Efforts to streamline and codify wave energy resource characterization and assessment for regional energy planning and wave energy converter (WEC) project development have motivated the recent development of resource classification systems. Given the unique interplay between WEC absorption and resource attributes, viz, available wave power frequency, directionality, and seasonality, various consensus resource classification metrics have been introduced. However, the main international standards body for the wave energy industry has not reached consensus on a wave energy resource classification system designed with clear goals to facilitate resource assessment, regional energy planning, project site selection, project feasibility studies, and selection of WEC concepts or archetypes that are most suitable for a given wave energy climate. In this work, a primary consideration of wave energy generation is the available energy that can be captured by WECs with different resonant frequency and directional bandwidths. Therefore, the proposed classification system considers combinations of three different wave power classifications: the total wave power, the frequency-constrained wave power, and the frequency-directionally constrained wave power. The dominant wave period bands containing the most wave power are sub-classification parameters that provide useful information for designing frequency and directionally constrained WECs. The bulk of the global wave energy resource is divided into just 22 resource classes representing distinct wave energy climates that could serve as a common language and reference framework for wave energy resource assessment if codified within international standards.
While a great deal of research has been performed to quantify and characterize the wave energy resource, there are still open questions about how a wave energy developer should use this wave resource information to design a wave energy converter device to suit a specific environment or, alternatively, to assess potential deployment locations. It is natural to focus first on the impressive magnitudes of power available from ocean waves, and to be drawn to locations where mean power levels are highest. However, a number of additional factors such as intermittency and capacity factor may be influential in determining economic viability of a wave energy converter, and should therefore be considered at the resource level, so that these factors can influence device design decisions. This study examines a set of wave resource metrics aimed towards this end of bettering accounting for variability in wave energy converter design. The results show distinct regional trends that may factor into project siting and wave energy converter design. Although a definitive solution for the optimal size of a wave energy converter is beyond the reaches of this study, the evidence presented does support the idea that smaller devices with lower power ratings may merit closer consideration.
Joint and marginal distributions in the frequency, direction, and time domain are employed to demonstrate their value for wave energy resource characterization when full spectra are available. Insights gained through analysis of these distributions support wave energy converter concept design, operation and maintenance. Spatial trends in the wave energy resource and contributing wave energy systems along the continental shelf of the West Coast of the United States are investigated using the most recent two-dimensional wave spectra measurements at four buoys over an eleven year period (2008 to 2018). Resource hot spots and dominant resolved energy resource bands in the frequency-direction-time domain are delineated. Resource attributes, including frequency and directional spreading, and seasonal variability, are characterized using joint distributions and marginal distributions of wave power spectra. North Pacific westerly swells in the winter season, augmented by Aleutian low-pressure southwesterly swells, are the principal suppliers of the dominant resource and main drivers influencing resource attributes. The modification of these systems southward, especially the North Pacific westerly swells, explains the observed spatial resource trends. The dominant resource wave period shifts two seconds to higher wave periods, thirty degrees in the dominant direction band to a more northward orientation, and forward by one month.
The thirty-year non-stationary historical trends in the wave energy climate for United States coastal waters between 1980 and 2009 are investigated using spectral partitioned wave data generated from a WaveWatch III® (version 5.05) hindcast. In addition to historical trends in the omni-directional wave power, frequency and directionally resolved wave power, frequency and directional spreading, and seasonal variability, are examined for the first time, including their geographical distribution. These historical wave energy climate trends are linked to changes to the dominant wave systems and commensurate trends in the historical wind climate. Total wave power trends are consistent with other studies, but the present study identifies regions, and specific frequency and direction bands, where significant wave energy climate changes have. For some regions, changes to wave climate parameters exceeded one-percent annually, more than thirty-percent over the study period. Non-stationary trends of this magnitude have significant implications for ocean and coastal engineering projects designed assuming wave climates are stationary and warrant their consideration in design practices.
While some engineering fields have benefited from systematic design optimization studies, wave energy converters have yet to successfully incorporate such analyses into practical engineering workflows. The current iterative approach to wave energy converter design leads to sub-optimal solutions. This short paper presents an open-source MATLAB toolbox for performing design optimization studies on wave energy converters where power take-off behavior and realistic constraints can be easily included. This tool incorporates an adaptable control co-design approach, in that a constrained optimal controller is used to simulate device dynamics and populate an arbitrary objective function of the user’s choosing. A brief explanation of the tool’s structure and underlying theory is presented. To demonstrate the capabilities of the tool, verify its functionality, and begin to explore some basic wave energy converter design relationships, three conceptual case studies are presented. In particular, the importance of considering (and constraining) the magnitudes of device motion and forces in design optimization is shown.
The marine and hydrokinetic (MHK) industry is at an early stage of development and has the potential to play a significant role in diversifying the U.S. energy portfolio and reducing the U.S. carbon footprint. Wave energy is the largest among all the U.S. MHK energy resources, which include wave energy, ocean current, tidal-instream, ocean thermal energy conversion, and river-instream. Wave resource characterization is an essential step for regional wave energy assessments, Wave Energy Converter (WEC) project development, site selection and WEC design. The present paper provides an overview of a joint modelling effort by the Pacific Northwest National Laboratory and Sandia National Laboratories on high-resolution wave hindcasts to support the U.S. Department of Energy’s Water Power Technologies Office’s program of wave resource characterization, assessment and classifications in all US coastal regions. Topics covered include the modelling approach, model input requirements, model validation strategies, high performance computing resource requirements, model outputs and data management strategies. Examples of model setup and validation for different regions are provided along with application to development of classification systems, and analysis of regional wave climates. Lessons learned and technical challenges of the long-term, high-resolution regional wave hindcast are discussed.
Opportunities and constraints for wave energy conversion technologies and projects are evaluated by identifying and characterizing the dominant wave energy systems for United States (US) coastal waters using marginal and joint distributions of the wave energy in terms of the peak period, wave direction, and month. These distributions are computed using partitioned wave parameters generated from a 30 year WaveWatch III model hindcast, and regionally averaged to identify the dominant wave systems contributing to the total annual available energy (AAE) for eleven distinct US wave energy climate regions. These dominant wave systems are linked to the wind systems driving their generation and propagation. In addition, conditional resource parameters characterizing peak period spread, directional spread, and seasonal variability, which consider dependencies of the peak period, direction, and month, are introduced to augment characterization methods recommended by international standards. These conditional resource parameters reveal information that supports project planning, conceptual design, and operation and maintenance. The present study shows that wave energy resources for the United States are dominated by long-period North Pacific swells (Alaska, West Coast, Hawaii), short-period trade winds and nor'easter swells (East Coast, Puerto Rico), and wind seas (Gulf of Mexico). Seasonality, peak period spread, and directional spread of these dominant wave systems are characterized to assess regional opportunities and constraints for wave energy conversion technologies targeting the dominant wave systems.