The value of wake steering wind farm control in U.S. energy markets

Abstract

Abstract. Wind farm flow control represents a category of control strategies for increasing wind plant power production and/or reducing structural loads by mitigating the impact of wake interactions between wind turbines. Wake steering is a wind farm flow control technology in which specific turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thus increasing overall wind plant power production. In addition to promising results from simulation studies, wake steering has been shown to successfully increase energy production through several recent field trials. However, to better understand the benefits of wind farm flow control strategies such as wake steering, the value of the additional energy to the electrical grid should be evaluated—for example, by considering the price of electricity when the additional energy is produced. In this study, we investigate the potential for wake steering to increase the value of wind plant energy production by combining model predictions of power gains using the FLOw Redirection and Induction in Steady State (FLORIS) engineering wind farm control tool with historical electricity price data for 15 existing U.S. wind plants in four different electricity market regions. Specifically, for each wind plant, we use FLORIS to estimate power gains from wake steering for a time series of hourly wind speeds and wind directions spanning the years 2018–2020, obtained from the ERA5 reanalysis data set. The modeled power gains are then correlated with hourly electricity prices for the nearest transmission node. Through this process we find that wake steering increases annual energy production (AEP) between 0.5 % and 2 %, depending on the wind plant, with average increases in potential annual revenue (i.e., annual value production (AVP)) 10 % higher than the AEP gains. For all wind plants, AVP gain was found to exceed AEP gain. But the ratio between AVP gain and AEP gain is greater for wind plants in regions with high wind penetration because electricity prices tend to be relatively higher during periods with below-rated wind plant power production, when wake losses occur and wake steering is active; for wind plants in the Southwest Power Pool—the region with the highest wind penetration analyzed (31 %)—the increase in AVP from wake steering is 21 % higher than the AEP gain. Consequently, we expect the value of wake steering, and other types of wind farm flow control, to increase as wind penetration continues to grow.

The value of wake steering wind farm flow control in US energy markets

“…The hourly time series of wind speed, wind direction, and FLORIS-modeled wind plant power used to compute the AEP results in this paper are available at https://doi.org/10.5281/zenodo.10493111 (Simley et al, 2024). The hourly electricity price data used to determine the ARP results -obtained from the commercial Hitachi Energy Velocity Suite product -are proprietary and cannot be shared publicly.…”

Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities

“…To address these challenges, wind farm control has multiple objectives that have to be considered simultaneously [1,2]. Among others, these include being able to adjust the wind farm power output accurately according to the grid demands providing flexibility, reducing the power production losses due to wake interactions, reducing the operational costs, extending the lifetime of the machines as much as possible [3][4][5], as well as maintaining profitability for the operators to maintain the financial attractiveness of projects [5][6][7]. In order to leverage the potential of digitalization and optimization towards these goals, an important aspect is to have models that can predict quickly and accurately enough the response of the entire turbine including power production and structural loads, for various operating modes and wind conditions replacing the computationally expensive aeroelastic simulations.…”

Stochastic gradient descent for wind farm optimization

“…This study benchmarks the performance of the proposed SGD approach when compared to conventional gradientbased optimization within the TOPFARM framework (DTU Wind Energy Systems, 2023b), considering wind farms with different shapes and sizes. We examine the open-source SLSQP algorithm (Kraft, 1988), which is employed in many engineering frameworks (King et al, 2017;Allen et al, 2020;Wu et al, 2020;Zilong and Wei, 2022;Kölle et al, 2022;Clark et al, 2022;Simley et al, 2023) and has been used in previous comparisons of optimization algorithms (Lam et al, 2018;Li and Zhang, 2021;Fleming et al, 2022). The TOPFARM framework has been used with SLSQP in several wind farm optimization studies (Riva et al, 2020;Ciavarra et al, 2022;Criado Risco et al, 2023;Rodrigues et al, 2023).…”