Wind Farm Energy Production Optimization Via Wake Steering

Abstract

The wake steering technique consists in misaligning turbines with respect to the incoming wind with the goal of displacing their wake region and reducing the wake wind speed deficit, thus increasing the power production of other turbines downwind. In this paper, an algorithm is proposed to estimate the amount of production gain a wind farm could achieve by employing this technique. Details about data treatment are provided and an analytical wake model that can represent the wakes displacement is described. A minimization algorithm to calibrate the wake model parameters based on SCADA data is presented, and the complete math needed to solve this problem is developed. Similarly, to find the optimal yaw angle values that maximize the wind farm production, a maximization problem is described along with the full development of the equations behind it. Insights on an efficient computational implementation to solve these problems are shown, based on matrix representation of the described variables. A case study is presented for a small wind farm owned by Voltalia, consisting of five turbines. Results point to an AEP gain in the order of 1%, a relevant value over the course of a project’s lifetime.

Review of wake management techniques for wind turbines

“…Fortunately, and in contrast to AIC, many of the lessons learned with a column of turbines still apply in larger arrays. Both optimization techniques 3,36,102,103,111,114,119,120,128,137,140,145,148–150,157,158,161,163–165 and trial and error comparisons 108,123,164 of different arrangements of yaw misalignment angles have been tested and have arrived at different conclusions. Several studies have found that total power is optimized when the upstream turbines are misaligned the most and the misalignment angle is decreased as turbines are located farther downstream 3,108,123,146,164 .…”

“…Similarly, with wake steering the goal is to balance yawing frequently enough to maintain power maximization while avoiding overuse of the yawing components. One approach to implementing an optimization in a wind farm is to simulate all (or a subset of) the possible conditions and create a lookup table of optimal yaw angles 112,121,130,132,133,145,150 . Campagnolo et al 112 demonstrated the need for good models when generating these tables, and Howland et al 142 showed the strong dependence on atmospheric conditions.…”

“…Most of the literature on wake steering agrees about what happens to the turbines downstream of misaligned turbines, especially in terms of power production. The potential for increased power production is highest when turbines are aligned with the inflow 36,103,150 and have smaller downstream spacing between them, 49,119,130,161 and the ambient turbulence levels are low 40,132,133,161,164 . These are the conditions for the largest gains because the baseline of a wind farm with these conditions produces less power than offset turbines with large spacing and high TI .…”

“…Since wake steering has advanced to the point of multiple field tests, it now runs up against the difficulty of interfacing with proprietary controllers. Similar to AIC, the few studies that have attempted to calculate a true change in AEP including realistic wind data find that increases are in the low single digit percents 105,139,150,156 . In contrast to AIC, reducing the loads on turbines may be at odds with power maximization when using wake steering, though the reductions in loads that wake steering can offer may be in the tens of percents rather than only single percents as estimated for AIC.…”