Optimization performance analysis and validation of the FLOW Estimation and Rose Superposition (FLOWERS) model

A common objective of a wind plant layout optimization study is to maximize total annual energy production (AEP). AEP is typically calculated as a numerical integral of a wind farm's power production across discrete wind speed-direction bins, each of which requires a separate simulation. The FLOW Estimation and Rose Superposition (FLOWERS) model estimates the annually-averaged wake velocity flow field by taking an analytical integral of a wake deficit model across every wind direction. This new approach is well-suited to the layout optimization problem, as a more efficient method to calculate AEP can yield substantial savings in computational time over potentially thousands of model evaluations. We explore further proof-of-concept of the FLOWERS model in this work. First, we conduct a comprehensive comparison of optimization performance and cost between FLOWERS and the conventional layout optimization framework. Second, we validate the FLOWERS estimates of average wake velocity compared with large eddy simulation predictions.