A layout optimization method based on wave wake preprocessing concept for wave-wind hybrid energy farms

Abstract Major investment of renewable energy currently focuses on wind and solar, which are commercially mature. However, there is no large commercial application of wave energy, despite more than four decades of continuous development. Previous research has indicated that wave energy could supply a significant portion of world electricity consumption. Therefore, it is critical to incentivize the utilization of wave energy. The hybrid energy farms, combining wave energy with wind energy, have been considered as one of the most viable solutions to promote mature grid integration of wave energy. However, combining wind and wave requires the identification of adequate locations for both resources and development of layout optimization algorithms capable of handling the complexity of wave wakes. Wave wake analysis has been one of the biggest hurdles for the development of recursive wave farm layout optimization algorithms due to the required extremely time consuming computation processes for each wave wake iteration. This research proposes a new approach by preprocessing the wave wakes beforehand the actual execution of the recursive layout optimization algorithm. This proposed preprocessed wave wake model can be integrated with the different optimization algorithms to identify optimal layouts for hybrid wave-wind farms. The new approach was tested in two selected locations in the Gulf of Mexico with over 36 years (1979–2015) of historical meteorological data. It identifies locations capable of sustaining commercially viable levels of wind and wave energy while simultaneously avoiding risk from extreme oceanic conditions that in the past have damaged or destroyed wave energy converters. Although the two locations have different meteorological conditions, the new approach was able to identify layouts with promising results in both locations. Results indicated that the selected locations could produce very good power output with a wave-wind hybrid energy farm, and most wave and wind energy devices generated capacity factor with values higher than commercial threshold limits.

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