Greedy Wind Farm Layout Optimization Using Pre-Averaged Losses

Abstract

Wind turbine placement in a wind farm can be optimized to limit power losses due to wakes and improve the economic value of the plant. Seeing as wind farms are increasing in number and size, fast methods to generate good quality layouts can be beneficial for designers. Wind Farm Layout Optimization (WFLO) consists in finding a layout that maximizes the Annual Energy Production (AEP) of a wind farm. The procedure is driven by an algorithm that generates possible layouts and by a framework that evaluates their AEP. If the traditional method to assess AEP is adopted, layout optimization is computationally demanding due to the impact of each turbine's position on the productivity of the surrounding ones. Indeed, for any change in the layout, this inter-dependency forces designers to calculate wake effects and the wind resource-average energy production of the wind farm. This thesis proposes an approach to reduce the computational load of WFLO by pre-computing the power losses. Indeed, the approach avoids recalculating the expected power loss among turbines during the optimization procedure. This optimization strategy employs a novel approach, called Pre-Averaged Model (PAM), that expresses the expected power loss of a wake source at representative points around it. Firstly, the wind farm is discretized, and fictitious turbines are placed at each given spot. Secondly, PAM calculates the expected power loss caused by each fictitious turbine for the surrounding ones. Discontinuities introduced by wind resource discretization and top-hat wake deficit profiles affect PAM's accuracy substantially. Binning wind measurements in 72 wind directions solves the problem for typical engineering wake models. Then, a greedy algorithm uses the power losses of the fictitious turbines to build layouts constructively by adding an extra turbine per iteration. The effect of multiple wake sources on a wake target is modelled by linear superimposition of the pre-computed power losses. PAM is tested in combination with three greedy algorithms, namely, Basic Greedy (BG), Add-Remove-Move Greedy (ADREMOG), and ADREMOG II. This research demonstrates that the PAM and the superposition of the power losses can be reliably used for WFLO. Also, the joint use of PAM and greedy algorithms achieve an interesting trade-off between speed and quality of the layouts. Indeed, PAM is beneficial as it speeds up greedy algorithms. Furthermore, greedy algorithms allow generating better layouts at the cost of slowing down the algorithm. The balance between speed and quality can be regulated by using a finer discretization, testing different locations for the first turbine placement, or acting on the nature of the algorithm. In particular, the use of a re-location stage at each constructive iteration increases the quality of the layouts substantially but reduces the speed of execution. As a result, the proposed algorithms present different characteristics: the BG is the fastest but its median layout is the worst; ADREMOG produces the best layouts in the longest time; ADREMOG II is a compromise between the two former algorithms.