Improved Energy Production Estimates from Wave Energy Converters through Spectral Partitioning of Wave Conditions

As global need for electrical energy increases and mandated carbon emission reductions come into play, renewable source of power generation are increasingly viable and securing increasingly significant portions of the electrical market share. As the wave energy conversion industry matures, the need to provide accurate predictions of power production become increasingly important. In this novel study, a spectral partitioning algorithm is implemented to reduce uncertainty in the gross resource assessment for the West Coast of Canada. Through direct knowledge of individual wave systems, the spectral partitioning method eliminates the need for assumptions of single peakedness and the use of the numerical wave energy period. Utilizing detailed knowledge of the significant wave heights and peak periods for each wave system, an annual power production error bias of 7.59 kW from standard single peaked methods is determined and the annual energy production estimates are reduced by 16.1%.