Conference Paper

NREL/CP-550-48944

July 2010

Advancing Wind Integration Study 

Methodologies: Implications of 

Higher Levels of Wind

Michael Milligan, Erik Ela, Debra Lew, Dave Corbus, 

and Yih-huei Wan 

Presented at WindPower 2010

Dallas, Texas

May 23–26, 2010
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1

Advancing Wind Integration Study Methodologies:

Implications of Higher Levels of Wind

Michael Milligan, Erik Ela, Debra Lew, Dave Corbus, and Yih-huei Wan

National Renewable Energy Laboratory (NREL)

michael.milligan@nrel.gov

1617 Cole Blvd. 

Golden, CO 80401

Abstract

Wind integration studies are now routinely undertaken by utilities and system operators to 

investigate the operational impacts of the variability and uncertainty of wind power on the 

grid. There are widely adopted techniques and assumptions that are used to model system 

operation, examine impacts on the regulation, load following, and unit commitment 

timeframes, and quantify costs. As wind penetration levels increase, some of the 

assumptions and methodologies are no longer valid and new methodologies have been 

devised. Based on involvement in conducting studies, reviewing studies, and/or developing 

datasets for studies in WECC, the Eastern Interconnect, Hawaii, and other regions, the 

authors report on the evolution of techniques to better model high penetrations (generally,

20% or more energy penetration) of wind energy. 

Introduction

Many wind integration studies have been performed in the past several years. As more high 

quality data have become available, the studies have evolved significantly, bringing a 

higher degree of realism and sophistication to modeling techniques and statistical 

approaches that are typically utilized for wind integration analysis. NREL has recently 

completed two very large integration studies; the Western Wind and Solar Integration 

Study (WWSIS) and the Eastern Wind Integration and Transmission Study (EWITS), under 

the sponsorship of the United States Department of Energy. These studies have provided 

many significant insights on integration, methods, and data. However, there is much yet to 

be learned, and methods can still evolve. In this paper we discuss the key technical areas of 

wind integration studies:

• Weather and wind plant modeling and diagnostics.

• How wind forecasts play a role in the integration study and issues surrounding the 

development of synthetic wind forecasts for power systems operational modeling.

• Balance-of-generation assumptions and the impact this has on the operational 

characteristics of the power system, and ability to manage the increased uncertainty 

and variability that wind brings to power system operations.

• Approaches to develop wind scenarios for high-penetration studies. 
2

We also discuss other factors that influence the ability of the system to absorb high wind 

penetration levels, such as baseload turn-down levels, and the complex topic of the 

interaction of wind with various types of reserves. 

Many wind integration studies have pursued the goal of estimating the incremental 

operating cost that is caused by wind’s variability and uncertainty. This is usually 

performed by comparing a simulation with wind power with some “no-wind” base case. 

While this appears to be a simple analysis, the many complex interactions among 

components of the power system and assumptions regarding the no-wind base case all 

have important influences on integration cost estimates. We discuss many of these 

concerns and implications, shedding some light on the difficulties involved in measuring 

and interpreting integration cost estimates. Of particular concern is whether the 

integration analysis (especially those with less formal modeling frameworks) correctly 

treats the separation of operating reserve requirements and energy, especially related to 

wind forecast errors.

Other studies focus on operational impacts and total production costs. These studies find 

that operating cost can be substantially reduced with wind. This conclusion is driven 

primarily by the essentially zero-marginal cost of wind, which in turn reduces (sometimes 

to zero or below) locational marginal prices. While this is an attractive result in the short 

run for consumers, it is not clear how these prices are sustainable, and whether they 

provide the right signals to investors and developers of new generation to provide for an 

economically optimal (or at least functional) generation portfolio with the required 

flexibility characteristics.

Because wind energy requires more flexibility from the remaining generation fleet, there are 

many potential sources of this flexibility. Examples range from different types of generation, to 

responsive load, plug-in hybrid vehicles, or storage. The required flexibility depends heavily on 

the wind and load characteristics, and on the institutional framework involved. Little work has 

been done to systematically evaluate the role of these and other ways of managing wind 

variability and uncertainty. 

Similarly, there is evidence that production simulation tools may not always be well suited 

to the task at hand. Unit commitment, the process of starting generation so that it will be 

available when needed, is more complicated with significant variable generation such as 

wind. During the period when the committed unit may be online, there could be a 

significant operating range that the unit would need to cover, subject to its physical 

capabilities. The use of wind forecasting in the unit commitment process further 

complicates this problem. In fact, we are aware of anomalous results from one integration 

study that found that a smooth wind forecast results in lower wind integration cost than an 

accurate forecast. What is not clear is whether this is caused by weaknesses in the 

simulation model or by the assumptions used to measure wind integration cost, or a 

combination of both. 

This paper begins with a discussion of wind data requirements and scenario development. The 

ability of the grid to manage high wind penetrations will be heavily influenced by the study’s 

electrical footprint, transmission network, and mix of non-wind generation. The quantity and 

characteristics of the non-wind portion of the generation fleet will depend on wind’s 
3

contribution to planning reserves and incremental requirements for flexible generation and 

scheduling practice. Because of the interdependence of these factors, they comprise the first 

chapter of this document.

We then focus on operational analysis. Recent wind integration studies in the United States

have utilized electricity production simulation models and market models to analyze the 

impact that wind will have on unit commitment decisions and operating reserves.1

Finally, we discuss integration cost analysis and describe some of the evolution in methods 

of assessing integration cost. Early methods were developed to allow the side-by-side 

comparison of costs between wind and other generation technologies. We show some of

the issues and shortcomings of these approaches, and suggest possible paths forward.

Standard production models may not have the ability to correctly manage commitment and

dispatch efficiently with high levels of wind, so separate statistical analysis of wind and 

load data is typically used to help develop operating reserve levels that are dynamically 

determined based on what the wind (and load) is doing at the time. 

Data, Scenarios, and Planning

The quality of the wind data drives the results of any wind integration analysis. In this 

section, we discuss the evolution of datasets and suggest approaches for reasonability 

checks to ensure reasonableness of the data.

Wind Data

Early studies relied on crude representations of wind plant output. With the introduction of 

numerical weather prediction (NWP) models that can create high-quality time series data 

of wind speed that can be used to produce plausible wind plant generation output, it 

became possible to simulate and evaluate alternative wind plant development options with 

realistic representations of the alternative power delivery profiles associated with each 

scenario. Early studies did not always do extensive testing and diagnostics of the wind 

speed and power data that was produced (although some of this may have been done 

internally by the meteorologists who developed the data). Today, more sophisticated 

analyses of the simulated wind plant data may check the frequency spectrum, ramping 

characteristics and density functions, statistical checks on variability that may be 

introduced by modeling artifacts such as 24-hour observational assimilation or seams 

resulting from geographic or temporal discontinuities. 

 1 We more fully define and develop the concepts related to “operating reserve” in a later section of this 

document.
4

Figure 1 − Modeling domains used for the WWSIS included the Northwest Wind Integration Action 

Plan (NWIAP) dataset shown in red, along with three other domains.

Both the WWSIS and EWITS have large datasets that are available for other uses and are 

publicly available.2

NWP models are complex and have many different parameter settings. This requires a 

knowledgeable user to exercise judgment on how best to run the model. The frequency of 

dataset assimilation, rolling in observed data to make sure the model doesn’t wander too 

far from reality, can have an influence on the model behavior and ultimately on the wind 

speed output from the model. 

The datasets each cover 2004-2006 using a 10-minute time step and a 2 

square km resolution (not all of this data is available on the web site). For integration studies, 

it is critical to use time-synchronized wind and load data to ensure that underlying weather 

drivers are properly accounted for in the statistical analyses and operational simulations. 

Unfortunately, the existing datasets are becoming somewhat stale with no immediate 

prospects to update them. This is leading to the development of questionable ad hoc methods 

to attempt the creation of high-quality, consistent wind datasets to be used for integration 

studies. Unfortunately, such methods will likely compromise integration analysis and 

interconnection planning until up-to-date datasets can be developed and kept up to date.

Figure 2 illustrates. When the model assimilates 

observations every 12 hours, the graph shows the difference (between red and green). The 

difference between the green and blue lines shows the impact of restarting or not 

restarting the NWP on Oct 16. This is clearly a large difference, and in fact led to the largest 

drop in wind power of the entire two years modeled. Of course this drop in wind power 

was simply an artifact of the modeling, and does not represent a real event.

 2 http://www.nrel.gov/wind/systemsintegration/
5

Figure 2 − Modeling in Hawaii shows the impact of alternatlve data assimilation approaches.

The WWSIS contained a component of analysis that evaluated the modeled wind speed at a 

number of selected sites (see http://wind.nrel.gov/public/WWIS/ValidationReports/NRELTower06008-Validation.pdf ).

Example Validation of WWSIS Data

The WWSIS data validation had several components. The data from the NWP (also known 

as the mesoscale model) was compared to observed data from actual wind plants in 

Colorado and Texas to perform the validation.

Five wind power plants (King Mountain, Big Spring, Brazos, Texas Wind Power Project, and 

Delaware Mountain) in Texas were chosen for comparison because of availability of wind 

sites at those locations in the 3Tier mesoscale dataset. Eighteen wind sites in the mesoscale 

dataset that closely map these 5 wind power plants were identified and their 10-minute 

hysteresis-corrected power time series of 2004 through 2006 were summed up for 

comparison with the actual 2004-2006 combined output power of these 5 wind plants.

Table 1 lists the installed capacity of the 5 wind plants and their observed peak 10-minute 

power during the 3 year period. The last row of the table lists the coincidental factor of 

these 5 wind plants. The coincidental factor (ratio of the coincidental peak to the non-
6

coincidental, which is the sum of individual plant peak output) is an indication of the 

degree of spatial and temporal diversity of wind resources among these 5 wind plants. By 

comparison, the combined mesoscale power output had a coincidental peak of 539 MW in 

all 3 years, or a coincidental factor of 99.8%, suggesting that the mesoscale model did not 

capture the full diversity of the wind resources.

Table 1 − Installed capacity for validation from 5 wind plants.

Table 2 lists the production and capacity factors of each of these locations. It can be seen from 

the table that the NWP consistently overestimates energy production for the 3-year period.

Table 2 − Comparison of NWP and actual wind energy generation.

Figure 3 shows the annual average diurnal patterns from the actual data and from the NWP 

data. It is clear from the graphs that the NWP does a reasonably good job of capturing the 

diurnal patterns of wind generation, but that does change somewhat from year to year.

Step Changes and Their Distribution

This is the area where the mesoscale model shows some deficiencies. Table 3 lists statistics 

of 10-minute and hourly step changes of actual and mesoscale data for 2004, 2005, and 2006. 

Average values are not listed because they are all very small as expected. It is clear that the 

mesoscale model overestimates the variability of wind power at those locations—large 

standard deviation values and larger extreme values.

10-minute Peak (MW)

Installed Capacity

(MW)

2004 2005 2006

King Mountain 278 237 247 253

Big Spring 34 34 33 33

Brazos 160 159 160 161

Texas Wind Power 

Project

35 39 34 40

Delaware Mountain 29 25 25 25

Combined

(coincidental)

536 461 456 474

Non-coincidental Peak 494 499 512

Coincidental Factor 93.3% 91.4% 92.6%

2004 2005 2006

GWh CF GWh CF GWh CF

Actual 1,263 31.2% 1,317 33.0% 1,415 34.1%

Mesoscale1,636 34.6% 1,512 32.1% 1,505 33.8%

% Change 29.5% 14.8% 6.4%
7 

Figure 3 − Comparison of modeled vs. actual diurnal patters of wind energy, 2004-2006 

(respectively from top to bottom). 

0

50

100

150

200

250

300

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Power (MW)

Actual Meso-scale

0

50

100

150

200

250

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Power (MW)

Actual Meso-scale

0

50

100

150

200

250

0:002:004:006:008:00

10:0012:0014:0016:0018:0020:0022:00

Power (MW)

Actual Meso-scale
8

Table 3 − 10-minute and hourly step changes.

In general the wind power time-series data from the NWP model have larger step changes 

and more extreme values. The next table shows the distribution of step change values 

grouped in terms of wind plant installed capacity. The percentage values of each row heading 

(the first column) should be read as less than the value. For example, there are 52,576 10-

minute power level changes with a magnitude of less than 10% of the plant capacity. The 

NWP model always produces more large step changes than the actual data shows.

Table 4 − Step change distributions.

10-minute 1-hour

Actual MesoscaleActual Meso-scale

2004

Std Dev 9.4 15.8 27.0 41.7

Max (+) 121 234 222 303

Max (–) -145 -216 -166 -255

2005

Std Dev 10.1 15.3 30.9 38.3

Max (+) 122 332 158 291

Max (–) -116 -311 -202 -225

2006

Std Dev 10.7 14.1 33.2 36.9

Max (+) 148 249 241 285

Max (–) -128 -190 -198 -244

2004 2005 2006

Actual MesoscaleActual MesoscaleActual Mesoscale

10-minute Data

10% 52576 52050 52400 51990 52331 52073

20% 119 560 155 449 204 401

30% 8 51 4 55 23 37

40% 0 6 0 22 1 9

50% 0 1 0 5 0 3

60% 0 0 0 2 0 0

70% 0 0 0 1 0 0

Hourly Data

10% 8257 7505 8004 7698 7732 7761

20% 498 1022 689 865 864 778

30% 24 189 65 137 142 159

40% 3 47 1 40 18 38

50% 1 13 0 12 2 13

60% 0 2 0 2 1 4

70% 0 0 0 0 0 0
9

Other Validation Approaches

The Oahu Wind Integration and Transmission Study (OWITS) utilized spectral analysis to 

diagnose and correct large 10-minute step changes in wind speed (AWS Truewind, 2009). The 

original power spectral density chart showed a divergence between the actual and modeled 

data, as evidenced by peaks in the spectra at high frequencies in the modeled data. The data 

problem was caused by binning of 2-second data blocks based on season. This limited the 

sample size and led to the data anomaly. The corrected spectral density graph shows a close 

correspondence between the actual and modeled data, as shown in Figure 4.

Figure 4 − Power spectral density of observed (blue) and modeled (red) high-frequency data from 

OWITS.

In our view, analyses such as these are critical so that the limitations to the underlying wind 

datasets are well understood. From the integration studies that have been done to date, it is 

apparent (and is probably obvious) that the renewable energy data drives the results of the 

models; therefore, efforts to ensure accuracy, or at least understand data limitations, appear 

quite valuable and desirable.
10

Synthetic Wind Forecasts

Modeling a prospective wind plant requires the use of a NWP that can simulate the 

atmosphere in time and space. Once this is done, usually at 5 to 10-minute intervals for a 3-

year period, there is a large simulated dataset that represents wind energy generation at 

the appropriate time step. However, to successfully model power systems operation, in 

addition, it is necessary to have a wind forecast that can be used in the unit commitment 

process. Because the wind data itself is simulated, methods to develop a wind forecast from 

the simulated data are challenging. Several approaches have been used and we believe that 

much more research needs to be done in this area. In our view, it is unclear which of 

several methods are appropriate for developing simulated forecasts.

Examples of approaches that we have seen used include: (1) using different NWP inputs or 

models from those used in generating the wind plant “actuals”, (2) applying statistical 

estimation techniques that are based on actual forecast error statistics. In one early study,

there was insufficient budget to develop a high-quality wind forecast, so forecast errors from 

another part of the country were applied to the area of interest, causing problems with the 

simulation results. (3) Perturbing inputs to the NWP to simulate inaccurate wind forecasts.

In addition, it is no longer sufficient to produce day-ahead forecasts and 1 to 2-hour ahead 

forecasts. Because wind generation can be more accurately forecast for shorter time 

horizons, and because of recent advances in more advanced simulation tools, frequent 

wind forecast updates (both in reality and in a modeling framework) can help with system 

scheduling. Advanced production simulation models that use rolling planning and

stochastic unit commitment, based on forecasts that can be updated every few hours, can 

reduce unit commitment errors in real time, reducing system costs. This further 

complicates the problem of producing a synthetic forecast because multiple forecasts with 

different time horizons are necessary for robust modeling with new simulation techniques.

In any case, part of a modern integration study also involves diagnostic checking of forecast 

errors using similar techniques as described for the “actual”3

Potential forecast error bias can needlessly increase integration costs, and can have 

significant impacts on other aspects of wind integration. Forecast error distributions also 

need to be checked because actual forecasting errors are typically lowest when wind output 

is stable and highest when wind output is ramping. Other systematic errors can result in 

forecasts that are “too good” or unrealistically inaccurate.

wind data. In addition, various 

comparisons between forecasts and actual data should be performed.

Figure 5 shows an example of an unbiased simulated forecast that has an over-forecasting 

trend. At low-wind power output levels, the wind tends to be over-forecast. At higher wind 

power output levels, forecast errors tend to become negative. It seems clear that diagnostic 

checking of simulated wind forecasts should include analyses that examine the statistical 

distribution of forecast errors as a function of the actual wind energy output.

 3 For the remainder of this discussion, we use the term actual to denote the simulated actual wind energy 

output based on the NWP model after applying appropriate power conversion algorithms. In cases where the 

wind energy data is from an operating wind plant, there is no need to develop simulations, and our term 

actual would therefore apply to real wind energy data.
11

Simulated forecasts and the resulting forecast errors must be accurately estimated for 

individual utility integration studies, but this may be more critical for large 

interconnection-wide integration or transmission analysis. Because the wind energy level 

over a large geographic footprint has complex, dynamic correlations based on weather 

front movements, the errors will also tend to have complex temporal relationships. These 

characteristics are not currently well known. Additional analysis of forecast errors would 

help provide the ability to improve the modeling of forecasts for integration studies, 

possibly resulting in new statistical approaches that can take weather phenomena into 

account, both temporally and geographically.

Figure 5 − This example set of wind forecasts is unbiased, but has an over-forecasting trend 

related to the level of wind.

Stochastic Unit Commitment and Wind Forecasts

There has been much recent interest in more advanced approaches to unit commitment, 

including stochastic unit commitment. This type of algorithm depends on a series of scenario 

trees, representing alternative futures that may range from one to several hours, or even 

days, in advance. Each branch of the tree represents a single scenario with a probability 

attached to its likelihood of occurrence. Although different objective functions are possible, 

expected operational cost is typically minimized with respect to the various system 

constraints and costs. For wind integration modeling, branches on the scenario tree can be 

based at least in part on the alternative realized wind, based on the wind forecast error 

distribution. When combined with a rolling unit commitment algorithm, a more robust unit 
12

commitment stack can be developed that can hedge against unlikely but severe wind forecast 

errors. In studies that utilized this type of model, it is clear that a suite of forecast values and 

associated probabilities are necessary, and need to be developed consistently and on rolling 

time periods. The role of the simulated wind forecasts in developing the unit commitment 

stack is clearly critical, and in our view this is the key limiting factor in achieving a robust 

understanding of the benefit of this type of advanced modeling. 

Stochastic methods require information about probability density functions that can 

accurately describe wind power data characteristics at any time of the day, month, or year. 

These characterizations are necessarily complex because they must take into account 

geographic locations, both absolute and relative, and reflect realistic underlying weather 

characteristics. Development of the proper scenario trees that are used for stochastic unit 

commitment remain an arcane subject, with little widespread involvement and 

understanding by the wind integration community and the meteorological community. 

More on stochastic unit commitment appears later in this paper.

Anomalies in Unit Commitment with Forecast Filters

In one recent wind integration study that NREL was involved with, some unusual results 

were found when applying wind forecasts from another region to the study footprint. 

Experiments with wind forecasting errors were carried out to assess the sensitivity of unit 

commitment and operational cost to the forecasts. 

One such analysis utilized a 6-hour rolling average of wind forecast errors. The rolling 

average filter smooths the forecast errors and reduces the accuracy of the forecasts 

generally. However, production simulation that used the rolling average forecast errors 

resulted in less production costs than when the actual, more accurate, forecasts were used.

Unfortunately, the study did not have the resources available for detailed follow-up 

analysis, so the reasons for the production cost anomaly is unknown. However, the 

following hypotheses have been suggested:

• Unit commitment algorithm does not perform well when there is a high range of net 

load values in the commitment time frame;

• The rolling average may act as a simplified form of stochastic unit commitment, 

putting the system in a “center” position that is within closer reach of the range of 

net loads that must be met;

• Actual forecasts result in putting the unit commitment stack too far in one direction 

(high or low), making it difficult to cover variations from forecasts if there are severe.

In any case, it is clear that wind forecast errors play an important role in wind integration 

studies. We think that this area of research is critical if the impacts of large-scale wind 

development on power system operations will be well understood.
13

Wind and Load Forecast Errors Both Contribute to Imbalance

Although wind forecast errors play a significant role in wind integration analysis because of 

the impact on uncertainty, these errors must be netted against load forecast errors that 

occur simultaneously. Failure to do this will result in either over-estimating or underestimating what the system operator must do in real time to bring the system into balance. 

For example, if wind unexpectedly increases its output 100 MW above forecast at the same 

time that load also increases by 100 MW, then the system operator would not need to 

modify the economic dispatch of other units.4

In our view, much more work is needed on this issue. It is clear that there is no widespread 

agreement on which approach is most appropriate to estimate future wind forecasting 

errors. In our view, we don’t know the right way to model wind forecast errors for 

integration studies.

However, if in this case the load dropped 100 

MW unexpectedly, the system operator would need to reduce output of the generation fleet 

by 200 MW. Although generally this would be done by reducing output of the non-wind 

fleet, it may be the case that some or all of the wind power would be curtailed, depending 

on the operating posture of the system at the time.

Scenario Development

Depending on the scope of the study, specific regions may be modeled in detail with entire 

grids (the Eastern or Western Interconnects) modeled at a coarser level to capture 

interchanges between specific study regions and their neighbors. As wind power 

penetrations increase, it becomes increasingly important to model the entire grid because 

significant power flows across the interchanges during times of high wind . Additionally, it 

becomes more important to model wind penetrations in neighboring regions, and 

significant wind penetrations in both a study region and neighboring regions may constrain 

how much the study region can ‘lean’ on its neighbors in accommodating large amounts of 

variable generation. In the WWSIS, it was found that increasing the penetration of wind in 

the rest of WECC had a significant operational impact on the study region.

Both WWSIS and EWITS developed a set of “book-end” cases to analyze the impact of 

alternative wind build-out scenarios on transmission and costs. For the WWSIS, alternative 

wind development patterns were based on:

• In-area development − all wind and solar generation necessary to satisfy state 

renewable energy standards were built in-state;

• Mega-projects − wind (and solar) sites were based on annual capacity factor. The 

best sites were developed irrespective of location (other than screening for 

locations that cannot be reasonably used for wind power development);

• Local-priority − a blend of the previous two cases.

The In-area scenario results in no additional required transmission between balancing 

zones because the energy is generated close to load. However, more installed wind is 

 4 Of course this discussion is somewhat simplistic, but captures the point that we are pressing; the load net 

wind is what must be balanced.
14

needed because of the relatively low capacity factors. This puts upward pressure on wind 

technology costs, but does not require significant transmission build-out or costs. The 

Mega-project case does require additional transmission build-out, which is expected 

because so much wind power is developed in low-load areas like Wyoming. WWSIS did not 

do a detailed transmission plan, but did provide an overall estimate of likely transmission 

additions required to enable the wind/solar delivery to load. These two build-out scenarios 

appear in Figure 6.

Figure 6 − WWSIS scenarios include in-area and mega-projects that result in different 

transmission expansions.

The EWITS project did a more refined analysis of transmission, but contained similar bookend scenarios to help bracket relatively extreme differences in wind development patterns. 

Three cases that represent a 20% wind energy penetration were examined, along with a 

single 30% penetration case. For this discussion we focus on the 20% cases.

Figure 7 illustrates two scenarios from EWITS. The upper panel shows a large 

concentration of wind power in the northwest portion of the footprint, which is generally 

where the best wind resources are. The lower panel shows some off-shore development 

along the Atlantic coast and less development in the northwest.
15

Figure 7 − EWITS wind locations based on heavy development in western MISO (above) and 

blended offshore/onshore case (below).

Transmission Scenario Development

Transmission has a significant impact on the ease with which wind power can be 

integrated into the power system. EWITS did considerable analysis on transmission since 

that was a part of the study. In developing the transmission scenario, there is a natural 

iteration between the resource scenario and transmission. This is best explained with 

reference to Figure 8.
16

Figure 8 − The EWITS process to develop consistent transmission and resource assumptions.

This figure illustrates the iterative process of developing a robust transmission plan, which 

is in part a function of the future load demand, and in part a function of the location and 

characteristics of the generation fleet.

Once the wind and load scenario has been developed, it is merged with the other 

generators and their characteristics. Then a ‘copper sheet’ analysis is undertaken, in which 

there are no binding transmission constraints in the study footprint. This allows the energy 

to flow unconstrained towards load areas based on prices and costs. To help enable this 

efficient flow of power, a transmission overlay plan is developed and tested by running a 

loss-of-load expectation (LOLE) study to determine whether the system has sufficient 

installed capability to meet a reliability target.5

 5 The reliability target is set by the entity performing the study. Often a 0.1 day/year LOLE target is used. 

These metrics and their use is discussed in a later section of this report.

At this stage, a wind integration analysis 
17

can be performed if desired, and the contribution of wind to resource adequacy, or capacity 

value, can be calculated using some form of the effective load carrying capability (ELCC) 

metric. Depending on the outcome of the reliability analysis, additional generation can now 

be added to the system or redundant generation can be removed. The production 

simulation can then be re-run and a new set of transmission constraints will likely emerge. 

The study then iterates between adding/evaluating transmission additions/subtractions, 

and generation additions/subtractions. Recent studies that have used this approach 

include the Joint Coordinated System Plan (www.jcspstudy.org) and EWITS, although in 

EWITS, only one iteration of the flowchart was performed because of limited resources.

Characteristics of other generation and sources of flexibility

Wind integration depends on sufficient operating range and flexibility from the balance of 

generation on the system. At low penetration rates, this is not an important issue because 

little additional flexibility, beyond that required to serve load, is typically needed. But as wind 

penetration rates increase to the level currently being analyzed (10-30% of annual electricity 

demand), the characteristics of the remaining generation fleet become more important. 

The primary types of flexibility needed to integrate large penetrations of wind power 

include lower turn-down levels on conventional generation, along with increased ramping 

capability. These are illustrated in Figure 9 and Figure 10, which are based on data from 

Milligan and Kirby (2008). The first of these graphs shows load and net load in the upper 

panel. Load ramping requirements are approximately 4,000 MW over a few-hour period, 

varying somewhat from day to day. The ramps of the net load sometimes cover a wider range 

in a similar time frame. This sample week is based on a 25% wind energy penetration 

annually, and there can be significant changes from week to week. The next of these two 

graphs shows the impact of high levels of wind energy during low load periods. The annual 

minimum load is approximately 8,000 MW, but when wind is added to the system, the 

minimum net load is less than half that amount. During those periods, it may be difficult for 

the remaining generation fleet to reduce output low enough so that the wind energy can be 

integrated. In some cases, units may be de-committed, but if they will be needed the next day 

to serve higher loads when the wind energy output is less, de-commitment may not be 

possible. There are several potential solutions to this issue; (1) markets may exist that allow 

for the excess energy to be exported, and (2) over time, the relatively inflexible generation 

can either be modified so that it can achieve lower turn-down levels, or can be retired and 

replaced with units that can be turned to a lower level of output.
18

Figure 9 − High levels of wind power penetration increase the required ramping range of the 

remaining generation fleet.

Figure 10 − The minimum net load is less than the minimum load, so lower turn-down capability is 

needed.
19

Many wind integration studies begin with a portfolio of generation that can adequately 

serve load without wind. The wind scenario is then added to the generation mix, and the 

analysis proceeds. In some ways this can be a fair representation of reality, for example, as 

was the case with Xcel Colorado which added approximately 750 MW of wind in a single 

year. However, many other studies focus on future years and the transition between the 

present and the future can include the development of complementary generation with the 

flexibility attributes that would help manage large amounts of wind. Older, less flexible 

generating units may be excluded from the scenario to test the impact of a more flexible 

generation fleet. There may be valid reasons for using either approach, but this should be 

made clear in the study.

EWITS modified the remaining generation fleet by running a generation expansion model 

(GEM) that considered the load and wind requirements together. GEMs have been utilized 

for many years, and often use a load duration curve (LDC) approach to reduce potentially 

extensive computer run-time so that large numbers of expansion cases can be examined in 

a timely fashion. As can be seen in Figure 10, a LDC does indeed consider the minimum net 

load level, so that the generation fleet chosen by the model will be able to operate during 

low load and high wind periods.

However, it is clear that the LDC does not include chronological information that can be 

used to determine the depth and frequency of ramping requirements on the system, such 

as those shown in Figure 9. It is possible that the algorithms in GEMs may need to be 

modified so that ramping can be considered in the generation expansion process, and 

research into this important issue is needed.

WWSIS took a different approach of building out the generation portfolio and achieving 

resource adequacy before adding wind and solar generation to the mix. This results in a 

generation portfolio that may make it challenging to integrate high levels of wind and solar 

energy. Baseload units are called upon to cycle more than they have in the past, raising the 

potential for higher operation and maintenance costs over the lifetime of the unit. The 

resulting system is also overbuilt somewhat, because both the wind and solar generation 

have some capacity value that is not accounted for by the system expansion model. 

However, this study is useful because it does show that, with changes to the way the power 

system is operated, even this relatively inflexible generation mix can physically respond to 

the needs of the significantly higher level of variability and uncertainty, although this will 

be a challenge at the 35% penetration rate in the study.

In addition to physically available flexibility to help mitigate wind impacts, institutional 

factors can also be significant. We discuss work that has examined some of these 

institutional constraints, and discuss limitations in the current state of knowledge about 

the institutional flexibility that is available from physically flexible hydro systems. 

Controllable hydro may offer promise in integrating wind power, but the lack of publicallyavailable information and data about hydro operations has provided another institutional 

roadblock to determining how hydro can play a potentially significant role in helping to 

manage wind generation.

Determining the flexibility that can be tapped in the existing generation fleet can also be 

complicated. For example, WWSIS uses data from WECC that contains maximum and 
20

minimum capacities and ramp rates for each thermal generator. However, at least one 

utility expressed concern that the coal units may have operated outside of their limits 

during some particularly high-wind time periods, causing the coal units to be dispatched to 

minimums. It is clear that assumptions regarding minimum turn-down levels, the type of 

minimum turn down (emergency or economic), and the duration that the unit can operate 

in these ranges are all important and will have potentially significant impacts on the ability 

of the power system to successfully absorb significant wind energy. WWSIS addressed this 

issue by examining a range of minimum run levels on the coal fleet.

Kirby and Milligan (2005) calculated the ramping capability of thermal generation in 

several regions of the United States. The overall approach was to utilize hourly emissions 

data collected by the U.S. Environmental Protection Agency in the Continuous Emissions 

Monitoring System (CEMS). The Platts database was used to extract hourly generation 

levels from all thermal plants. From that data, ramping and turn-down levels can be 

calculated based on actual plant performance. Units’ startup and shutdown cycles must be 

evaluated carefully so as not to distort the actual capability of the unit. This approach can 

be used to estimate a lower bound of the flexibility in a given power system. Because only 

data from emitting plants are collected by CEMS, hydro and nuclear data are not available 

for this type of analysis, and must be obtained elsewhere.

It has also become clear that small balancing areas (BAs) will be limited in their ability to 

integrate wind, as demonstrated by Kirby and Milligan (2008), Kirby and Milligan (2008), and 

the WWSIS final project report. Many different approaches can be used to move towards either 

a full physical consolidation of BAs, or other cooperative approaches to manage variability over 

a larger electrical footprint. Furthermore, wind scenarios that feature wind that is exported 

from the host BA to the load center will place large integration challenges on the host if 

scheduling rules are not flexible and fast (see Milligan and Kirby, 2010b). For example, a large 

wind plant (or multiple plants) developed in a wind-rich area like Montana and exported to the 

Pacific Northwest would likely need to be dynamically scheduled out of the host BA and into 

the load BA. But there may be other economic options; a third BA could provide some or all of 

the required ancillary services to enable wind integration. The assumptions regarding these 

issues will drive the integration analysis and conclusions. There may be other approaches, 

such as the ACE6

Assumptions regarding these aspects of flexibility sources, whether hardware or 

institutional, along with the size of the geographic and electrical footprint that is analyzed, 

will have a highly significant impact on how wind and solar can be integrated on the power 

system.

Diversity Interchange or Dynamic Scheduling System and other ways to 

enable the sharing of ancillary services over a larger footprint. Because pooling the variability 

of larger regions is effective in reducing the per-unit variability that the power system must 

respond to, dynamic scheduling of ACE or other aggregate metrics of imbalance would have a 

larger benefit than dynamic scheduling of individual loads or resources. Other approaches are 

discussed in Milligan, Kirby, and Beuning (2010).

There are several types of reserves maintained by power system operators for distinct 

purposes. Examples include contingency reserves and regulating reserves. Both of these are 

 6 Area control error.
21

reasonably well-defined and commonly understood. However, because of its uncertainty and 

variability, wind induces the system operator to ensure that sufficient flexibility or ramping

capability is available when needed. This additional flexibility must be accounted for by the 

unit commitment and dispatch algorithms, and does not have a commonly used definition7

Contribution to planning reserves

or 

calculation method. Some recent wind integration studies have developed a dynamic reserve

concept that accounts for this needed flexibility, using a variety of ad hoc approaches, some 

of which may be quite promising. Surprisingly, some wind integration studies have used a 

static reserve level for the entire year. This technique will typically over-schedule reserves 

and will needlessly increase costs without providing additional reliability.

A fundamental planning task is to perform an evaluation to determine whether there is 

sufficient installed capacity at some future date to serve loads. In market areas, this 

function is typically used by the system operator to quantify the resource adequacy going 

forward, whereas in regulated monopoly environments, this activity can be tied to resource 

acquisition. There are two primary components to this type of analysis. The first is to 

determine whether there is sufficient resource adequacy for some base system, possibly 

today’s system at a future date. This is done by a probabilistic study whose goal is to 

provide an estimate of LOLE (or another related metric) of the future system or alternative 

future systems. A resource adequacy target is developed, and any plan that hits the target is 

judged to be adequate. A common target is 0.1 day/year LOLE.8

Figure 8

An iterative process such 

as the one in is commonly used to develop a plan that meets the reliability target.

The second aspect of this type of study is to determine the particular contribution to 

resource adequacy that is made by a given generator or group of generators. This yields the 

capacity value, measured in ELCC. Figure 11 shows a graphical example using the common 

target of 1 day per 10 years LOLE.

 7 Possible terms include variability reserve and flexibility reserve. 8 Although technically not correct, the term LOLP, loss of load probability, is often used in place of LOLE.
22 

Figure 11 − ELCC measures a generator's contribution to resource adequacy, holding reliability 

constant. 

This issue is the subject of an IEEE Task Force paper (Keane et al, 2010) and the North 

American Electricity Reliability Corporation (NERC) Variable Generation Task Force. 

Integration studies often include an element of resource adequacy and calculation of wind 

capacity value. The traditional approach to reliability analysis is the calculation of a daily 

LOLP for the peak hour of each day. However, because wind can fundamentally change the 

time of the peak net load, there has been much interest in alternative approaches to use 

when there is a significant contribution of wind or solar generation. Examples of 

alternative approaches include: 

• Retaining the LOLP calculation on the daily peak, but allow the algorithm to look for 

the highest net-load hour, which may differ from the peak hour. The highest netload for the day would be used in the calculation; 

• Move to an hourly LOLE calculation. This would involve developing a new target of 

loss of load hours (LOLH). Although this target need not be equivalent to 0.1 

day/year LOLE, the differences should be well understood; 

• Move towards an expected energy not served (EENS) metric (sometimes called 

expected unserved energy, EUE). With more energy resources such as wind in the 

power system of the future, this may be the most appropriate metric to utilize. 

More research is needed to better understand the implications of these alternative 

approaches. The North American Electric Reliability Corporation (NERC) has convened a 

task force as part of its Integrating Variable Generation Task Force to examine the capacity 

value methods for variable generation such as wind. 

Available margin as a percentage of peak demand

0

Probability

0.02

10 20

Area under curve = 0.1 days/year
23

To calculate the ELCC for a conventional generator, the long-term forced outage rate (FOR) 

for that size and type of unit is the most appropriate. Data can usually be found in the 

NERC Generation Availability Data Set (GADS). However, for wind energy there is no such 

dataset. Thus ELCC calculations for wind have typically been done year by year for up to 3 

years of data, and concern has arisen regarding the variability of wind’s capacity value from 

year to year. Longer datasets are clearly required so that the wind capacity value can be put 

on the same footing as those of conventional generators. We also point out that a 

conventional generator that trips during the high-risk peak season may have a significantly 

higher inter-annual variation in ELCC (about 90% of its rated capacity) than a wind plant, 

which may vary by 3-10% of its rated capacity.

As noted elsewhere in this paper, the development of a long-term database for wind data, 

both from NWP models and from actual wind plants, is a critical element for providing solid 

analytical understandings of the behavior and resource adequacy contribution of wind 

power plants.

Operational Analysis

Early integration studies focused on single utilities and modest wind penetration rates. As 

interest in wind energy has grown and wind energy has been adopted into the generation 

mix around the United States, larger study footprints accompanied higher penetration 

analyses. For example, Xcel Energy in Minnesota was the subject of two early integration 

studies which were eventually followed by a state-wide study that analyzed wind energy 

penetration of 25% of annual electricity demand. The larger the system sizes and wind 

energy penetrations become, the more difficult it is to accurately model large wind plants

and the remainder of the power system.

Many different questions are pursued in wind power integration studies. Once the study 

participants understand and agree on the study objectives, different techniques are used to 

answer each question. Most studies evaluate the effect that wind energy has on system 

operation, total annual production costs, and on energy spot prices. Some studies evaluate

the changes in the generation mix that would result in more efficient operation, and the 

energy shares of different fuel types or technologies will produce. Generation schedule 

results show how existing generation types may be operating differently; ramp rates or 

commitment hours may change, for example. Sometimes the level of wind curtailment on 

the system is also investigated. Since many studies also evaluate transmission expansion 

scenarios, many studies will also analyze the flows on the lines. Other areas that some, but 

not all studies, will look at include operating reserve requirements, voltage stability, 

transient stability, loss of load probability, power exchange between regions, and many 

other topics.

Before the modeling of the power system begins, there are a number of assumptions that 

must be made. Assumptions on the generation mix, transmission expansion, and locations 

and amounts of the wind in the scenario have already been discussed. Operation of the 

power system, however, also depends on the market structure assumptions. The first is 

that the study system is usually optimized in its entirety. For large regional studies, this is 
24

somewhat different than today’s actual operations. Many small BAs will operate their own 

system individually, and power transfers between regions may not be fully optimized 

based on economics. Sometimes special techniques are used to replicate this behavior. 

However, with very large penetrations of wind power, it has been concluded that the 

seamless coordination of BAs and rational economic operation between them is essential to 

fostering an efficient integration of wind power. This is why many studies will perform the 

simulations based on operation of the power system by a single entity.

Other assumptions are made before the simulations are performed. Many studies assume 

that the areas operate with day-ahead and real-time energy markets. They also assume that 

the energy and ancillary services (e.g., operating reserves) markets are co-optimized with 

each other so that the most efficient solution is made. Many studies also assume that subhourly energy markets exist in the study area and this assumption helps drive the operating 

reserve analysis. Lastly, most studies will assume that the market participants will offer into 

the energy market with bids based on their marginal cost to operate. The subject of changing 

of market behavior with high penetrations of wind power has not been studied in detail in 

any U.S. wind integration study. Lastly, much of the analysis that goes into the study needs to 

make the assumption of keeping the power system at the same general level of reliability.

Production simulation

To get a realistic view of how the system operates in these studies, detailed simulations are 

run with the system conditions modeled. Production cost simulation tools are commonly 

used when simulating the power system behavior of the generating units and the power 

flows on the transmission system. These tools are very popular in use by utilities, 

independent system operators (ISOs), and others when evaluating profit outlooks based on 

locational marginal pricing (LMP), financial transmission rights price forecasting, and 

transmission planning, to name a few applications. The use of these programs as the main 

tool in wind power integration studies is new and challenges arise because they were not 

built with this type of application in mind. That being said, their use of security constrained 

unit commitment (SCUC) and security constrained economic dispatch (SCED) show great 

realizations of how the system may behave with high wind power penetrations. A very 

large portion of the analysis on steady state operations can be seen by the use of these 

powerful tools.

SCUC performs an optimization to determine the least cost solution of units that should be 

on-line to meet the load and reserve demands without violation of certain constraints. 

These constraints include limitations on the generation fleet like the following: minimum 

run times, ramp rate limits, minimum generation limits and startup times. These are actual 

constraints that some generators have that must be replicated in the simulation. SCUC also 

models transmission constraints usually using a dc power flow. It makes sure that its 

decision creates power flows that are within reliable operation of the transmission system. 

In some cases, it also ensures that following “n-1” contingencies, power flows are still 

within limits. It is important to note that commitments must be made well in advance since 

many thermal generating units need substantial time to start up and be ready to serve load.

Once those units have started up, they are committed to stay online in real-time even if 

system conditions change.
25

In SCED, dispatch is adjusted to meet load while minimizing production costs and meeting 

all generator and transmission constraints. This is the second stage of the production cost 

tool. In most cases, quick start units (e.g., combustion turbines) are allowed to be turned on 

and off, but all other thermal units must remain in their unit commitment status from the 

SCUC. The costs and LMPs that come from the SCED are those used in the results, and 

represent the actual generation and load.

As discussed elsewhere in this report, the wind power forecast errors become very 

important when analyzing the operational impacts of wind. These simulations have to use 

the wind power forecasts in a way that represents how BAs use or are planning on using 

them. Generally, this means that the wind power forecast is used in SCUC and the actual 

wind power production is used in SCED. The same can be done for load, and both of these 

usually will represent the day-ahead forecasts. The result is that unit commitment is made 

with consideration of the forecast, while the actual dispatch is made towards the actual 

production. The larger the error in forecast, the more inefficient the unit commitment 

decision. More inefficient unit commitment will lead to higher production costs. In some 

studies, sensitivities are run to show the value of using wind power forecasts. The general 

process of the production cost simulation tool is shown in Figure 12.

Figure 12 − General production cost simulation model process.

The general application of these models works fairly well for a predicted system operation. 

New techniques have been performed in recent studies to discover new results as 

sensitivity studies using these models. However, as many market rules begin to change and 

standards and practices are being developed to better accommodate new resources, it 

becomes difficult to keep the models up to date. Many of these enhancements are not 

currently being practiced in actual system operations; therefore, it is likely that tools that 

simulate system operations would not be up to date. However, since the studies are often 

looking at time frames of more than ten years in the future, it is becoming critical that 

future enhancements that may help facilitate wind integration be part of the modeling 

exercise. The enhancements can also be used in comparison with the base case models so 

that additional benefits of these enhancements towards the integration of high 

penetrations of wind power can be better realized.
26

 

0

10,000

20,000

30,000

40,000

50,000

MON APR 10 TUE APR 11 WED APR 12 THU APR 13 FRI APR 14 SAT APR 15 SUN APR 16

MW

Nuclear Steam Coal Wind

Solar CSP w/ Storage Solar PV Combined Cycle

Gas Turbine Pumped Storage Hydro Hydro

 

0

10,000

20,000

30,000

40,000

50,000

MON APR 10 TUE APR 11 WED APR 12 THU APR 13 FRI APR 14 SAT APR 15 SUN APR 16

MW

Figure 13 shows an example of the types of outputs that are created from typical 

integration studies. The figure (see Lew et al, 2009) shows one day in April with no wind 

(left panel) with a typical dispatch – nuclear, coal, combined cycle, and hydro generation 

that meets local load within WestConnect and some exports. When the 20% annual wind 

energy plus 3% solar penetration is added, the dispatch changes significantly. Combined 

cycle generation is reduced significantly, and hydro is moved around somewhat. There is 

even an impact on the coal stack. This particular day is the most challenging of the 3-year 

study period, so is not representative. Detailed simulation of multiple years is a valuable 

way to find rare situations that may be very challenging for system operations.

Figure 13 − Example dispatch when wind energy is added to the system. Left: no wind, right, 20% 

annual wind penetration during low-load and high wind period.

A common goal of wind integration studies is to examine how production costs change as wind 

is added to the system. Production costs do not include capital costs, and consist primarily of 

fuel cost, and are a function of the unit commitment and dispatch schedules. Because wind is a 

price-taker, the economically optimum situation is to use all the wind that is available unless 

there is a reliability concern. This will displace other fuels, reducing production costs. Figure 14

is taken from the EWITS study, and shows how production costs change with wind penetration 

and location.9

 9 Scenarios 1-3 have a 20% annual wind energy penetration, (a) concentrated in the western part of MISO, (b) 

more geographically dispersed with some off-shore wind, and (c) heavy off-shore. Scenario 4 is a 30% 

penetration.
27 

Figure 14 − EWITS production cost by year and scenario (EnerNex, 2010). 

Stochastic unit commitment 

The traditional SCUC used in both day-ahead markets and in production cost simulation 

tools that are used in wind power integration studies solves toward deterministic 

conditions. In other words, uncertainty in day-ahead predictions against real-time 

outcomes is not modeled explicitly. Instead, operating reserves are used to cover any 

uncertainty in a loss of generation or load forecast error. The probability of outages 

occurring is generally pretty consistent during all times, and system operators usually will 

carry enough reserves to cover the largest single contingency. Load forecast errors are 

often not as significant and any large errors can be accommodated by different types of 

operating reserves or system flexibility in real-time. With high wind power penetrations, 

the uncertainty is not as straight forward. There has been lot of research (see Ruiz et al, 

2009, Tuohy et al, 2009, Lei et al, 2007, and Risoe, 2006) that looked at a stochastic unit 

commitment program that solves the unit commitment towards a robust set of resources 

being able to meet multiple possible scenarios. This creates an efficient solution that in the 

long term is both more economic and more reliable if probabilistic forecasts are 

representative of their outcomes. 

The objective of a deterministic SCUC can be written as: 

௜ܥܷܵ כ ௛௜ݖ ൅ ௜ܥܮܰ כ ௛௜ݑ ൅ ௜ܿ כ ௛௜ܲ ෍ ෍ ൌ ࣦ ݁ݖ݉݅݊݅݉݅

ேீ

௜ୀଵ

ேு

௛ୀଵ
28 

The objective of the stochastic SCUC differs: 

௜ܥܷܵ כ ௦௜௛ݖ ൅ ௜ܥܮܰ כ ௦௜௛ݑ ൅ ௜ܿ כ ௦௜௛ܲ ෍෍כ ௦ߨ ෍ ൌ ࣦ ݁ݖ݉݅݊݅݉݅

ேீ

௜ୀଵ

ேு

௛ୀଵ

ேௌ

௦ୀଵ

where: h = hour index; NH = number of hours; i = generator index; NG = number of 

generators; P = power schedule; c = variable energy cost; u = unit status; NLC = no load 

cost; z = unit startup; SUC = startup cost; s = scenario index; NS = number of scenarios; and 

p = probability. 

As can be seen, stochastic unit commitment will minimize the expected cost based on 

weighting its solution on their probability. Therefore, it is more important to reduce costs 

for more likely scenarios and scenarios with very low probabilities are not as significant for 

making cost efficient. Stochastic unit commitment represents a two stage process (or 

sometimes more) where the first stage ensures a unit commitment and the second is a 

multiple branch scenario tree of possible real-time outcomes as seen in Figure 15. Unit 

commitment, however, must be made no matter what the scenario turns out to be since it 

must be made in advance. 

Figure 15 – Two-stage scenario tree. 

The constraint is specified as: 

 ܵܮܩܰ א ݅ ׊ ;ܰܵ א ݏ ׊ ௛௜ݑ ൌ ௦௜௛ݑ

where NGLS is the set of generators with long start times. This ensures that one single-unit 

commitment solution is made robust toward multiple solutions. When different outcomes 

occur in real-time, the system is already built to meet conditions reliably, and because costs 

are reduced for all scenarios, it will be met efficiently as well. 

The main issue with stochastic SCUC is its computation times. For increases in the number 

of scenarios, computation times increase as well. Scenario reduction techniques have been 

researched as well to help reduce computation times. However, reduction in scenarios may 

cause a reduction in accuracy and efficiency. It is likely that research will continue in this 

area, and that sophisticated programs will eventually be able to solve the stochastic unit 

commitment problem in reasonable computation times. 
29

Rolling unit commitment

Generally, in the U.S. market regions, there is a day-ahead market and a real-time market. 

The day-ahead market runs the SCUC program and the real-time market runs the SCED 

program. Unit commitment is considered fixed after the day-ahead market solution is 

complete. This is identical to how production cost simulation models are run for wind power 

integration studies. However, many resources have start times of only a few hours and 

therefore would be able to change the commitment decision any time throughout the day.

Figure 16 shows wind forecast errors as a function of the forecast time horizon. As one 

would guess, forecast errors are generally higher as one looks further ahead in time. When 

decisions are made closer to real-time, those decisions may be more efficient than if they 

were made further ahead in time. Some research has looked at using rolling unit 

commitment strategies, where the unit commitment solution can be adjusted on frequent 

intervals throughout the day. The unit commitment process would have the opportunity to 

change the previous commitment decision for any one generator if its start time has not 

exceeded the time horizon needed if it is initially on and is to be turned off, or if its start 

time is not less than the time horizon needed if it was initially committed and is to be 

turned on. This strategy could further facilitate the integration of wind power by increasing 

the efficiency of unit commitment and reducing integration costs.

Figure 16 − Forecast errors (mean absolute error) as a function of time horizon (courtesy of AWS 

Truepower).
30 

Sub-hourly time resolution 

The original application of these production cost simulation tools was made for hourly 

scheduling resolution. Hourly averages of wind generation and load were used and their 

hourly variability had to be met by the system resources. This means that ramp rates on 

conventional units were limited by hourly rates. Any errors or variability inside an hour 

are ignored in the simulation runs. Instead, the common technique is to use statistical 

analysis to determine an amount of operating reserve that can be used to meet the withinhour variability and uncertainty. 

It is unclear as to whether the statistical analysis is capturing the integration impacts of 

wind power that would occur within the hour. It is likely that sophisticated simulations 

would validate the analysis. Many of the studies have discussed the use of sub-hourly 

scheduling that can help facilitate the integration of wind. Rarely can the studies show 

detailed quantitative results on these benefits. More research is needed to model power 

system behavior and response to better replicate the U.S. energy markets, most of which 

operate at intervals as fast as five minutes. 

Transmission analysis 

As discussed, models that are run in wind power integration studies may have different 

representations of the transmission system. Some studies ignore the transmission system, 

others model the system as a transportation model that allows as much power to go in any 

direction under the transfer limits given, and some finally will use a dc power flow 

representation. It is very rare for production cost models to model the transmission system 

using an ac power flow, which gives an actual representation of power flow on the system. 

Generally, dc power flow is a good approximation and errors are usually less than 5%. DC 

power flow ignores reactive flow on transmission lines and assumes all voltage magnitudes 

on the system to be 1 per unit. DC power flow also ignores resistances on lines when solving 

the power flow. Generally, reactive flow, which can increase total current on the line, is fairly 

small compared to real power. Voltage magnitudes are also generally pretty close to 1 per 

unit. Resistances are usually quite small compared to reactance of the line. Therefore, dc 

power flow is a very good approximation and because it ensures a linear problem, keeps 

computation times reasonable compared to using ac power flow, a non-linear problem. 

Differences in how dc power flow is modeled and how ac power flow is modeled and how 

transmission flow is calculated can be seen in the following general equations. 

DC power flow equation: 

ࡼ ௜ൌ ሾܤԢ௜,௝ሿࣂכ௝

DC line flow calculation: 

ܲ௜௝ ൌ 

௝ߠ െ௜ ߠ

௝௜ݔ

AC power flow equation: 

ሽ௝ࢂכሿ௜௝ܻሼሾ כ ௜ࢂ ൌ ࢏ࡽ כ ݆ ൅ ௜ࡼ
31 

AC line flow calculation: 

 ൌ௜௝ ܳ כ ݆ ൅ ௝௜ܲ

|ܸ௜|

ଶ כ ሺ݃௜௝ ൅݆כܾ௝௜ሻ െ |ܸ௜| כ หܸ௝ห כ ሾܿݏ݋ሺߠ ௜െ ߠ௝ሻ ൅ ݆ כ ݏ݅݊ሺߠ ௜െ ߠ௝ሻሿ כ ሺ݃௜௝ െ݆כܾ௝௜ሻ 

where P = real power; Q = reactive power; B’ is the dc power flow admittance matrix; ܻ௜௝ is 

the complex Ybus admittance matrix; θ = the voltage angle; |V| and ࢂ ௜are the voltage magnitude 

and complex voltage, respectively; x = reactance of the line; g = the conductance of the line 

(inverse of resistance); and b = susceptance (inverse of reactance). 

The difference in linearity and non-linearity and complexity can be seen from these 

equations. The assumption that dc power flow is a good approximation of system 

conditions is very much based on current system flows. Changing of flows due to variation 

in wind can possibly change this assumption. In dc power flow, losses on transmission lines 

are also approximated since losses are also non-linearly dependent on the line current. 

Since wind power can be sited in locations distant from the load and line losses increase 

with distance, it is very important that system losses with high penetrations of wind be 

correctly modeled. It is expected that more research on how to better model power flow 

simultaneously with SCUC and SCED will be ongoing, and better assumptions on how the 

system will be modeled for wind power integration studies will be discovered. 

Most studies generally analyze the power system’s steady state behavior. Other impacts 

can occur on a more dynamic time frame. More studies are attempting to analyze the 

system at all time frames including system frequency response. Wind turbines do not have 

physical capabilities of providing inertia10 to the system as do synchronous machines. 

Therefore, it is important to analyze system frequency response with high penetrations of 

wind to analyze predicted behavior and determine appropriate mitigation procedures if 

issues arise. 

Operating Reserves 

Current practice for power system operation involves carrying reserve capacity to ensure a 

reliable and secure system. In the United States, operating reserves are typically separated 

into contingency reserve and regulation reserve. Contingency reserve is generally used for 

system failures and usually a BA will make sure it has enough to withstand the largest 

single contingency. Regulation reserve is used to keep system frequency stable and BA-ACE 

close to zero. The amount of regulation reserve usually carried is based on meeting NERC 

Control Performance Standards in North America. These reserves are based on current 

variability and uncertainty on the system and most experts agree that the types and 

amounts of these reserves will change with higher penetrations of wind power. For 

instance, reliability events due to wind power changes do occur, but the speed at which 

they do is on a different time scale than that of generation contingency and therefore the 

same rules generally don’t apply. 

 

10 At least one turbine vendor has developed a method of simulating physical inertia via software controls on 

the turbine. 
32 

Most wind power integration studies put a lot of effort into analysis on quantifying reserve 

requirement increases due to wind. Methods are evolving with each study and with wind 

power penetrations of up to 30% of total energy, current methods used today cannot be 

used anymore. One basic assumption is that the amount of operating reserves with higher 

wind penetrations must be made based on meeting the same level of reliability as today. 

In the United States, the first two large scale wind power integration studies were performed 

in New York State and Minnesota. Both of these had extensive analysis on determining 

operating reserve increases. In New York, the study evaluated 3,300 MW of wind power on 

the 33,000-MW peak load NYISO system (see GE Energy, 2005). The study concluded that no 

incremental contingency reserves would be needed since the largest single severe 

contingency would not change. The study then concluded that an additional 36 MW of 

regulating reserve was required on top of the current 175 to 250 MW procured today. This is 

a result of analyzing the standard deviation of 6-second changes in load net of wind 

compared with that of load alone. The standard deviation with wind increased from 71 MW 

to 83 MW, presenting a 12 MW increase that was multiplied by three to achieve 99.7% 

confidence. In Minnesota, a study (EnerNex, 2006) evaluated 15, 20, and 25% wind energy as 

a percentage of total annual demand (3441 MW, 4582 MW, and 5688 MW on a system with a 

peak demand of roughly 20,000 MW). Similar to New York, it was concluded that there 

would be no impact on contingency reserve requirements with the added wind penetrations. 

The regulating reserve requirement similarly evaluated the added variability of wind, but 

calculated it to be a 2-MW standard deviation for every 100-MW wind plant installed. This 

calculation was based on operational data from existing wind plants. The ratio was used to 

calculate the regulating reserve requirement as seen in the following equation: 

ܴ݁݃ ܴ݁ݍ ൌ ݇ටߪ௔௢௟ௗ

ଶ ൅ ܰሺߪௐଵ଴଴

ଶ ሻ 

where k is a factor relating regulation capacity requirements to the standard deviation of 

the regulation variations (assumed to be 5 in this study reflecting current practices); σload is 

the standard deviation of regulation variations from load; σW100 is the standard deviation of 

regulation variations from a 100-MW wind plant; and N is the wind generation capacity in 

the scenario divided by 100. The results showed increases of 12, 16, and 20 MW for the 15, 

20, and 25% cases, respectively. The Minnesota study quantified two other defined 

categories that the New York study did not. In the Minnesota study, these are defined as 

load following and operating reserve margin. These categories are not usually defined in 

current system procedures and unique methods were used to determine how variability 

and uncertainty of wind impacted their results. 

Most recently, three large regional wind power integration studies have been performed 

for the WestConnect footprint of the Western Interconnection, the Eastern Interconnection, 

and the Southwest Power Pool (SPP). Each of these produced highly sophisticated 

engineering techniques to determine additional operating reserve needs on its system. We 

discuss each of these methods below. 

In WWSIS, the team used the term variability reserve to note the capacity that must be 

available to meet the increased variability apparent from wind power. The team analyzed 
33

10-minute wind net load data and compared its variability to that of load-alone data. The 

team investigated how different wind penetrations and load levels influenced the total net 

load variability on the system. Three standard deviations of this variability were then used 

and simple formulas based on wind and load levels were created to achieve the reserves 

needed. For example, Table 5 shows these rules for the 30% wind, local priority scenario. 

For each area, variability reserves were calculated based on a percentage of hourly 

forecasted load, plus a percentage of hourly wind forecast up to some level of wind. Once 

above that level of wind, it was seen that variability reserves did not need to increase as 

wind power was increased. The project also looked at more non-linear functions that better 

represented statistical distribution of variability.

Table 5 − WWSIS reserve rules for 30% local priority scenario.

In EWITS, reserve requirements were determined slightly differently than WWSIS. The 

team basically showed increases in two types of operating reserves, with contingency 

reserves remaining based on the largest single contingency for each region. For regulation 

reserves, the minute-to-minute variability of wind that had been the main driver of the 

requirement in past studies was deemed to be insignificant because of geographic diversity 

impacts of the large geographic scope of the study area. However, it was noted that because 

economic dispatch signals that are created and sent to generating units every five or ten 

minutes cannot be changed inside those same five or ten minutes. Therefore, any forecast 

errors made with each dispatch signal must be met with regulation reserves. The team then 

calculated the amount necessary by assuming that 10-minute persistence forecasts were 

used. The actual requirements also took note that these errors were larger at different 

percentages, mainly towards the 50% portion and therefore a function was used that 

depended on the hourly wind production by hour. Three standard deviations of this value 

were used and the totals were geographically added with the regulation required due to 

load, because of their lack of correlation. Lastly, an additional reserve requirement was 

used for the hour-ahead forecast errors. This was also an hourly function of wind, but only 

one standard deviation was required to be spinning reserve due to its slower response 

requirement. The full EWITS methodology can be seen in Figure 17.

Load Only

(% of load)

Load Term 

(% of load)

Wind Term 

(% of wind 

production)

up to 

(% of wind 

nameplate)

Footprint 1.3 1.1 5 47

Arizona 2.2 2.2 5.6 36

Nevada 2.1 1 10.7 54

Colorado East 2.4 2 5.7 68

New Mexico 2 3.1 3.5 70

Wyoming 1.3 2.7 8.7 33

Colorado West 1.8 3.1 7.3 100

30% LP Scenario
34 

Figure 17 − EWITS reserve methodology overview. 

In the SPP Wind Integration Task Force (WITF) Integration Study, the reserve determination 

methodologies were unique once again. The study evaluated reserve requirement needs for 

regulation reserves, load following reserves, and contingency reserves. For regulation 

reserves, the team explicitly used the NERC-CPS2 standard to determine the increases. 

11Therefore, to equate to the 90% compliance requirement of CPS2, the 5th and 95th

percentiles were used as the requirement boundaries. Also, because ACE is out of 

compliance only if the ten-minute average is above the BA-L10, this is taken into the overall 

equation of determining the requirement. The overall equations are shown below: 

ܴ௨௣ ൌ ටሺ0.01݈௣௘௔௞ ൅ ܮଵ଴ሻଶ ൅ ܽ∆ܹଽହ

ଶ െ ܮଵ଴

ܴௗ௢௪௡ ൌ ටሺ0.01݈௣௘௔௞ ൅ ܮଵ଴ሻଶ ൅ ܽ∆ܹହ

ଶ െ ܮଵ଴

Where lpeak = peak load; ܽ is a calculated coefficient, and ΔW are the respective percentile 

of ten-minute deltas. Figure 18 shows the regulation requirements for peak days. Like in 

the other two studies, the project team proposes that regulation requirements be dynamic 

and are time varying based on system conditions. The study also recommends the 

possibility of a load following reserve requirement and provides analysis on some of the 

ramping requirement increases in this time frame. Lastly, the contingency reserve 

requirement was not changed for the study, but it was recommended that it be reevaluated 

if extensive high-voltage transmission expansion occurs needed to transfer high 

penetrations of wind power from remote locations. 

 

11 An alternative approach is under consideration and is receiving field trials in WECC. See NERC, 2009. 
35

Figure 18 − Total regulation requirements for seasonal peak loads (MW).

These three studies have opened new doors on how system operators think of operating 

reserves. All propose time varying requirements, which differ from how systems act today. 

Different reserve types have been proposed that are not currently part of standards or 

common operating practice. Many other new methods have been proposed as well in other 

integration studies, for example Makarov et al, (2008). Even though there are 

commonalities between the approaches, it is very interesting to see methods that differ. 

More work should examine the strengths and weaknesses of different methodologies. 

Simulations that model sub-hourly and sub-minute market operations that attempt to 

replicate operator actions when deploying operating reserves with high wind penetrations 

should help validate the various methods. Having enough reserves is never really the issue 

since additional generation can always be committed. The real question is how to use the 

information available to determine the efficient amount of operating reserve for a given 

situation to keep a certain pre-defined reliability level. It may be appropriate to ignore 

current standards and start from scratch to discern the most optimal standards to maintain 

a reliable system. Some of the examples of further research questions we recommend are

listed below. The full list, however, is much longer. An example of how different operating 

reserves may look and relate to each other is shown in Figure 19.

Examples of further operating reserve analysis:

• What is the right level of spinning vs. non-spinning reserve for the different 

categories? (Tradeoff between high standby cost of spinning reserve vs. high 

utilization cost of non-spinning reserve)

• Can certain reserves be shared or should specific causes trigger each reserve type?

• What is the correct response time of non-spinning reserve?
36

• When is it appropriate for load following reserves to have specific requirements 

rather than allow them to be met through energy markets?

• How to determine a specific requirement for frequency responsive reserve? Is there 

a need for it?

• What are the true causes of regulation reserve? (short term uncertainty vs. 

variability)

• What are the appropriate standards for regulation reserve requirements? (the 

current CPS or the new proposed Balancing Authority Area Limits (BAAL) or others)

• Should ramping reserve requirements require a response time that is a function of 

ramping predictions or static responses?

Figure 19 − Example of operating reserves on a system with high wind power penetrations.

Integration Cost

Many wind integration studies have examined the issue of wind integration cost. These 

studies typically have had an objective of attempting to determine how the increased 

variability and uncertainty of wind translates into increased operating costs. Sometimes 

the objective has been to compare these costs to a benign generating unit that has neither 

variability nor uncertainty. In a pure sense, such a unit does not exist. As a result, various 

types of theoretical benchmark units have been proposed, most of which have been some 

form of flat energy blocks that are equivalent to wind energy over some agreed upon time 

period (such as a daily-equivalent block of energy).

In this section, we discuss the issue of integration cost, and whether wind is the only 

technology that might impose additional variability or uncertainty on the power system. 

We also discuss shortfalls that have been identified in recent analysis.

Operating 

Reserve

Regulating 

Reserve

Contingency 

Reserve

Load 

Following 

Reserve

Frequency 

Responsive 

Reserve

Supplemental 

Reserve

Ramping 

Reserve

Non-event

Event

Fast Slow Fast Slow

Spinning Reserve

Non-Spinning Reserve
37

Other generating units may impose an integration cost by altering the efficiency 

of other plants.

Wind integration costs generally consist of the cost associated with increased cycling and 

additional reserves that must be provided by other generators. For example, a thermal unit 

that increases its cycling to help accommodate the wind will typically perform less 

efficiently, using more fuel per unit of output. The additional cycling duty may also result in 

increased operations and maintenance cost, and potentially a shorter useful life.12

Because of the many complex relationships between units in the commitment and dispatch 

stacks, and the requirement that loads and resources must be balanced, a new entry into 

the generation fleet may cause changes in the way that one or more incumbent units are 

operated, imposing additional cycling, wear and tear, and reduced efficiency. Consider the 

following simplistic example.

This has 

been identified in Milligan and Kirby (2009).

A power system has two types of generators; one is base loaded and the others are cycling 

units. A sample daily dispatch curve appears in Figure 20. The baseload generation never 

changes its output, incurring no cycling costs or efficiency losses.

Figure 20 − A simple system with generation lumped into two categories: incumbent baseload and 

cycling units.

Starting with this simple scenario, we now add a new fleet of base-load generation that is 

less expensive than the incumbent baseload units. This changes the merit order in the 

economic dispatch stack. The new, less expensive generation is dispatched first. This moves 

 12 The wear-and-tear cost of additional deep cycling is widely acknowledged, but little if any public data are 

available to inform analysis.
38

both of the incumbent units up the stack, reducing the capacity factor (and revenue) and 

reducing efficiency. Excess cycling may impose additional wear and tear on the incumbent 

baseload units. 

Figure 21 − After introducing new, lower marginal-cost units, the incumbent generation now 

begins to cycle, and the incumbent cycling units are out of market.

Into this simple example, we introduce some parametric costs to illustrate the concept. If 

the incumbent base units were selling energy at $50/MWh before the new competition 

arrived, and assuming that it would continue to sell at $50/MWh after reducing its output, 

it would lose revenue. Depending on the efficiency losses, its generation (fuel) cost would 

be higher per-unit of output, imposing another cost.

We capture this in Table 6, which shows the two cases: before and after introduction of 

new baseload competition. In keeping with the simple nature of the example, the 

incumbent base generation receives the same price in both cases. We also calculate 

potential efficiency cost at two levels: $2.00/MWh and $5.00/MWh. The actual efficiency 

cost may be higher or lower, depending on the frequency and depth of cycling, heat rates, 

and other unit properties. 
39

Table 6 − Hypothetical example of integration cost of baseload generation.

Flat proxy

Most, if not all, integration studies that calculate wind integration cost use a flat-block 

proxy resource for the no-wind case. The method develops the proxy resource by 

calculating the wind energy-equivalent for each day of the study, and then inserting this 

daily flat energy block at zero cost into the dispatch stack. Since the block has no variability 

or uncertainty during its 24-hour lifetime, it has been used as the no-wind case by which 

wind integration cost can be calculated. Integration cost is then the difference in total 

operating cost between the wind case and the flat-block proxy case, divided by the wind 

energy in MWh to obtain a cost per MWh.

Applying the flat-block proxy in EWITS revealed significant problems with that approach. To 

be fair, this method was developed to answer a particular question about wind integration: 

How much does incorporating wind’s additional variability and uncertain cost affect the 

system operator? As more studies have been performed that look at higher wind 

penetrations, the usefulness of this metric and approach appears to have become limited. In 

EWITS, there were extremely large transitions between days whenever wind output changed 

as a result of a large frontal passage or other significant change in daily wind energy. 

Milligan and Kirby analyzed the performance of alternative proxy resources, including 

various flat block durations, and found that at high wind penetrations they all imposed a 

significant artificial ramp during the intra-block transition. Wind integration studies up 

through 2009 that used flat proxy resources used 24-hour blocks, which have extremely 

high intra-block proxy ramps. Figure 22 is adapted from that work, and shows that both the 

daily block and 6-hour block have much higher extreme ramping behavior than wind.

Case 1 Case 2

Energy Revenue $6,907,200 $5,822,800

MinGen (MW) 5756 3756

Variability (COV) 0.0 14.6

Cycling/Efficiency cost

 at $2/MWh $0 $43,376

 at $5/MWh $0 $108,440

Net Revenue

 at $2/MWh $6,907,200 $5,779,424

 at $5/MWh $6,907,200 $5,714,360

Capacity Factor (%) 100.0 82.3
40

Figure 22 − 24-hour and 6-hour flat blocks introduce large positive artificial ramps to the proxy 

resource.

A similar impact is seen with large negative ramps, as shown in Figure 23

Figure 23 − Large negative ramps also appear in flat proxy resources.
41

There are also concerns regarding the intermixing of value with cost. These are also detailed by 

Milligan and Kirby (2009). Because of the timing of wind energy delivery, it will likely have a 

different (lower) value than the proxy resource. In that eventuality, part of the differential in 

production cost between the wind as-delivered and the proxy resource will be caused by this 

value differential, and is not the same as the cost of additional reserves or efficiency.

Figure 24 − Prices from the Midwest Independent System Operator and wind data show the 

average diurnal value swing of wind energy (Milligan and Kirby, 2009).

MISO LMP and Wind (2004)

0

500

1000

1500

2000

2500

3000

0:00 6:00 12:00 18:00 0:00

Wind MW

$0

$10

$20

$30

$40

$50

$60

$70

$80

Wind MW Average

System $/MWH

Hourly 

Energy Price

Average
42

Figure 25 − Comparison of 24-hour flat block proxy with 6-hour block proxy (adapted from Milligan 

and Kirby, 2009).

A further concern arose with EWITS. Because of the high penetration of wind analyzed, 

some regions found it economic to secure additional ancillary services from adjacent areas. 

This occurs often in areas that have electricity markets. Area A has a need for ancillary 

services that it could provide itself, but area B can provide the services at lower cost. A pays 

B for the service. B incurs additional operating cost because it may have moved generation 

units to less efficient points on the heat rate curve, or incurred more ramping, both of 

which come at a cost. However, these costs were compensated by A. But when integration 

costs are tallied across a multi-region footprint, there may be no recognition that B

benefited by making a sale, and the benefit from the revenue stream was not counted in the 

integration cost.

For this and other reasons, it may be time to move in a new direction. Because of the difficulty 

in assessing wind integration cost (compared to what?) and because it can be difficult to 

untangle costs from benefits, it may be time to assess total operational cost with and without 

wind. Of course this comparison will be complicated in cases that have different generation 

mixes. The flat-block comparisons, although problematic, have provided significant insights 

into wind integration impacts and costs. A full accounting of all operating costs, assuring that 

the system is reliable and secure and that the modeling is realistic, encompasses all such costs 

of integrating wind. 
43

Not all conventional generators can follow control signals.

Some generators have difficulty following an automatic generation control (AGC) signal, 

which is sent to AGC units to provide frequency regulation. In cases like this, the generator 

can actually increase the need for regulation instead of providing helpful regulation. This 

imposes an integration cost on the system that is rarely, if ever, assessed. Figure 26

illustrates two coal units in the Midwest. The unit in the left panel of the graph does a good 

job in following the AGC signal, whereas the unit on the right imposes a 31 MW regulation 

burden for this hour because of its inability to follow the AGC signal.

Figure 26 − The coal unit on the left provides regulation to the system, whereas the unit on the 

right cannot follow AGC and imposes an additional 31 MW of regulation during this hour.

Market Considerations 

Milligan and Kirby (2010) analyze the role of energy markets in helping to integrate 

variable generation. A complete discussion of that work is outside the scope of the current 

paper, however it is worthwhile to extract key points from that work and show the 

relevance to wind integration analysis.

Fast energy markets, operating at 5-minute intervals, allow variability in load, wind, and 

other variable generation to be managed with a potentially large fleet of generation. The 

economic dispatch calls upon units that are economic to move to new operating points, 

increasing or decreasing output as needed. At each of these time steps, units on AGC that 

are providing regulation can move back to their preferred operating points near the middle 

of their respective operating ranges. Conversely, when the dispatch is changed only once an 

hour, the economic dispatch stack is constrained and all of those units will keep their 

position until the top of the next hour (allowing time at the top of the hour for units to 

ramp into their new position).

Current practice in integration analysis typically involves a statistical analysis of wind and 

load data, respecting the chronology by using synchronized data, focusing on time steps 

that include sub-hourly (5 or 10 minutes) to days, weeks, or even months. Usually the subhourly analysis is done to complement the hourly production simulation. Therefore, the 

operation of a fast market is often beyond the resolution of the production model, since 

those models rarely have the ability to run at a faster time step. At this point it is likely that 

97094A

620

640

660

680

700

720

740

0 15 30 45 60

TIME (minutes)

ACTUAL AND REQUESTED

GENERATOR OUTPUT (MW)

ACTUAL

AGC SIGNAL

97094

390

400

410

420

430

440

450

0 15 30 45 60

TIME (minutes)

ACTUAL AND REQUESTED

GENERATOR OUTPUT (MW)

ACTUAL

AGC SIGNAL

31 MW
44

the combination of tools comes quite close to representing economic dispatch, load 

following, and regulation requirements, although it is also possible that models will evolve 

to run at relevant sub-hourly time steps.13

As shown in Milligan and Kirby, fast energy markets can often supply needed ramping 

capability, which is a capacity service, at little or no cost. However, there are times that the 

units on economic dispatch may not have sufficient response capabilities to ramp quickly 

enough, and an out-of-merit dispatch of a fast-start unit may be required to supply the 

ramp. A simplified diagram appears in Figure 27. The figure illustrates a case that has 

insufficient ramping capability online, causing a peaking unit to follow the ramp until the 

baseload capacity can catch up. The peaking unit sets the price for the hour. In such cases, 

it might be beneficial to design a load following market as a supplement to the energy 

market. It would not be necessary to invoke the load following market at all times, because 

sufficient ramping capability may be available much of the time. In cases where ramping 

supply is limited, the load following market could be used to procure more, without 

distorting the energy price.

Figure 27 − Insufficient ramping capability from the dispatch stack may result in peaking response 

which could set the energy price for the period.

Other concerns regarding the sustainability of existing energy markets at high wind 

penetration rates may be more important. The low LMPs that are often seen in wind 

integration studies are not due solely to wind; they are exacerbated by the inability of baseload 

 13 At the time of this writing, WECC is investigating whether its hourly production simulation model can be 

“tricked” into running at a faster time step if it is provided with appropriate input data.

3200

3000

2800

2600

2400

4:00 AM 6:00 AM 8:00 AM 10:00AM 12:00PM 2:00 PM 4:00 PM

Base Load - $10/MWh

Peaking -

$90/MWh

Energy Price

$10/MWh

Energy Price $10/MWh Energy Price Increases

to $90/MWh because

base unit can't ramp

fast enough
45

units to move to a lower generation level or shut down. During high-wind and low-load times, 

this situation causes an excess of generation and consequently low or negative prices. With a 

different non-wind generation mix or transmission infrastructure, this situation might be 

alleviated. Will the markets successfully encourage a workable generation mix that is 

sustainable economically/financially? Is there a need for more targeted markets for ramping 

products?

Summary and Conclusions

Wind integration analysis has progressed significantly in the last several years. Advances in 

wind data development, reserves modeling, operational analysis, incorporating sub-hourly 

information into hourly models, and performing unit commitment based on forecasts have 

all enhanced our collective knowledge about integration. New advanced methods that 

include rolling stochastic unit commitment, and introducing ramp constraints and other 

factors into unit commitment algorithms will further improve the quality and accuracy of 

the analysis. Further work in variability reserves, incorporating sub-hourly constraints into 

hourly models, developing models with sub-hourly economic dispatch, improved methods 

for simulating actual wind power and forecasted wind power, and continued validation of 

datasets are needed. A detailed analysis of generation expansion model characteristics and 

need may lead to the development of new algorithms and models. All of these 

improvements will greatly enhance resource development, both wind and non-wind, and 

better transmission planning. Furthermore, with the anticipated increases in solar energy 

utilization and possibly other forms of variable generation, these improvements in 

methods should not be undertaken solely with wind in mind.

Further work is needed to either refine methods for integration cost analysis, or propose 

entirely new approaches with transparent accounting for the complex interactions among 

generating units. Additional work is also needed to understand the complex interactions 

between transmission build-out, resource mix, BA size, and markets.

Markets cover a large fraction of the U.S. power grid, and much valuable experience has 

emerged from operating the various markets over the past several years. However, 

questions remain about whether the markets in their current form will support needed 

services and provide market signals that will induce an economically efficient level of 

flexibility in the long-term with significant levels of variable generation like wind. There 

may be a need for a supplemental ramp product so that energy prices are not severely 

distorted during fast ramps. Hypothetically, very low LMP’s over a significant fraction of 

the year will induce generation developers to favor flexible units, with a disincentive to 

overly-develop baseload generation. Whether this is realistic remains to be seen.

Finally, it is critical to improve, update, and keep current the existing public wind databases 

developed from NWP models to help inform the critical decision-making that must occur 

over the next decade if the nation wants to pursue a future with significant quantities of 

wind energy.
46

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of operating reserve determination in wind power integration studies,” Proc. of IEEE PES 

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Milligan, M.; Kirby, B.; Beuning, S. (2010),”Combining Balancing Areas’ Variability: Impacts 

on Wind Integration in the Western Interconnection,” 26 pp. Available at www.nrel.gov.
47

Milligan, M.; Kirby, B. (2010), “Market Characteristics for Efficient Integration of Variable 

Generation in the Western Interconnection,” NREL Technical Report.. Available at:

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the Bulk Electric Systems of North American.” Available at: www.nerc.com.

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Ruiz, Pablo, Russ Philbrick, Eugene Zak, Kwok Cheung, and Peter Sauer (2009),

“Uncertainty Management in the Unit Commitment Problem,” IEEE Trans. Power Syst., pp. 

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Advancing Wind Integration Study Methodologies: Implications of 

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5a. CONTRACT NUMBER

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NREL/CP-550-48944

5e. TASK NUMBER

WE10.4221

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401-3393

8. PERFORMING ORGANIZATION

REPORT NUMBER

NREL/CP-550-48944

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)

NREL

11. SPONSORING/MONITORING

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service

U.S. Department of Commerce

5285 Port Royal Road

Springfield, VA 22161

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

The authors report on the evolution of techniques to better model high penetrations (generally, 20% or more energy 

penetration) of wind energy. 

15. SUBJECT TERMS

WECC; Eastern Interconnect; Hawaii; load following; operational impacts; unit commitment; grid integration; wind; 

solar; variable generation; responsive load;

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT

UL

18. NUMBER 

OF PAGES

19a. NAME OF RESPONSIBLE PERSON

a. REPORT

Unclassified

b. ABSTRACT

Unclassified

c. THIS PAGE

Unclassified 19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98)

Prescribed by ANSI Std. Z39.18