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.

目前可再生能源的主要投资集中在商业成熟的风能和太阳能。然而，尽管经过了四十多年的不断发展，波浪能还没有大规模的商业应用。先前的研究表明，波浪能可以供应世界电力消耗的很大一部分。因此，激励波浪能的利用至关重要。将波浪能与风能相结合的混合能源农场被认为是推动成熟的波浪能并网的最可行的解决方案之一。然而，结合风和浪需要为资源确定足够的位置，并开发能够处理波浪尾流复杂性的布局优化算法。由于每次波浪尾流迭代所需的计算过程极其耗时，因此波浪尾流分析一直是开发递归波场布局优化算法的最大障碍之一。本研究提出了一种新方法，即在递归布局优化算法的实际执行之前对波尾进行预处理。这种提出的预处理波浪尾流模型可以与不同的优化算法相结合，以确定混合波浪风电场的最佳布局。新方法在墨西哥湾的两个选定地点进行了测试，其中包含超过 36 年（1979-2015）的历史气象数据。它确定了能够维持商业上可行的风能和波浪能水平的位置，同时避免极端海洋条件带来的风险，这些条件过去已经损坏或摧毁了波浪能转换器。尽管这两个位置的气象条件不同，但新方法能够识别出在两个位置都具有良好效果的布局。结果表明，选定的位置可以通过波浪风混合能源农场产生非常好的功率输出，并且大多数波浪能和风能设备产生的容量系数值高于商业阈值限制。