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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设施的最佳选址

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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:提出了一种用于选择新风电场安装地点的自动化过程。感兴趣的区域被划分为一个1平方公里的网格,并使用诸如高度和风速之类的地理数据将网格单元分类为适合风电场安装的区域。在对网格进行分组之前,使用一组约束提取用于构建风电场的可行网格。我们通过最大化年平​​均风速或通过最小化组中每个单元的功率输出之间的协方差来测试将可行区域分组的两个不同约束。如果目标是每年达到预期的输出功率水平,则第一种策略更具吸引力,而第二种策略旨在提供最稳定的功率。然后将投资组合理论应用于根据总预期功率输出的均值和方差来评估两个选址结果的有效边界曲线。分析表明,对单位面积进行分组以最大程度地提高平均风速,可以最有效地抑制设备预期输出的差异,并有效地分配最佳风电场位置。

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