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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Biases in preconstruction estimates of wind plant annual energy production

Estimating the energy yield of a wind plant during the preconstruction phase is a historically difficult task, even with industry improvements in these estimations. We build on prior research comparing the realized energy production of wind plants and their estimated annual energy production P50 values (median energy production), using owner-provided energy production and losses. We produced similar results to prior studies but with a slightly increasing bias of overestimating median energy production (a bias between realized and estimated energy production of −7.4 % to −6.6 %, depending on the scenario, as opposed to −6.7 % to −5.5 % from earlier studies). In addition to assessing annual energy production P50 bias, we compared both the 1-year and the long-term annual energy production P90 and uncertainty energy yield assessment estimates to the observed long-term-corrected energy production. We found that neither the energy yield assessment uncertainty nor the P90 is conservative enough compared to the observed distribution of prediction errors, suggesting significant room for improvement in the energy yield assessment process.

17 WIND ENERGY↗
DOE OSTI2020

Operational-based annual energy production uncertainty: are its components actually uncorrelated?

Calculations of annual energy production (AEP) from a wind power plant – whether based on preconstruction or operational data – are critical for wind plant financial transactions. The uncertainty in the AEP calculation is especially important in quantifying risk and is a key factor in determining financing terms. A popular industry practice is to assume that different uncertainty components within an AEP calculation are uncorrelated and can therefore be combined as the sum of their squares. We assess the practical validity of this assumption for operational-based uncertainty by performing operational AEP estimates for more than 470 wind plants in the United States, mostly in simple terrain. We apply a Monte Carlo approach to quantify uncertainty in five categories: revenue meter data, wind speed data, regression relationship between density-corrected wind speed (from reanalysis data) and measured wind power, length of long-term-correction data set, and future interannual variability. We identify correlations between categories by comparing the results across all 470 wind plants. We observe a positive correlation between interannual variability and the linearized long-term correction; a negative correlation between wind resource interannual variability and linear regression; and a positive correlation between reference wind speed uncertainty and linear regression. Then, we contrast total operational AEP uncertainty values calculated by omitting and considering correlations between the uncertainty components. We quantify that ignoring these correlations leads to an underestimation of total AEP uncertainty of, on average, 0.1% and as large as 0.5% for specific sites. Although these are not large increases, these would still impact wind plant financing rates; further, we expect these values to increase for wind plants in complex terrain. Based on these results, we conclude that correlations between the identified uncertainty components should be considered when computing the total AEP uncertainty.

17 WIND ENERGY↗
DOE OSTI2021

Verification of induction zone models for wind farm annual energy production estimation

Fast engineering wake models are the backbone of wind farm annual energy production (AEP) estimators, whereas the addition of induction zone models in existing tools is a more recent response to rising concerns over wind farm blockage associated losses. Here, "blockage" describes the combined induction fields of all wind turbines inside a farm. Unlike the term might suggest, blockage not only reduces flow speeds, but also increases them; for instance along the outer edges of wind farms. Evaluating the overall impact on AEP of these gains and losses necessitates accurate wind farm induction models. Whilst engineering wake models are tuned to predict wind farm performance, existing induction zone models are all derived for accurately predicting the near field induction not the far field value—important for wind farm simulations. This paper presents the induction models implemented in the wind farm simulation tool PyWake, as well as results from novel analytical models and compares their far field predictions to RANS-AD simulations of different turbines. We demonstrate that when including blockage in AEP simulations, the downstream speed-ups need to be included to avoid an unrealistic bias toward AEP loss and that wake expansion significantly impacts induction at rated thrust levels.

17 WIND ENERGY↗
DOE OSTI2024

Integrated floating wind farm layout design and mooring system optimization to increase annual energy production

As we cluster wind turbines in wind farms to gain energy from sites with high wind speeds, wake losses occur within the wind farm. Wake loss is a term used to describe the lower energy production of a downwind turbine that is totally or partially in the wake of an upwind turbine. To decrease wake losses inside the wind farm, the wind farm’s layout is optimized. However, a variety of factors constrain the wind farm layout optimization, such as the size of the lease area relative to the number of turbines to be placed, or the shape of the lease area. Therefore, many wind farms end up with a regular grid layout, such as the Horns Rev 1 wind farm in the North Sea. The ability of a floating offshore wind turbine (FOWT) to change its position based on the wind direction and its mooring system design presents an opportunity to further decrease wake losses in floating wind farms. In this work, we integrate the design of the FOWT mooring systems with the floating wind farm layout design with the goal of increasing the farm’s annual energy production. We use the Horns Rev 1 wind farm as a case study to demonstrate our method. The results show that allowing the FOWT to relocate can decrease wake losses up to 18%. Moreover, the newly developed mooring systems are less stiff and therefore allow larger motion of the FOWT; hence, the material cost of the mooring system decreases by an estimated 17%.

17 WIND ENERGY↗
DOE OSTI2025

Model to predict annual energy production loss based on blade erosion class

Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) has created the Task 46 to undertake cooperative research in the key topic of blade erosion. Participants in the task are given in Table 1.

17 WIND ENERGY↗
DOE OSTI2024

Dynamic performance of a passively self-adjusting floating wind farm layout to increase the annual energy production

One of the main differences between floating offshore wind turbines (FOWTs) and fixed-bottom turbines is the angular and translational motions of FOWTs. When it comes to planning a floating wind farm (FWF), the translational motions introduce an additional layer of complexity to the FWF layout. The ability of a FOWT to relocate its position represents an opportunity to mitigate wake losses within an FWF. By passively relocating downwind turbines out of the wake generated by upwind turbines, we can reduce wake-induced energy losses and enhance overall energy production. The translational movements of FOWTs are governed by the mooring system attached to it. The way a FOWT relocates its position changes if the design of the mooring system attached to it changes. Additionally, the translational motion of a FOWT attached to a given mooring system is different for different wind directions. Hence, we can tailor a mooring system design for a FOWT to passively control its motions according to the wind direction. In this work, we present a new self-adjusting FWF layout design and assess its performance using both static and dynamic methods. The results show that relocating the FOWTs in an FWF can increase the energy production by 3 % using a steady-state wake model and 1.4 % using a dynamic wake model at a wind speed of 10 m s -1 . Moreover, we compare the fatigue and ultimate loads of the mooring systems of the self-adjusting FWF layout design to the mooring systems in a current state-of-the-art FWF baseline design. The comparison shows that with smaller mooring system diameters, the self-adjusting FWF design has similar fatigue damage compared to the baseline design with bigger mooring system diameters at rated wind speed. Finally, the ultimate loads on the mooring systems of the self-adjusting FWF design are lower than those on the mooring systems of the baseline design.

17 WIND ENERGY↗
DOE OSTI2021

An overview of wind-energy-production prediction bias, losses, and uncertainties

Abstract. The financing of a wind farm directly relates to the preconstruction energy yield assessments which estimate the annual energy production for the farm. The accuracy and the precision of the preconstruction energy estimates can dictate the profitability of the wind project. Historically, the wind industry tended to overpredict the annual energy production of wind farms. Experts have been dedicated to eliminating such prediction errors in the past decade, and recently the reported average energy prediction bias is declining. Herein, we present a literature review of the energy yield assessment errors across the global wind energy industry. We identify a long-term trend of reduction in the overprediction bias, whereas the uncertainty associated with the prediction error is prominent. We also summarize the recent advancements of the wind resource assessment process that justify the bias reduction, including improvements in modeling and measurement techniques. Additionally, because the energy losses and uncertainties substantially influence the prediction error, we document and examine the estimated and observed loss and uncertainty values from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 wind resource assessment standard. From our findings, we highlight opportunities for the industry to move forward, such as the validation and reduction of prediction uncertainty and the prevention of energy losses caused by wake effect and environmental events. Overall, this study provides a summary of how the wind energy industry has been quantifying and reducing prediction errors, energy losses, and production uncertainties. Finally, for this work to be as reproducible as possible, we include all of the data used in the analysis in appendices to the article.

17 WIND ENERGY↗
DOE OSTI2024

Evaluation of Rooftop Solar Potential in Chernihiv and Lviv, Ukraine, and Efficacy of High-Resolution 3D Data Digital Twins [Slides]

NREL evaluated rooftop solar photovoltaic (PV) siting opportunities in the cities of Chernihiv and Lviv, Ukraine, leveraging very-high-resolution 3D elevation data to calculate technical potential. The study assessed total rooftop solar capacity and annual energy production. In Chernihiv and Lviv, 116,503 buildings were analyzed for their rooftop solar potential. The buildings in the study areas include a mixture of residential, commercial, and industrial buildings, characterized by diverse roof shapes and sizes. The total estimated rooftop solar capacity is 332 MWDC in Chernihiv and 873 MWDC in Lviv with annual energy production up to 376.2 GWhDC in Chernihiv and 995.5 GWhDC in Lviv. This accounting provides a clear estimate of the potential rooftop solar installations that could be realized under optimal conditions, considering both technical constraints and the geographic distribution of available rooftop space. Related to Russia's invasion of Ukraine, NREL estimates loss from buildings damaged or destroyed of 2,754 buildings, 20.15 MWDC of capacity, and 22,869 MWhDC of annual energy production in Chernihiv as well as a loss of 1,316 buildings, 34.49 MWDC of capacity, and 39,369 MWhDC of annual energy production in Lviv. The study also assessed the feasibility of adapting this methodology on a national scale using either simulated digital surface models (DSMs) or a digital twin approach. While the very-high-resolution DSM provided more precise results, the simulated DSM demonstrated reasonable accuracy for broader applications in modeling aggregated distributed solar supply. The feasibility of using a digital twin for Ukraine's national rooftop solar potential is considered promising, with certain limitations in areas with highly variable building stock and heavy war damage.

14 SOLAR ENERGY↗
DOE OSTI2023

Modeling Annual Electricity Production and Levelized Cost of Energy from the US East Coast Offshore Wind Energy Lease Areas

Offshore wind energy development along the East Coast of the US is proceeding quickly as a result of large areas with an excellent wind resource, low water depths and proximity to large electricity markets. Careful planning of wind turbine deployments in these offshore wind energy lease areas (LA) is required to maximize power output and to minimize wake losses between neighboring wind farms as well as those internal to each wind farm. Here, we used microscale wind modeling with two wake parameterizations to evaluate the potential annual energy production (AEP) and wake losses in the different LA areas, and we developed and applied a levelized cost of energy (LCoE) model to quantify the impact of different wind turbine layouts on LCoE. The modeling illustrated that if the current suite of LA is subject to deployment of 15 MW wind turbines at a spacing of 1.85 km, they will generate 4 to 4.6% of total national electricity demand. The LCoE ranged from $68 to $102/MWh depending on the precise layout selected, which is cost competitive with many other generation sources. The scale of the wind farms that will be deployed greatly exceed those currently operating and mean that wake-induced power losses are considerable but still relatively poorly constrained. AEP and LCoE exhibited significant dependence on the precise wake model applied. For the largest LA, the AEP differed by over 10% depending on the wake model used, leading to a $10/MWh difference in LCoE for the wind turbine layout with 1.85 km spacing.

58 GEOSCIENCES↗
DOE OSTI2024

Low-Cost Heliostat for High-Flux Small-Area Receivers (Final Technical Report)

This project analyzed a two-stage heliostat concept consisting of a tracking stage and a concentrating stage. The tracking stage uses mirrors mounted on a common drive that move to track the sun. The concentrating stage consists of stationary mirrors that each have a unique angle to direct rays towards a small-area, high-flux, point-focused receiver. By splitting the collection and concentrating process into two stages, multiple small, inexpensive mirrors can share a structure and be controlled by a single drive in the tracking stage. The project effort developed modeling techniques that were specifically relevant to this two-stage heliostat concept. Both field-level and unit-level models were developed. The field-level model does not explicitly consider unit-level losses which are predicted by the unit-level model and then integrated into the field-level model through a correlation referred to as an efficiency modifier. This approach is referred to as the two-model approach; the development and demonstration of this two-model approach for a multi-stage heliostat technology is a key outcome of this work. The field-level model is used to design a field that hits a specific design day power given a set of heliostat design parameters. An oversized field is simulated and then heliostat units are removed based on their annual energy production in order to generate the highest performing field. The field reduction procedure fits a smooth curve fit to annual energy production as a function of position in the field which has the effect of reducing the noise that is otherwise caused by the Monte Carlo ray tracing technique. This approach is referred to as the annual energy fit method and substantially reduces computational run time for a given field level modeling accuracy. The annual energy fit approach enables the selection of a properly sized, high-performing field using orders of magnitude fewer rays than would otherwise be possible and the development of this approach is a second key outcome of this work. These models are used within a genetic optimization algorithm in order to optimize the geometric parameters associated with a heliostat in order to achieve the lowest cost per unit of collected design day power. The cost modeling that underlies the optimization is a simple, scaling type analysis backed up by a much more detailed Design for Manufacture and Assembly (DFMA) analysis. Although the figure of merit used for optimization was not cost per mirror area, this metric is reasonable to use as a means of comparison. The optimally designed 500 kW design has a tracking mirror specific cost of $181.85/m 2 , which is significantly larger than the target value and also larger than the current state of the art. The cost of the torque-tube type linkages contributed substantially to the overall cost. Based on this observation, potentially attractive alternative design configuration utilizing a capstan type actuation system should be investigated. Finally, NREL compared the performance of the two-stage heliostat to the performance of a focused and different sized flat conventional heliostats and showed that, as expected, additional losses versus the convention heliostat caused by a worse cosine efficiency, two stages of reflection, and interstage interactions. The two-stage heliostat requires around 75% more reflective area than a flat 1x1 meter conventional heliostat (similar to a focused heliostat) and 40% more than a flat 2x2 meter conventional heliostat.

14 SOLAR ENERGY↗
DOE OSTI2023

Projecting Future Energy Production from Operating Wind Farms in North America. Part III: Variability

Abstract Daily expected wind power production from operating wind farms across North America are used to evaluate capacity factors (CF) computed using simulation output from the Weather Research and Forecasting (WRF) Model and to condition statistical models linking atmospheric conditions to electricity production. In Parts I and II of this work, we focus on making projections of annual energy production and the occurrence of electrical production drought. Here, we extend evaluation of the CF projections for sites in the Northeast, Midwest, southern Great Plains (SGP), and southwest U.S. coast (SWC) using statewide wind-generated electricity supply to the grid. We then quantify changes in the time scales of CF variability and the seasonality. Currently, wind-generated electricity is lowest in summer in each region except SWC, which causes a substantial mismatch with electricity demand. While electricity of residential heating may shift demand, research presented here suggests that summertime CF are likely to decline, potentially exacerbating the offset between seasonal peak power production and current load. The reduction in summertime CF is manifest for all regions except the SGP and appears to be linked to a reduction in synoptic-scale variability. Using fulfillment of 50% and 90% of annual energy production to quantify interannual variability, it is shown that wind power production exhibits higher (earlier fulfillment) or lower (later fulfillment) production for periods of over 10–30 years as a result of the action of internal climate modes. Significance Statement Electrical power system reassessment and redesign may be needed to aid efficient increased use of variable renewables in the generation of electricity. Currently wind-generated electricity in many regions of North America exhibits a minimum in summertime and hence is not well synchronized with electricity demand, which tends to be maximized in summer. Future projections indicate evidence of reductions in wind power during summer that would amplify this offset. However, electrification of heating may lead to increased wintertime demand, which would lead to greater synchronization.

Coburn, Jacob↗
DOE OSTI2024

Seasonal variability of wake impacts on US mid-Atlantic offshore wind plant power production

Abstract. The mid-Atlantic will experience rapid wind plant development due to its promising wind resource located near large population centers. Wind turbines and wind plants create wakes, or regions of reduced wind speed, that may negatively affect downwind turbines and plants. We evaluate wake variability and annual energy production with the first yearlong modeling assessment using the Weather Research and Forecasting model, deploying 12 MW turbines across the domain at a density of 3.14 MW km−2, matching the planned density of 3 MW km−2. Using a series of simulations with no wind plants, one wind plant, and complete build-out of lease areas, we calculate wake effects and distinguish the effect of wakes generated internally within one plant from those generated externally between plants. We also provide a first step towards uncertainty quantification by testing the amount of added turbulence kinetic energy (TKE) by 0 % and 100 %. We provide a sensitivity analysis by additionally comparing 25 % and 50 % for a short case study period. The strongest wakes, propagating 55 km, occur in summertime stable stratification, just when New England's grid demand peaks in summer. The seasonal variability of wakes in this offshore region is much stronger than the diurnal variability of wakes. Overall, yearlong simulated wake impacts reduce power output by a range between 38.2 % and 34.1 % (for 0 %–100 % added TKE). Internal wakes cause greater yearlong power losses, from 29.2 % to 25.7 %, compared to external wakes, from 14.7 % to 13.4 %. The overall impact is different from the linear sum of internal wakes and external wakes due to non-linear processes. Additional simulations quantify wake uncertainty by modifying the added amount of turbulent kinetic energy from wind turbines, introducing power output variability of 3.8 %. Finally, we compare annual energy production to New England grid demand and find that the lease areas can supply 58.8 % to 61.2 % of annual load. We note that the results of this assessment are not intended to make nor are they suitable to make commercial judgments about specific wind projects.

17 WIND ENERGY↗
DOE OSTI2020

An Overview of Wind Energy Production Prediction Bias, Losses, and Uncertainties

The financing of a wind farm directly relates to the preconstruction energy yield assessments which estimate the annual energy production for the farm. The accuracy and the precision of the preconstruction energy estimates can dictate the profitability of the wind project. Historically, the wind industry tended to overpredict the annual energy production of wind farms. Experts have been dedicated to eliminating such prediction errors in the past decade, and recently the industry is recording near-zero average energy prediction bias. Herein, we present an overview of the energy yield assessment errors across the global wind energy industry. We identify a long-term trend of reduction in the overprediction bias, whereas the uncertainty associated with the prediction error is prominent. We also summarize the recent advancements of the wind resource assessment process that justify the bias reduction, including the improvements in modeling and measurement techniques. Additionally, because the energy losses and uncertainties substantially influence the prediction error, we document and examine the estimated and observed loss and uncertainty values from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 wind resource assessment standard. From our findings, we highlight the opportunities for the industry to move forward, such as the validation and reduction of prediction uncertainty, and the prevention of energy losses caused by wake effect and environmental events. Overall, this study provides a summary on how the wind energy industry has been quantifying and reducing prediction errors, energy losses, and production uncertainties. Finally, for this work to be as reproducible as possible, we include all of the data used in the analysis in appendices to the manuscript.

literature review↗
DOE OSTI2022

An independent analysis of bias sources and variability in wind plant pre-construction energy yield estimation methods

The wind resource assessment community has long had the goal of reducing the bias between wind plant pre-construction energy yield assessment (EYA) and the observed annual energy production (AEP). This comparison is typically made between the 50% probability of exceedance (P50) value of the EYA and the long-term corrected operational AEP (hereafter OA P50), and is known as the P50 bias. The industry has critically lacked an independent analysis of bias reduction investigated across multiple consultants to identify the greatest sources of uncertainty and variance in the EYA process and the best opportunities for uncertainty reduction. The present study addresses this gap by benchmarking consultant methodologies against each other and against operational data at a scale not seen before in industry collaborations. We consider data from 10 wind plants and evaluate discrepancies between eight consultancies in the steps taken from estimates of gross to net energy. Consultants tend to overestimate the gross energy produced at the turbines and then compensate by further overestimating downstream losses, leading to a mean P50 bias near zero, still with significant variability among the individual wind plants. Within our data sample, we find that consultant estimates of all loss categories, except environmental losses, tend to reduce the project-to-project variability of the P50 bias. The disagreement between consultants, however, remains flat throughout the addition of losses. Finally, we find that differences in consultants’ estimates of project performance can lead to differences up to $10/MWh in the levelized cost of energy for a wind plant.

Todd, Austin C.↗
DOE OSTI2024

Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States

Abstract. A major issue in quantifying potential power generation from prospective wind energy sites is the lack of observations from heights relevant to modern wind turbines, particularly for offshore where blade tip heights are projected to increase beyond 250 m. We present analyses of uniquely detailed data sets from lidar (light detection and ranging) deployments in New York State and on two buoys in the adjacent New York Bight to examine the relative power generation potential and power quality at these on- and offshore locations. Time series of 10 min wind power production are computed from these wind speeds using the power curve from the International Energy Agency 15 MW reference wind turbine. Given the relatively close proximity of these lidar deployments, they share a common synoptic-scale meteorology and seasonal variability with lowest wind speeds in July and August. Time series of power production from the on- and offshore location are highly spatially correlated with the Spearman rank correlation coefficient dropping below 0.4 for separation distances of approximately 350 km. Hence careful planning of on- and offshore wind farms (i.e., separation of major plants by > 350 km) can be used reduce the system-wide probability of low wind energy power production. Energy density at 150 m height at the offshore buoys is more than 40 % higher, and the Weibull scale parameter is 2 m s−1 higher than at all but one of the land sites. Analyses of power production time series indicate annual energy production is almost twice as high for the two offshore locations. Further, electrical power production quality is higher from the offshore sites that exhibit a lower amplitude of diurnal variability, plus a lower probability of wind speeds below the cut-in and of ramp events of any magnitude. Despite this and the higher resource, the estimated levelized cost of energy (LCoE) is higher from the offshore sites mainly due to the higher infrastructure costs. Nonetheless, the projected LCoE is highly competitive from all sites considered.

Foody, Rebecca↗
DOE OSTI2024

Synergistic Integration of Multiple Wave Energy Converters with Adaptive Resonance and Offshore Floating Wind Turbines through Bayesian Optimization

We developed a synergistic ocean renewable system where an array of Wave Energy Converters (WEC) with adaptive resonance was collocated with a Floating Offshore Wind Turbine (FOWT) such that the WECs, capturing wave energy through the resonance adapting to varying irregular waves, consequently reduced FOWFT loads and turbine motions. Combining Surface-Riding WECs (SR-WEC) individually designed to feasibly relocate their natural frequency at the peak of the wave excitation spectrum for each sea state, and to obtain the highest capture width ratio at one of the frequent sea states for annual average power in a tens of kilowatts scale with a 15 MW FOWT based on a semi-submersible, Bayesian Optimization is implemented to determine the arrangement of WECs that minimize the annual representation of FOWT’s wave excitation spectra. The time-domain simulation of the system in the optimized arrangement is performed, including two sets of interactions: one set is the wind turbine dynamics, mooring lines, and floating body dynamics for FOWT, and the other set is the nonlinear power-take-off dynamics, linear mooring, and individual WECs’ floating body dynamics. Those two sets of interactions are further coupled through the hydrodynamics of diffraction and radiation. For sea states comprising Annual Energy Production, we investigate the capture width ratio of WECs, wave excitation on FOWT, and nacelle acceleration of the turbine compared to their single unit operations. We find that the optimally arranged SR-WECs reduce the wave excitation spectral area of FOWT by up to 60% and lower the turbine’s peak nacelle acceleration by nearly 44% in highly occurring sea states, while multiple WECs often produce more than the single operation, achieving adaptive resonance with a larger wave excitation spectra for those sea states. The synergistic system improves the total Annual Energy Production (AEP) by 1440 MWh, and we address which costs of Levelized Cost Of Energy (LCOE) can be reduced by the collocation.

Engineering↗