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Optimized Site Selection for New Wind Farm Installations Based on Portfolio Theory and Geographical Information

机译:基于投资组合理论和地理信息的新风电场安装优化的网站选择

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An automated process for selecting sites for new wind farm installations is proposed. The region of interest is divided into a 1-km-square mesh, and geographical data such as altitude and wind speed are used to sort the mesh cells into regions that are feasible for wind farm installations. Before grouping the meshes, feasible meshes for constructing wind farms are extracted using a set of constraints. We tested two different constraints for grouping the feasible areas, either by maximizing the annual mean wind speed or by minimizing the covariance between the power outputs of each cell in the group. The first strategy is more attractive if the goal is to meet an expected level of power output each year, while the second strategy is intended to supply the most-stable power. Portfolio theory was then applied to the evaluate efficient-frontier curves of the two site-selection results from the mean and variance of the total expected power outputs. The analysis showed that grouping unit areas to maximize average wind speed most effectively suppresses variance in the expected output of an installation, and efficiently distributes the optimum wind farm locations.
机译:提出了一种为新风电场设施选择网站的自动化过程。感兴趣的区域被分成1公里的网格,并且使用高度和风速等地理数据来将网状电池对风电场安装可行的区域。在分组网状物之前,使用一组约束来提取用于构建风电场的可行网格。我们测试了两个不同的约束,用于分组可行区域,通过最大化年平​​均风速或最小化组中每个单元的电力输出之间的协方差。第一个策略如果目标是每年达到预期的电力产出水平,而第二次策略旨在提供最稳定的权力。然后将投资组合理论应用于两个站点选择的评估高效 - 前沿曲线来自总预期功率输出的平均值和方差。分析表明,分组单元区域最大化平均风速最大化最有效地抑制了安装的预期输出中的方差,有效地分配了最佳的风电场位置。

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