Wind farm layout optimization under uncertainty with landowners\u27 financial and noise concerns

Current wind farm layout research focuses on advancing optimization methods. The research includes the assumption that a continuous piece of land is readily available. In reality, landowners\u27 decisions and concerns play a crucial role in wind projects, and some land parcels are more important to project success than others. During early farm development stages, developers must model many important factors, such as wind resource, land availability, topography, and etc. These factors are associated with great uncertainties. In this dissertation, three system-level optimization models, which include landowners\u27 concerns and optimization-under-uncertainty formulation, are developed progressively. System Model 1 applies a realistic cost model, including landowner remittances, to determine optimal turbine placement under three landowner participation scenarios and two land-plot shapes. The formulation represents landowner participation scenarios as a binary string variable, along with number of turbines. The optimal Cost-of-Energy results are compared to actual Cost-of-Energy data and found to be realistic. System Model 2 advances Model 1 with an optimization-under-uncertainty formulation. A farm layout is optimized under multiple sources of uncertainty including wind shear and farm cost. Landowner participation is represented as uncertain with a novel model of willingness-to-accept compensation. System Model 3 advances Model 2 by modeling landowners\u27 noise concerns and associated compensation. This uncertain model, together with a noise propagation model is then incorporated into the optimization-under-uncertainty system model. Including uncertain parameters and compensation models leads to a total farm cost estimate that is more accurate than the most current publicly-available model used by the National Renewable Energy Laboratory, which requires the addition of an arbitrary term to match industry-reported Cost-of-Energy data. Additionally, the framework presented here can help developers identify land plots that are worth the extra investment during early farm development. It can provide developers with a robust farm design that is not only profitable but also has minimal noise disturbance for landowners. It can also give landowners an idea of where turbines are likely to be placed, and the likely auditory impacts. This improved transparency-of-information can potentially facilitate the negotiation process between developers and landowners during early farm planning and ultimately improve the success rate of projects