Uncertainty analysis of wave energy farms financial indicators

In this work, an analysis of the uncertainty that influences wave energy farm financial returns is carried out. Firstly, a methodology to analyze the financial uncertainty through cash flow analysis is developed. A reconstruction of a set of power production life-cycles is made based on the methodology proposed in Ref. [1] using a selection and an interpolation technique. This set of lifecycles allowed to obtain the statistical distributions of the main financial indicators (IRR, NPV and PBP). The high variability of these parameters is explained by the climate variability. Therefore, for the economic study of a wave energy project the influence of the climate conditions is demonstrated and for this purpose a long climate data series is needed. Finally, a second uncertainty source related with the political and legislation environment is studied, focusing on the effect of feed in tariff. This sensitivity analysis of the feed in tariffs is made based on cash-flow algorithm. Current feed in tariffs seemed insufficient in order to get profitable wave energy projects and also an increase in feed in tariff resulted in an increase of variability of financial indicators. Therefore an increase in feed in tariff is not recommended for early stage technologies. Finally, learning curve was also included in this investigation and it appeared as a key parameter in order to get cost effective financial returns.

[1]  P. Camus,et al.  A hybrid efficient method to downscale wave climate to coastal areas , 2011 .

[2]  Bilal M. Ayyub,et al.  Elicitation of expert opinions for uncertainty and risks: Answer to the Book Review by Roger M. Cooke , 2003, Fuzzy Sets Syst..

[3]  Inigo J. Losada,et al.  Time domain model for a two-body heave converter: Model and applications , 2013 .

[4]  T. P. Wright,et al.  Factors affecting the cost of airplanes , 1936 .

[5]  Michael O'Connor,et al.  Techno-economic performance of the Pelamis P1 and Wavestar at different ratings and various locations in Europe , 2013 .

[6]  Lars Johanning,et al.  Mooring systems for wave energy converters: A review of design issues and choices , 2004 .

[7]  A. Clément,et al.  Wave energy in Europe: current status and perspectives , 2002 .

[8]  Jerome P. Lavelle,et al.  Essentials of engineering economic analysis , 2002 .

[9]  Raymond Alcorn,et al.  A 10 year installation program for wave energy in Ireland: A case study sensitivity analysis on financial returns , 2012 .

[10]  I. Losada,et al.  Variability of multivariate wave climate in Latin America and the Caribbean , 2013 .

[11]  Inigo J. Losada,et al.  Factors that influence array layout on wave energy farms , 2014 .

[12]  T. Lewis,et al.  Operational expenditure costs for wave energy projects and impacts on financial returns , 2013 .

[13]  Inigo J. Losada,et al.  A Global Ocean Wave (GOW) calibrated reanalysis from 1948 onwards , 2012 .

[14]  R. Mínguez,et al.  Directional calibrated wind and wave reanalysis databases using instrumental data for optimal design of off-shore wind farms , 2011, OCEANS 2011 IEEE - Spain.

[15]  Aurélien Babarit,et al.  Numerical benchmarking study of a selection of wave energy converters , 2012 .

[16]  Haitao Yu,et al.  Offshore wave energy generation devices: Impacts on ocean bio-environment , 2012 .

[17]  Julien De Rouck,et al.  A methodology for production and cost assessment of a farm of wave energy converters , 2011 .