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A system-level cost-of-energy wind farm layout optimization with landowner modeling

机译：土地所有者建模的系统级能源成本风电场布局优化

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This work applies an enhanced levelized wind farm cost model, including landowner remittance fees, to determine optimal turbine placements under three landowner participation scenarios and two land-plot shapes. Instead of assuming a continuous piece of land is available for the wind farm construction, as in most layout optimizations, the problem formulation represents landowner participation scenarios as a binary string variable, along with the number of turbines. The cost parameters and model are a combination of models from the National Renewable Energy Laboratory (NREL), Lawrence Berkeley National Laboratory, and Windustry. The system-level cost-of-energy (COE) optimization model is also tested under two land-plot shapes: equally-sized square land plots and unequal rectangle land plots. The optimal COEs results are compared to actual COE data and found to be realistic. The results show that landowner remittances account for approximately 10% of farm operating costs across all cases. Irregular land-plot shapes are easily handled by the model. We find that larger land plots do not necessarily receive higher remittance fees. The model can help site developers identify the most crucial land plots for project success and the optimal positions of turbines, with realistic estimates of costs and profitability.
机译：这项工作应用了包括土地所有者的汇款费用在内的增强的分层风电场成本模型，以确定在三种土地所有者参与方案和两种土地情节形状下的最佳涡轮机布置。与大多数布局优化中一样，与其假定连续的土地可用于风力发电场建设，问题公式还用二进制字符串变量以及涡轮机数量将土地所有者的参与方案表示为二进制字符串变量。成本参数和模型是国家可再生能源实验室（NREL），劳伦斯伯克利国家实验室和Windustry的模型的组合。系统级别的能源成本（COE）优化模型也在两个地形图上进行了测试：大小相等的正方形土地图和不相等的矩形土地图。将最佳COE结果与实际COE数据进行比较，发现它是现实的。结果表明，在所有情况下，土地所有者的汇款约占农场运营成本的10％。该模型可以轻松处理不规则的地形图形状。我们发现较大的地块不一定会收取更高的汇款费用。该模型可以帮助站点开发人员确定对项目成功最关键的地块，以及涡轮机的最佳位置，并对成本和盈利能力进行实际估算。

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