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 natural microgrids. Hence, accurate wave predictions to manage the interactions of a WEC array with microgrids is especially important. Recently developed, low-cost wave measurement buoys allow for operational assimilation of wave data at remote, site specific locations where real-time data have previously been unavailable. We present the development and assessment of a wave modeling framework with real-time data assimilation capabilities for WEC power prediction. The availability of real-time wave spectra from low-cost wave measurement buoys allows for operational data assimilation with the ensemble Kalman filter technique within a hybrid modeling procedure whereby physics-based numerical wave models are combined with data-driven error models that aim to capture the discrepancy in prescribed boundary conditions. With that aim, measured wave spectra are assimilated for combined state and parameter estimation while taking into account model and observational errors. The analysis allows for more accurate and precise wave characteristic predictions at the locations of interest. Initial deployment data obtained offshore Yakutat, Alaska, indicated that measured wave data from one buoy that were assimilated into the wave modeling framework resulted in improved forecast skill in comparison to traditional numerical forecasts.

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.

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 natural microgrids. Hence, accurate wave predictions to manage the interactions of a WEC array with microgrids is especially important. Recently developed, low-cost wave measurement buoys allow for operational assimilation 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 data assimilation capabilities for WEC power prediction. The availability of real-time wave spectral components from low-cost wave measurement buoys allows for operational data assimilation with the Ensemble Kalman filter technique, whereby measured wave conditions within the numerical wave forecast model domain are assimilated onto the combined set of internal and boundary grid points while taking into account model and observation error covariances. The updated model state and boundary conditions allow for more accurate wave characteristic predictions at the locations of interest. Initial deployment data indicated that measured wave data from one buoy that were assimilated into the wave modeling framework resulted in improved forecast skill for a case where a traditional numerical forecast 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 assimilation modeling with real-time wave observations.

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.

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.

This document summarizes existing Marine and Hydrokinetic (MHK) performance metrics known to the United States Department of Energy and national laboratories. This document was updated based on feedback from the MHK Energy community, however, this summary still may not be exhaustive. There are a wide variety of needs and uses for metrics. All stakeholders, such as developers, funding agencies, investors, and researchers, have a need for metrics and their many uses. It is evident that the sector will benefit from clear techno economic performance metrics to guide development towards success. There are international efforts underway to bring the community together to (1) understand what metrics/approaches are being used currently and (2) reach a global framework on the approach to the measurement of success. This document serves to list existing metrics known to the U.S. at the present, and is not meant to represent international efforts or consensus.

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.

据估计,美国沿海地区的波浪能资源大约为1200万亿瓦时/年(或1.2万亿千瓦时/年) (美国电力研究协会(EPRI),2011),规模相当于天然气和煤炭能源发电量的总和.虽然从商业角度看,波浪能产业相对较新,但波浪能转换(WEC)技术正在以越来越快的速度发展.要加大波浪能转换阵列部署的商业规模,需要证明与其他能源发电方式相比波浪能转换技术在经济成本方面具有竞争力.国际电工委员会(IEC)为波浪能资源评估和波浪能资源表征提供了技术规范,但最终还是要由开发商来为波浪能发电装置竞争力的提升创造途径.本次研究通过采用不同的波浪能转换策略对几处沿岸地点的波浪能年发电能力进行了评估,并探索了能够使波浪能转换装置具有较强竞争力的部署方式.此外,还对美国海岸沿线各地点的波浪能资源进行了评估,并通过波浪能建模研究和波浪能资源基础性评估分析了影响沿岸各处波浪能资源开发成本的因素.通过本次研究不仅加强了对波浪能资源的认识,同时还提升了对波浪能转换装置性能的评估水平,从而能够更好地对波浪能转换装置商业规模部署的效果进行评价.

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.

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.

Hindcasts from third generation (3G) phaseaveraged wave models are commonly used to estimate standard statistics for energy resource assessment and characterization. It is, therefore, important to investigate wave models and best modeling practices to evaluate and improve hindcast accuracy. We present results from hindcast simulations using two of the most widely used 3G phase-averaged wave models, WaveWatchIII (WWIII)(Tolman et al. 2010, 2014) and Simulating WAves Nearshore (SWAN)(The SWAN Team 2015). Results from the WWIII ST2 and ST4 physics packages are compared to investigate new source term formulations within the ST4 package, which, unlike its ST2 counterpart, models swell dissipation effects, and includes different growth and wave dissipation formulations. Results from the stationary (SWANS) and nonstationary (SWAN-NS) modes of the SWAN model are also compared to evaluate benefits of modeling the unsteady term in the spectral wave action balance equation.

It is expected that wave energy technologies will play a future role in providing clean renewable energy and diversifying energy portfolios; however, they are still at an early stage of development compared to other renewables, with varying archetypes proposed. As technologies advance toward commercialization, benchmarking is needed to quantify performance and costs. In this review, experimental datasets of Wave Energy Converter (WEC) devices tested in the final stage of the Wave Energy Prize (WEPrize) are compared and ranked using performance metrics found in the literature and those developed as WEPrize judging metrics at both U.S. and European representative wave climates. Because the WEPrize devices were tested under a set of identical sea states, which ranged from typical operating conditions to extreme storm events, consistent datasets were produced to facilitate comparison. This allows for a rare addition to the open literature on device performance trends. In addition, a reevaluation of trends established in previous power performance benchmarking studies is given. Trends found in previous studies were confirmed, except for the absorbed energy per characteristic mass metric, in which some of the WEPrize devices had higher values. Each of the metrics considered in this study has limitations due to the assumptions in simplifying the economic potential (e.g., power absorbed vs. a proxy to cost). In addition, each of these proxies is limited to the capital cost of a device, unlike the final metric used in the WEPrize, HPQ, which includes limited proxies of operational and capital expenditures, as well as array considerations. Recommendations are given for the use and potential modification of the metrics considered. Specifically, it is recommended that the ACE metric (from the WEPrize) be modified to more accurately include the other important system costs, such as the PTO and mooring, as well as installation, operation and maintenance costs.

The U.S. Department of Energy's Wave Energy Prize Competition encouraged the development of innovative deep-water wave energy conversion technologies that at least doubled device performance above the 2014 state of the art. Because levelized cost of energy (LCOE) metrics are challenging to apply equitably to new technologies where significant uncertainty exists in design and operation, the prize technical team developed a reduced metric as proxy for LCOE, which provides an equitable comparison of low technology readiness level wave energy converter (WEC) concepts. The metric is called 'ACE' which is short for the ratio of the average climate capture width to the characteristic capital expenditure. The methodology and application of the ACE metric used to evaluate the performance of the technologies that competed in the Wave Energy Prize are explained in this report.

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.