NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Wind Data Inputs for Regional 

Wind Integration Studies 

Preprint

D. Lew, K. Orwig, and Y. Wan

National Renewable Energy Laboratory

C. Alonge, M. Brower, and J. Frank

AWS Truepower

L. Freeman

GE Energy

C. Potter

3TIER Group

To be presented at the Power & Energy Society Annual Meeting

Detroit, Michigan

July 24-29, 2011

Conference Paper

NREL/CP-5500-50636

March 2011
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1 

Abstract—Wind integration studies are conducted routinely to 

examine the operational impacts of wind on the power system. 

Wind plant power outputs and forecasts are needed as inputs to 

these studies and this data is often synthesized by a variety of 

methods. This paper examines the methodologies used to create 

these datasets, the pitfalls that may be encountered, and the 

tradeoffs between different methodological approaches.

Index Terms—Wind power modeling, wind speed, mesoscale 

modeling, forecast error, wind forecasting, and wind integration. 

I. INTRODUCTION

tilities in the United States have seen high interest in 

wind development, as evidenced by the fact that wind 

leads all other fuels and technologies in interconnection 

requests [1] and that wind competes with solar as the nation’s 

fastest growing energy source [2]. As a result of this interest, 

utilities, Regional Transmission Organizations (RTOs), and 

Independent System Operators (ISOs) are increasingly 

undertaking integration studies to examine the operational 

impacts of growing penetrations of wind power on their grids. 

Many wind integration studies have been conducted to date 

[3]-[7], including two new regional studies looking across 

much of the eastern and western interconnections [8],[9]. 

These studies typically include hourly production cost 

modeling of at least one year of the power system with various 

levels of wind power. Each wind plant is modeled, at least on 

an hourly basis, and provided as inputs to the production cost 

model, along with hourly load data. In addition to wind output 

data, wind forecast data is also typically used to more 

accurately model how a utility or RTO/ISO might commit or 

dispatch units based on a forecast.

While existing wind plants can be represented with 

historical output data, hypothetical future plants cannot. Onsite 

measurements for wind speed can be converted to power and 

used as data inputs. However, some of these studies, for 

 This work was supported by the U.S. Department of Energy Wind 

Program.

D. Lew, K. Orwig and Y-H. Wan are with the National Renewable Energy 

Laboratory, Golden, CO 80401 USA (email: debra.lew@nrel.gov, 

kirsten.orwig@nrel.gov, yih-huei.wan@nrel.gov).

C. Alonge, M. Brower, and J. Frank are with AWS Truepower, Albany, 

NY 12205 USA (email: calonge@awstruepower.com, 

mbrower@awstruepower.com, jfrank@awstruepower.com).

L. Freeman is with GE Energy, Schenectady, NY 12345 USA (email: 

Lavelle.freeman@ge.com).

C. Potter is with the 3TIER Group, Seattle, WA 98121 USA (email: 

cpotter@3tier.com).

example, the Eastern Wind Integration and Transmission 

Study (EWITS) and the Western Wind and Solar Integration 

Study (WWSIS), examine up to 30% wind penetration, which 

requires modeling of 330 GW and 75 GW of wind plants, 

respectively. Even those studies with modest penetration 

levels typically don’t have onsite measurement data to 

represent the hypothetical plants.

Many studies turn to modeled wind data to represent 

hypothetical future plants. One methodology seeks to scale 

and/or time-shift actual measurements to represent nearby 

hypothetical plants [10]. These may provide realistic outputs 

in regions where there is a predominant wind direction and 

where wind plants do not cover a large region. However, for 

wide geographical regions with more complex terrain, and in 

order to capture a variety of weather patterns, more 

sophisticated methodologies may be needed to capture 

correlations in both space and time across that region. A more 

commonly used methodology involves physics-based 

modeling of the atmosphere and the weather patterns over 

time and on a three-dimensional spatial grid. This Numerical 

Weather Prediction (NWP) model can then be sampled in time 

and space to yield hypothetical wind speed measurements that 

can then be converted to wind plant output. These mesoscale 

models are typically run at a high horizontal spatial resolution 

(less than 10 km).

In this paper, historical observations of actual plant output 

are called observations, modeled plant output is called 

modeled or actuals (to distinguish them from forecasts), and 

modeled forecasts of plant output are called forecasts. The 

difference between the forecasts and the actuals are the 

forecast errors. A good wind dataset for use in integration 

studies will have the following attributes: 

• Appropriate characterization of variability on the 1-

minute, 10-minute, hourly, daily, and seasonal time scales. 

Accurate characterization of variability and forecast error is 

the most important criterion for a dataset. Integration studies 

are conducted to determine the impact of wind variability and 

uncertainty on various reserve requirements.

• Appropriate characterization of forecast errors. 

Forecast errors tend to be higher during wind ramp events.

Capturing the appropriate magnitude of forecast error over a 

single plant, state, or region is important. Forecast error 

distributions tend to be wider than Gaussian distributions.

• Both modeled and forecasted data should display 

reasonable spatial correlation, both within a single plant and 

between plants in a region. 

• Both modeled and forecasted data should display 

reasonable temporal correlation over short, medium, and 

Wind Data Inputs for Regional Wind Integration 

Studies

Debra Lew, Member, IEEE, Charles Alonge, Michael Brower, Jaclyn Frank, Lavelle Freeman,

Member, IEEE, Kirsten Orwig, Cameron Potter, Member, IEEE, and Yih-Huei Wan, Senior Member, 

IEEE

U
2 

longer term timescales. Forecast errors should exhibit

appropriate autocorrelation characteristics. 

• Accuracy in resource quality as defined by the capacity 

factor of the site is deemed a less important criterion for 

datasets. If the capacity factor is low, then additional sites will 

be needed to reach a target penetration level. This may impact 

the overall variability since more sites will be aggregated.

II. RESULTS

This paper reviews methodologies used to create regional 

wind datasets for integration studies, with a focus on the 

EWITS and WWSIS datasets. A recent Hawaii wind dataset, 

which built upon the lessons learned in the EWITS and 

WWSIS datasets and which required higher temporal and 

spatial resolution and a higher degree of accuracy, is also 

discussed. The paper investigates the tradeoffs between 

different approaches as well as some of the pitfalls 

encountered in creation of these datasets.

A. Spatial Correlation and Consistency

Various mesoscale models are employed to simulate the 

weather. For example, the WWSIS used the Weather Research 

and Forecasting (WRF) model and EWITS used the 

Mesoscale Atmospheric Simulation System (MASS). In both 

models, a key input was the National Center for 

Environmental Prediction (NCEP)/National Center for 

Atmospheric Research (NCAR) Global Reanalysis (NNGR) 

dataset, which represents the overall state of the atmosphere 

based on surface and upper air observations. 

Modeling a large region at high resolution can quickly 

exhaust computational capacity limits. EWITS and WWSIS 

required modeling half of the continental United States at a 2-

km, 10-minute resolution over three years. In WWSIS, this 

meant modeling 1.2 million grid cells. The region was divided 

into four overlapping domains (see Figure 1), partly because 

the northwest domain had already been modeled under a 

separate project and partly to satisfy computational limitations 

[11]. To eliminate spatial seams resulting from this approach, 

data from overlapping regions were blended.

Fig. 1. Four modeling domains used in the WWSIS [11]. 

EWITS used a nested grid approach, using a 30-km 

resolution parent grid shown by the large box in Figure 2 and 

three smaller grids of 8 km and then finally 2-km resolution 

child grids shown by the red boxes [12]. In this way, grids of a 

manageable size could be run at a high resolution while 

maintaining the correlation of the parent grid.

At this time, detailed analysis of the spatial correlation and

consistency between these two approaches has not been 

undertaken. However, it is noted that the blending process 

used in modeling individual domains is an extra computational 

step and that such steps are opportunities for additional errors 

(especially on datasets of this size).

Fig. 2. Nested grids for the EWITS mesoscale model [12]. 

Mesoscale modeled data can display less spatial diversity 

than observations. For example, the WWSIS dataset was 

compared to six operating wind plants in Texas, totaling 

nearly 540 MW. The modeled data shows a coincident peak of 

99.8% of installed capacity for the years 2004-2006, while the 

observations show a coincident peak of 86.5%.

B. Temporal Correlation and Consistency

The mesoscale models are run for specific time intervals and 

then restarted using NNGR input data. The length of this time 

interval must strike a balance between allowing the mesoscale 

model to establish and capture mesoscale flows and patterns, 

and aligning the model with the higher level NNGR analysis 

data. Typically, the mesoscale model for the next time interval 

is spun-up for about 12 hours before data from that model is 

used, to allow the model to establish the mesoscale 

circulations. Model runs in WWSIS and EWITS were 3 and 

15-16 days, respectively.

Fig. 3. Hourly changes in wind power output for wind sites in WWSIS 

scenarios across the Western Electricity Coordinating Council/WECC for 

2006. Data has been parsed into 3-day intervals. The long rectangles show the 

one sigma variability and the whiskers show the maxima and minima [8]. 

In WWSIS, the five hours around the model restart were 

blended to minimize the impact of the temporal seam. In 

addition, one-hour deltas (hour-to-hour ramps) were checked 
3 

to ensure that no hourly ramp was outside of plausible limits. 

Despite this, the hours around the model restart displayed 

statistically significantly higher variability than other hours. 

While this did not impact the hourly production simulation 

analysis, it did impact the statistical analysis of WWSIS. As a 

result, every third day was removed from the dataset in the 

statistical analysis. Figure 3 shows the average profiles of onehour deltas for the wind plant output data for all of the 

Western Electricity Coordinating Council (WECC) when the 

data has been parsed into three-day intervals. The restarts 

occurred at 00 GMT, or 4 pm local time on Day 1.

Another potential entry for temporal issues is the 

assimilation of rawinsonde data. In EWITS, rawinsonde and 

surface observation data were assimilated every 12 hours (at 

00 and 12 GMT) to align the model with observations. Figure 

4, top, shows the abrupt jumps that were found on many of the 

days. Figure 4, bottom, shows the effect of replacing the 

affected hours with synthesized data.

Fig. 4. Jumps in wind power output due to abrupt assimilations of 

observations every 12 hours, showing before (top) and after (bottom) the fix

[12]. The left Y-axis shows wind plant output in megawatts/MW, the right Yaxis shows changes in wind output in MW, and the X-axis shows time in 

GMT. The red curve is the mean output (left axis), the purple curve is the 

absolute value of the change in output from one 10-minute record to the next 

(right axis), and the blue curve is the change in output (right axis).

As a follow-up to EWITS, the same methodology was used 

to develop a higher resolution dataset for Hawaii [13]. While 

the fix shown in Figure 4 resolved the discontinuity in the 

wind power output, a discontinuity remained in the first 

derivative. The Hawaii power system is much smaller and the 

penetration levels studied much higher than in EWITS, so this 

discontinuity in the first derivative needed to be eliminated for 

the integration analysis. The high frequency signal was 

retained and 10-minute wind speed changes (eliminating the 

unrealistic jump) from that signal was randomly applied to the 

wind speeds (see Figure 5).

In the Hawaii dataset, the largest wind event (a significant 

wind down ramp at three islands) happened to occur at a time 

both when the model was restarted and when rawinsonde data 

was assimilated. To determine whether the event was due to 

the model restart or the observational assimilations, three runs 

were compared: the original run with the restart and 

assimilation, the original run without the assimilation of data 

and a run without the restart but with assimilation of data. The 

results of these three runs are shown in Figure 6 for one site. 

They show that the model restart was the culprit for the large 

ramp event at 00 GMT on Oct. 16, and that the assimilation of 

rawinsonde data was not a factor.

Fig. 5. Ten-minute absolute changes in wind speed at wind plant in Hawaii 

before adjustment (red), after adjustment (green), and observed (blue) [13]. 

Fig. 6. Comparison of three different model runs: original run with restart and 

data assimilation (blue), data assimilation with no restart (green), and no 

assimilation/no restart (red) [13]. Y-axis shows wind speed in m/s and x-axis 

shows time in GMT. Prior to 00 GMT on 16 Oct, the green trace overlays the 

blue trace so that it is not easily seen in this graph. 

The temporal seam at model restart was judged too difficult 

to fix. In integration studies, detailed examination of 

significant events is often conducted to ensure the power 

system can operate through these events or to determine what 

mitigation options must be undertaken so the system can 

operate through these events. It was recommended that the 

selection process for significant events ignore the model 

restarts (every 15-16 days).

It is important that integration analysts have a detailed 

understanding of how their wind data was generated so that 

they can identify and resolve periods of increased variability 
4 

that may be artifacts of the modeling process and not truly 

representative of wind plant behavior.

C. Wind Speed Validation

To check the accuracy of the wind speed modeling, detailed 

validation analyses are conducted to compare modeled data to 

anemometer observations during the same time period. It is 

important to ensure that the same data used in developing the 

modeled data is not used in validating the modeled data.

Fig. 7. Locations of publicly available tower data that were used to validate 

wind speeds in WWSIS [11]. 

TABLE I 

SUMMARY OF RESULTS FROM WIND SPEED VALIDATION OF WWSIS DATASET 

AGAINST 28 PUBLICLY AVAILABLE TOWER OBSERVATIONS [11]

Tower State Height [m] Observed wind 

speed [m/s]

Modeled wind 

speed [m/s]

3001 AZ 30 5.47 5.95

3002 AZ 30 6.40 6.23

3003 AZ 30 5.43 5.47

3006 AZ 30 4.80 4.47

4402 CA 47 4.24 4.40

4403 CA 61 5.89 4.55

6001 CO 44 7.37 6.47

6008 CO 20 5.29 4.84

6009 CO 20 4.30 4.36

6013 CO 80 4.80 6.02

6029 CO 20 5.87 5.27

6039 CO 50 7.71 7.56

12111 ID 60 6.49 6.03

12131 ID 20 5.90 5.01

12439 ID 20 5.51 4.25

12500 ID 20 6.29 4.39

12505 ID 20 5.45 3.79

26007 MT 30 7.39 6.24

26010 MT 40 7.62 6.59

28001 NV 50 4.62 4.82

28002 NV 50 4.46 5.50

28003 NV 50 5.46 4.97

31010 NM 70 8.54 8.07

31011 NM 39 7.02 6.96

34018 ND 40 7.07 6.98

44003 UT 20 3.98 3.76

44022 UT 20 4.90 4.48

44999 UT 50 6.42 6.31

WWSIS used a MOS (model output statistics) correction to 

adjust the raw mesoscale model output. The correction was 

used with the Rapid Update Cycle (RUC) dataset produced by 

the National Oceanic and Atmospheric Administration 

(NOAA) and NCEP. The RUC dataset extensively uses 10-m 

towers which can make this correction less valid in areas of 

complex terrain, so the correction was weighted.

In WWSIS, wind speed validation was undertaken with 34 

publicly available towers and also some proprietary tower 

data. The locations of 28 of the public towers (Pacific 

Northwest towers are not shown here) are shown in Figure 7. 

Note that there is limited publicly available observational data, 

especially in Wyoming where some of the best resources are 

located. Proprietary data can help to some degree. Even where 

there are publicly available observations, many of these are far 

below hub height (20-50 meters). Periods of record are often a 

small subset of the three years (2004-2006) modeled. This 

underscores the need for publicly available wind data.

Summary results from the validation against the 28 publicly 

available towers shown in Figure 7 are shown in Table I. 

Accuracy and bias varies, even within a single state. For 

example, northeast Colorado winds are underestimated while 

winds just north of Denver are overestimated. This makes it 

difficult to apply a simple adjustment to further increase the 

accuracy of the dataset. It is important to note that these are 

wind speeds, and that wind power is proportional to the cube 

of the wind speed.

D. Wind Power Conversion

Ultimately for wind integration studies, wind power output 

is needed. Conversion of a single wind speed measurement to 

wind plant power output is a non-trivial exercise. Wind speeds 

vary across the 2 km resolution of the grids in 

EWITS/WWSIS and across wind plant layouts, which 

themselves vary from plant to plant. Output of a single wind 

turbine is stochastic, as is the output of a wind plant.

In WWSIS, a modified version of 3TIER’s SCORE 

(Statistical Correction to Output from a Record Extension) 

process was used [14]. SCORE creates probability density 

functions (PDF) from actual wind turbine output and samples 

from those PDFs to generate hypothetical wind turbine output. 

SCORE-lite simplified the process for the large number of 

sites in WWSIS; instead of applying the PDFs to each wind 

turbine, the PDFs were applied to each grid cell which 

contained 10 wind turbines. The Vestas V90 3-MW turbine 

was modeled with a hub height of 100 m, based on observed 

PDFs from wind plants with these turbines.

Mesoscale models tend to produce wind speeds that are 

excessively smooth. The SCORE process adds back in some 

variability so that the wind plant output is more realistic.

In EWITS, AWS Truepower used their SynOutput 

procedure. AWS Truepower’s 20-km historical dataset for 

1997-2007 and 200-m wind maps were used to scale the mean 

wind speeds for each site. Ten validation towers were used to 

adjust the raw data, depending on which tower was most 

representative of each particular site. An annual adjustment 

was made. Three synthetic power curves were used for each 

IEC class with the IEC 1 and 2 turbines having a hub height of 

80 m, and the IEC 3 turbine at 100 m.
5 

Corrections are then made to the wind speed output to 

ensure that the wind plant power output is realistic. This 

includes adding in the turbulent kinetic energy (TKE) and

applying a time filter to mimic spatial averaging across the 

grid cell. Wake losses, electrical losses, and plant availability 

were all applied to produce final wind plant outputs.

It should be noted that neither of these methods is 

attempting to exactly recreate specific plant output over a 

specific time window. Instead, they attempt to capture the 

statistical characteristics of the variability of wind plant output 

over different timescales. Aggregated over a region, wind 

ramps tend to occur over the tens of minutes and hours 

timeframe, so these methods may provide suitable accuracy 

for system studies. However, for small systems with a small 

number of wind plants, this data may need further evaluation 

to determine applicability in system studies.

E. Wind Ramp Validation

The most important validation of a wind dataset for use in 

integration studies is of the variability of wind plant output. 

Integration studies examine the impacts of increased 

variability due to wind on the power system and what may be 

needed to maintain reliability of the power system. Capturing 

this variability correctly is important to ensure reliability 

without overinvestment in unnecessary mitigation options.

Figure 8 shows the comparison of modeled and observed 

wind plant output ramps for a wind plant in Hawaii.

Fig. 8. Comparison of modeled and observed 10-minute and 60-minute wind 

plant output ramps for a Hawaiian wind plant [13]. 

Figure 9 shows the comparison of 10-minute wind plant 

output ramps for a region in the western U.S. It should be 

noted that when validating ramping behavior at specific wind 

plants, significantly different results were found, depending on 

which sites were selected to model the plant even though all 

sites were very close to each other.

F. Issues with Wind Plant Output Observations

Even observed wind plant output is not free from problems. 

Wind plants are curtailed; there may be higher than normal 

outages in the period of record; there may be ramp rate 

limitations, etc. While validating the wind modeling in the 

Hawaii dataset, it was found that the modeled wind speeds 

closely matched the observed wind speeds, but that the wind 

power outputs were not well-matched (see Figure 10). The 

difference between the modeled and observed wind plant 

outputs resulted from a frequent curtailment of the wind plant 

at night.

Fig. 9. Comparison of modeled and observed 10-minute wind plant output 

ramps for a wind plant in the western U.S. [8]

Fig. 10. Observed and modeled power output and wind speed at a Hawaiian 

wind plant [13]. 

The Hawaii wind dataset required wind plant output at a 2-

second interval. Modeling wind speeds at a 2-second level and 

converting to power would have introduced too many 

potential sources of error. Therefore, the entire process was 

conducted in the power domain. Ten-minute wind plant output 

data was developed using mesoscale modeling as described 

above. Two-second power output observations were collected 

from a wind plant in Hawaii. The underlying 10-minute trend 

was removed from the output so that the 2-second residuals 

remained. These 2-second residuals were applied to the 10-

minute wind plant output data. However, when the ramping 

histograms were analyzed (see Figure 11 top), it was found 

that the ramping behavior was asymmetric. This asymmetry 

was found to be due to an up-ramp rate limit of 2 MW/minute 

on the wind plant, and no down-ramp rate limit. In order to 

model a wind plant without ramp-rate limits, the up ramps 

were assumed to have the same distribution as the down
6 

 

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Arizona

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Forecast Error (%)

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Study Footprint Rest of WECC

ramps, and the 2-second data was re-run (see Figure 11 

bottom).

Power spectral density (PSD) was used to ensure the high 

frequency modeled data accurately modeled actual wind plant 

behavior. Using this technique, high frequency harmonics 

were identified in the spectra which were caused by limiting 

the sample size of the 2-second data inputs. When this 

limitation was removed, the improved spectra in Figure 12 

resulted. A kink in the spectra at 20 minutes remained, which 

was due to a fix of the abrupt jumps from the assimilation of 

observations in the modeling process.

Fig. 11. Frequency distribution of step changes in simulated 1-minute and 2-

second output before (top) and after (bottom) the workaround [13]. 

G. Forecasts 

In addition to examining the operational impacts of 

variability (ramping behavior) of wind plants, integration 

studies also typically examine the impact of uncertainty 

(forecast error). There are many ways to develop forecast 

datasets and it is difficult to determine which is most 

appropriate because it is typically not clear how forecasts will 

be developed and utilized. For example, will there be a 

centralized forecasting provider or will individual wind plant 

owners develop their own forecasts? This would affect the 

level of forecast error correlation between sites. What 

methodology will be used in the future to develop the 

forecasts? Will they be statistical or NWP-based? All of these 

would affect forecast error characteristics such as biases, 

spatial and temporal correlation as well as the overall error.

Fig. 12. Power spectral density of observed (blue) and modeled (red) highfrequency data for a wind plant in Hawaii [13]. 

In addition to these considerations, it is difficult to 

synthesize both ‘actual’ wind output and ‘forecasted’ wind 

output without generating forecasts that are ‘too good’ 

because the same systematic biases were built into both 

datasets.

Fig. 13. Forecast error by state/region [8].

In WWSIS, the forecasts were developed by running the 

same NWP and WRF datasets, but instead of using NNGR as 

the input dataset, it used the Global Forecast System (GFS), 

which is the information used to perform state-of-the-art 

forecasting. This was run at an hourly, 6-km resolution as a 

single large domain across the western U.S. Day-ahead hourly 

forecast errors were 12-16% mean absolute error (MAE) on a 

state basis, but 8% over the study footprint and 7% across all 

of WECC, which seemed reasonable. However, there was a 
7 

significant positive bias in the forecasts, with approximately 

10% high forecasts in the study footprint and 20% in the rest 

of WECC (see Figure 13). The forecast bias differed from 

state to state and from month to month, and the reason for the 

bias was not identified. As a workaround, in the WWSIS 

analysis, forecasts were reduced by 10% in the study footprint 

and by 20% in the rest of WECC.

After the bias was removed, forecast error was plotted 

against wind output. Figure 14 shows that highest overforecast errors occur in the mid-range of the wind output, 

when the wind output is most volatile. The under-forecast 

errors occur when the wind plants are producing their 

maximum output.

Fig. 14. Forecast error versus wind output in WWSIS [8]. 

Fig. 15. Distribution of day-ahead forecast errors for 10, 20, 30% wind 

scenarios in WWSIS [8]. 

When the bias is removed, the distribution of errors is 

reasonably symmetric (see Figure 15). While the MAE of the 

forecast errors is not very high, there are events at the tails of 

the distribution that exhibit very high error: +11,771 MW and 

– 11,515 MW. These errors are for installed wind capacity of 

26,760 MW, or nearly half of the installed capacity. These tail 

events are the events that cause issues in the operations of the 

power system. These events drive the increased need for 

reserves or other mitigation options.

In EWITS, the forecasts were generated by a statistical tool 

called SynForecast. Observations of forecasts and wind plant 

output from operational wind plants were used to develop a set 

of transition probabilities. Using a Markov chain approach, 

these were applied to the modeled wind actuals to develop the 

forecasts. Using this methodology, the forecasts had no bias 

and the forecast error distribution was the same as that of the 

input observational data.

Fig. 16. Spatial correlation between sites in ERCOT for modeled wind plant 

output (top) and modeled wind plant forecasts (bottom). Higher correlation is 

shown by warmer colors. Correlation results are shown in the upper left 

triangular halves of these plots; the bottom right does not show any data.

Because there were no observations of forecasts available as 

inputs, AWS Truepower ran its eWind forecasting service on 

the observed wind plant output from four operational wind 

plants. They ran the MASS mesomodel to develop the input 

data for the forecasting service. The resulting forecasts were 

used to develop four transition probability matrices. To 

generate forecasts for each project site, one of the four 

matrices was selected at random.

Autocorrelation, i.e., if this hour forecasts high wind, the 

next hour is likely to also forecast high wind, is captured by 

basing the probability matrix on the forecast for the current 

hour and the forecast for one hour earlier.

Spatial correlation of forecasts may be underestimated with 

this methodology. This methodology was applied to develop 

forecasts for a modeled dataset in ERCOT (Electric Reliability 

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2000

2500

< -9000-9000 – -8400-8400 – -7800-7800 – -7200-7200 – -6600-6600 – -6000-6000 – -5400-5400 – -4800-4800 – -4200-4200 – -3600-3600 – -3000-3000 – -2400-2400 – -1800-1800 – -1200-1200 – -600-600 – 00 – 600600 – 12001200 – 18001800 – 24002400 – 30003000 – 36003600 – 42004200 – 48004800 – 54005400 – 60006000 – 66006600 – 72007200 – 78007800 – 84008400 – 9000> 9000

MW

Number of Hours

FP (10%)

FP (20%)

FP (30%)

Over-forecast

Under-commitment

Under-forecast

Over-commitment
8 

Council of Texas) [15]. Figure 16, top, shows the correlation 

between different sites in ERCOT for the mesomodeled wind 

plant output and the bottom graph shows the correlation for 

the same sites for the forecasts. The forecasts display 

significantly less site-to-site correlation (~45% on average) 

than the actuals. This is an artifact of the methodology used to 

generate the forecasts. Since the wind forecasts are created by 

overlaying an error (pulled from a probability distribution of 

forecast errors) onto the actuals, this tends to weaken the 

underlying spatial correlation seen in the actuals. Over a large 

area, forecast errors may then cancel, and the accuracy of the 

aggregate forecast could be overstated. This can have a 

significant impact on day-ahead commitment decisions and 

reserve requirements in shorter timeframes.

III. CONCLUSION

In conclusion, it is essential that the developers of the data 

inputs for integration studies work closely with the analysts 

conducting the studies. Developers of wind data inputs need to 

understand which characteristics of the data are most 

important to replicate and select methodologies accordingly. 

Integration study analysts, on the other hand, need to 

understand how the data were developed and what aspects of 

the data may be artifacts of the modeling process and not 

necessarily representative of wind output. They also need to 

understand in which ways the data best represents reality and 

in which ways the data does not. Finally, both the wind data 

developers and the integration study analysts should run 

appropriate checks on the data to determine caveats for use of 

the data: where seams or abrupt jumps may exist, when 

variability or uncertainty may be under- or overstated, when 

correlation may be under- or overestimated, etc.

While significant analysis has been conducted on the 

EWITS and WWSIS datasets, they have not been exhaustively 

analyzed or checked. Further work is still needed to ascertain 

the ability of various methodologies to replicate temporal and 

spatial diversity and correlation, especially for forecasted data.

IV. ACKNOWLEDGMENT

The authors gratefully acknowledge the support of the U.S. 

Department of Energy’s Wind Program that funded this work; 

3TIER Group and AWS Truepower, who created the datasets 

analyzed in this paper; and the contributions of D. Corbus, G. 

Jordan, M. Milligan, and K. Porter in overseeing the dataset 

creation and identifying issues throughout these undertakings.

V. REFERENCES

[1] R. Wiser, et al., "2009 Wind Technologies Market Report," 

NREL/TP-6A2-48666, 2010.

[2] R. Gelman, et al., "2009 Renewable Energy Data Book," 

DOE/GO-102010-3074, 2010.

[3] EnerNex, "Xcel Energy and the Minnesota Department of 

Commerce Wind Integration Study - Final Report," September 28,

2004.

[4] CAISO, "Integration of Renewable Resources: Operational 

Requirements and Generation Fleet Capability at 20% RPS," 

August 31, 2010.

[5] GE, "Analysis of Wind Generation Impact on ERCOT Ancillary 

Services Requirements," March 28, 2008.

[6] EnerNex, "Wind Integration Study for Public Service of Colorado 

Addendum Detailed Analysis of 20% Wind Penetration," 

December 1, 2008. 

[7] E. A. DeMeo, et al., "Accomodating Wind's Natural Behavior," 

IEEE power and energy magazine, vol. 5, No. 6, Nov/Dec. 2007. 

[8] GE, "Western Wind and Solar Integration Study," NREL/SR-550-

47434, 2010.

[9] EnerNex, "Eastern Wind Integration and Transmission Study," 

NREL/SR-550-47086, 2010.

[10] B. McManus, Bonneville Power Administration, personal 

communication, 2010. 

[11] 3TIER, "Development of Regional Wind Resource and Wind Plant 

Output Datasets," NREL/SR-550-47676, Mar. 2010. 

[12] M. Brower, "Development of Eastern Regional Wind Resource 

and Wind Plant Output Datasets," NREL/SR-550-46764, Dec. 

2009. 

[13] J. Manobianco, et al., "Development of Regional Wind Resource 

and Wind Plant Output Datasets for the Hawaiian Islands," 

NREL/SR-550-48680, Jul. 2010. 

[14] C.W. Potter, et al., "Wind Power Data for Grid Integration 

Studies," Proc. 2007 IEEE Power Engineering Society Generatl 

Meeting, Tampa, FL, USA, paper No. 07FM0808, Jun. 2007.

[15] M. Brower, "Wind Generation and Forecasting Profiles," Oct. 23, 

2007.

VI. BIOGRAPHIES

Debra Lew (M’08) graduated with a MS and PhD in Applied Physics from 

Stanford University, CA, and with BS degrees in Physics and Electrical 

Engineering from the Massachusetts Institute of Technology, MA. Debra 

presently works as a Senior Project Leader in the Transmission and Grid 

Integration Group at the National Renewable Energy Laboratory. 

Charles Alonge graduated with a M.S. and B.S. in Atmospheric Science from 

University at Albany, State University of New York. He is Scientific 

Programmer in the Information Services Group at AWS Truepower where he

develops applications to analyze and disseminate atmospheric model output to 

meet various operational and research requirements.

Michael Brower received a PhD in Experimental Atomic Physics from 

Harvard University and a B.Sc. in Physics from the Massachusetts Institute of 

Technology. Dr. Brower is a Principal and the Chief Technical Officer at 

AWS Truepower, LLC where he is primarily responsible for company-wide 

research and development.

Jaclyn Frank holds a M.S. in Atmospheric Sciences from the University at 

Albany, State University of New York and a B.S. in Meteorology from 

Valparaiso University. Jaclyn currently works as a research scientist in the 

Research and Technical Services group at AWS Truepower.

Lavelle Freeman (M) received the M.S.Cp.E. degree from North Carolina 

State University, an M.S. in Electrical Engineering from University of North 

Carolina at Charlotte, and a B.S. in Electrical Engineering from the University 

of Alabama. He is a Principal in GE Energy’s Energy Consulting group where 

he manages the DSTAR program (www.dstar.org).

Kirsten Orwig graduated with a PhD and M.S. in Wind Science and 

Engineering from Texas Tech University and a B.S in Chemistry and Physical 

Science from Wayland Baptist University. She is a Systems Integration 

Analyst in the Transmission and Grid Integration Group at the National 

Renewable Energy Laboratory.

Cameron Potter (M) graduated with a PhD and BE in Power Systems 

Engineering and Artificial Intelligence from University of Tasmania, Hobart, 

Tasmania, Australia. Cameron is presently the Managing Director of 3TIER 

Group’s Australia office. 

Yih-huei Wan (M’83) received the B.S. degree in electrical engineering from 

the National Cheng Kung University, Taiwan, in 1973, and the M.S. degree in 

electrical engineering from Southern Illinois University, Carbondale, in 1979.

He is currently a Senior Engineer with the National Wind Technology Center, 

National Renewable Energy Laboratory, Golden, CO, where he manages the 

Wind Farm Monitoring Program.
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