A comparison of deterministic refinement techniques for wind farm layout optimization

Abstract

With over 200 wind farm projects underway in 33 states in America, now more than ever, innovation and precision are necessary in wind farm design. The Wind Farm Layout Optimization Problem (WFLOP) calls for optimally positioning turbines within a wind farm so that a particular objective function is optimized in the presence of wake effect. To make the WFLOP tractable, many solution methods begin by modeling the wind farm as a n x n square grid where the centers of each of the n2 cells serve as potential locations for wind turbines. After making this modeling assumption, there are 2n2 potential layouts to consider, and so heuristic algorithms are often employed in order to search the solution space and generate a near-optimal layout. In this paper, we propose a local, continuous refinement technique that seeks to improve the layouts generated by these heuristic algorithms. In particular, we consider the objective function Levelized Cost of Energy (LCOE) and capture wake using the simple, yet effective, Jensen Model. Given the anemometer data for two potential wind farm sites, we begin by generating initial layouts using a specific heuristic algorithm (the Distributed Genetic Algorithm) and then refine these layouts using our proposed continuous, deterministic refinement scheme. We compare the performance of this deterministic refinement technique to another deterministic refinement technique in the space: a Heuristic Hill-climbing approach. Results suggest that our refinement technique outperforms the compared refinement technique by generating layouts that increase overall energy production, consequently reducing the LCOE. To our knowledge, this is the first paper to compare refinement techniques in wind farm layout optimization, and in doing so, we employ a simulation framework that examines the energy production of the generated wind farm layouts for 10 min intervals.