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

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Contract No. DE-AC36-08GO28308

Short-Term Load Forecasting

Error Distributions and 

Implications for Renewable 

Integration Studies

Preprint

B.-M. Hodge, D. Lew, and M. Milligan

To be presented at the IEEE Green Technologies Conference

Denver, Colorado

April 4–5, 2013

Conference Paper

NREL/CP-5500-57340

January 2013
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1 

Abstract—Load forecasting at the day-ahead timescale is a 

critical aspect of power system operations in the unit 

commitment process. It is also an important factor in renewable 

energy integration studies, where the combination of load and 

wind or solar forecasting techniques create the net load 

uncertainty that must be managed by the economic dispatch 

process or with suitable reserves. An understanding of the load 

forecasting errors that may occur in this process can lead to 

better decisions about the amount of reserves necessary to 

compensate for the errors that do occur. In this work, we 

performed a statistical analysis of the day-ahead (and two-dayahead) load forecasting errors observed in two independent 

system operators for a one-year period. Comparisons were made 

with the normal distribution commonly assumed in power system

operation simulations used for renewable power integration 

studies. Further analysis identified time periods when the load is 

more likely to be under- or overforecast.

Index Terms—forecasting, load modeling, power system 

analysis computing, statistical distributions

I. INTRODUCTION

oad forecasting has always played an important role in 

power system operations, giving an estimate of the 

amount of generating capacity that must be available at any 

future point in time. Accurate forecasting in the short-term, 

defined here as the hours-to-days time frame, can lead to more 

economic system operation. This increased efficiency is 

accomplished through more accurate scheduling, which 

decreases the amount of out-of-merit dispatch caused by the 

combination of forecasting errors and unit ramping limits, and 

a reduction in reserves. Scheduling in power system 

operations is conducted through the unit commitment and 

economic dispatch process. Unit commitment involves 

determining which generating units will be turned on during 

future time periods. It is a necessary process and is often 

performed at a day-ahead interval because some thermal units 

require long start-up and shutdown times. Because the unit 

commitment process determines the availability of units for 

the next day, accurate day-ahead load forecasts are required to 

ensure that production can meet demand in an economic 

 The authors are with the National Renewable Energy Laboratory, Golden, 

CO 80020 USA (e-mail: bri-mathias.hodge@nrel.gov, debra.lew@nrel.gov, 

michael.milligan@nrel.gov).

manner. Dispatch is the process by which the unit outputs are 

fine-tuned to match the more accurate load forecasts produced 

at shorter timescales.

 The unit commitment and economic dispatch processes 

form the basis for renewable power integration study 

simulations such as the Western Wind and Solar Integration 

Study [1]. Load forecasting can be included in these studies, 

to see how the interaction of load and wind or solar 

forecasting errors impact system operations. Many previous 

studies have assumed that the load forecast errors follow a 

normal distribution [2-5]. This assumption has been made for 

the day-ahead and hour-ahead timescales commonly used to 

represent the unit commitment and economic dispatch time

frames. This assumption is often made without an examination 

of real forecast data from the area under study and can impact 

study results.

In this work, we studied the statistical properties of 

short-term load forecast errors, made comparisons to the 

commonly assumed normal distribution, and suggested an 

alternative model distribution. 

II. METHODS AND DATA

In this section, we describe the load forecasting data used 

in this study and the methods used in the analysis. Section IIA describes the load forecasting data used in the study, while 

Section II-B discusses the statistical background and methods 

used for the characterization of load forecasting errors.

A. Data Utilized

In this work, we analyzed load forecasting data from two

timescales and geographic locations. The data came from two 

independent system operators in the United States: the 

California Independent System Operator (CAISO) and the 

New York Independent System Operator (NYISO). Dayahead and two-day-ahead load forecasts for each hour of the 

day, as well as matching actual load data, were obtained for 

the entire year 2010 from the CAISO Open Access SameTime Information System [6]. The mean load value during the 

course of the year was 26,186 MW, the maximum load value 

was 47,282 MW, and the minimum load value was 17,890

MW. The NYISO hourly load data and day-ahead load 

forecasts from 2010 were obtained from the NYISO Market & 

Operations Data website [7]. The mean load value for the year 

was 18,664 MW. The minimum and maximum loads were 

Short-Term Load Forecasting Error 

Distributions and Implications for Renewable 

Integration Studies

Bri-Mathias Hodge, Member, IEEE, Debra Lew, Member, IEEE, and Michael Milligan, Senior 

Member, IEEE

L
2 

11,859 MW and 33,452 MW, respectively. Fig. 1 shows the 

hourly values of the NYISO load during the course of the year 

2010.

Fig. 1. NYISO hourly load for the year 2010, in MW. 

B. Statistical Background

The range of values that a random variable may take can be 

described through the use of a probability density function.

The normal (or Gaussian) distribution is among the most 

common and has often been assumed to describe load 

forecasting errors [2-5]. The truncated normal distribution is 

often used to provide support over a fixed interval. The 

normal distribution can be fully described by the first two 

statistical moments: mean and variance; however, the third 

and fourth moments—skewness and kurtosis, respectively—

can be utilized to provide additional information about an 

observed distribution. If an observed distribution is well 

represented by the normal distribution, the skewness and 

excess kurtosis values should both be close to zero. Skewness 

is a measure of the asymmetry of a distribution; kurtosis 

provides a measure of the distribution’s peakedness and the 

weight of the distribution’s tails. The normal distribution has a 

kurtosis value of three, and thus the excess kurtosis is the 

kurtosis value minus three. Because we used the normal 

distribution as a point of reference in what follows, when we 

subsequently refer to kurtosis we specifically refer to the 

excess kurtosis value. A distribution with a high kurtosis 

value is known as a leptokurtic distribution; a lower kurtosis 

value is described as platykurtic.

The R statistical computing environment [8] was used in the 

analysis work performed in this study, and the hyperbFit

function of the HyperbolicDist package [9], in particular, was

used to characterize the distributions and provide estimates of 

the hyperbolic distribution parameter values. The Shapiro-Wilk 

[10] normality test was used to test whether an observed 

distribution comes from a normally distributed population.

III. RESULTS

 To understand the impact of load forecasting errors on 

system operations and renewable power integration studies, 

we analyzed and characterized the distribution of load forecast 

errors that occurred during 2010 in the NYISO and CAISO 

balancing areas. Section III-A examines the distribution of 

day-ahead load forecast errors in the CAISO system. A similar 

analysis of the two-day-ahead load forecast errors in CAISO 

is presented in Section III-B. The NYISO day-ahead load 

forecast errors are examined in Section III-C. Section III-D 

discusses some of the consequences of erroneously assuming 

that load forecasting errors are normally distributed.

A. CAISO Day-Ahead Load Forecasts

Day-ahead load forecasts are important in power system 

operations because, through the unit commitment process,

they help determine which slow-starting thermal power plants 

(e.g., coal and nuclear plants) will be on during which hours 

of the next day. Load forecast errors can therefore cause a 

suboptimal commitment of thermal generation in the dayahead market. System operators are accustom to uncertainty in 

load and therefore have methods of dealing with forecasting 

inaccuracies (e.g., regulation reserve and hour-ahead 

dispatch); however, large load forecast errors can have large 

negative consequences for system operation. One traditional 

method of examining statistical distributions is through the 

plotting of a histogram. A critical parameter in the plotting of 

a histogram is the choice of the number of bins. Because we 

are particularly interested in the tails of the distributions—i.e., 

the large forecast errors—we chose n = 200, a number that is 

greater than the recommendation from Scott’s rule [11]. Fig. 2 

shows the observed day-ahead load forecast errors for the 

CAISO system in 2010. The dotted line shows a normal 

distribution with the same mean and standard deviation as the 

observed errors. The blue line is a hyperbolic distribution fit 

to the observed errors. The observed error distribution is more 

peaked, with narrower shoulders and larger tails than the 

normal distribution assumption would suggest. One of the 

most critical features of the observed distribution is the 

negative mean bias, represented by a mean value of -84.71 

MW. The distribution is also positively skewed and 

leptokurtic. It is also important to note the spread of the 

forecast errors, with both positive and negative errors of 

approximately 4 GW. These are very significant errors on a 

system with a mean load of approximately 26 GW, and would 

require very large corrective actions before the actualization 

time, at high economic cost, to prevent reliability issues.
3 

Fig. 2. Histogram of the observed day-ahead forecast errors in the CAISO 

system in MW, μ = -84.71; σ = 851.05; γ = 0.44; κ = 0.92. The blue line 

represents a hyperbolic distribution fit to the data, with parameter values: π = 

0.274, ζ = 3.076, δ = 1,156.279, μ = -564.815. The black dashed line 

represents a normal distribution with the same mean and standard deviation.

Although the examination of the histogram of day-ahead 

load forecast errors seems to indicate that the distribution is 

non-normal, additional assurance was provided by utilizing a 

normal quantile-quantile (Q-Q) plot. Fig. 3 shows a normal QQ plot of the CAISO day-ahead load forecast errors. The line 

drawn passes through the first and third quartiles of the 

observed errors and should pass through all of the data points 

if the data is from a normal distribution. However, we noticed 

significant deviations from the normal distribution, most 

pointedly in the tails of the observed error distribution.

Additional assurance of non-normality was provided by 

performing a Shapiro-Wilk [10] test on the data. The null 

hypothesis of the CAISO day-ahead forecast error data 

coming from a normal distribution was rejected at a 

significance level of α = 0.000001, i.e., the 99.9999% 

confidence interval.

Fig. 3. Normal Q-Q plot for the CAISO day-ahead forecast errors. The line in 

the graph passes through the first and third quartiles of the observed data and 

should pass through all of the data points if the distribution is normal. 

Another means by which distributions may be compared is 

through the plotting of their cumulative distribution functions.

A cumulative distribution plot—like that shown in Fig. 4—

also allows us to compare the fit of the proposed hyperbolic 

distribution to both the observed errors, and a normal 

distribution with the same first two statistical moments. One 

can observe that the normal distribution shows significant 

deviations from the observed errors in the tails and shoulders 

of the distribution. On the other hand, the hyperbolic 

distribution fit to the data parallels the observed error 

distribution almost exactly, with only minor deviations in the 

transition area between the shoulders and tails of the 

distributions.

Fig. 4. Cumulative distribution plot for the day-ahead forecast errors in the 

CAISO system during a 12-month period. The red line represents a normal 
4 

distribution with the same mean and standard deviation. The blue line 

represents a hyperbolic distribution fit to the data with: π = 0.274, ζ = 3.076, δ 

= 1,156.279, μ = -564.815. 

Although the figures above are useful for examining the 

whole range of errors observed, they do not give any 

indication as to the timing of the errors. The histogram, Q-Q, 

and cumulative distribution plots provide an aggregated view 

of the errors that is not particularly useful for improving 

forecasts. By disaggregating the data into hourly and monthly 

pieces, we can analyze when the forecasts are most accurate

and inaccurate, with the goal of considering the temporallyspecific conditions at those times to improve the forecasts.

Fig. 5 provides a heat map of the mean hourly forecast error 

per month. This indicates that load forecasting tends to 

underforecast the demand during the morning load ramp, 

especially during the summer months. The forecasting also 

tends to underforecast significantly during the early evening 

hours of the winter months. Another interesting result is that 

the summer late night months tend to be overforecast. That the 

summer months provide the most trouble for the forecast is 

not surprising; they have the largest daily variability, as shown 

in Fig. 6, which shows the hourly CAISO load for the entire

year 2010. Based on the results of examining the heat maps, 

we noticed that the forecasts have difficulty forecasting the 

timing of the morning and evening up- and down-ramps, a 

problem that is most pronounced during the summer months.

Fig. 5. Heat map of the mean CAISO day-ahead forecast errors for each hour 

of the day per month of the year. 

Fig. 6. CAISO hourly load for the year 2010, in MW. Notice the increased 

daily variability during the summer months. 

B. CAISO Two-Day-Ahead Load Forecasts

 Two-day-ahead load forecasts are not as widely utilized as 

day-ahead forecasts, but they can play an important role in 

reliable system operations. Sometimes a look ahead greater 

than 24 hours is used in the unit commitment and economic 

dispatch process to prevent time boundary issues with slowstarting thermal units. In this case, a two-day-ahead load 

forecast enables more efficient commitment through the 

generation of a 48-hour schedule, where only the first 24 

hours are binding. Alternatively, the two-day-ahead forecast 

can be used to ensure supplemental resource availability from 

units that have longer than day-ahead start or minimum run 

times. Although small errors in the two-day-ahead forecast are 

not important (because of the subsequent additional unit 

commitment performed at the day-ahead timescale), large 

errors could result in insufficient reserve capacity availability.

For this reason, the tails of the two-day-ahead load forecast 

errors are the most important feature of the distribution. Fig. 7 

shows a histogram of the two-day-ahead load forecasting 

errors observed during one year in the CAISO system.

Compared to the day-ahead forecast errors, the two-day errors 

were more biased, with a mean value of approximately -180 

MW, and had a slightly higher standard deviation. They also 

were less skewed (or more symmetric) and more leptokurtic.

Interestingly, the maximum positive forecast error was greater 

for the day-ahead forecast (4,163 MW vs. 3,380 MW), while 

for the negative forecast errors the two-day-ahead forecast 

error is larger (-3,838 MW vs. -4,972 MW), as expected, 

because the day-ahead forecast can incorporate more recent 

information.
5 

Fig. 7. Histogram of the observed two-day-ahead forecast errors in MW for 

the CAISO system, μ = -181.63; σ = 879.52; γ = 0.03; κ =1.46. The blue line 

represents a hyperbolic distribution fit to the data, with parameter values: π = 

0.084, ζ = 1.872, δ = 873.105, μ = -318.944. The black dashed line represents 

a normal distribution with the same mean and standard deviation.

Examining the Q-Q plot of the two-day-ahead forecasts in 

Fig. 8, the deviations from normality are clearly demonstrated 

when a comparison was made between the tails of the 

observed distribution and a line through the first and third 

quartiles. Again, a Shapiro-Wilk test confirmed the nonnormality with the null hypothesis of the CAISO two-dayahead forecast error data coming from a normal distribution 

being rejected at a significance level of α = 0.000001, i.e., the 

99.9999% confidence interval.

Fig. 8. Normal Q-Q plot for CAISO two-day-ahead forecast errors. The line in 

the graph passes through the first and third quartiles of the observed data and 

should pass through all of the data points if the distribution is normal. 

 As shown in the cumulative distribution plot of the twoday-ahead forecasts in Fig. 9, the fit hyperbolic distribution 

provided a more accurate representation of the observed errors 

than did the normal distribution. This is particularly evident in 

the tails of the distribution, which are underrepresented by the 

assumption of normality.

Fig. 9. Cumulative distribution plot for the two-day-ahead forecasts in the 

CAISO system during a 12-month period. The red line represents a normal 

distribution with the same mean and standard deviation. The blue line 

represents a hyperbolic distribution fit to the data with: π = 0.084, ζ = 1.872, δ 

= 873.105, μ = -318.944. 

The same general trends that were noted in the hourly 

timing of day-ahead forecasting errors were also apparent in 

the two-day-ahead forecasts, as shown in Fig. 10. The load 

was often underforecast during the morning ramp—especially 

during the summer months—and the significant 

overforecasting events tended to occur late at night and, again, 

were most severe during the summer months. There were also 

significant underforecasting tendencies noticeable during the 

early evening hours in the winter. These results intuitively 

made sense because the times of greatest errors tended to 

occur during the times of greatest variability in load, which 

coincided with the times of greatest uncertainty.
6 

Fig. 10. Heat map of the mean CAISO two-day-ahead forecast errors for each 

hour of the day per month of the year. 

C. NYISO Day-Ahead Load Forecasts

Examining the statistical moments of the NYISO day-ahead 

load forecast error distribution revealed some interesting 

results. For example, the mean of the data was nearly -450 

MW, indicating a very significant negative mean bias in the

forecast. Additionally, the distribution was significantly 

leptokurtic, with a kurtosis value of 3.8. This is clearly visible 

in Fig. 11, where the normal distribution is much less peaked 

than the observed error distribution. There was also 

significantly more kurtosis than was evident in the CAISO 

day-ahead load forecasts. In addition, the observed error 

distribution was thinner through the shoulders than the normal 

distribution and has heavier tails. The spread of the forecast 

errors was similar to that of the CAISO errors, but the NYISO 

system had a significantly lower mean yearly load. The high 

kurtosis value indicated that although the forecasts were more 

accurate than expected from the normal distribution much of 

the time, when there were very significant forecasting errors, 

they were greater than they would have been assumed with the 

normal distribution. The distribution also had significant 

positive skewness.

Fig. 11. Histogram of the observed day-ahead forecast errors in MW for the 

NYISO system, μ = -447.26; σ = 665.21; γ = 0.38; κ =3.80. The blue line 

represents a hyperbolic distribution fit to the data, with parameter values: π = -

4.189 E-5, ζ = 0.023, δ = 10.076, μ = -446.904. The black dashed line 

represents a normal distribution with the same mean and standard deviation.

A Q-Q plot of the forecast errors provided evidence of nonnormality, as shown in Fig. 12. The observed errors form the 

S shape commonly seen in heavy-tailed distributions. A 

Shapiro-Wilk test confirmed the non-normality with the null 

hypothesis, that the NYISO day-ahead forecast error data 

came from a normal distribution, being rejected at a 

significance level of α = 0.000001.

Fig. 12. Normal Q-Q plot for the NYISO day-ahead forecast errors. The line 

in the graph passes through the first and third quartiles of the observed data 

and should pass through all of the data points if the distribution is normal. 
7 

The cumulative distribution plot in Fig. 13 shows 

significant differences between the normal distribution and 

observed errors for the NYISO day-ahead load forecast errors.

Most important were those observed in the tails of the 

distribution. There were also noticeable, though much smaller, 

differences between the fit hyperbolic distribution and the 

observed errors. This demonstrates that although the 

hyperbolic distribution shows significant improvements from 

the normal distribution in use as a model distribution, it was 

not an exact representation of the observed forecast errors.

Fig. 14 shows an alternative means of comparing the fit of the 

hyperbolic distribution, a Q-Q plot between the observed data 

and the fit hyperbolic distribution. Note the improved fit 

compared to Fig. 12.

 Fig. 15 shows a heat map of the mean hourly forecast 

error per month. The figure shows a tendency to strongly 

underforecast the load during the morning ramp, with the 

largest errors occurring during the month of March. The 

variable weather during this month also led to the strongest 

tendency to overforecast, namely during the late-night hours.

Generally speaking, load was often underforecast in the 

NYISO system, with the mean forecast error of approximately 

-450 MW. By comparison, the CAISO day-ahead forecast 

errors showed a mean bias less than -100 MW.

Fig. 13. Cumulative distribution plot for the day-ahead forecasts in the NYISO 

system during a 12-month period. The red line represents a normal distribution 

with the same mean and standard deviation. The blue line represents a 

hyperbolic distribution fit to the data with: π = -4.189 E-5, ζ = 0.023, δ = 

10.076, μ = -446.904. 

Fig. 14. Q-Q plot for the NYISO day-ahead forecast errors compared with the 

hyperbolic distribution fit to the data. 

Fig. 15. Heat map of the mean NYISO day-ahead forecast errors for each hour 

of the day per month of the year. 

D. Implications of Non-Normality

We have shown that day-ahead and two-day ahead load 

forecasting errors do not follow a normal distribution for these 

individual independent system operators, but what are some of 

the consequences of this finding for power system operations 

and renewable power integration studies? Because the 

distribution of observed errors is more leptokurtic than the 
8 

normal distribution, this implies that assuming the normal 

distribution underrepresents the tails of the distribution.

Therefore, there will be more frequent large forecasting errors 

that occur than are assumed, which can lead to 

undercommitting generating units in the day-ahead market.

This suboptimal commitment will lead to more expensive, 

fast-acting units having to come online after the unit 

commitment phase, or perhaps during the economic dispatch 

stage, instead of cheaper baseload units. This will raise the 

cost of electricity during these time frames. In addition, both 

systems examined show significant negative mean bias in the 

forecasts. This contrasts sharply with the zero mean bias 

normal distribution assumption commonly made in the 

renewable integration study literature. This consistent 

underforecasting of load can have significant economic 

consequences, as if the forecasts were zero mean biased more 

of the load could be fulfilled through lower-cost baseload 

units, and more expensive, fast-acting units would be utilized 

less to accommodate the underforecasting errors.

IV. CONCLUSION

In this study, we analyzed and characterized the load 

forecasting errors that occur in relatively large balancing areas 

at the day-ahead and two-day-ahead timescales. The forecast 

error distributions have been shown to not follow a normal 

distribution, and the hyperbolic distribution has been proposed 

as a more accurate means of modeling the distribution. This is 

consistent with findings from wind and solar power forecasts, 

both at the day-ahead, and smaller timescales, and for many 

different geographic locations [12-16]. In addition, the 

forecasting errors were disaggregated into months and hours 

of the day, and the times where large forecasting errors are 

most likely to occur were identified. These results can be 

utilized in power system operations studies, including wind 

and solar integration studies, to more accurately model the 

variability and uncertainty faced in system operations. The 

significant non-normality and mean biases of the load 

forecasting error distributions could have significant economic 

consequences in these studies. The examination of the 

operating cost results from simulations run with the zero mean 

bias normal distribution forecast error assumption and those 

performed with forecast error results based on historical data 

for the system under study is an area for future examination.

V. REFERENCES

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

47434, 2010.

[2] M. Ortega-Vazquez and D. Kirschen, "Estimating Spinning 

Reserve Requirements in Systems With Significant Wind Power 

Generation Penetration," IEEE Transactions on Power Systems, 

vol. 24, pp. 114-124, 2009.

[3] Y. Makarov, C. Loutan, J. Ma, and P. de Mello, "Operational 

Impacts of Wind Generation on California Power Systems," IEEE 

Transactions on Power Systems, vol. 24, pp. 1039 - 1050, 2009.

[4] R. Doherty and M. O'Malley, "A New Approach to Quantify 

Reserve Demand in Systems With Significant Installed Wind 

Capacity," IEEE Transactions on Power Systems, vol. 20, pp. 587 - 

595, May 2005.

[5] F. Bouffard and F. Galiana, "Stochastic security for operations 

planning with significant wind power generation," in 2008 IEEE 

Power and Energy Society General Meeting, Pittsburgh, PA, 2008.

[6] CAISO. (2012). CAISO Open Access Same-time Information 

System (OASIS). Available: http://www.oasis.caiso.com

[7] NYISO. (2011). Market & Operation Data. Available: 

http://www.nyiso.com/public/markets_operations/market_data/pow

er_grid_data/index.jsp

[8] "R: A Language and Environment for Statistical Computing," ed. 

Vienna, Austria: R Foundation for Statistical Computing, 2010.

[9] D. Scott, "HyperbolicDist: The hyperbolic distribution," ed, 2009.

[10] S. Shapiro and M. Wilk, "An analysis of variance test for normality 

(complete samples)," Biometrika, vol. 52, 1965.

[11] D. Scott, "On optimal and data-based histograms," Biometrika, vol. 

66, pp. 605-610, 1979.

[12] B.-M. Hodge, E. Ela, and M. Milligan, "The Distribution of Wind 

Power Forecast Errors from Operational Systems," in 10th 

International Workshop on Large-scale Integration of Wind 

Power, Aarhus, Denmark, 2011.

[13] B.-M. Hodge, E. Ela, and M. Milligan, "Characterizing and 

Modeling Wind Power Forecast Errors from Operational Systems 

for Use in Wind Integration Planning Studies," Wind Engineering, 

vol. 36, pp. 509 - 524, 2012.

[14] B.-M. Hodge, M. Hummon, and K. Orwig, "Solar Ramping 

Distributions over Multiple Timescales and Weather Patterns," in 

The 1st International Workshop on Integration of Solar Power into 

Power Systems, Aarhus, Denmark, 2011.

[15] B.-M. Hodge and M. Milligan, "Wind Power Forecasting Error 

Distributions over Multiple Timescales," in IEEE Power & Energy 

Society General Meeting, Detroit, MI, 2011.

[16] B.-M. Hodge, D. Lew, M. Milligan, H. Holttinen, S. Sillanpaa, E. 

Gomez Lazaro, R. Scharff, L. Soder, X. G. Larsen, G. Giebel, D. 

Flynn, and J. Dobschinski, "Wind Power Forecasting Error 

Distributions: An International Comparison," in 11th Annual 

International Workshop on Large-Scale Integration of Wind Power 

into Power Systems as weel as on Transmission Networks for 

Offshore Wind Power Plants, Lisbon, Portugal, 2012.

Bri-Mathias Hodge (M’10) received a B.S. degree in chemical engineering 

from Carnegie Mellon University. He received an M.S. from Åbo Akademi 

and completed a Ph.D. in chemical engineering at Purdue University. He is 

currently a research engineer in the Transmission and Grid Integration Group 

at NREL. His research interests include energy systems modeling, simulation, 

and optimization.

Debra Lew (M’08) graduated with M.S. and Ph.D. degrees in applied physics 

from Stanford University, and with B.S. degrees in physics and electrical 

engineering from the Massachusetts Institute of Technology. Lew presently 

works as a senior project leader in the Transmission and Grid Integration 

Group at NREL. Her fields of interest include high penetrations of wind 

power in power systems and transmission planning to accommodate wind 

power. 

Michael Milligan (M’98, SM’10) received a B.A. degree from Albion 

College, in Albion, Michigan, and M.A. and Ph.D. degrees from the 

University of Colorado at Boulder. He is part of the Transmission and Grid 

Integration Group at NREL, where he has worked on various aspects of 

electricity supply related to wind energy since 1992. He has worked on issues 

such as the ancillary service impacts of wind generation, the value of accurate 

wind forecasting, optimal selection of geographically dispersed wind power 

plants, modeling wind plant variability, and reliability contribution of wind 

power plants. He has authored or coauthored more than 120 reports and book 

chapters and has served on numerous technical review committees for wind 

integration studies around the United States.