Validation of Power Output for the WIND Toolkit

King, J.; Clifton, A.; Hodge, B. M.

doi:10.2172/1159354

Cited by 47 publications

(52 citation statements)

References 7 publications

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“…Each turbine is assumed to be 2 MW with 100‐m hub height. The power curve used in each grid cell depends on the assessed wind class and is made up of a number of representative manufacturer power curves for similar turbines . Grid cell power production is estimated using the appropriate power curve and wind speeds from meteorological reanalysis data.…”

Section: Alternative Methods For Power Predictionmentioning

confidence: 99%

“…The correction is

C_{w a k e} = 1 - \frac{1}{20} (\frac{n_{t u r b i n e s} - 1}{7}),

where n t u r b i n e s is the number of turbines in the grid cell, and with a maximum of 8, the wake reduction is at most 5%. C w a k e is multiplied by the reanalysis wind speeds, resulting in corrected grid cell wind speed for all turbines …”

Section: Alternative Methods For Power Predictionmentioning

confidence: 99%

“…The power curve used in each grid cell depends on the assessed wind class and is made up of a number of representative manufacturer power curves for similar turbines. 21 Grid cell power production is estimated using the appropriate power curve and wind speeds from meteorological reanalysis data. Instead of explicitly modeling wake effects, an empirical correction is applied to reduce the wind speed to account for the presence of wakes.…”

Section: Nrel Wind Integration National Datasetmentioning

confidence: 99%

“…There has thus been a strong focus on advancing the modeling techniques to better understand and represent fluid flow within a wind farm. Even with an accurate forecast for wind speed, conversions from wind speed to power are typically done by heuristics in practice, such as simply adding up the individual power curves of each turbine and applying a penalty to account for wakes . Although these estimates are far from perfect, they are still useful for many large‐scale planning decisions.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of methods for assessing power output in non‐uniform onshore wind farms

2017

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Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshore farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non-uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. We show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice. KEYWORDSonshore wind farms, statistical modeling, turbine wakes, wind power assessment INTRODUCTIONWind energy is a fast-growing segment of the energy sector worldwide. As wind starts to play a larger role in our electricity systems, there is increasing interest in using it efficiently. Wind power is, for the most part, not a dispatchable form of energy; forecasts are not perfect, and an individual wind farm cannot be set to a desired production level unless the wind conditions are sufficient for that level of power. Managing a farm's variability and intermittency is still a big challenge, but recent research shows promising results in optimizing farm production. 1-4 Knowledge of the amount of power that a wind farm will produce in a given hour, day, or year will improve the way that the farm is operated and financed. Perfect knowledge of future wind power production is impossible, but recent research has made great strides in estimating these quantities. Power estimation and prediction is important for multiple applications within the timeline of a wind energy project. In the initial planning stages of a farm, one must develop accurate estimates of what the wind conditions will be once the farm is built. This typically involves the installation of meteorological towers (or met towers) for data collection. This information is used to determine the turbine layout and alignment within the farm that will best capture the available energy. Initial estimates of long-term wind conditions are used to assess the financial viability of a wind farm. Thus, the decision of farm design and resource assessment is critical, as the return on investment depends on the ability of the wind farm to generate enough en...

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Section: Alternative Methods For Power Predictionmentioning

confidence: 99%

“…The correction is

C_{w a k e} = 1 - \frac{1}{20} (\frac{n_{t u r b i n e s} - 1}{7}),

Section: Alternative Methods For Power Predictionmentioning

confidence: 99%

Section: Nrel Wind Integration National Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comparison of methods for assessing power output in non‐uniform onshore wind farms

2017

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“…In this paper, we perform a comparison of three deterministic and probabilistic wind resource assessments based on NWP models: (i) a low resolution reanalysis data set, the Modern-Era Retrospective Analysis for Research and Applications (MERRA) [21]; (ii) an analog ensemble methodology based on the MERRA model and on-site observations, which provides both deterministic and probabilistic predictions [22]; and (iii) a high resolution NWP data set, the Wind Integration National Dataset (WIND) Toolkit based on WRF model [23,24]. The remainder of the paper is organized as follows: the three NWP-based methods are presented in Section 2.…”

Section: Research Motivation and Objectivesmentioning

confidence: 99%

Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods

et al. 2015

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h i g h l i g h t sThree wind resource assessment methodologies for grid integration studies are compared. The analog-based method provides deterministic and probabilistic estimates. The WIND Toolkit data set is more suitable for a large study area. a b s t r a c tNumerical weather prediction (NWP) models have been widely used for wind resource assessment. Model runs with higher spatial resolution are generally more accurate, yet extremely computational expensive. An alternative approach is to use data generated by a low resolution NWP model, in conjunction with statistical methods. In order to analyze the accuracy and computational efficiency of different types of NWP-based wind resource assessment methods, this paper performs a comparison of three deterministic and probabilistic NWP-based wind resource assessment methodologies: (i) a coarse resolution (0.5°Â 0.67°) global reanalysis data set, the Modern-Era Retrospective Analysis for Research and Applications (MERRA); (ii) an analog ensemble methodology based on the MERRA, which provides both deterministic and probabilistic predictions; and (iii) a fine resolution (2-km) NWP data set, the Wind Integration National Dataset (WIND) Toolkit, based on the Weather Research and Forecasting model. Results show that: (i) as expected, the analog ensemble and WIND Toolkit perform significantly better than MERRA confirming their ability to downscale coarse estimates; (ii) the analog ensemble provides the best estimate of the multi-year wind distribution at seven of the nine sites, while the WIND Toolkit is the best at one site; (iii) the WIND Toolkit is more accurate in estimating the distribution of hourly wind speed differences, which characterizes the wind variability, at five of the available sites, with the analog ensemble being best at the remaining four locations; and (iv) the analog ensemble computational cost is negligible, whereas the WIND Toolkit requires large computational resources. Future efforts could focus on the combination of the analog ensemble with intermediate resolution (e.g., 10-15 km) NWP estimates, to considerably reduce the computational burden, while providing accurate deterministic estimates and reliable probabilistic assessments.

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Simulating wind power forecast error distributions for spatially aggregated wind power plants

Miettinen

Holttinen

2019

Wind Energy

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Dispersion and aggregation of wind power plants lower the uncertainty of wind power by reducing wind power forecasting errors. Using quantitative methods, this paper studies the dispersion's impact on the uncertainty of the aggregated wind power production. A method to simulate day‐ahead forecast error distributions at different dispersion and forecasting skill scenarios is presented. The proposed method models the uncertainty of wind power forecasting on an annual basis and at different levels of production. As a result, the uncertainty in the forecasting of spatially dispersed wind power plants is modelled using two continuous distributions: Laplace and beta distribution. The analysis shows that even the production level uncertainty can be modelled in various dispersion and forecasting skill scenarios. The model for aggregated forecast error distributions requires only four variables: capacity‐weighted distance of the total wind power plant fleet, mean of the site‐specific mean absolute errors (MAEs), number of aggregated wind power plants, and the mean variability of the elevations from the proximities of the aggregated wind power plants. The results are especially promising when the number of aggregated wind power plants exceeds five, the terrain complexity is low or moderate, and the aggregation region is large. This is demonstrated through a case study for Texas, United States.

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Validation of Power Output for the WIND Toolkit

Cited by 47 publications

References 7 publications

A comparison of methods for assessing power output in non‐uniform onshore wind farms

A comparison of methods for assessing power output in non‐uniform onshore wind farms

Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods

Simulating wind power forecast error distributions for spatially aggregated wind power plants

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