Refining technical wave energy resource assessment through data assimilation

초록

Wave energy is a promising renewable resource, but its reliable assessment remains challenging due to uncertainties in wave model hindcasts. Data assimilation plays a crucial role in improving wave model accuracy, and its success has motivated interest in enhancing accuracy and reliability of wave energy resource assessment. The present study provides compelling evidence that incorporating data assimilation techniques leads to substantial advancements in wave energy resource assessments. The assessments from assimilated and non-assimilated hindcasts are compared by estimating sea states, electrical power production, and capacity factors of typical wave energy converters (WECs) and by identifying their resource hotspots. Through a case study on the Korean Peninsula with one-year WaveWatch III hindcasts, the study reveals that the assimilation shifts the distribution of sea state occurrences closer to observations and results in more reliable estimates of electrical power production and capacity factors for typical WECs. The assimilated hindcast increases estimates of electrical power production by up to 50 % with increasing capacity factors in some coastal regions, e.g., Jeju Island and mid-east coast of the mainland, emerging as technical resource hotspots. The assimilation shifts wave occurrences from low-energy to moderate-energy sea states, resulting in more favorable wave conditions for WEC operations. These findings demonstrate the necessity of improving regional wave hindcasts and resource assessments through the data assimilation, to investment decisions and planning, ultimately enhancing the feasibility of wave energy development.

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