Genetic algorithm selection of the weather research and forecasting model physics to support wind and solar energy integration
Genetic algorithm selection of the weather research and forecasting model physics to support wind and solar energy integration
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DOI：
10.1016/j.energy.2022.124367
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发表时间：
2022-05
期刊：
影响因子：
9
通讯作者：
J. Sward;T. Ault;K.M. Zhang
中科院分区：
文献类型：
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作者：
J. Sward;T. Ault;K.M. Zhang
为了使可再生能源的未来成为可能，我们必须设计我们的电力系统，使其能够无缝地收集、储存和运输地球上自然产生的能量流——即太阳能和风能。这样的未来将要求风能和太阳能资源及其相关可变性的准确表示渗透到电力系统规划和操作工具中。实际上，我们必须合并天气和电力系统建模。尽管许多影响风能和太阳能发电的气象现象在孤立的情况下得到了很好的研究，但没有协调一致的努力寻求利用数值天气预报（NWP）模型改善中长期电力系统规划。一个现代的开源NWP工具——天气研究和预报（WRF）模型——提供了将天气预报与任何地区的电力系统模型集成所需的复杂性和灵活性。然而，有超过一百万种不同的方式来建立WRF。在这里，我们提出了一种利用遗传算法优化WRF模型物理来预测风力密度和太阳辐照度的方法。我们的算法创建的前五种设置优于所有推荐的设置。利用模拟结果，我们训练了一个随机森林模型来识别哪些WRF参数对预测误差最小，并绘制了描绘关键物理选项性能的图，以指导能源研究人员快速建立准确的WRF模型。
To make a future run by renewable energy possible, we must design our power system to seamlessly collect, store, and transport the Earth's naturally occurring flows of energy – namely the sun and the wind. Such a future will require that accurate representations of wind and solar resources and their associated variability permeate power systems planning and operational tools. Practically speaking, we must merge weather and power systems modeling. Although many meteorological phenomena that affect wind and solar power production are well-studied in isolation, no coordinated effort has sought to improve medium- and long-term power systems planning using numerical weather prediction (NWP) models. One modern open-source NWP tool – the weather research and forecasting (WRF) model – offers the complexity and flexibility required to integrate weather prediction with a power systems model in any region. However, there are over one million distinct ways to set up WRF. Here, we present a methodology for optimizing the WRF model physics for forecasting wind power density and solar irradiance using a genetic algorithm. The top five setups created by our algorithm outperform all of the recommended setups. Using the simulation results, we train a random forest model to identify which WRF parameters contribute to the lowest forecast errors and produce plots depicting the performance of key physics options to guide energy researchers in quickly setting up an accurate WRF model.