A test-bed modeling study for wave resource assessment

Zhaoqing Yang1, Vincent S Neary2, Taiping Wang3, Budi Gunawan2 and Ann Dallman2, (1)Pacific Northwest National Lab, Coastal Division, Seattle, WA, United States, (2)Sandia National Laboratories, Water Power Technologies, Albuquerque, NM, United States, (3)Pacific Northwest National Lab, Seattle, WA, United States
Abstract:
Hindcasts from phase-averaged wave models are commonly used to estimate standard statistics used in wave energy resource assessments. However, the research community and wave energy converter industry is lacking a well-documented and consistent modeling approach for conducting these resource assessments at different phases of WEC project development, and at different spatial scales, e.g., from small-scale pilot study to large-scale commercial deployment. Therefore, it is necessary to evaluate current wave model codes, as well as limitations and knowledge gaps for predicting sea states, in order to establish best wave modeling practices, and to identify future research needs to improve wave prediction for resource assessment. This paper presents the first phase of an on-going modeling study to address these concerns. The modeling study is being conducted at a test-bed site off the Central Oregon Coast using two of the most widely-used third-generation wave models – WaveWatchIII and SWAN. A nested-grid modeling approach, with domain dimension ranging from global to regional scales, was used to provide wave spectral boundary condition to a local scale model domain, which has a spatial dimension around 60km by 60km and a grid resolution of 250m – 300m. Model results simulated by WaveWatchIII and SWAN in a structured-grid framework are compared to NOAA wave buoy data for the six wave parameters, including omnidirectional wave power, significant wave height, energy period, spectral width, direction of maximum directionally resolved wave power, and directionality coefficient. Model performance and computational efficiency are evaluated, and the best practices for wave resource assessments are discussed, based on a set of standard error statistics and model run times.