Marine Hydrokinetic Energy Site Identification and Ranking Methodology Part I: Wave Energy

Kilcher, Levi; Thresher, R. W.; Tinnesand, Heidi

doi:10.2172/1330617

Cited by 11 publications

(6 citation statements)

References 8 publications

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“…The definition of "sites" in this work is more specific than the "locales" defined in the companion wave study (Kilcher and Thresher 2016). This study focuses on sites because a) tidal energy density is more spatially localized compared to wave energy, which facilitates a more specific analysis, and b) there has been relatively less published work in identifying U.S. tidal energy sites, so the specificity of this analysis adds unique information to our understanding.…”

Section: Methodology and Datamentioning

confidence: 99%

“…This report is the second in a two-part series on methods for identifying and ranking commercial MHK deployment locations. This document is focused on tidal energy site identification, and its companion focuses on wave energy site identification (Kilcher and Thresher 2016). Each of these reports utilizes publically available data for the U.S. coastline to provide a high-level assessment of the potential for MHK deployment in the United States.…”

Section: Introductionmentioning

confidence: 99%

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Marine Hydrokinetic Energy Site Identification and Ranking Methodology Part II: Tidal Energy

Kilcher

Thresher

Tinnesand

2016

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Section: Methodology and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Marine Hydrokinetic Energy Site Identification and Ranking Methodology Part II: Tidal Energy

Kilcher

Thresher

Tinnesand

2016

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“…On the East Coast of the United States, Cape Cod, Massachusetts, was chosen as it is near major population hubs, has a medium-sized wave resource, and is close in proximity to the proposed Vineyard Wind Farm location, opening potential opportunities for tandem wave-wind converter devices [22]. Finally, Miami, Florida, was chosen for analysis since it has been classified as unusable for present state-of-the-art WEC devices [24]; this study aims to determine if small WECs would be suitable to capture wave energy off Florida's southern coast. Figure 7 shows the study locations and the gross wave energy transport, as developed and calculated by Cornett [22].…”

Section: Case Study Wec Assessment Locations and Study Scenariosmentioning

confidence: 99%

Wave Resource Assessments: Spatiotemporal Impacts of WEC Size and Wave Spectra on Power Conversion

2022

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Wave energy has the potential to power significant portions of economies around the world. Standard International Electrotechnical Commission methods for determining wave energy quantifies the gross wave resource available in the ocean, yet a significant portion of this resource is not usable by specific wave energy converters (WECs). This can provide a misleading assessment of the spatiotemporal opportunities for wave energy in deployment locations. Therefore, there is a need to develop a new technique to assess potential wave power from a device point of view that is generally applicable across WEC sizes. To address this challenge, a novel net power assessment methodology is proposed, which implements Budal’s upper bound (which describes the power available to a WEC based on its stroke), the radiation power limit (which describes the maximum radiation-based amount of wave power a WEC can absorb), and total gross incident wave power as absorbable power upper bounds. Spatiotemporal opportunities for WECs were re-evaluated based on this new technique. Numerical simulations were conducted to quantify the net wave resource available for different sized WECs (1, 2, 5, 10) at five different ocean sites in the U.S. based on wave data. The simulation results show the predicted potential wave power through the net power assessment for a 5 m device is 0.8% of the International Electrotechnical Commission assessment results at PacWave, Oregon. For the monthly average power, the results show PacWave has the most energetic wave resource (up to 406 kW in January) and WETS, Hawaii, has the steadiest wave power available (maximum COV of 0.8) among the sites. Regarding the size of the devices, results show that larger devices (e.g., 10 m) have better performance in terms of both magnitude and steadiness of power available at WETS and Los Angeles, California. Finally, the wave power potential of different sized WECs at varying locations was compared at a 3-h resolution. The maximum instantaneous power available for a 1 and 10 m device at PacWave throughout the time period was 47.8 and 3.52 × 103 kW, respectively.

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“…Of the four sites, two are located on the West Coast and two on the East Coast ( Figure 2 [17]. Based on a recent analysis by the National Renewable Energy Laboratory (NREL) [18], the Massachusetts coast is among the highest-ranked sites in terms of wave resources and market potential. Therefore, the Boston Harbor and coast were selected for their relatively high wave resource as being representative of the New England region.…”

mentioning

confidence: 99%

“…NDBC 41025, Diamond Shoals, NC, USA. North Carolina's coast is the only region identified as a high-resource and -market potential site south of the New England coast in NREL's study [18]. NDBC 41025 is located near the edge of the continental shelf break and the Hatteras Canyon.…”

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confidence: 99%

A Sensitivity Analysis of the Wind Forcing Effect on the Accuracy of Large-Wave Hindcasting

Wang

Yang

et al. 2018

JMSE

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Deployment of wave energy converters (WECs) relies on consistent and accurate wave resource characterization, which is typically achieved through numerical modeling using deterministic wave models. The accurate predictions of large-wave events are critical to the success of wave resource characterization because of the risk on WEC installation, maintenance, and damage caused by extreme sea states. Because wind forcing is the primary driver of wave models, the quality of wind data plays an important role in the accuracy of wave predictions. This study evaluates the sensitivity of large-wave prediction to different wind-forcing products, and identifies a feasible approach to improve wave model results through improved wind forcing. Using a multi-level nested-grid modeling approach, we perform a series of sensitivity tests at four representative National Data Buoy Center buoy locations on the U.S. East and West Coasts. The selected wind-forcing products include the Climate Forecast System Reanalysis global wind product and North American Regional Reanalysis regional wind product as well as the observed wind at the buoys. Sensitivity test results indicate a consistent improvement in model predictions for the large-wave events (e.g., >90th percentile of significant wave height) at all buoys when observed-wind data were used to drive the wave model simulations.

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Marine Hydrokinetic Energy Site Identification and Ranking Methodology Part I: Wave Energy

Cited by 11 publications

References 8 publications

Marine Hydrokinetic Energy Site Identification and Ranking Methodology Part II: Tidal Energy

Marine Hydrokinetic Energy Site Identification and Ranking Methodology Part II: Tidal Energy

Wave Resource Assessments: Spatiotemporal Impacts of WEC Size and Wave Spectra on Power Conversion

A Sensitivity Analysis of the Wind Forcing Effect on the Accuracy of Large-Wave Hindcasting

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