极品飞车快速风电场优化

Maria Sarcos, Julian Quick, A. Hahmann, Nicolas G. Alonso-De-Linaje, Neil Davis, M. Friis-Møller
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引用次数: 0

摘要

我后院的风"(WIMBY)项目正在开发一个网络界面,以帮助社区为风能项目选址。作为选址工具的一部分,用户可以在一定的限制条件下,为欧洲的任何拟议选址找到逼真的风电场布局。在设计该工具时,对速度提出了要求:必须在适合网络界面的计算时间内设计出逼真的布局。在这项研究中,我们比较了两种优化算法:一种是基于梯度的算法,即随机梯度下降算法(SGD),另一种是无梯度的方法,即智能启动算法(smart-start)。以丹麦的一个地点为例,通过参数扫描分析了最佳能源产出和优化计算时间之间的权衡。该分析考虑了拥有 10、25 和 50 台涡轮机的发电场。我们发现,在计算时间很短的情况下,智能启动法能获得最佳结果,而在计算时间较长的情况下,SGD 布局能获得更高的发电量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Need For Speed: Fast Wind Farm Optimization
The Wind in my Backyard (WIMBY) project is developing a web interface to aid communities in siting wind energy projects. As part of this siting tool, users will be able to find realistic wind farm layouts for any proposed site in Europe, given certain constraints. When designing this tool, there arises a need for speed: realistic layouts must be designed in computational times that are appropriate for a web interface. In this study, we compare two optimization algorithms: a gradient-based algorithm, referred to as stochastic gradient descent (SGD), and a gradient-free method, referred to as smart-start. The trade-offs between the optimal energy yield and optimization computational time are characterized via a parameter sweep, considering a site in Denmark. This analysis considered farms with 10, 25, and 50 turbines. We find that smart-start yielded the best results for very short computational times, and that SGD yielded layouts with higher energy yields when considering larger computational times.
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CiteScore
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