veatch Ltd., Glasgow, UK

Philipp R. Thies

University of Exeter, Penryn, UK

Claire Cohen

Black & Veatch Ltd., Redhill, UK

Tom Bruce

University of Edinburgh, Edinburgh, UK

Paper No. OMAE2017-61667, pp. V010T09A008; 8 pages
  • ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering
  • Volume 10: Ocean Renewable Energy
  • Trondheim, Norway, June 25–30, 2017
  • Conference Sponsors: Ocean, Offshore and Arctic Engineering Division
  • ISBN: 978-0-7918-5778-6
  • Copyright © 2017 by ASME


The financial performance of a marine energy project is based on assumptions with significant uncertainty. To fully appraise the risk, potential investors require an understanding of the likelihood of deviations from the assumed most likely case for a project’s financial performance. A Monte Carlo Analysis (MCA) model with flexible user defined uncertainty definitions for all inputs is developed for this study. A realistic tidal energy project is used as a case study to compare the central, optimistic and pessimistic Levelised Cost of Energy (LCOE) and Internal Rate of Return (IRR) values derived using commonly used deterministic methods and the probabilistic MCA model. The improvement in decision support due to the probabilistic analysis is shown and the possibility for misinterpreting the deterministic results in highlighted. Two sensitivity analysis methods are employed to identify key risks and emphasise the need to use the most appropriate method for the type of analysis being conducted. Finally, the significance of some commonly ignored parameters is tested and shown to be important for accurately appraising the investment risk in a real project. Thus this paper provides guidance and tools to help investors make informed decisions with confidence.

Copyright © 2017 by ASME



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