David Hennecke

We address the Wind Farm Layout Optimization (WFLO) problem and tackle the optimal placement of several turbines within a specific (wind farm) area by incorporating additional aspects of an economically driven target function. With this, we contribute three refinements for WFLO research: First, while many research contributions optimize the turbines' locations subject to maximum energy production or energy efficiency, we instead pursue a strategy of maximizing a profit objective. This enables us to incorporate inner-farm wiring costs (underground cable installation). For this, we explore the impact of using MSTs (Minimum Spanning Trees) and adding junction (so-called "Steiner") points to the terrain plane. Second, while most research focuses on finding optimal x and y coordinates (i.e., address two-dimensional turbine placement), we also optimize the turbines' hub heights z. Third, we also provide a software implementation of the Gaussian wake model. The latter finds entrance to the open-source WFLO research framework that comes as package wflo for statistical software R. We find that taking wiring cost into account may lead to very different turbine placements, however, increasing overall profit significantly. Allowing the optimizer to vary the hub heights may have an ambiguous impact on the wind farm profit.

Before a new wind farm can be built, politics and regional planning must approve of the respective area as a suitable site. For this purpose, large-scale potential computations were carried out to identify suitable areas. The calculation of wind power plant potential usually focuses on capturing the highest energy potential. In Germany, due to an energy production reimbursement factor defined in the Renewable Energy Sources Act (“Erneuerbare-Energien-Gesetz”, EEG) in 2017, the influence of energy quantities on the power plant potential varies, economically and spatially. Therefore, in addition to the calculation of energy potentials, it was also necessary to perform a potential analysis in terms of economic efficiency. This allows, on the one hand, an economic review of the areas tendered by the regional planning and, on the other hand, a spatial-economic analysis that expands the parameters in the search for new areas. In this work, (a) potentials with regard to the levelized cost of electricity (LCOE) were calculated by the example of the electricity market in Germany, which were then (b) spatially and statistically processed on the level of the federal states.

In this study, we address the wind farm layout optimization (WFLO) problem and tackle the issue of optimal turbine placement by incorporating additional aspects of an economically driven target function. Firstly, we have analyzed the effect of wind direction on a given turbine arrangement. Based on the direction-dependent wake pattern, practitioners sometimes shut down certain turbines on their farms. Our method computes which turbines should be shut down in which wake situation. On this basis, we have developed a method of finding new turbine setups that rarely require shutdowns and are, in a certain sense, "robust" against changes to the wind direction. Secondly, we have presented a partial coverage Jensen wake model in three-dimensional space and have provided the tools for reducing or avoiding wake-induced asymmetric thrust on the rotor disc of the turbine, which leads to reduced energy yield and accelerated wear. This aspect can also be used for finding new turbine setups that take partial coverage into account and avoid it if necessary. Overall, the application of the refinements suggested in this study will result in an increased yearly profit achieved from the produced energy in a wind farm. This is an aspect that decision-makers, such as farm planners/operators, might depend on in a market that typically possesses narrow profit margins. Our methods find entrance into the open-source research framework that comes as the package wflo for the statistical software R.