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Abstract Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J , kW/m) and its distribution with peak period ( T p , s). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs. Highlights • Classification parameters discriminate distinct trends in US wave climates. • Key attributes classified for energy planning and project development. • Key attributes classified for design and operation of WEC technologies. [ABSTRACT FROM AUTHOR]

Abstract: The wave power potential along the southeast Atlantic coast of the United States of America bounded by latitudes 27° N and 38° N and longitudes 82° W and 72° W (i.e. North Carolina, South Carolina, Georgia, and northern Florida) is investigated. The available data from National Data Buoy Center wave stations in the given area are examined. Temporal trends of the wave heights, wave periods and the wave power are analyzed for a time scale of weeks. The time series from the wave stations are downsampled with a 15-day moving average filter with near 50% overlapping to study the seasonal trends. Power calculated from hourly significant wave heights and average wave periods is compared to power calculated using spectral wave density. It is found that a factor of 0.61 needs to be applied to the wave power calculated from hourly significant wave heights and average periods in order to get the same results with the power calculated from spectral wave density. The mean power within 50km of the shore is determined to be ∼9kW/m, whereas higher power (∼15kW/m) is available further offshore. [Copyright &y& Elsevier]

• Wave energy for United States coastal waters is assessed using WaveWatchIII hindcast. • New wave energy resource attribute parameters are proposed. • Frequency, directional, and temporal variability of the wave energy are characterized. • The resource attributes corroborate with distinct regional wave climate features. The wave energy resource is characterized and assessed for coastal waters of the United States to evaluate regional opportunities and constraints for wave energy converter (WEC) projects. Partitioned wave parameters generated from a 30-year WaveWatch III model hindcast are used to estimate the total wave energy potential as an annual available energy (AAE), which is a theoretical annual energy production per unit energy capture length without considering energy conversion losses. The distribution of AAE by peak period, wave direction, month, and year is quantified using summary statistics, including peak period spread, AAE -weighted period, AAE -directionality coefficient, and inter-annual and seasonal temporal variability. Geographical distributions of the AAE and these five resource attributes delineate distinct wave energy resource regions within United States coastal waters, where different opportunities and constraints may influence regional energy planning, WEC project development, and WEC conceptual design. [ABSTRACT FROM AUTHOR]

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. 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. • Novel Global WERCS is developed considering three levels of physical WEC constraints. • Total, frequency-, and frequency-directionally constrained wave power classifications are developed. • Dominant peak period bands containing the most wave energy content are delineated. • Most global coastal wave sites fall into just 22 resource classes. • KEYWORDS: Wave energy resource classification, Global wave energy resource, Global wave climate. [ABSTRACT FROM AUTHOR]