Wave resource assessment in Oregon and southwest Washington, USA

García‐Medina, Gabriel; Özkan-Haller, H. Tuba; Ruggiero, Peter

doi:10.1016/j.renene.2013.11.014

Cited by 61 publications

(20 citation statements)

References 24 publications

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“…However, correlations for the direction of the maximum directionally resolved wave power and directionality coefficient are not very strong, as shown in Figure 4e,f. For each of six wave resource parameters recommended by the IEC TS, the following model performance metrics were computed to show the model skills, which are commonly used in other modeling studies [24,26]. All of these metrics represent an average estimate of the difference between predicted and measured values over a defined period of simulation.…”

Section: Model Performance Metricsmentioning

confidence: 99%

“…The low correlation coefficient of 0 is caused by the higher-order moments of the variance spectrum. For θ and d θ , the low correlation coefficients are due to the uncertainties in both modeled and measured directional metrics [20,22,24]. …”

Section: Model Performance Metricsmentioning

confidence: 99%

“…Akpinar et al presented a potential wave energy assessment in the Black Sea and showed spatial distribution maps based on monthly, seasonal, and annual averages for the establishment and design of a wave energy converter (WEC) system [23]. García-Medina et al conducted a seven-year hindcast, using nested grid WWIII and structured grid SWAN models to assess the temporal and spatial variability as well as the trend of wave resources in Oregon and southwestern Washington [24,25]. Yang et al conducted a wave resource assessment at a test bed off the central Oregon coast, using structured grid WWIII and SWAN with a four-level nested grid approach.…”

Section: Introductionmentioning

confidence: 99%

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Wave Resource Characterization Using an Unstructured Grid Modeling Approach

Yang

Wang

2018

Energies

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Notably, spectral analysis indicates that the ST4 physics package improves upon the ST2 physics package's ability to predict wave power density for large waves, which is important for wave resource assessment, load calculation of devices, and risk management. In addition, bivariate distributions show that the simulated sea state of maximum occurrence with the ST4 physics package matched the observed data better than with the ST2 physics package. This study demonstrated that the unstructured grid wave modeling approach, driven by regional nested grid WWIII outputs along with the ST4 physics package, can efficiently provide accurate wave hindcasts to support wave resource characterization. Our study also suggests that wind effects need to be considered if the dimension of the model domain is greater than approximately 100 km, or O (10 2 km).

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Section: Model Performance Metricsmentioning

confidence: 99%

Section: Model Performance Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wave Resource Characterization Using an Unstructured Grid Modeling Approach

Yang

Wang

2018

Energies

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“…A comprehensive analysis of expected wave conditions on the Oregon coast was conducted using seven years (2005-2011) of modeled wave conditions [33]. This analysis provided approximately 300 discrete sea state bins, characterized by significant wave height (Hs), peak period (Tp), and mean wave direction (Dp) [34], along with the probability of occurrence of each sea state.…”

Section: Wave Modelmentioning

confidence: 99%

Spatial Environmental Assessment Tool (SEAT): A Modeling Tool to Evaluate Potential Environmental Risks Associated with Wave Energy Converter Deployments

et al. 2018

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Wave energy converter (WEC) arrays deployed in coastal regions may create physical disturbances, potentially resulting in environmental stresses. Presently, limited information is available on the nature of these physical disturbance or the resultant effects. A quantitative Spatial Environmental Assessment Tool (SEAT) for evaluating the potential effects of wave energy converter (WEC) arrays on nearshore hydrodynamics and sediment transport is presented for the central Oregon coast (USA) through coupled numerical model simulations of an array of WECs. Derived climatological wave conditions were used as inputs to the model to allow for the calculation of risk metrics associated with various hydrodynamic and sediment transport variables such as maximum shear stress, bottom velocity, and change in bed elevation. The risk maps provided simple, quantitative, and spatially-resolved means of evaluating physical changes in the vicinity of a hypothetical WEC array in response to varying wave conditions. The near-field risk of sediment mobility was determined to be moderate in the lee of the densely spaced array, where the potential for increased sediment deposition could result in benthic habitat alteration. Modifications to the nearshore sediment deposition and erosion patterns were observed near headlands and topographic features, which could have implications for littoral sediment transport. The results illustrate the benefits of a risk evaluation tool for facilitating coastal resource management at early market marine renewable energy sites.

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“…For calibration purposes, the modelled and measured significant wave height data sets at the bottom mounted wave recorder area point were compared for the period from the 1 to 5 September 2012 with a model time step of 6 h. The model accuracy is assessed by calculating the root mean square error, bias and the correlation coefficient [7,21]. These values for various magnitudes of the Nikuradse roughness parameter are presented in the Table 2.…”

Section: Model Calibrationmentioning

confidence: 99%

Spatial Distribution of the Baltic Sea Near-Shore Wave Power Potential along the Coast of Klaipėda, Lithuania

et al. 2017

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Abstract:Wave power is an abundant source of energy that can be utilized to produce electricity. Therefore, assessments of wave power resources are being carried out worldwide. An overview of the recent assessments is presented in this paper, revealing the global distribution of these resources. Additionally, a study, which aims to assess the spatial distribution of the Baltic Sea near-shore wave power potential along the coast of Klaipėda (Lithuania), is introduced in this paper. The impacts of the wave propagation direction and decreasing depth on wave power resources were examined using the numerical wind-wave model MIKE 21 NSW. The wave height loss of the design waves propagating to shore was modelled, and the wave power fluxes in the studied depths were calculated using the JONSWAP wave spectrum modified for the Baltic Sea. The results revealed that all waves that propagate to the shore in the Baltic Sea near-shore area along the coast of Klaipėda from 30 m depth to 5 m depth lose at least 30% of their power. Still, most common waves in this area are low, and therefore, they start to lose their power while propagating to the shore at relatively low (10-14 m) depths. To turn this into an advantage the wave power converter would have to work efficiently under low power conditions.

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Wave resource assessment in Oregon and southwest Washington, USA

Cited by 61 publications

References 24 publications

Wave Resource Characterization Using an Unstructured Grid Modeling Approach

Wave Resource Characterization Using an Unstructured Grid Modeling Approach

Spatial Environmental Assessment Tool (SEAT): A Modeling Tool to Evaluate Potential Environmental Risks Associated with Wave Energy Converter Deployments

Spatial Distribution of the Baltic Sea Near-Shore Wave Power Potential along the Coast of Klaipėda, Lithuania

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