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DOE OSTI2021

Aeroacoustic Assessment of Wind Plant Controls

The Aeroacoustic Assessment project aims to quantify changes in aeroacoustic noise generation by a utility scale wind turbine operating under imposed yaw offsets common for wake steering and wind plant control strategies. Active plant control utilizing wake deflection control strategies have been shown experimentally and computationally to reliably produce 1-2% of additional annual energy production (AEP) without significant changes in turbine structural loads, the potential impact on aeroacoustic emissions has yet to be quantified or completely understood. Yawed operation of a wind turbine changes the three-dimensional aerodynamic interaction between the rotor blades and the incoming atmospheric flow, leading to changes in noise generation. This work quantifies the extent to which active control induces additional aeroacoustic emissions from additional separation and other flow interaction dynamic effects. Given public concerns about wind turbine noise and the need for observational data required for regulators to establish noise restrictions, we must understand potential acoustic emissions resulting from active control prior to commercial deployment and the development of practical noise reduction methods and technologies. The work outlined in this report details the new aeroacoustic measurement capability developed at NREL and its application to making full-field observations of noise from a utility-scale wind turbine. Preliminary noise modeling with the aeroacoustics module in OpenFAST helped to establish the design of the experiment, specifically targeting the noise observed at observers distributed around the DOE-owned GE 1.5 MW wind turbine. Low-frequency and infrasound measurement capability implemented in this project establish an operational baseline for the wind turbine, setting the stage for future control, rotor blade, and turbine design research.

17 WIND ENERGY↗

DOE OSTI2021

Machine learning enables national assessment of wind plant controls with implications for land use

Summary As deployment of wind energy continues to expand, computationally efficient tools for predicting wind plant performance over a wide range of layout designs, technology innovations, and spatial locations are increasingly important for policy and investment decisions. We demonstrate two approaches to training a surrogate model to predict annual energy production (AEP) of parameterized wind plant layouts: one using a Gaussian process (GP) and the other using a fully convolutional neural network (FCNN). We leverage the powerful FCNN architecture by encoding wind plant design parameters and output response surface as an image. The FCNN produces more accurate results than the GP with mean absolute errors equivalent to 1% and 1.9% of plant rated power, respectively, although the GP performs well under limited training data and provides useful uncertainty information. We also evaluate a surrogate model for wake steering, enabling a nationwide assessment of the impact of plant control strategies and plant layout decisions. Across two million locations, we find that wake steering strategies boost AEP with relative gains upwards of 3%. Gains are most pronounced at sites without a dominant wind direction and where layout optimization is less fruitful. Additionally, we perform a nationwide sensitivity analysis showing that wake steering can mitigate wake losses from higher density plant layouts. Our results suggest that regions which have not been previously viable for wind deployment due to moderate wind resources are especially well enhanced by wake steering strategies that could help overcome land constraints and inflexible layout options, potentially identifying new deployment opportunities.

17 WIND ENERGY↗

DOE OSTI2021

Demonstration of Wake Steering Through Yaw Control in a Wind Plant Field Experiment: Cooperative Research and Development Final Report, CRADA Number CRD-16-00629

Over the last few decades, wind energy has evolved into a large international industry involving major players in the manufacturing, construction, and utility sectors. Coinciding with the industry’s growth, significant innovation in the technology has resulted in larger turbines with lower associated costs of energy and more complex designs in all subsystems. However, as the deployment of the technology has grown and its role within the electricity sector become more prominent, so have the expectations of the technology in terms of performance, reliability, and cost. The industry currently partitions its efforts into separate paths for turbine design, plant design and development, finance, grid interaction and operation, mitigation of adverse community and environmental impacts, and other areas. One prominent area where this partition is evident is in wind turbine control. Traditionally, each wind turbine in a wind plant has been controlled separately – via its own internal controller using only its own sensors. However, wind turbines in a plant interact with each other through the plant-level fluid dynamics. Wake losses (due to upstream turbines extracting energy from the winds and “waking” downstream turbines) can be up to 10% or even 20% of the gross energy production (if each turbine experienced the free stream wind inflow to the plant). A series of studies and experiments have demonstrated that there is potential for improving energy output at existing plants through plant control methods which seek to optimize total wind plant energy production over the current “greedy” approach where each turbine maximizes its own production. Wake steering induced by yaw offsets (turning the turbine to be out of the plane perpendicular to wind inflow) for upstream turbines has shown significant promise in simulations and wind tunnel experiments. In simulation studies, annual energy production has been shown to increase by 2% or more depending on the particular aspects of the wind plant (turbine spacing, meteorological conditions, etc). This project seeks to demonstrate the potential of plant-level controls via wake steering at a commercial wind plant. This is an important step towards commercialization and industry adoption of this plant-level modeling and analysis capability.

17 WIND ENERGY↗

DOE OSTI2023

Enabling control co-design of the next generation of wind power plants

Abstract. Layout design and wake steering through wind plant control are important and complex components in the design and operation of modern wind power plants. They are currently optimized separately, but with more and more computational and experimental studies demonstrating the gains possible through wake steering, there is a growing need from industry and regulating bodies to combine the layout and control optimization in a co-design process. However, combining these two optimization problems is currently infeasible due to the excessive number of design variables and large solution space. In this article, we present a method that enables the coupled optimization of wind power plant layout and wake steering with no additional computational expense than a traditional layout optimization. We developed a geometric relationship between wind turbines to find an approximate optimal yaw angle, bypassing the need for either a nested or coupled wind plant control optimization. It also provides a significant and immediate improvement to wind power plant design by enabling the co-design of turbine layout and yaw control for wake steering. A small co-designed plant shown in this article produces 0.8 % more energy than its sequentially designed counterpart. This additional energy production comes with no additional infrastructure, turbine hardware, or control software; it is simply the outcome of optimizing the turbine layout and yaw control together, resulting in millions of dollars of additional revenue for the wind power plants of the future.

17 WIND ENERGY↗

DOE OSTI2022

Blade planform design optimization to enhance turbine wake control

Abstract This study considers optimizing the planform of wind turbine blades to ultimately enhance wind plant controls, namely, wake steering strategies. Adjoint‐enabled unsteady actuator line simulations are carried out to obtain gradients for optimization of several different performance objectives with respect to blade chord length at 10 locations along blade span. We demonstrate different blade design optimizations that can maximize time‐averaged lateral wake deflection, entrainment of kinetic energy, or total power of multiple turbines. Our optimized designs can produce a 4+% increase in wake deflection, a 4× increase in vertical kinetic energy entrainment, or a 3.6% increase in power when compared with the baseline case. While lateral wake deflection is only modestly sensitive to chord changes, we find that increasing the outboard chord length can dramatically increase kinetic energy entrainment, resulting in faster wake recovery and gains in net power. While this work develops only a few case studies emphasizing relative performance improvements and general trends, these results show the promise of a framework that combines mid‐fidelity computation with adjoint‐based optimization for control and design problems.

WIND ENERGY↗

DOE OSTI2021

Data-Driven Adaptive Damping Controller for Wind Power Plants with Doubly-Fed Induction Generators

This paper presents an adaptive damping controller for wind power plants in which the turbines are equipped with doubly-fed induction generators. The controller is designed to respond to an input control signal that is triggered according to the system operating conditions. A processing unit continuously estimates the electromechanical modes of oscillation based on real-time streaming data acquired from a phasor measurement unit that is strategically positioned on the grid. The decision to trigger (or not trigger) the control signal is automatic, based on the relative damping of the dominant mode. The modes are estimated using the dynamic mode decomposition algorithm with time-delay embedding. Numerical simulations performed on the two-area system demonstrate that the proposed controller enhances the rotor angle stability for both small-signal and large disturbances, and is adaptive to changing grid conditions.

damping controller↗

DOE OSTI2023

Coordinated Wind Power Plant and Battery Control for Active Power Services

This article considers joint active power control of wind turbines and battery storage to follow a plant-level power reference signal. The joint control dynamically curtails the energy from a subset of the wind turbines and stores or withdraws energy from the battery to meet the power reference setpoint while accounting for wind plant aerodynamic interactions, such as wake losses. As a use case, we study the performance of the controller in maintaining a constant power output over hourly periods. A wind plant operating in this way would rely much less on other grid resources to meet its contractual agreements, thereby improving grid reliability, especially in grids with high penetration of wind and solar generation. We compare the operation of the wind plant under joint active power control to standard power-maximizing control with battery support. We present an analysis of the performance of the control system architecture. To study the impact of the battery size on performance, we simulate a 50-MW wind plant supported by batteries ranging from 8 to 64?MWh. We then evaluate the over and undergeneration penalties incurred by the plant during the simulation period.

17 WIND ENERGY↗

DOE OSTI2021

Data-Driven Adaptive Damping Controller for Wind Power Plants with Doubly-Fed Induction Generators: Preprint

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2022

Acoustic Travel-Time Tomography for Wind Energy

New remote sensing technologies for direct observation of the atmospheric flow in the proximity of operating wind turbines are necessary for the advancement of wind turbine design, wind plant control, and the validation of high-fidelity numerical models; fundamental goals of the DOE Wind Energy Technology Office's (WETO's) A2e program. Modern remote sensing technologies for commercial and research applications rely on the backscatter of light or sound waves, which limits spatial and temporal resolution and cannot produce reliable flow estimates near solid bodies or reflective surfaces. Acoustic tomography (AT) is an innovative sensing technology based on fundamental thermodynamic properties of the atmosphere and an inverse algorithm that can reconstruct both velocity and temperature fields within a network of acoustic transducers. AT relies on the direct travel path of acoustic signals between speakers and microphones in the network and can achieve much higher resolutions than conventional backscatter technologies. For wind energy R&D applications, AT represents an extremely economical option over other remote sensing technologies, integrating mature and commonly available acoustic and signal processing hardware. This report is intended as an update on the development of the AT array on the NREL Flatirons Campus (FC) and includes a brief review of the instrumentation, the calibration of equipment, signal processing, and resulting flow field estimates. Designs for future AT systems and applications are detailed in this report, including summaries of new capabilities, engineering and theoretical challenges, and anticipated costs help to outline future directions for AT technology R\&D. The potential for application of AT to utility-scale turbines is also discussed. Investing in the development of the proposed instrument will establish AT as a transformational RS technique that represents a significant advance in measurement instrumentation, enabling new fundamental insights into the structure of the atmospheric boundary layer and supporting applications for wind energy, pollution transport, and resource characterization. AT is also an extremely economical option over other RS instruments, integrating mature and commonly available acoustic and signal processing hardware.

17 WIND ENERGY↗

DOE OSTI2024

Wind plant wake losses: Disconnect between turbine actuation and control of plant wakes with engineering wake models

Wake losses from neighboring plants may become a major factor in wind plant design and control as additional plants are constructed in areas with high wind resource availability. Because plant wakes span a large range of physical scales, from turbine rotor diameter to tens of kilometers, it is unclear whether conventional wake models or turbine control strategies are effective at the plant scale. Wake steering and axial induction control are evaluated in the current work as means of reducing the impact of neighboring wind plants on power and levelized cost of electricity. FLOw Redirection and Induction in Steady State (FLORIS) simulations were performed with the Gauss–Curl Hybrid and TurbOPark wake models as well as two operation and maintenance models to investigate control setpoint sensitivity to wake representation and economic factors. Both wake models estimate losses across a range of atmospheric conditions, although the wake loss magnitude is dependent on the wake model. Annual energy production and levelized cost of electricity are driven by wind direction frequency, with frequently aligned plants experiencing the greatest losses. However, both wake steering and axial induction are unable to mitigate the impact of upstream plants. Wake steering is constrained by plant geometry, since wake displacement is much less than the plant wake width, while axial induction requires curtailing the majority of turbines in upstream plants. Individual turbine strategies are limited by their effective scale and model representation. New wake models that include plant-scale physics are needed to facilitate the design of effective plant wake control strategies.

Scott, Ryan (ORCID:0000000328107574)↗

DOE OSTI2021

Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind

Here, we present a proof-of-concept distributed reinforcement learning framework for wind farm energy capture maximization. The algorithm we propose uses Q-Learning in a wake-delayed wind farm environment and considers time-varying, though not yet fully turbulent, wind inflow conditions. These algorithm modifications are used to create the Gradient Approximation with Reinforcement Learning and Incremental Comparison (GARLIC) framework for optimizing wind farm energy capture in time-varying conditions, which is then compared to the FLOw Redirection and Induction in Steady State (FLORIS) static lookup table wind farm controller baseline.

17 WIND ENERGY↗

DOE OSTI2022

Accelerated Wind-Turbine Wake Recovery Through Actuation of the Tip-Vortex Instability

Advances in wind-plant control have often focused on more effectively balancing power between neighboring turbines. Wake steering is one such method that provides control-based improvements in a quasi-static way, but this does little to fundamentally change the wake recovery process, and thus, it has limited potential. This study investigates use of another control paradigm known as dynamic wake control (DWC) to excite the mutual inductance instability between adjacent tip-vortex structures, thereby accelerating the breakdown of the structures. The current work carries this approach beyond the hypothetical by applying the excitation via turbine control vectors that already exist on all modern wind turbines: blade pitch and rotor speed control. The investigation leverages a free-vortex wake method (FVWM) that allows a thorough exploration of relevant frequencies and amplitudes of harmonic forcing for each control vector (as well as the phase difference between the vectors for a tandem configuration) while still capturing the essential tip-vortex dynamics. The FVWM output feeds into a Fourier stability analysis working to pinpoint candidate DWC strategies suggesting fastest wake recovery. Near-wake length reductions of >80% are demonstrated, although without considering inflow turbulence. Analysis is provided to interpret these predictions considering the presence of turbulence in a real atmospheric inflow.

17 WIND ENERGY↗

DOE OSTI2022

Design of the American Wake Experiment (AWAKEN) field campaign

The American WAKE experimeNt (AWAKEN) is a multi-institutional collaborative field campaign, starting in March 2022, that will gather an unprecedented data set including both atmospheric observations and wind plant operational data. This comprehensive data set will be used to characterize the wind plant performance and turbine loading in different operational and atmospheric conditions and validate the use of different wind plant control strategies and simulation frameworks. An extensive field campaign like AWAKEN requires proper coordination and long-term planning to be successful. In this paper, we review the major activities planned during AWAKEN to provide information for current and future project partners. Specifically, we provide information about the project sites, their planned instruments, and how these will relate to the scientific objectives of the overall AWAKEN project.

17 WIND ENERGY↗

DOE OSTI2025

WHOC (Wind Hybrid Open Controller) [SWR-25-54]

The Wind Hybrid Open Controller (WHOC) is a python-based tool for real-time plant-level wind farm control and wind-based hybrid plant control. WHOC is primarily run in simulation, although we intend that it could be used for physical plants in future. WHOC provides simple farm-level (and hybrid plant-level) controls such as wake steering control, spatial filtering/consensus, active power control, and coordinated control of hybrid power plant assets; and creates an entry point for the development of more advanced controllers.

Sinner, Michael (Misha) [National Renewable Energy↗

DOE OSTI2023

A2E2G (Atmosphere to Electrons to the Grid platform) [SWR-23-22]

A2E2G is a platform that integrates 1) forecasting tools to account for weather uncertainty, with 2) aerodynamic wind plant models to account for wake dynamics and wind plant operation, and 3) economic models to advise on operation for a wind power plant that offers grid services in addition to energy. The A2E2G platform can be used as a high-level controller for a wind plant for market participation and real-time wind plant control. The A2E2g platform is a holistic Python tool with modules that can be run to 1) advise on market participation and 2) control and operate a wind power plant in real time. The A2E2g framework assumes two stages: the first stage is in day-ahead and the second stage is in real-time. Managing uncertainty is key in the first stage and managing variability is key in the second stage. The different components have models written and developed in the Python programming language. The code is assembled into a Python package and can be easily downloaded and installed from the A2E2g repository (https://github.com/NREL/a2e2g).

Sinner, Michael↗

DOE OSTI2021

Evaluation of the potential for wake steering for U.S. land-based wind power plants

The wind power plant-wide control strategy known as wake steering involves the misalignment of upstream turbines with the wind direction to deflect wakes away from downstream turbines, increasing net wind plant power production. In this paper, we evaluate the potential of wake steering for U.S. land-based wind power plants. First, we outline a method for simulating and optimizing wake steering control for existing wind plants by combining the flow redirection and induction in steady state wake steering engineering model with the U.S. Wind Turbine Database and Wind Integration National Dataset Toolkit wind resource dataset. Next, to better understand the potential benefits of wake steering beyond those for existing wind plants, we evaluate the relative impacts of turbine specific power, turbine spacing, and mean wind speed on energy gain and levelized cost of energy (LCOE) using a model land-based wind power plant. For a subset of 60 existing wind plants, assuming a constant turbulence intensity of 8%, wake steering was found to yield an average annual energy production (AEP) gain of 0.80%, equivalent to recovering 13.85% of baseline wake losses. Further, we present a linear approximation between baseline wake losses and AEP gains that can be used to estimate wake steering gains for other wind power plants. Highlighting additional benefits of wake steering, for the model wind power plant we found that energy gains from wake steering enabled an approximate 30% reduction in turbine spacing while keeping LCOE constant.

17 WIND ENERGY↗

DOE OSTI2024

Grid Integration of Offshore Wind Power: Standards, Control, Power Quality and Transmission

Offshore wind is expected to be a major player in the global efforts toward decarbonization, leading to exceptional changes in modern power systems. Understanding the impacts and capabilities of the relatively new and uniquely positioned assets in grids with high integration levels of inverter-based resources, however, is lacking, raising concerns about grid reliability, stability, power quality, and resilience, with the absence of updated grid codes to guide the massive deployment of offshore wind. To help fill the gap, this paper presents an overview of the state-of-the-art technologies of offshore wind power grid integration. First, the paper investigates the most current grid requirements for wind power plant integration, based on a harmonized European Network of Transmission System Operators (ENTSO-E) framework and notable international standards, and it illuminates future directions. The paper discusses the wind turbine and wind power plant control strategies, and new control approaches, such as grid-forming control, are presented in detail. The paper reviews recent research on the ancillary services that offshore wind power plants can potentially provide, which, when harmonized, will not only comply with regulations but also improve the value of the asset. The paper explores topics of wind power plant harmonics, reviewing the latest standards in detail and outlining mitigation methods. The paper also presents stability analysis methods for wind power plants, with discussions centered on validity and computational efficiency. Finally, the paper discusses wind power plant transmission solutions, with a focus on high-voltage direct-current topologies and controls.

17 WIND ENERGY↗

DOE OSTI2021

Progress on Optimizing Wind Farms and Rotor Designs Using Adjoints

Modern wind plants are increasingly tasked with multiple performance objectives. In addition to designing plants that maximize power output and minimize the levelized cost of energy (LCOE), the design and operation of wind plants is increasingly influenced by challenges regarding grid integration of variable generation renewables. This places a growing emphasis on making wind plants more controllable and predictable. WindSE is a Reynolds-averaged Navier-Stokes (RANS) model designed around analytical gradient and adjoint methods, with the ability to capture terrain-induced effects, as shown in Figure 1. The recent addition of an unsteady solver with an actuator line method (ALM) and ongoing work to enable massively parallel optimizations gives it a unique niche to explore coupled plant-level controls and design problems. This code is an open source python package built on the FEniCS framework that utilizes fast, parallel PETSc solvers to model fluid flow throughout wind-farm scale domains. Two recent studies performed using WindSE demonstrate the capability to optimize under a wide variety of flow conditions and objective functions. In the first, we present an optimization focused on modifying the layout of a wind farm with a fixed number of turbines for maximum total power output [1]. This study highlights the ability to quickly perform simulations using the steady Navier-Stokes solver combined with rotors represented as actuator disks while also stressing the importance of capturing terrain-induced effects. Gradient-based optimization using the RANS equations is viable due to the inclusion of efficiently computed adjoint derivatives. We interpret the physical results of the optimal layout and also discuss the computational cost of scaling to larger problems. In the second study, we present the capabilities of the unsteady Navier-Stokes solver, where rotor-blade profiles represented by actuator lines are optimized to enhance wake steering effects and overall power production [2]. We quantify the wind plant performance gains obtained from this type of simultaneous control co-design optimization as compared to optimizing the blade design and yaw independently. Figure 2 shows the differences between a baseline two-turbine system and an optimized system where we fine-tune the blade chord profile. Results and challenges from each study are quickly summarized and used to motivate the current development efforts within WindSE. Current and future work is focused on enabling higher-resolution studies with more degrees of freedom through parallelization of both the simulation and optimization algorithms. We present benchmarking results to show that WindSE performs well in both weak- and strong-scaling tests and further demonstrate that the optimizer obtains the same convergence rates in both shared- and distributed-memory environments. Using larger wind farms, we can study deep-array effects within an optimization context, allowing the use of objective functions that have been previously unstudied. As an example, we present ongoing work on a blockage metric which characterizes the loss of available kinetic energy due to wake effects from multiple upstream turbines.

adjoint optimization↗