Influence of wave resource assessment methodology on wave energy production estimates. (February 2016)

• Record Type: Journal Article
• Authors: Robertson, Bryson; Bailey, Helen; Clancy, Dan; Ortiz, Juan; Buckham, Bradley
• Is Part Of: Renewable energy. Volume 86(2016)
• Page Start: 1145
• Page End: 1160
• Publication Date: 2016-02
• Subjects: Wave energy resource assessment -- Wave energy converters -- SWAN model -- Spectral wave partitioning; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2015.09.020
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Renewable energy resource inventories show that ocean waves are one of the most energy dense untapped resources in the world, and present an opportunity to generate significant quantities of electricity. To accurately assess the levels of usable energy over long periods, a parametric representation of the raw wave resource is required. This study investigates the variability across four wave energy assessment methods, and two input data sources, to quantify the uncertainties in WEC power production assessments. Two conventional methods were tested: a time-series method and a standard spectral method with a generic spectral shape. Two higher fidelity techniques were additionally studied; an aggregate spectral and a partitioned spectral method. Annual WEC energy production assessments varied between 472 MWh and 543 MWh, a difference of 15%. The partitioned spectral method is shown to minimize prediction uncertainties, yet results in a 14% reduction in annual WEC energy production and increasing power variability. Spectral shape has limited impact on power estimates and energy production assessments, while the numerical wave model data can underestimate annual energy estimates by up to 13%. These uncertainties significantly impact the feasibility of wave energy developments and need to be accounted for as the industry matures. Highlights: Assess impact on annual energy production due to differing wave resource assessment methodologies and input data sources. 15% variation in annual wave energy production due to differing resource assessment methods despite using same input data. Wave buoy data generally predicts up to an additional 13% of wave energy production when compared against SWAN model data. Partitioned spectral method reduces annual energy estimates by 14% and increases standard deviation of power production. Spectral shape is shown to have limited effect of annual energy production estimates.