利用机器学习方法对风力发电厂进行长期技术经济评估

Ali Omidkar, Razieh Es'haghian, Hua Song
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引用次数: 0

摘要

碳氢化合物储量的枯竭和全球变暖的影响对化石燃料的持续使用构成了重大挑战。因此,可再生能源已引起相当大的注意,有些国家目前从这些替代能源中获得其总能源需求的很大一部分。在可再生能源中,风能被认为是最容易获得和最清洁的能源之一。然而,对风力发电厂进行技术和经济评估势在必行。这包括计算与化石能源相比的能源平化成本,并预测长期的最小和最大能源输出。实现这一目标需要对特定地点的风速进行长期预测,这涉及复杂的数学建模和计算,通常由超级计算机执行。在这项研究中,一个数据驱动的机器学习模型被用来预测卡尔加里25年期间的风速,而CPU时间最短。在电厂的整个运行周期内,利用最优模型计算年发电量。基于模型精度指标,CNN-LSTM混合模型显示出更高的精度。因此,该厂生产能源的平均成本计算为每千瓦时0.09美元,这在加拿大电力市场上具有竞争力。投资在大约六年的时间里达到了盈亏平衡点,这是可以接受的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Using machine learning methods for long-term technical and economic evaluation of wind power plants

Using machine learning methods for long-term technical and economic evaluation of wind power plants
The depletion of hydrocarbon reserves and the impact of global warming have posed significant challenges to the continued use of fossil fuels. Consequently, renewable energy sources have garnered substantial attention, with some countries now deriving a significant portion of their total energy needs from these alternatives. Among renewable sources, wind energy has been recognized as one of the most accessible and clean. However, it is imperative to evaluate wind power plants both technically and economically. This involves calculating the levelized cost of energy in comparison to fossil-based energy sources and predicting the minimum and maximum energy output over the long term. Achieving this requires long-term forecasts of wind speeds at specific locations, which involve complex mathematical modeling and computations typically performed by supercomputers. In this study, a data-driven machine learning model has been employed to predict wind speeds in Calgary over a 25-year period with minimal CPU time. Throughout the power plant's operational life, the optimal model was also used to calculate the annual energy production. The hybrid CNN-LSTM model demonstrated superior accuracy based on model accuracy metrics. Consequently, the levelized cost of energy produced by the plant was calculated at $0.09 per kWh, which is competitive within the Canadian electricity market. The investment reached a breakeven point in approximately six years, which is deemed acceptable.
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