A layout optimization method based on wave wake preprocessing concept for wave-wind hybrid energy farms
A layout optimization method based on wave wake preprocessing concept for wave-wind hybrid energy farms
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DOI：
10.1016/j.enconman.2021.114469
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发表时间：
2021-09
影响因子：
10.4
通讯作者：
Francisco Haces-Fernandez;Hua Li;David Ramirez
中科院分区：
文献类型：
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作者：
Francisco Haces-Fernandez;Hua Li;David Ramirez
目前可再生能源的主要投资集中在商业上成熟的风能和太阳能。然而，尽管经过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，波浪能可以提供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。将波浪能和风能结合起来的混合能源农场被认为是促进波浪能成熟并网的最可行的解决方案之一。然而，结合风和波需要确定足够的位置，为资源和布局优化算法的发展能够处理复杂的波尾。波浪尾流分析一直是递归波浪场布局优化算法开发的最大障碍之一，因为每次波浪尾流迭代都需要非常耗时的计算过程。本研究提出了一种新的方法，通过预处理波尾流的递归布局优化算法的实际执行之前。该预处理后的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-风力发电场的最佳布局。新方法在墨西哥湾的两个选定地点进行了测试，使用了超过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地点，同时避免了过去损坏或摧毁波浪能转换器的极端海洋条件的风险。虽然这两个地点的气象条件不同，但新方法能够确定两个地点的布局，并取得有希望的结果。结果表明，所选的位置可以产生很好的功率输出与波-风混合能源农场，和大多数波和风能设备产生的容量因子的值高于商业阈值限制。
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.