Wave energy resource classification system for US coastal waters

• Published in: Renewable & sustainable energy reviews Vol. 104; pp. 54 - 68
• Main Authors: Ahn, Seongho, Haas, Kevin A., Neary, Vincent S.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2019
• Subjects: Annual Available Energy; Peak period partition; Wave energy assessment; Wave power; Wave resource classification; WEC operating period bandwidth; Wave resource classification; Wave power; WEC operating period bandwidth; Peak period partition; Wave energy assessment; Annual Available Energy
• ISSN: 1364-0321; 1879-0690
• DOI: 10.1016/j.rser.2019.01.017

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 (Tp,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. •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.