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At least 163 records · Page 9

Wind power production from very large offshore wind farms

In this work, we provide the first quantitative assessment of power production and wake generation from offshore wind energy lease areas along the U.S. east coast. Deploying 15-MW wind turbines, with spacing equal to the European average, yields electricity production of 116 TWh/year or 3% of current national supply. However, power production is reduced by one-third due to wakes caused by upwind wind turbines and wind farms. Under some flow conditions whole wind-farm wakes can extend up to 90 km downwind of the largest lease areas, and the frequency-weighted average area with a 5% velocity deficit is 2.6 times the footprint of the lease areas. Simulations including maritime corridors demonstrate reduction in the wake effects leading to power-efficiency gains and may offer contingent benefits. First-order scaling rules are developed that describe how “wake shadows” from large offshore wind farms scale with prevailing meteorology and wind turbine installed densities.

15 MW↗

Predicting wind farm operations with machine learning and the P2D‐RANS model: A case study for an AWAKEN site

Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models.

17 WIND ENERGY↗

Identification of Climatological Representative Days in the Mid-Atlantic for High-Fidelity Offshore Wind Energy Modeling

The goal of reaching 30 GW of offshore wind energy by 2030 becomes more realistic with the continued approval of offshore wind energy areas by the Biden Administration. In the Mid-Atlantic, where wind energy projects are in the most advanced stages of development, there is increased research focus on the eventual interaction of these wind farms. These interactions, in the form of wakes and cluster wakes, or wakes from multiple wind farms, could have detrimental effects on power production and forecastability for downwind wind farms (Pryor et al. 2022, Golbazi et al. 2022, Rosencrans et al. 2023). To help alleviate these issues, numerical simulations in the form of numerical weather prediction (NWP) and large eddy simulations (LES) can provide insight into when cluster wake situations may occur, but running such simulations can be expensive and difficult to run for multiple years. In this study, we leverage and build upon existing techniques in the literature (Fischereit et al. 2022) to identify climatologically representative days for wind energy areas in the Mid-Atlantic where conditions would promote cluster wake situations. We select meteorological variables (wind speed, wind direction, atmospheric stability, boundary-layer height, TKE) critical to understanding wind energy production and wake propagation. We then consider two different NWP datasets of varying spatial and temporal resolution: ERA5 provides data at hourly intervals from 1940 to present at 0.25 deg (31 km) spatial resolution (Hersbach et al. 2020), and the NOW-23 dataset provides data at 5-minute resolution for 21 years at 2-km spatial resolution (Bodini et al. 2020). Our first step is to compare these two datasets for an overlapping 21-year time period. Initial results show that the required number of days to represent the long-term climate increases with each additional variable considered. In their study of the German Bight, Fischereit et al. (2022) found that they could represent the long-term wind and wave climate in a "near-perfect" way with -180 days, by reaching a Perkins Skill Score (PSS) of 0.9; our investigation of the mid-Atlantic wind resource region with ERA5 and NOW-23 data suggests that we will need -100 days to reach a PSS of 0.9. As we expand our parameter space to include multiple variables, the number of required days will likely grow. These results will ultimately be used to select case studies to best represent cluster wake conditions that apply to this region for the lifetime of likely wind farms in this mid-Atlantic region.

clusterwakes↗

Predicting and Managing Risk to Bats at Commercial Wind Farms using Acoustics

Bat populations in North America face novel threats from white-nose syndrome and widespread turbine-related mortality related to the rapidly expanding wind power industry in addition to long-standing pressures from habitat loss and degradation. Bats, unlike most small mammals, are long-lived and slow to reproduce, highlighting the importance of understanding and managing anthropogenic sources of mortality. My dissertation research used acoustic bat detectors to measure bat activity at commercial wind projects, predict patterns in risk, and design strategic measures to reduce fatality rates by curtailing turbine operation during periods when bats are most active. Bats collide with wind turbines only when their rotors are spinning, and risk of turbine-related fatality is therefore a dynamic factor that can be manipulated by curtailing turbine operation when bats are active. We first measured inter-detector variation in metrics of acoustic bat activity to understand how the acoustic detection process may affect inferences related to spatial and temporal variation in bat activity. Using acoustic detectors mounted on top of wind turbines at two commercial wind farms in West Virginia, we then demonstrated that the amount of bat activity recorded when turbines were operating aligned closely with bat fatality rates on multiple scales. Accordingly, the metric of bat activity exposed to turbine operation provides a meaningful, quantitative indicator of turbine-related bat fatality risk. Further, bats responded consistently to changing wind speed and temperature at turbines in both wind farms across multiple years, enabling exposed bat activity to be predicted accurately among turbines and years. Building on these results, we simulated exposure of bats to turbine operation and energy loss for curtailment strategies recommended by state and federal agencies in the United States and Canada. By adjusting parameters such as cut-in wind speeds and temperature thresholds, we demonstrated the ability to design strategic curtailment programs that achieve equivalent or greater predicted reductions in bat activity exposure for substantially less energy-production loss. Characterizing fatality risk on a finer scale using acoustics will help regulatory agencies and the wind industry alike reduce risks of population-level impacts to vulnerable bat species while continuing to expand large-scale renewable energy generation.

17 WIND ENERGY↗

Advanced Turbulence Models for Large-Scale Atmospheric Boundary Layer Flows

We present high-fidelity large-eddy-simulation (LES) modeling approaches for the turbulent atmospheric boundary layer (ABL) flows. Wind energy is a prime example of an application driven by ABL. Generation of electrical energy from farms of wind turbines at night in the stable ABL is a particularly interesting situation. In this report, we consider the well-known GEWEX (Global Energy and Water Cycle Experiment) Atmospheric Boundary Layer Study (GABLS) stably stratified benchmark LES case. We use a high-order spectral element code Nek5000/RS, which is supported under the DOE's Exascale Computing Project (ECP) Center for Efficient Exascale Discretizations (CEED) project, targeting application simulations on various acceleration-device based exascale computing platforms. In our earlier ANL report, we demonstrated our newly developed subgrid-scale (SGS) models based on high-pass filter (HPF), mean-field eddy viscosity (MFEV), and Smagorinsky (SMG) with no-slip and traction boundary conditions, provided with low-order statistics, convergence and turbulent structure analysis. In this report, we extend the range of our SGS modeling approaches in the context of the mean-field eddy viscosity (MFEV), to include the solution of an SGS turbulent kinetic energy equation (TKE). We demonstrate the model fidelity of Nek5000/RS in comparison to that of AMR-Wind, a block-structured second-order finite-volume code with adaptive-mesh-refinement capabilities, with which we studied scaling performance for both codes in comparison on DOE's leadership computing platforms.

17 WIND ENERGY↗

Wind farm blockage effects: comparison of different engineering models

The work presents four engineering methods to estimate the induction zone in front of a wind turbine and account for the wind farm blockage effect. The methods comprise the vortex cylinder model, vortex dipole model, self-similar model, and wake projection model. The majority of the models presented account for yaw misalignments and ground effect. Actuator disk simulations are used to verify the individual models. The performance of each model is evaluated both in terms of precision and computational time. The induction models are coupled to wake models within the FLOw Redirection and Induction in Steady State framework to provide the full velocity field within a wind farm. Sample wind farm computations are presented, and the impact of including induction effects into wind farm performance predictions is reported. The different codes are publicly available online.

17 WIND ENERGY↗

Observations of wind farm wake recovery at an operating wind farm

Abstract. The interplay of momentum surrounding wind farms significantly influences wake recovery, affecting the speed at which wakes return to their freestream velocities. Under stable atmospheric conditions, wind farm wakes can extend over considerable distances, leading to sustained vertical momentum flux downstream, with variations observed throughout the diurnal cycle. Particularly in regions such as the US Great Plains, stable conditions can induce low-level jets (LLJs), impacting wind farm performance and power output. This study examines the implications of wake recovery using long-term observations of vertical momentum flux profiles across diverse atmospheric conditions. In these observations, several key findings were observed, such as (a) LLJ heights being altered downstream of a wind farm, especially when the LLJs are below 250 m above ground level; (b) a notable impact of LLJ height on wake recovery being observed using momentum flux profiles at upwind and downwind locations, wherein LLJs between 250 and 500 m above ground level resulted in larger momentum transfer within the wake (i.e., smaller velocity deficit) compared to LLJs below 250 m above ground level; (c) the largest momentum flux variability being observed during stable atmospheric conditions, with non-negligible variability observed during neutral and unstable atmospheric conditions; (d) detection of wake effects almost always being observed throughout the atmospheric boundary layer height; and finally (e) enhancement of wake recovery being observed in the presence of propagating gravity waves. These insights deepen our understanding of the intricate dynamics governing wake recovery in wind farms, advancing efforts to model and predict their behavior across varying atmospheric contexts. In addition, the performance of large-eddy-simulation-based semi-empirical internal boundary layer height model estimates incorporating real-world atmospheric and turbine inputs was evaluated using observations during LLJ conditions.

17 WIND ENERGY↗

Hourly gap-filled meteorological data from PIE LTER measurements (2004-2023) used as drivers to run ELM PFLOTRAN simulations

This dataset contains continuous gap-filled precipitation, solar radiation, photosynthetically active radiation (PAR), air temperature, relative humidity, wind speed, and barometric pressure data recorded primarily at the Marshview Farm weather station within the Plum Island Long Term Ecosystems Research (PIE LTER) in Newbury Massachusetts (MA) from 2004 to 2023. We compiled the data set from published annual data packages in 15min resolution available on DataOne. Gaps were filled using different statistical techniques or available observations from the vicinity, e.g. the US-PLo and the US-PHM Ameriflux sites, also located within the PIE LTER. Flags are included in this dataset to indicate the origin of each data point. Metadata files ELMPFLOTRAN_met_dd.csv and ELMPFLOTRAN_met_flmd.csv contain more information on site locations, gap filling protocols, data variables, flags, and QA/QC methods. The data set was used in the spin up and simulations of a land surface model coupled to a biogeochemical reaction network (ELM PFLOTRAN) assessing impacts of hydrology and salinity input on methane fluxes in 2022 and 2023 (Sulman et al., 2024).

54 ENVIRONMENTAL SCIENCES↗

Efficient wind farm layout optimization with the FLOWERS AEP model and analytic gradients

Wind farm layout optimization (WFLO) studies often aim to maximize the annual energy production (AEP) of a wind farm by choosing an arrangement of turbines that minimizes wake interactions. One way to reduce the cost of WFLO studies is by using more computationally efficient AEP models. The cost of standard AEP modeling approaches, based on the numerical integration of low-fidelity engineering wake models, scales poorly with the number of simulated discrete wind conditions. A second way to reduce cost when using a gradient-based algorithm is to supply exact gradient information instead of finite-difference estimates. However, analytical functions for the derivatives of AEP with respect to turbine positions are not always available in the conventional modeling approach. FLOWERS is a computationally inexpensive, analytical model for wind farm AEP that is specifically developed for WFLO applications. In this paper, we analyze the performance of the FLOWERS AEP model with analytic gradients in a layout optimization study compared with a reference optimization framework across three wind farm case studies. We find that the FLOWERS-based approach reduces computation time by a factor of 50–4000 and improves optimal AEP by about 0.3% with less than half of the variability in AEP across instances with randomized initial conditions. We also find the optimal layouts to be insensitive to model parameter tuning, making FLOWERS-based layout optimization a streamlined, user-friendly approach.

17 WIND ENERGY↗

Assessing the impacts of wakes on floating wind farms with shared anchors

This paper examines wake effects for floating wind farms with shared anchors. Three 20-turbine farms are examined: a grid-formation baseline with no shared anchors, a farm based on 3-line anchors, and a farm based on 6-line anchors, governed by a wind turbine spacing of 8 rotor diameters. The IEA 15 MW reference turbine on the UMaine semisubmersible platform was used with a taut mooring system in deep- water depths representative of U.S. west coast lease areas. A steady-state wake model showed that, when evaluating a sweep of wind headings, the baseline design had the lowest wake losses, with a value of 11.7 %, followed by the 6-line at 12.9% and the 3-line at 13.8%. Dynamic simulations were run in FAST.Farm to analyse the effects of wakes on mean anchor loads for wind headings that showed significant wake losses. The baseline farm showed the largest anchor load reductions due to wake effects (up to 16%), followed by the 3-line farm (up to 8%), and the 6-line farm, which showed relatively consistent load magnitudes on the 6-line anchors across all headings.

17 WIND ENERGY↗

Wake steering of multirotor wind turbines

Abstract In this paper, wake steering is applied to multirotor turbines to determine whether it has the potential to reduce wind plant wake losses. Through application of rotor yaw to multirotor turbines, a new degree of freedom is introduced to wind farm control such that wakes can be expanded, channelled or redirected to improve inflow conditions for downstream turbines. Five different yaw configurations are investigated (including a baseline case) by employing large‐eddy simulations (LES) to generate a detailed representation of the velocity field downwind of a multirotor wind turbine. Two lower‐fidelity models from single‐rotor yaw studies (curled‐wake model and analytical Gaussian wake model) are extended to the multirotor case, and their results are compared with the LES data. For each model, the wake is analysed primarily by examining wake cross‐sections at different downwind distances. Further quantitative analysis is carried out through characterisations of wake centroids and widths over a range of streamwise locations and through a brief analysis of power production. Most significantly, it is shown that rotor yaw can have a considerable impact on both the distribution and magnitude of the wake velocity deficit, leading to power gains for downstream turbines. The lower‐fidelity models show small deviation from the LES results for specific configurations; however, both are able to reasonably capture the wake trends over a large streamwise range.

17 WIND ENERGY↗

Oceanic mixing and waves in the presence of a suspended canopy

Large-eddy simulations are analysed to determine the influence of suspended canopies, such as those formed in macroalgal farms, on ocean mixed layer (OML) deepening and internal wave generation. In the absence of a canopy, we show that Langmuir turbulence, when compared with wind-driven shear turbulence, results in a deeper OML and more pronounced internal waves beneath the OML. Subsequently, we examine simulations with suspended canopies of varying densities located in the OML, in the presence of a background geostrophic current. Intensified turbulence occurs in the shear layer at the canopy’s bottom edge, arising from the interaction between the geostrophic current and canopy drag. Structures resembling Kelvin–Helmholtz (KH) instability emerge as the canopy shear layer interacts with the underlying stratification, radiating internal waves beneath the OML. Both intensified turbulence and lower-frequency motions associated with KH-type structures are critical factors in enhancing mixing. Consequently, the OML depth increases by up to a factor of two compared with cases without a canopy. Denser canopies and stronger geostrophic currents lead to more pronounced KH-type structures and internal waves, stronger turbulence and greater OML deepening. Additionally, vertical nutrient transport is enhanced as the OML deepens due to the presence of the canopy. Considering that the canopy density investigated in this study closely represents offshore macroalgal farms, these findings suggest a mechanism for passive nutrient entrainment conducive to sustainable farming. Overall, this study reveals the intricate interactions between the suspended canopy, turbulent mixing and stratification, underscoring their importance in reshaping OML characteristics.

Bo, Tong (ORCID:0000000260300561)↗

Characterization of HRRR-simulated rotor layer wind speeds and clouds along the coast of California

Stratocumulus clouds, with their low cloud base and top, affect the atmospheric boundary layer wind and turbulence profile, thereby modulating wind energy resources. GOES satellite data reveal an abundance of stratocumulus clouds in the late spring and summer months off the coast of northern and central California, where there are active plans to deploy floating offshore wind farms at two lease areas (near Morro Bay and Humboldt). Since the fall of 2020, two buoys equipped with multiple instruments, including Doppler lidar, have been deployed for about 1 year in these wind farm lease areas to assess the rotor layer wind conditions in these locations. The objective of this study is to evaluate how well the High-Resolution Rapid Refresh (HRRR) model represents stratocumulus cloud characteristics and turbine-relevant rotor layer winds (surface to 300 m) by comparing HRRR simulations with buoy and satellite observations. We first find that the HRRR model reproduces the seasonal cycle of cloud top height reasonably well in these regions. However, during the warm season – especially at Morro Bay – the HRRR-simulated stratocumulus clouds tend to have lower tops by about 150 m and exhibit weaker diurnal cycles than satellite observations. Our analysis also shows that rotor layer wind speeds and vertical shear are stronger at Humboldt than at Morro Bay, and both are generally stronger under clear-sky conditions. Finally, the HRRR model bias in rotor layer wind speed is small under cloudy conditions but larger and dependent on observed wind speed under clear skies. Specifically, HRRR underestimates wind speeds at Morro Bay and overestimates them at Humboldt under clear-sky conditions.

17 WIND ENERGY↗

Aeroelastic code comparison using the IEA 22MW reference turbine

Reference wind turbine designs and the associated aeroelastic models are widely used in both research and industry. Reference models representing future concepts are of particular interest. Current state of the art aeroelastic tools are relied upon to design the next generation of large wind turbines. However, modelling assumptions may be invalidated by upcoming very large turbines, and different aeroelastic tools may give inconsistent results. A 22MW turbine model has been defined as part of International Energy Agency (IEA) Wind Task 55 on Reference Wind Turbines and Farms to represent future turbines to be deployed in the 2030s. In this study, an aeroelastic model of this turbine has been created in four tools; Bladed, HAWC2, OpenFAST, and QBlade. Code comparisons are presented for steady state operation, linear stability analysis, and time domain power production simulations in steady and turbulent wind. Generally, the codes show a good agreement, but with some differences present in the linear stability analysis, periodic azimuthal variation, and time domain simulations. The models are a good basis for further study with the IEA 22MW turbine, and further code comparison exercises.

17 WIND ENERGY↗

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↗

Design space exploration and decision‐making for a segmented ultralight morphing 50‐MW wind turbine

Abstract Wind turbine design encompasses many different aspects including aerodynamic, structural, electrical, and control system design. To achieve optimal plant performance, a system design approach is utilized in which the performance of the whole wind turbine is evaluated and quantified during operational scenarios with subsystem interactions. In this paper, the design for a Segmented Ultralight Morphing Rotor (SUMR) 50‐MW wind turbine is presented utilizing levelized cost of energy (LCOE) for design choices, with additional quantification of simulated performance shortcomings at the 50‐MW scale. The multi‐disciplinary design process results in a final ultra‐scale turbine configuration that outperforms other existing offshore wind farms regarding the LCOE.

Kianbakht, Sepideh↗

Total Power Factor Smart Contract with Cyber Grid Guard Using Distributed Ledger Technology for Electrical Utility Grid with Customer-Owned Wind Farm

In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially owned DERs, present a risk to power system operations. A common protective measure is to use relays located at the PCC to isolate poorly behaving or out-of-tolerance DERs from the grid. Ensuring the integrity of the data from these relays at the PCC is vital, and blockchain technology could enhance the security of modern electrical grids by providing an accurate means to translate operational constraints into actions/commands for relays. This study demonstrates an advanced power system application solution using distributed ledger technology (DLT) with smart contracts to manage the relay operation at the PCC. The smart contract defines the allowable total power factor (TPF) of the DER output, and the terms of the smart contract are implemented using DLT with a Cyber Grid Guard (CGG) system for a customer-owned DER (wind farm). This article presents flowcharts for the TPF smart contract implemented by the CGG using DLT. The test scenarios were implemented using a real-time simulator containing a CGG system and relay in-the-loop. The data collected from the CGG system were used to execute the TPF smart contract. The desired TPF limits on the grid-side were between +0.9 and +1.0, and the operation of the breakers in the electrical grid and DER sides was controlled by the relay consistent with the provisions of the smart contract. The events from the real-time simulator, CGG, and relay showed a successful implementation of the TPF smart contract with CGG using DLT, proving the efficacy of this approach in general for implementing electrical grid applications for utilities with connections to customer-owned DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Simulation of the Multi-Wake Evolution of Two Sandia National Labs/National Rotor Testbed Turbines Operating in a Tandem Layout

The future of wind power systems deployment is in the form of wind farms comprised of scores of such large turbines, most likely at offshore locations. Individual turbines have grown in span from a few tens of meters to today’s large turbines with rotor diameters that dwarf even the largest commercial aircraft. These massive dynamical systems present unique challenges at scales unparalleled in prior applications of wind science research. Fundamental to this effort is the understanding of the wind turbine wake and its evolution. Furthermore, the optimization of the entire wind farm depends on the evolution of the wakes of different turbines and their interactions within the wind farm. In this article, we use the capabilities of the Common ODE Framework (CODEF) model for the analysis of the effects of wake–rotor and wake-to-wake interactions between two turbines situated in a tandem layout fully and partially aligned with the incoming wind. These experiments were conducted in the context of a research project supported by the National Rotor Testbed (NRT) program of Sandia National Labs (SNL). Results are presented for a layout which emulates the turbine interspace and relative turbine emplacement found at SNL’s Scaled Wind Technologies Facility (SWiFT), located in Lubbock, Texas. The evolution of the twin-wake interaction generates a very rich series of secondary transitions in the vortex structure of the combined wake. These ultimately affect the wake’s axial velocity patterns, altering the position, number, intensity, and shape of localized velocity-deficit zones in the wake’s cross-section. This complex distribution of axial velocity patterns has the capacity to substantially affect the power output, peak loads, fatigue damage, and aeroelastic stability of turbines located in subsequent rows downstream on the farm.

Baruah, Apurva (ORCID:0000000252354068)↗