Publications

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This study presents a numerical model of a WEC array. The model will be used in subsequent work to study the ability of data assimilation to support power prediction from WEC arrays and WEC array design. In this study, we focus on design, modeling, and control of the WEC array. A case study is performed for a small remote Alaskan town. Using an efficient method for modeling the linear interactions within a homogeneous array, we produce a model and predictionless feedback controllers for the devices within the array. The model is applied to study the effects of spectral wave forecast errors on power output. The results of this analysis show that the power performance of the WEC array will be most strongly affected by errors in prediction of the spectral period, but that reductions in performance can realistically be limited to less than 10% based on typical data assimilation based spectral forecasting accuracy levels.

Two material types identified by Sew-EZ were tested in various configurations, and under various conditions, by Sandia National Laboratories (SNL). The primary focus of this study was to assess the filtration performance of these two materials and identify if they perform similarly to certified N95 respirators. Testing was conducted on two systems which use distinctly different techniques to characterize the aerosol penetration characteristics of materials: a) R&D Filtration System: A large-scale R&D filtration system was used with testing parameters that mimicked NIOSH guidelines, where possible. Efficiency data as a function of particle size was attained using NaC1 as the test aerosol and a Scanning Mobility Particle Sizer (SMPS) for measurements. A more detailed system description can be found in Omana et al. 2020. b) Automated Tester: A commercial, automated filter tester (100Xs, Air Techniques International) was used to provide penetration/efficiency data for Sew EZ materials. The 100Xs aerosolizes a polydisperse NaC1 aerosol with a consistent concentration and size profile. The 100Xs manual (Air Techniques International 2018) states, "The aerosol particle size and distribution are designed to meet all requirements as defined in the relevant sections of NIOSH 42 CFR, Part 84 (pg. 32)."

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Integration of renewable power sources into grids remains an active research and development area,particularly for less developed renewable energy technologies such as wave energy converters (WECs).WECs are projected to have strong early market penetration for remote communities, which serve as naturalmicrogrids. Hence, accurate wave predictions to manage the interactions of a WEC array with microgridsis especially important. Recently developed, low-cost wave measurement buoys allow for operationalassimilation of wave data at remote locations where real-time data have previously been unavailable. This work includes the development and assessment of a wave modeling framework with real-time dataassimilation capabilities for WEC power prediction. The availability of real-time wave spectral componentsfrom low-cost wave measurement buoys allows for operational data assimilation with the Ensemble Kalmanfilter technique, whereby measured wave conditions within the numerical wave forecast model domain areassimilated onto the combined set of internal and boundary grid points while taking into account model andobservation error covariances. The updated model state and boundary conditions allow for more accuratewave characteristic predictions at the locations of interest. Initial deployment data indicated that measured wave data from one buoy that were assimilated intothe wave modeling framework resulted in improved forecast skill for a case where a traditional numericalforecast model (e.g., Simulating WAves Nearshore; SWAN) did not well represent the measured conditions.On average, the wave power forecast error was reduced from 73% to 43% using the data assimilationmodeling with real-time wave observations.

Variability in the predicted cost of energy of an ocean energy converter array is more substantial than for other forms of energy generation, due to the combined stochastic action of weather conditions and failures. If the variability is great enough, then this may influence future financial decisions. This paper provides the unique contribution of quantifying variability in the predicted cost of energy and introduces a framework for investigating reduction of variability through investment in components. Following review of existing methodologies for parametric analysis of ocean energy array design, the development of the DTOcean software tool is presented. DTOcean can quantify variability by simulating the design, deployment and operation of arrays with higher complexity than previous models, designing sub-systems at component level. A case study of a theoretical floating wave energy converter array is used to demonstrate that the variability in levelised cost of energy (LCOE) can be greatest for the smallest arrays and that investment in improved component reliability can reduce both the variability and most likely value of LCOE. A hypothetical study of improved electrical cables and connectors shows reductions in LCOE up to 2.51% and reductions in the variability of LCOE of over 50%; these minima occur for different combinations of components.

The wave energy resource for U.S. coastal regions has been estimated at approximately 1,200 TWh/ yr (EPRI 2011). The magnitude is comparable to the natural gas and coal energy generation. Although the wave energy industry is relatively new from a commercial perspective, wave energy conversion (WEC) technology is developing at an increasing pace. Ramping up to commercial scale deployment of WEC arrays requires demonstration of performance that is economically competitive with other energy generation methods. The International Electrotechnical Commission has provided technical specifications for developing wave energy resource assessments and characterizations, but it is ultimately up to developers to create pathways for making a specific site competitive. The present study uses example sites to evaluate the annual energy production using different wave energy conversion strategies and examines pathways available to make WEC deployments competitive. The wave energy resource is evaluated for sites along the U.S. coast and combinations of wave modeling and basic resource assessments determine factors affecting the cost of energy at these sites. The results of this study advance the understanding of wave resource and WEC device assessment required to evaluate commercial-scale deployments.

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Integration of renewable power sources into electrical grids remains an active research and development area, particularly for less developed renewable energy technologies, such as wave energy converters (WECs). High spatio-temporal resolution and accurate wave forecasts at a potential WEC (or WEC array) lease area are needed to improve WEC power prediction and to facilitate grid integration, particularly for microgrid locations. The availability of high quality measurement data from recently developed low-cost buoys allows for operational assimilation of wave data into forecast models at remote locations where real-time data have previously been unavailable. This work includes the development and assessment of a wave modeling framework with real-time data assimilation capabilities for WEC power prediction. Spoondrift wave measurement buoys were deployed off the coast of Yakutat, Alaska, a microgrid site with high wave energy resource potential. A wave modeling framework with data assimilation was developed and assessed, which was most effective when the incoming forecasted boundary conditions did not represent the observations well. For that case, assimilation of the wave height data using the ensemble Kalman filter resulted in a reduction of wave height forecast normalized root mean square error from 27% to an average of 16% over a 12-hour period. This results in reduction of wave power forecast error from 73% to 43%. In summary, the use of the low-cost wave buoy data assimilated into the wave modeling framework improved the forecast skill and will provide a useful development tool for the integration of WECs into electrical grids.

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A detailed methodology was used to select the sea states tested in the final stage of the Wave Energy Prize (WEPrize), a public prize challenge sponsored by the U.S. Department of Energy [1]. The winner was selected based on two metrics: a threshold value expressing the benefit to effort ratio (ACE metric) and a second metric which included hydrodynamic performance-related quantities (HPQ). HPQ required additional sea states to query aspects of the techno-economic performance not addressed by ACE. Due to the nature of the WEPrize, limited time was allotted to each contestant for testing and thus a limitation on the total sea states was required. However, the applicability of these sea states was required to encompass seven deployment locations representative of the United States West Coast and Hawaii. A cluster analysis was applied to scatter diagrams in order to determine a subset of sea states that could be scaled to find the average annual power flux at each wave climate for the ACE metric. Four additional sea states were selected, including two highly energetic sea states and two bimodal sea states, to evaluate HPQ. These sea states offer a common experimental testing platform for performance in United States deployment climates.

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A wave model test bed is established to benchmark, test and evaluate spectral wave models and modeling methodologies (i.e., best practices) for predicting the wave energy resource parameters recommended by the International Electrotechnical Commission, IEC TS 62600-101Ed. 1.0 ©2015. Among other benefits, the model test bed can be used to investigate the suitability of different models, specifically what source terms should be included in spectral wave models under different wave climate conditions and for different classes of resource assessment. The overarching goal is to use these investigations to provide industry guidance for model selection and modeling best practices depending on the wave site conditions and desired class of resource assessment. Modeling best practices are reviewed, and limitations and knowledge gaps in predicting wave energy resource parameters are identified.

This report presents met-ocean data and wave energy characteristics at eight U.S. wave energy converter (WEC) test and potential deployment sites. Its purpose is to enable the comparison of wave resource characteristics among sites as well as the selection of test sites that are most suitable for a developer’s device and that best meet their testing needs and objectives. It also provides essential inputs for the design of WEC test devices and planning WEC tests, including the planning of deployment, and operations and maintenance. For each site, this report catalogues wave statistics recommended in the International Electrotechnical Commission Technical Specification (IEC 62600-101 TS) on Wave Energy Characterization, as well as the frequency of occurrence of weather windows and extreme sea states, and statistics on wind and ocean currents. It also provides useful information on test site infrastructure and services.