Marine hydrokinetic devices, such as wave energy converters (WECs), can unlock untapped energy from the ocean's currents and waves. Acoustic impact assessments are required to ensure that the noise these devices generate will not negatively impact marine life, and accurate modeling of noise provides an a priori means to viably perform this assessment. We present a case study of the PacWave South site, a WEC testing site off the coast of Newport, Oregon, demonstrating the use of ParAcousti, an open-source hydroacoustic propagator tool, to model noise from an array of 28 WECs in a 3-dimensional (3-D) realistic marine environment. Sound pressure levels are computed from the modeled 3-D grid of pressure over time, which we use to predict marine mammal acoustic impact metrics (AIMs). We combine two AIMs, signal to noise ratio and sensation level, into a new metric, the effective signal level (ESL), which is a function of propagated sound, background noise levels, and hearing thresholds for marine species and is evaluated across 1/3 octave frequency intervals. The ESL model can be used to predict and quantify the potential impact of an anthropogenic signal on the health and behavior of a marine mammal species throughout the 3-D simulation area.
With the urgent need to mitigate climate change and rising global temperatures, technological solutions that reduce atmospheric CO2 are an increasingly important part of the global solution. As a result, the nascent carbon capture, utilization, and storage (CCUS) industry is rapidly growing with a plethora of new technologies in many different sectors. There is a need to holistically evaluate these new technologies in a standardized and consistent manner to determine which technologies will be the most successful and competitive in the global marketplace to achieve decarbonization targets. Life cycle assessment (LCA) and techno-economic assessment (TEA) have been employed as rigorous methodologies for quantitatively measuring a technology's environmental impacts and techno-economic performance, respectively. However, these metrics evaluate a technology's performance in only three dimensions and do not directly incorporate stakeholder needs and values. In addition, technology developers frequently encounter trade-offs during design that increase one metric at the expense of the other. The technology performance level (TPL) combined indicator provides a comprehensive and holistic assessment of an emerging technology's potential, which is described by its techno-economic performance, environmental impacts, social impacts, safety considerations, market/deployability opportunities, use integration impacts, and general risks. TPL incorporates TEA and LCA outputs and quantifies the trade-offs between them directly using stakeholder feedback and requirements. In this article, the TPL methodology is being adapted from the marine energy domain to the CCUS domain. Adapted metrics and definitions, a stakeholder analysis, and a detailed foundation-based application of the systems engineering approach to CCUS are presented. The TPL assessment framework is couched within the internationally standardized LCA framework to improve technical rigor and acceptance. It is demonstrated how stakeholder needs and values can be directly incorporated, how LCA and TEA metrics can be balanced, and how other dimensions (listed earlier) can be integrated into a single metric that measures a technology's potential.
Costs to permit Marine Energy projects are poorly understood. In this paper we examine environmental compliance and permitting costs for 19 projects in the U.S., covering the last 2 decades. Guided discussions were conducted with developers over a 3-year period to obtain historical and ongoing project cost data relative to environmental studies (e.g., baseline or pre-project site characterization as well as post-installation effects monitoring), stakeholder outreach, and mitigation, as well as qualitative experience of the permitting process. Data are organized in categories of technology type, permitted capacity, pre-and post-installation, geographic location, and funding types. We also compare our findings with earlier logic models created for the Department of Energy (i.e., Reference Models). Environmental studies most commonly performed were for Fish and Fisheries, Noise, Marine Habitat/Benthic Studies and Marine Mammals. Studies for tidal projects were more expensive than those performed for wave projects and the range of reported project costs tended to be wider than ranges predicted by logic models. For eight projects reporting full project costs, from project start to FERC or USACE permit, the average amount for environmental permitting compliance was 14.6%.
Surrogate models maximize information utility by building predictive models in place of computational or experimentally expensive model runs. Marine hydrokinetic current energy converters require large-domain simulations to estimate array efficiencies and environmental impacts. Meso-scale models typically represent turbines as actuator discs that act as momentum sinks and sources of turbulence and its dissipation. An OpenFOAM model was developed where actuator disc k-ε turbulence was characterized using an approach developed for flows through vegetative canopies. Turbine-wake data from laboratory flume experiments collected at two influent turbulence intensities were used to calibrate parameters in the turbulence-source terms in the k-ε equations. Parameter influences on longitudinal wake profiles were estimated using Gaussian process regression with subsequent optimization minimizing the objective function within 3.1% of those obtained using the full model representation, but for 74% of the computational cost (far fewer model runs). This framework facilitates more efficient parameterization of the turbulence-source equations using turbine-wake data.
This report summarizes the key contributions and lessons learned from SNL experience in technical reviews of Controls awardees in the DOE SPA program from 2013 - 2020. The purpose of this report is to provide observations and technical suggestions that are likely to be beneficial to the WEC industry as a whole. Over the course of the SPA FOA program, SNL has engaged in technical review for a total of 5 different Controls awardees. The awardees represent a diversity of WEC devices and the application of different control design approaches. The report begins with a summary of key performance metrics results reported by the 5 Controls awardees. This is followed by a summary of observations and lessons learned distilled from the technical reviews of the awardees . The report concludes with a list of general technical suggestions for future WEC controls projects.
For renewable ocean wave energy to support global energy demands, wave energy converters (WECs) will likely be deployed in large numbers (farms), which will necessarily change the nearshore environment. Wave farm induced changes can be both helpful (e.g., beneficial habitat and coastal protection) and potentially harmful (e.g., degraded habitat, recreational, and commercial use) to existing users of the coastal environment. It is essential to estimate this impact through modeling prior to the development of a farm, and to that end, many researchers have used spectral wave models, such as Simulating WAves Nearshore (SWAN), to assess wave farm impacts. However, the validity of the approaches used within SWAN have not been thoroughly verified or validated. Herein, a version of SWAN, called Sandia National Laboratories (SNL)-SWAN, which has a specialized WEC implementation, is verified by comparing its wave field outputs to those of linear wave interaction theory (LWIT), where LWIT is theoretically more appropriate for modeling wave-body interactions and wave field effects. The focus is on medium-sized arrays of 27 WECs, wave periods, and directional spreading representative of likely conditions, as well as the impact on the nearshore. A quantitative metric, the Mean Squared Skill Score, is used. Results show that the performance of SNL-SWAN as compared to LWIT is “Good” to “Excellent”.
The marine and hydrokinetic (MHK) industry plays a vital role in the U.S. clean energy strategy by providing a renewable, domestic energy source that may offset the need for traditional energy sources. The first MHK deployments in the U.S. have incurred very high permitting costs and long timelines for deploying projects, which increases project risk and discourages investment. A key challenge to advancing an economically competitive U.S. MHK industry is reducing the time and cost required for environmental permitting and compliance with government regulations. Other industries such as offshore oil and gas, offshore wind energy, subsea power and data cables, onshore wind energy, and solar energy facilities have all developed more robust permitting and compliance pathways that provide lessons for the MHK industry in the U.S. and may help inform the global consenting process. Based on in-depth review and research into each of the other industries, we describe the environmental permitting pathways, the main environmental concerns and types of monitoring typically associated with them, and factors that appear to have eased environmental permitting and compliance issues.
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.
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.
Flood irrigation benefits from low infrastructure costs and maintenance but the scour near the weirs can cause channeling of the flow preventing the water from evenly dispersing across the field. Using flow obstructions in front of the weir could reduce be a low cost solution to reduce the scour. The mitigation strategy was to virtually simulate the effects of various geometric changes to the morphology (e.g. holes and bumps) in front of the weir as a means to diffuse the high intensity flow coming from the gate. After running a parametric study for the dimensions of the shapes that included a Gaussian, semi-circle, and rectangle; a Gaussian-hole in front of the gates showed the most promise to reduce farm field shear-stresses with the added benefit of being easy to construct and implement in practice. Further the simulations showed that the closer the Gaussian-hole could be placed to the gate the sooner the high shear stress could be reduced. To realize the most benefit from this mitigation strategy, it was determined that the maximum depth of the Gaussian-hole should be 0.5 m. The width of the hole in the flow direction and the length of the Gaussian-hole normal to the flow should be 0.5 m and 3 m respectively as measured by the full width at half maximum.
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.
Scour beneath seafloor pipelines, cables, and other offshore infrastructure is a well-known problem. Recent interest in seafloor mounted wave energy converters brings another dynamic element into the traditional seafloor scour problem. In this paper, we consider the M3 Wave APEX device, which utilizes airflow between two flexible chambers to generate electricity from waves. In an initial at-sea deployment of a demonstration/experimental APEX in September 2014 off the coast of Oregon, scour beneath the device was observed. As sediment from the beneath the device was removed by scour, the device's pitch orientation was shifted. This change in pitch orientation caused a degradation in power performance. Characterizing the scour associated with seafloor mounted wave energy conversion devices such as the M3 device is the objective of the present work.
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.
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.
This three-year effort started in FY17 with the primary objective of detailing the environmental compliance costs and lessons learned from U.S. based MHK projects that have gone through the permitting and compliance process. The project goal is to find ways to improve the efficiency and effectiveness of the permitting and compliance process that reduce deployment uncertainties and associated risks/costs; ultimately encouraging investment in MHK projects. The project team is composed of Sandia National Laboratories, H. T. Harvey & Associates, Integral Consulting, and Kearns & West. Step one of the project process, collect data to determine permitting and compliance costs, was a focus during 2017, but is an ongoing process to ensure the project team is working with the most recent and accurate data as possible. Currently, the project team is focusing on step two of the project process, identify cost reduction pathways. Step three, develop cost reduction strategies, will follow during Fall 2018 and Winter 2019. Each step is envisioned as an iterative approach working with industry and regulators to best meet the project goal.
Marine hydrokinetic (MHK) devices generate electricity from the motion of tidal and ocean currents, as well as ocean waves, to provide an additional source of renewable energy available to the United States. These devices are a source of anthropogenic noise in the marine ecosystem and must meet regulatory guidelines that mandate a maximum amount of noise that may be generated. In the absence of measured levels from in situ deployments, a model for predicting the propagation of sound from an array of MHK sources in a real environment is essential. A set of coupled, linearized velocity-pressure equations in the time-domain are derived and presented in this paper, which are an alternative solution to the Helmholtz and wave equation methods traditionally employed. Discretizing these equations on a three-dimensional (3D), finite-difference grid ultimately permits a finite number of complex sources and spatially varying sound speeds, bathymetry, and bed composition. The solution to this system of equations has been parallelized in an acoustic-wave propagation package developed at Sandia National Labs, called Paracousti. This work presents the broadband sound pressure levels from a single source in two-dimensional (2D) ideal and Pekeris wave-guides and in a 3D domain with a sloping boundary. The paper concludes with demonstration of Paracousti for an array of MHK sources in a simple wave-guide.
This three-year effort started in FY17 with the primary objective of detailing the environmental compliance costs and lessons learned from U.S. based MHK projects that have gone through the permitting and compliance process. The project goal is to find ways to improve the efficiency and effectiveness of the permitting and compliance process that reduce deployment uncertainties and associated risks/costs; ultimately encouraging investment in MHK projects. The project team is composed of Sandia National Laboratories, H. T. Harvey & Associates, Integral Consulting, and Kearns & West. Step one of the project process, collect data to determine permitting and compliance costs, was a focus during 2017, but is an ongoing process to ensure the project team is working with the most recent and accurate data as possible. Currently, the project team is focusing on step two of the project process, identify cost reduction pathways. Step three, develop cost reduction strategies, will follow during Fall 2018 and Winter 2019. Each step is envisioned as an iterative approach working with industry and regulators to best meet the project goal.
Developing sound methods to evaluate risk of seabed mobility and alteration of sediment transport patterns in the near-shore coastal regions due to the presence of Offshore Wind (OW) infrastructure is critical to project planning, permitting, and operations. OW systems may include seafloor foundations, cabling, floating structures with gravity anchors, or a combination of several of these systems. Installation of these structures may affect the integrity of the sediment bed, thus affecting seabed dynamics and stability. It is therefore necessary to evaluate hydrodynamics and seabed dynamics and the effects of OW subsea foundations and cables on sediment transport. A methodology is presented here to map a site's sediment (seabed) stability and can in turn support the evaluation of the potential for these processes to affect OW deployments and the local ecology. Sediment stability risk maps are developed for a site offshore of Central Oregon. A combination of geophysical site characterization, metocean analysis, and numerical modeling is used to develop a quantitative assessment of local scour and overall seabed stability. The findings generally show the presence of structures reduces the sediment transport in the lee area of the array by altering current and wave fields. The results illustrate how the overall regional patterns of currents and waves influence local scour near pilings and cables.