Technical Report

NREL/TP-550-46273

July 2009

Impact of Electric Industry 

Structure on High Wind 

Penetration Potential 

M. Milligan and B. Kirby

National Renewable Energy Laboratory

R. Gramlich and M. Goggin

American Wind Energy Association
National Renewable Energy Laboratory

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Contract No. DE-AC36-08-GO28308

Technical Report

NREL/TP-550-46273

July 2009

Impact of Electric Industry 

Structure on High Wind 

Penetration Potential 

M. Milligan and B. Kirby

National Renewable Energy Laboratory

R. Gramlich and M. Goggin

American Wind Energy Association

Prepared under Task No. WER95501
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1 

The Impact of Electric Industry Structure on High Wind 

Penetration Potential

Michael Milligan

National Renewable Energy Laboratory 

michael_milligan@nrel.gov 

Brendan Kirby, Consultant 

National Renewable Energy Laboratory 

kirbybj@ieee.org

Robert Gramlich, Policy Director

American Wind Energy Association

rgramlich@awea.org 

Michael Goggin, Electric Industry Analyst 

American Wind Energy Association

mgoggin@awea.org 

Introduction 

Wind is an abundant renewable resource that provides a host of environmental and 

economic benefits. A record 8,358 megawatts (MW) of new wind generation was 

installed in the United States last year, bringing the total wind generation fleet capacity to 

25,170 MW. No longer a research novelty, wind is now a competitive mainstream 

generation technology. Along with the environmental benefits of reducing CO2, SOx, 

NOx and mercury emissions, wind is a domestic resource that increases energy security 

and reduces electricity price volatility. 

Because wind generator output varies with the wind itself rather than responding to 

operator dispatch commands to burn fuel or release water, it is a different type of 

resource than most conventional generators. Extracting the maximum benefit from wind 

requires understanding and accommodating its unique characteristics. This is actually a 

familiar process that the power industry goes through every time a new generation or 

transmission technology is introduced. For example, power systems must accommodate 

conventional generators’ startup times, minimum run times, minimum off times, and 

minimum loads. Guaranteeing the availability and reliability of off-site power to nuclear 

units also constrains current utility operations. 

Power system characteristics that mitigate and accommodate variability make it easier to 

integrate wind into grid operations. Wind-friendly physical characteristics include 

geographically and electrically large balancing areas, as well as generator characteristics 

such as fast-ramping, load-following capability.i They also include generation and 

transmission market structures that provide access to conventional generation flexibility 

and maneuverability; the ability of a generator to ramp up and down quickly and 
2 

accurately, to turn on and off quickly and at low cost, and the ability to operate at low 

minimum loads. Advanced wind forecasting, integrated into the power system control 

room, can also help wind integration costs by providing the power system operator with a 

reliable look at the near future balancing requirements.

This paper attempts to evaluate which balancing area (BA) characteristics best 

accommodate wind energy. Market structures are naturally dependent upon the physical 

and regulatory characteristics of the region they operate in and both physical and market 

characteristics are important. We find that open market structures with large geographic 

scope, along with day-ahead, hour-ahead, and sub-hourly market clearing accommodate 

wind integration by rewarding the maneuvering capability of conventional generators. Such 

markets are operated by Independent System Operators (ISOs) and Regional Transmission 

Organizations (RTOs), which also offer many of the characteristics necessary to effectively 

incorporate large amounts of wind generation. Today, these regions host a 

disproportionately large share of the wind generation in America. We also find that 

regional transmission planning, and implementing transmission expansion plans so that 

load and generation can be aggregated throughout a region, reduces the total amount of 

variability introduced by wind generation and further reduces wind integration costs. 

Lastly, in this paper we expand on a tool for evaluating a BA or region’s structural 

capabilities to accommodate high levels of wind integration first conceptually introduced 

by two of us (Gramlich and Goggin) in 2008. The tool allows a reviewer to numerically 

rate a BA in ten areas that are important for reducing wind integration costs. 

Comparisons of different BAs are possible as well as evaluating a single BA. Example 

ratings are provided which demonstrate how the tool might be used. 

Relevant Characteristics of Wind Power for System 

Operators 

Wind power itself has four principal characteristics important to power system planning 

and operations: the wind generation is variable because the wind itself is variable, wind 

generation has a near-zero variable cost, wind and wind generation are difficult to 

forecast precisely, and the best wind sites are often located far from load centers. Wind is 

often described as “intermittent,” but since that term implies sudden changes in output

and wind output changes vary over a period of hours—longer than the instantaneous 

outages that affect conventional units—we use the term “variable.” Failure to recognize 

these characteristics of wind generation will raise the financial and environmental costs 

of the nation’s overall electricity usage. It is important to note that the characteristics of 

wind are not unlike those of load. However, the magnitude of wind’s variability is greater 

than the variability of load on a per-unit basis, and wind is somewhat more difficult to 

forecast than load. Hence, the primary differences are more of degree than of kind. 

Wind is a variable and largely non-dispatchable resource. Given the high cost of energy 

fuels today, wind’s primary value is in supplying energy, not capacity. Wind saves fuel 

and reduces emissions. It is also a resource with a capital cost, but essentially no variable 

costs (fuel or operating costs). It almost always saves money to reduce production at fuel-
3 

consuming power plants instead, and generate as much as possible at wind plants. It also 

generally makes more sense to reduce generation at hydro plants if they have a reservoir 

for storing water. Most reservoir base hydro systems are energy limited so saving water 

with wind increases the capacity value of the hydro system. Although modern wind 

turbine technology allows an operator to precisely curtail wind production when 

necessary, “spilling” free wind is only desirable when it is required for reliability reasons, 

or in the event that wind could supply ancillary services in the future. 

While annual wind energy production can be forecast with reasonable accuracy, it is 

more difficult to predict wind generation output a few hours or a few days in advance. 

Wind generator operators cannot commit to and follow generation schedules like 

conventional generation operators. Wind forecasts are getting better, however, and power 

system costs can be reduced when power system operators have an advanced wind 

forecast in the control room.

The best wind resources are often located far from load centers and are frequently far 

from existing transmission lines. New wind plants often need significant transmission 

expansion before the full amount of new wind generation capacity can be accepted onto 

the grid and delivered to loads. Where new wind capacity precedes transmission 

expansion, the incremental wind generation may have to compete for transmission access 

with other generators and be delivered using non-firm capacity; such generation may 

often be curtailed due to reliability or transmission capacity limitations. A robust 

transmission system (coupled with sub-hourly scheduling) that allows wind and load 

variability to be aggregated over large distances further reduce the cost of wind 

integration. Therefore, wind greatly benefits from a long-term, regional approach to 

transmission planning. Of course, power system customers benefit from a robust regional 

transmission network with or without wind. 

System Operation with Wind

Wind’s characteristics can present challenges to the power system operator. Wind 

variability in itself is not unique; system operators continuously deal with load variability

over all time frames from seconds to seasons. However, because wind is variable, it does 

add to aggregate variability. While the power system does not need to respond to the 

variability of each individual wind turbine, it is necessary to meet the North American 

Electric Reliability Corporation (NERC) reliability standards and balance aggregate loadnet wind with aggregate generation. Adding wind to the generation mix will increase the 

control actions the conventional generators must take. Fortunately wind and load 

variability tend to be uncorrelated, so they do not add linearly, greatly reducing the net 

flexibility required from the conventional generators. This is based on the principle of 

statistical independence, described below. If additional flexibility is not valued by the 

market or incorporated into system plans, then there may not be sufficient response 

capability offered, making it more difficult to balance the system.

Forecast error is not unique to wind either. System operators regularly deal with load 

forecast uncertainty. A one-degree weather forecast error, for example, can result in a 

1000-MW summer peak-load forecast error for the California ISO. As with variability, 
4 

wind forecast errors add to load forecast errors, increasing the required conventional 

generator flexibility. Fortunately, as with variability, wind and load forecast errors tend to 

be uncorrelated, reducing the total required conventional generation flexibility. 

Accommodating Wind

Capturing the full environmental and economic benefits of wind generation requires 

looking at the power system slightly differently than in the past. One important difference 

is that energy production should be valued as well as capacity. This requires examining 

total annual fuel requirements (and emissions) as well as peak generation needs. 

Accommodating wind involves adjusting the power system structure to accept wind 

energy when and where it is available. This involves a combination of physical attributes 

and institutional support, along with incentives to achieve the desired physical attributes.

Physical Characteristics that Help Wind

Economically dealing with wind’s variability and predictability requires a large, flexible 

power system. Physical size is important because the correlation between production

from multiple wind plants diminishes as those plants are geographically farther apart

(Figure 1). In the graph below, correlation between wind farms is lowest (approaches 0) 

when distances between wind plants are large. 

To focus on the impact of geographic diversity impacts within the wind plant, Figure 2

illustrates the per-unit variability from a group of 15 turbines and a nearby group of 200 

turbines. The graph is normalized by the average wind output. The lower panel shows the 

group of 15 turbines, and it is apparent that this grouping has much more variability per 

unit as compared to the upper panel. The coefficient of variation (standard deviation 

divided by mean) is 0.184 for the small cluster, and 0.126 for the large cluster. 

Figure 1. Wind generator variability loses correlation as the distance between 

machines increases and as the time frame of interest decreases (Ernst, 1999). 

 

4h Average

12-hour 

1h Average

30min Average

5min Average

2h Average
5 

Figure 2. Geographic smoothing within a wind plant has a considerable smoothing impact 

using 1-second data for nearly 9 hours. 

Preliminary results from the Eastern Wind Integration Study (EWITS) provide dramatic 

evidence of the benefits of large area aggregation (Figure 3). Enernex studied the wind 

variability from 71,671 MW of wind generation spread over most of the eastern 

interconnection. Mesoscale wind modeling was used to generate three years of historic 

wind data on a ten-minute basis and 2-kilometer grid spacing. Note how the variability of 

the total wind fleet (red bar) is well below the variability of the same wind resource when 

dealt with on an individual regional basis. And these are large regions to start with. Total 

wind is expected to vary by 10% from hour to hour less than 14 times per year. When 

300,000 MW of wind are studied a 10% variation is expected less than four times per 

year. Large geography, coupled with robust transmission and market rules that support 

sub-hourly transactions allow wind aggregation over great distances and reduce the net 

variability which must be compensated for. 
6 

Figure 3. Enernex study performed for the Eastern Wind Integration and Transmission 

Study (EWITS) shows a dramatic reduction in wind variability when transmission supports 

aggregation over a large geographic area (Zavadil, 2009, draft results). 

As with load, larger geographic and electrical size also makes forecasting easier. Table 1

shows that the wind forecasting error is reduced significantly when wind output from all 

four regions of Germany are compared with wind output from a single region. (Rohrig, 

2005) This conclusion is reinforced by Ahlstrom (2008) – when aggregated over a broad 

geographic region, wind forecast errors can be reduced by as much as 30% to 50%. Thus, 

power system operators can more accurately predict and plan for changes in wind output 

when their systems are larger.

Not surprisingly, forecasting accuracy also improves closer to real time. It is easier to 

forecast for short periods ahead, compared to longer periods in the future. Markets that 

operate closer to real-time take advantage of the improved forecasting accuracy by 

allowing more frequent generator schedule changes. Hour-ahead markets better 

accommodate wind than day-ahead markets. Sub-hourly markets have the least forecast 

error. A coordinated series of regularly clearing markets provides the best ability for 

conventional generation to adjust to changing wind conditions at least cost. RTOs and 

ISOs typically operate sub-hourly markets while other regions do not. This feature is one 

of the drivers of costs of software systems for RTOs and ISOs.

Hourly Wind Variability By Region Reference Scenario

0

500

1000

1500

2000

2500

3000

3500

4000

4500

-10.0% -7.5% -5.0% -2.5% 0.0% 2.5% 5.0% 7.5% 10.0%

Hourly Production Changes on Nameplate

Hours per Year

MAPP

NYISO

ISO-NE

MISO

TOTAL

PJM

SERC

SPP

TVA
7 

 

Table 1. Wind forecasting accuracy improves when larger geographic areas are considered.

NRMSE Forecasting 

Error %

Germany (all 4 control 

zones) ~1000 km

1 German Control 

Zone ~350 km

Day ahead 5.7 6.8

4 hours ahead 3.6 4.7

2 hours ahead 2.6 3.5

The Principle of Statistical Independence

Electric power systems are comprised of a very large number of components. A 

typical utility service territory (or market area) includes many thousands of 

individual customers. The behavior of these customers exhibits some statistical 

correlation over some time periods, but has little correlation over other periods. 

During the morning load pickup, customers are generally increasing their usage of 

electrical devices, leading to an overall increase in electric demand. However, 

during very short periods of time, such as seconds to minutes, some loads are 

increasing at the same time that other loads are decreasing. There is no correlation 

between these random events; one customer turns on the lights at the same time as 

another customer turns off the lights. These events, when they occur 

simultaneously, have no net impact on electrical demand. 

Wind turbines have a similar statistical property. During the short time periods of 

seconds or minutes, one wind turbine may be experiencing an increase in wind 

speed, resulting in more wind power output from the turbine. At the same moment, 

another wind turbine may experience a decline in wind speed and power output. 

The random nature of these events can be captured statistically, and are formally 

described as uncorrelated events. It is important to note that if wind turbine A 

always runs counter to wind turbine B, then they are perfectly negatively correlated 

(correlation coefficient is -1). But if sometimes the turbines move together, and 

other times move in opposite directions, this lack of correlation has important 

implications for balancing requirements. 

The principle of statistical independence is the reason why each increase in 

customer demand (resulting from a switched on light, for example) does not need to 

be matched by a corresponding increase in generation. Because other customers are 

switching off their lights at the same time, statistical methods can be used to 

calculate the amount of generation required to match the aggregate change in load. 

This principle of statistical independence over short time frames applies to loads, 

wind turbines, and to load and wind combined. This article illustrates this concept 

in several different contexts: load, wind, load and wind, and wind forecasts are all 

subject to the principle of statistical independence.
8 

Aggregation and Large Balancing Area Size

Utilities have taken advantage of aggregation for decades. Since each balancing area only 

has to compensate for the variability in its aggregate load, and since random variations in 

individual loads partially cancel each other out, larger balancing areas require relatively 

less system balancing through “regulation” service than smaller balancing areas. As 

shown above, the same principle applies to integrating wind: larger balancing areas are 

better able to integrate large amounts of wind because the random variability of 

individual wind generators and individual loads partially cancel each other out. This is 

based on the principle of statistical independence. If multiple remote wind plants are 

grouped and operated together within a single balancing area, their overall variability 

falls and it costs less to integrate their production into grid operations. 

Having a deep pool of flexible generation that can respond to variations in wind output helps 

system operators and reduces the cost of system balancing. Larger balancing areas have 

larger generation pools. Greater flexibility is a function of the generation mix, but larger 

pools always provide greater flexibility than smaller pools of the same generation mix.

 

As an example of the benefit of the larger balancing areas, we analyzed the consequences 

of balancing area consolidation in Minnesota, both with and without wind (Milligan and 

Kirby, 2007). Neighboring balancing areas will sometimes need to redispatch their 

generation in different directions at the same time. This happens when the load in one 

balancing area is increasing during a period when the load is decreasing in another 

balancing area. During such times, it would be beneficial for both systems to net their 

load ramping requirements, which would result in less ramping of generation in both 

balancing areas. Using hourly data, we calculated the ramping that could be eliminated if 

the four balancing areas in Minnesota were to combine. The graph for one full year of 

hourly load data is shown in Figure 4. Opposite ramping does not occur in all hours, but 

it is apparent from the graph that 50 MW/hr or more can be reduced during much of the 

year, resulting in approximately a 14% reduction in ramping requirements (both up and 

down) annually if operations are combined. This reduction in load ramping 

requirements translates into lower cost of serving loads in all affected balancing areas. 

In Figure 4 below, the top graph shows the load ramping movements that would cancel 

out and need not be performed if the four Minnesota balancing areas were combined. 

Cancellation happens whenever one balancing area is ramping up while another is 

ramping down. Benefits are spread throughout the year, but can be seen to vary from 

hour to hour. The lower portion of Figure 4 reorganizes the same ramping information 

into a ramp-duration curve, which shows that, absent balancing area consolidation, there 

is as much as 75 MW of costly, unnecessary load-following generation in Minnesota 

attempting to compensate for the net variability of loads. This graph is based on loads 

only; there is no wind in the system portrayed by this graph. 
9 

Figure 4. Physical ramping requirements can be reduced by consolidating balancing 

areas (hourly load data). 

Combining balancing areas provides multiple benefits for loads, as seen in Figure 4. 

Because wind is also subject to the principle of statistical independence, wind variability 

declines on a per unit basis when more wind is added to the system. An example of this 

benefit for a large wind penetration is shown in Figure 5, where the benefits of 

consolidated operations, such as would be provided by an RTO or ISO, is more 

significant than portrayed for load alone in Figure 4. What these figures show is that 

excess ramping, which is unneeded and costly, is significant when balancing areas 

operate independently. Some balancing areas must ramp generation up at the same time 

that other balancing areas are ramping down. If operations could be coordinated, much of 

this ramping, and the associated costs, could be eliminated. The figure shows that, with 

wind, the maximum unnecessary ramp is approximately 400 MW, and is matched by a -

400 MW ramp. This bi-directional ramp requirement could be eliminated if the balancing 

areas would combine operations. 

Balancing areas can be consolidated either physically or virtually. Physically combining 

balancing areas is straightforward, but may not always be desirable. Two or more 

balancing areas can retain their autonomy and still capture much of the aggregation 

benefit by electronically combining their Area Control Errors (ACE). Each can control to 

an allocated portion of the combined ACE, assuring that reliability is met at lower cost.ii

Sub-hourly scheduling can provide fast access to neighboring markets, allowing trading 

of opposite ramping requirements. 
10 

Figure 5. Combining balancing areas can reduce ramping requirements for systems that 

have significant wind and load.

Additional work by Milligan and Kirby (2008, An Analysis of Sub-Hourly Ramping 

Impacts of Wind Energy and Balancing Area Size: 

http://www.nrel.gov/docs/fy08osti/43434.pdf) found that large, infrequent 5-minute 

ramps can be avoided when BA’s are combined. Figure 6 shows this impact does not 

only apply to regions with large wind penetrations, but also may apply to loads only. 
11 

Figure 6. Large infrequent ramps can be significantly reduced by combining balancing areas.

These results are corroborated by the New York State wind integration study which 

found that combined operation of the eleven zones in the New York State power system 

reduces hourly load variability by 5% and five-minute load variability by 55%.iii

Figure 4

 (GE 

Energy, 2005). Hourly wind variability is reduced by 33% and five-minute wind 

variability is reduced by 53% with state-wide operations. Hourly system variability is 

further reduced by 10% and five-minute system variability is reduced by 15% when wind 

and load are considered together. Note that while operating large balancing areas helps 

reduce the cost of wind integration, it also helps reduce the cost of serving load with or 

without wind (as pointed out in for the no-wind case). 

The benefits of large electricity markets apply to systems around the world. In a recent 

report for the International Energy Agency (Holttinen et al, 2007), the authors conclude 

“Larger balancing area size and wind aggregation: both load and generation benefit from 

the statistics of large numbers as they are aggregated over larger geographical areas. Larger 

balancing areas make wind plant aggregation possible. The forecasting accuracy improves 

as the geographic scope of the forecast increases; due to the decrease in correlation of wind 

plant output with distance, the variability of the output decreases as more plants are 

aggregated. On a shorter time scale, this translates into a reduction in reserve requirements; 
12 

on a longer time scale, it produces some smoothing effect on the capacity value. Larger 

balancing areas also give access to more balancing units.” (page 107).

The Value of Energy Markets

Markets help economically and reliably integrate wind both in how they treat wind 

generators and in how they treat conventional generators. Markets that allow variable 

resources to sell excess energy or purchase shortages at transparent and fair prices 

accommodate the natural characteristics of wind while reflecting the true real-time cost of 

maintaining reliability. 

More generally, generation scheduling rules and the energy market structure itself are the 

most important factors in tapping the physical flexibility of the conventional generation 

fleet. Sub-hourly energy markets provide economic signals that make it profitable for 

conventional generators to respond to fluctuations in load and wind. Scheduling rules that 

restrict generators to hourly movements artificially hobble the conventional generation 

fleet, resulting in lost opportunities for those generators and increased costs for all. 

Markets that encourage conventional generation movement when it helps increase 

reliability, and do not restrict generators to only changing output at the top of each hour, 

reduce costs. Markets can provide direct economic incentives for generation flexibility if 

the current fleet does not have enough. 

RTOs and ISOs in the United States have fast energy markets, which result in a new 

economic dispatch every 5 to 15 minutes, depending on the market. The fast energy 

markets make it possible to hold the regulating units closer to their preferred operating 

point because they can be brought back to the mid-point of their operating range much 

faster than if the redispatch did not occur for an hour. Therefore, there is less need for 

regulation in faster energy markets. This results in a significant reduction in costs because 

regulation is typically the most expensive ancillary service. Thus, when calculating wind 

integration costs, such features that reduce balancing costs generally will lead to lower 

wind integration costs. 

Enhancing the flexibility of the conventional generation fleet helps to accommodate 

wind. This can involve valuing physical flexibility as other generators are built: fast start 

generators, lower minimum load capability, and high ramp rates are all valuable. 

Participation in the fast energy markets also encourages generators to move to the 

operating level that is consistent with their energy bids. Table 2 provides average hourly 

prices from day-ahead, hour-ahead, and real-time 5-minute energy markets from five 

ISOs for all of 2008. Note that the average prices are reasonably consistent from dayahead to hour-ahead to real-time with only a dollar or two difference. The average 

within-hour price differences for the 5-minute markets (15-minute market in Texas) are 

very high by contrast. This implies that ISOs send strong economic signals to move 

economic generation within the hour but that the load following response they obtain 

does not have a high cost. If the within hour response were expensive the average prices 

from the 5-minute markets would be higher than the hourly prices, but they are not. This 
13 

can have important consequences for wind integration, and is discussed further in that 

context below. 

Table 2. Annual average prices for 2008 show consistency between hourly energy 

markets. Sub-hourly markets show strong volatility.

ISO Day-Ahead

$/MWh 

Hour-Ahead 

$/MWh 

5-Minute

$/MWh 

Average Within-Hour 

5-Minute Range

$/MWh

NYISO $67.70 $64.93 $63.31 $91.18

ISO-NE $81.38 $80.76 $81.22 $24.40

CAISO $69.78 $68.32 $59.87

ERCOT1 $71.69 $40.00

MISO $49.99 $48.62 $48.71 $67.75 1

ERCOT currently operates a 15-minute sub-hourly market rather than a 5-minute market.

It is possible for a power system to have insufficient ramping capability with 

inappropriate consequences for energy prices if maneuverability is not directly valued.

Figure 7 presents a simplified example where a fast energy market, which normally 

provides load following as a byproduct, may have difficulty providing ramp capacity 

under some conditions. Prior to 8:00 a.m., the example system is serving a 2,550 MW 

load with over 3,000 MW of baseload generation, and therefore clearing all energy at 

$10/MWh. At 8:00 a.m., a 300 MW ramp starts which the baseload generation cannot 

follow. There is ample baseload capacity; it simply cannot ramp fast enough. Peaking 

generation (the only other generation in this example system) is started to meet the ramp 

needs. The peaking generator stays on until baseload generation can ramp up. With no 

explicit ramping service, the price rises for the entire energy market (all 2,850 MW) from 

$10/MWh to $90/MWh for 5 hours, just to follow a 30-minute 300-MW ramp. In this 

case, it might be better to create a separate ramping or load following service and pay the 

peaking generator for its response, rather than distorting the price of the entire energy 

market. It is very important to determine if ramping requirements can be served at a low 

cost as a byproduct of the sub-hourly energy market (the typical condition), or if ramping 

requirements impose a high cost because dedicated resources must be used. Fortunately 

market monitors can detect this condition and recommend the establishment of a ramping 

service if the condition is persistent. (Milligan and Kirby, 2007) 
14 

Figure 7. In this simple example, load following is required from an expensive peaking 

generator, but energy is only an incidental product.

When there is a need for relatively slow response, non-spin is normally an economic 

option and is much less expensive than spin. Table 3 shows the average price of spinning 

reserve, non-spinning reserve, and replacement reserve for 4 market areas over a 7-year 

period, subject to data availability. Obtaining flexibility from non-spinning resources is 

not only an economic choice, it maps nicely with the low incidence of large ramp events 

that have been observed in many locations around the United States, such as the ones 

depicted in Figure 6. 
15 

Table 3. Non-spinning reserve is an order of magnitude less expensive than spinning reserve. 

The Benefits of RTOs and ISOs for Wind

Of the various utility structures operating in the United States today, ISOs and RTOs 

provide the best environment for wind generation development. They provide electrically 

and geographically large open markets for wind integration. They operate sub-hourly 

balancing markets which can tap the physical maneuvering capabilities of the 

conventional generators. Balancing payments are typically based upon cost causation 

rather than on arbitrary penalties. A summary of utility industry research by the Utility 

Wind Integration Group (UWIG - www.uwig.org) states that “well-functioning hourahead and day-ahead markets provide the best means of addressing the variability in wind 

plant output.” This assertion is based on evidence from wind integration studies such as 

GE Energy (2005) for the NY ISO. Markets are also cited as helping with wind 

integration in the International Energy Agency (IEA) report on wind integration on large 

power systems (Holtinnen, 2007) and by Smith et al, (2007). The UWIG document also 

says that, “consolidation of balancing areas or the use of dynamic scheduling can 

improve system reliability and reduce the cost of integrating additional wind generation 

into electric system operation.” This is also based on evidence from NY (GE Energy, 

2005) and MN (Zavadil, 2006, and Milligan & Kirby, 2007). 

Not surprisingly, ISOs and RTOs host a disproportionate amount of wind generation: 

72% of installed wind capacity is now located in ISO and RTO regions, even though only 

2002 2003 2004 2005 2006 2007 2008

Annual Average $/MW-hr

California (Reg = up + dn)

Regulation 26.9 35.5 28.7 35.2 38.5 26.1 33.4

Spin 4.3 6.4 7.9 9.9 8.4 4.5 6.0

Non-Spin 1.8 3.6 4.7 3.2 2.5 2.8 1.3

Replacement 0.90 2.9 2.5 1.9 1.5 2.0 1.4

ERCOT (Reg = up + dn)

Regulation 16.9 22.6 38.6 25.2 21.4 43.1

Responsive 7.3 8.3 16.6 14.6 12.6 27.2

Non-Spin 3.2 1.9 6.1 4.2 3.0 4.4

New York

Regulation 18.6 28.3 22.6 39.6 55.7 56.3 59.5

Spin 3.0 4.3 2.4 7.6 8.4 6.8 10.1

Non Spin 1.5 1.0 0.3 1.5 2.3 2.7 3.1

30 Minute 1.2 1.0 0.3 0.4 0.6 0.9 1.1

New England (Reg +”mileage”)

Regulation 54.64 30.22 22.26 12.65 13.75

Spin 0.27 0.41 1.67

10 Minute 0.13 0.34 1.21

30 Minute 0.01 0.09 0.06
16 

44% of wind energy potential and only 53% of electric demand is in these areas. Table 4

shows how wind was distributed inside and outside ISO and RTOs. 

Table 4. At the end of 2008 more wind was located in ISO/RTOs than outside ISO/RTOs (M. 

Goggin, AWEA Projects Database, www.awea.org). 

Wind in RTO/ISO Wind in Non-RTO

RTO State Capacity State Capacity

ERCOT Texas 7,116 Iowa 1,391

CAISO California 2,320 Washington 1,375

MISO Minnesota 1,752 Colorado 1,068

MISO Iowa 1,399 Oregon 1,067

MISO/PJM Illinois 915 Wyoming 676

NYISO New York 882 New Mexico 296

SPP Kansas 815 Montana 272

SPP Oklahoma 708 California 196

MISO North Dakota 539 North Dakota 175

MISO Wisconsin 395 Missouri 163

PJM Pennsylvania 361 Idaho 75

PJM West Virginia 330 Nebraska 73

SPP New Mexico 202 Hawaii 63

MISO South Dakota 143 South Dakota 45

MISO Michigan 129 Tennessee 29

NEISO Main 47 Utah 20

NEISO New Hampshire 25 Alaska 3

PJM New Jersey 8 Arkansas 0

PJM/MISO Ohio 7 Non-RTO Total 6,987

NEISO Vermont 6

NEISO Massachusetts 5

NEISO Rhode Island 1

RTO Total 18,104

A recent study required by the Minnesota legislature to assess the reliability and cost of 

providing 20% of the state’s electricity from wind provides a good example of how open 

markets can facilitate wind integration:

“The MISO [Midwest Independent System Operator] energy market also played a 

large role in reducing wind generation integration costs. Since all generating 

resources over the market footprint are committed and dispatched in an optimal 

fashion, the size of the effective system into which the wind generation for the 

study is integrated grows to almost 1200 individual generating units. The 

aggregate flexibility of the units on line during any hour is adequate for 
17 

compensating most of the changes in wind generation.” (See 

www.puc.state.mn.us/docs/windrpt_vol%201.pdf.) (Zavadil, 2006) 

ISO and RTO Characteristics

The ISOs grew out of the Federal Energy Regulatory Commission (FERC) Orders Nos. 

888 and 889. These orders required transmission operators to provide competing 

generators non-discriminatory access to the transmission system. Subsequently, FERC 

Order No. 2000 encouraged RTO formation, which included the characteristics of an ISO 

plus a requirement for sufficient regional scope. An organization must satisfy 12 

characteristics and functions to earn FERC approval as an RTOiv

1. independence 

: 

2. operational authority

3. regional scope (generally implies a large geographic and electrical scope)

4. reliability authority

5. tariff design

6. parallel path flow

7. market monitoring

8. transmission planning 

9. congestion management

10. supply ancillary services 

11. OASIS and transmission capacity

12. inter-regional coordination. 

Operational authority and regional scope capture the benefits of uncorrelated change in 

loads over various time frames, and can provide a larger, more robust dispatch stack that 

can potentially reduce per-unit costs. Figure 8 shows how RTOs and ISOs are distributed 

across the United States and Canada. Large areas benefit from aggregation of loads, 

which has been one of the driving principles behind the large number of reserve-sharing 

agreements that have been forged over the past several decades. Combining the 

characteristics of a large resource stack also provides significant benefits because of the 

increased aggregate response capability that exists in a larger generation portfolio.

Wind Integration Costs

Wind integration studies typically show lower wind integration costs for ISO and RTO 

markets than for non-ISO/RTO areas. These studies quantify the costs of additional 

reserves, changes in unit commitment and dispatch, gas nominations, etc. Integration 

costs are separate from energy and emissions benefits. Table 5 shows results from several 

recent wind integration studies (Smith et. al, 2007, Northwest Wind Integration Action 

Plan, 2007, http://www.nwcouncil.org/energy/wind/Default.asp).v In general, the studies 

show lower integration costs in ISO/RTOs than in smaller, single-utility service areas. 

The integration costs for the three ISO/RTO studies range from zero to $4.41/MWh of 

wind, while the integration costs for the two non-ISO/RTO studies range from $8.84 to 

$16.16/MWh. One reason for these results is that the three ISO/RTOs operate sub-hourly 

markets, i.e. they dispatch generation on a five to fifteen-minute time frame, while the 

two non-ISO/RTOs require generators to follow hourly schedules and obtain all sub-
18 

hourly balancing from regulating units. Another reason for these results is the large size 

of ISOs and RTOs, which means there is much more conventional generation with 

ramping capability available to respond to changes in wind output while maintaining the 

balance between generation and load, thereby reducing wind integration costs. (ISO/RTO 

Council, 2007) 

Figure 8. ISOs and RTOs in North America have broad geographic and electrical reach. 

Note that Nebraska is now part of SPP.
19 

Table 5. Wind Integration Cost Study Results. 

Date Study ISO/RTO Wind 

Capacity 

Penetration

Integration Cost: 

$/MWh of Wind 

Output

Energy 

Market 

Interval

3/05 NYISO ISO/RTO 10% Very Low 5 minute

12/06 Minnesota/MISO ISO/RTO 31% $4.41 5 minute

2/07 GE/Pier/CAIAP(a) ISO/RTO 33% $0-$0.69 10 minute

3/07 Avista No 30% $8.84 1 hour

3/07 Idaho Power(b) No 30% $7.92 1 hour

(a) Includes two-thirds wind and one-third solar and includes cost increases of regulation and load 

following assigned to regulation.

(b) Reduced from $16.16 in September, 2007, settlement proceedings.

Wind Integration is facilitated by Energy Markets in Europe

Evidence from the United States is corroborated by evidence in Europe. Denmark, 

Germany, and Spain have all integrated large amounts of wind generation into their 

power systems. Their experience points to the benefits of operating in a region with a 

robust spot electricity market. (Holttinen, 2007) 

West Denmark already receives 24% of its electric energy from wind. Participation in the 

Nordpool spot market greatly helps wind integration. Holttinen notes that added reserve 

requirements in the Nordic countries would be double if they operated as single countries 

compared to operating as a combined pool. Pool operation also reduces the need to 

curtail wind when there is excess production in one country. Denmark has not needed to 

increase the amount of operating reserves because of wind, but it does use the reserves 

more often. 

Holttinen (2007) shows that West Denmark has experienced times when wind power has 

exceeded the load. Figure 9, taken from the IEA publication, illustrates one such time 

period. Note the wind generation exceeding load during light-load nighttime conditions at 

hours 13, 82-87 and 107-113. Wind generation nearly exceeded load during hours 156, 

204 and 228. During the high-wind/low-load events, West Denmark was able to export 

the surplus wind energy to markets in Norway and Sweden using DC transmission 

connections. Since Norway and Sweden have hydroelectric generation with water 

storage capabilities, they use the imported Danish wind in lieu of hydropower and use the 

hydropower at other times. 
20 

Figure 9. West Denmark can export excess wind power to other energy markets when 

wind exceeds load. 

North Germany receives 33% of its energy from wind. As noted earlier, (Table 1) 

forecasting errors are reduced by integrating wind generation across the four German 

regions. Transmission ties are being strengthened to increase the size of the region over 

which wind variability and load-following generation can be aggregated. Germany too 

has seen an increase in the use of operating reserves, but no increase in the amount of 

reserves needed. 

Spain, with 24% to 30% of electric energy coming from wind in various regions, has not 

required increased operating reserves though it also uses the reserves more often. Spain 

also derives large benefits from integrated interregional operations. 

The European Wind Energy Association (EWEA) advocates regional markets as an 

important policy for the integration of wind: “the capacity of the European power system 

to absorb significant amounts of wind power is determined more by economics and 

regulatory rules than by technical or practical constraints.” The European Commission 

cites the need for increased cross-border transmission links and increased liquidity in 

wholesale electricity markets as barriers to increased wind integration in its 2005 

Benchmarking report. EWEA also notes that the large geographical spread of wind 

power will reduce variability, increase predictability, and decrease the occurrences of 

near-zero or peak wind output. (Van Hulle, 2005) 

West Denmark January 3-15, 2005

0

500

1000

1500

2000

2500

3000

3500

4000

1 49 97 145 193 241 289

hour

MW

Wind

Load
21 

Some Non-RTOs are Adopting or Evaluating RTO-like 

Functions 

In large parts of the western part of the U.S., there is no RTO or ISO, and energy markets 

are not robust. However, there is significant interest in developing cooperative 

agreements among balancing areas that would provide some of the benefits of 

consolidation. The Northern Tier Transmission Group (NTTG) (www.nttg.biz) developed 

an ACE Diversity Interchange (ADI) pilot program that allows for the sharing of 

regulation across regions. There is significant interest in this project, and WestConnect 

(www.westconnect.com) has joined the NTTG ADI project, and continues to investigate 

wholesale market enhancements and seams issues in the footprint. 

The National Renewable Energy Laboratory (NREL) has begun a large Western Wind 

and Solar Integration Study. The focus of the study is the WestConnect footprint, but the 

entire U.S. portion of the Western Electricity Coordinating Council (WECC) will be 

modeled and high wind and solar penetrations will be analyzed. Figure 10 shows the 

study footprint. One of the scenarios will consider the benefit of consolidated balancing 

area operations and examine the potential benefit for integrating a high penetration of 

renewable energy sources. 

In the Pacific Northwest, the Bonneville Power Administration convened stakeholders 

and utilities to examine how the region could best position itself to integrate up to 6,000 

MW of wind that may be developed in the next several years. The result of this effort is 

the Northwest Wind Integration Action Plan 

(http://www.nwcouncil.org/energy/Wind/library/2007-1.pdf). 

Among the items on the agenda for the Northwest Wind Integration Action Plan are “(1) 

developing more cooperation between regional utilities to spread the variability of wind 

more broadly; (2) developing markets that will reward entities who choose to market 

their surplus flexibility.” Other parts of the report indicate a need for “developing more 

robust markets for control area services that will provide needed electric services for 

smaller control areas with substantial wind resources” (page 13).
22 

Figure 10. NREL's Western Wind and Solar Integration Study focuses on the WestConnect 

footprint and models the U.S. portion of the WECC footprint. 

Although the outcomes of these various initiatives cannot be precisely predicted, they are 

further indication that when analysts consider how to integrate wind, market structure and 

design changes can offer significant benefits. The combination of regions in the 

Northwest and in WestConnect covers nearly the entire West that is not currently part of 

the California ISO or the Southwest Power Pool (parts of eastern New Mexico).

System Evaluation Tool

The System Evaluation Tool (Figure 11) is a spreadsheet based instrument for assessing 

how accommodating the structure of a balancing area (BA) or region is to the integration 

of large amounts of wind generation. It is especially useful in comparing regions or BAs. 

The judgments are necessarily subjective but the subjectivity is limited and provided 

some structure. The evaluator provides a numeric rating between 1 and 10 for each BA in 

the ten areas discussed previously: one is the poorest performance and ten is the best. 
23 

Figure 11. The System Evaluation Tool helps quantify and structure assessment of 

accommodating wind integration (see Score Card Excel spreadsheet for access to this tool). 

The evaluator can choose to give greater different weighting factors to each category. 

Sub-hourly markets could be given twice as much weight as responsive load (or half as 

much) for example. In this example, all categories are given equal weight.

The tool provides example evaluations for five hypothetical utility structures. The large 

RTO gets a high score because of its large size and also because the wind generation in 

this hypothetical example is assumed to be dispersed geographically. It is also using an 

advanced wind forecast in the control room. Sub-hourly energy markets and sub-hourly 

schedules with neighboring RTOs are beneficial as well. This RTO conducts joint 

planning with others throughout the interconnection. The transmission system itself is 

less than perfect however, with congestion occurring more often than is optimal, so the 

RTO received a rating of six for “Robust Electrical Grid.” This RTO offers conditional 

firm transmission and re-dispatch services to partly compensate for the less than ideal 

transmission system. Sub-hourly energy markets provide access to generation response 

and the generation mix itself has sufficient flexibility to gain a high score on “Flexibility 

in Generation.” Load response has not been fully developed, however, so the RTO has a 

relatively low “Responsive Load” score. Overall this hypothetical large, progressive RTO 

receives a fairly high score for having market structures and physical characteristics that 

help reduce wind integration costs.

A smaller ISO might not receive such a high score. Smaller size, without some 

mechanism to share diversity with neighbors, results in a lower score. Similarly, if wind 

Large BA

 Geographically Dispersed Wind

 Wind Forecasting Effectively Integrated Into System Operations

 Sub-Hourly Energy Markets

 Fast Access to Neighboring Markets

 Non Spinning and 30-Minute Reserves for Wind Event Response

 Regional Transmission Planning For Economics and Reliability

 Robust Electrical Grid

 More Flexible Transmission Service

 Flexibility in Generation

 Responsive Load

 Overall

 Example Utility Structures

10 8 7 10 7 2 7 6 7 7 3 7 Large RTO with spot markets

6 6 6 3 3 2 6 4 7 2 2 4 Smaller ISO

1 3 2 1 2 1 2 3 2 2 2 2 Interior west & upper Midwest (non-MISO) 

7 6 6 2 2 2 5 4 2 5 2 4 Large vertically integrated utility

1 3 2 1 2 1 2 4 2 2 2 2 Smaller vertically integrated local utility

8 Unconstrained hydro system

3 Heavily fish constrained hydro system

Accommodating Wind Integration
24 

generation is concentrated within the ISO the score for wind diversity is reduced. This 

ISO has not developed sub-hourly energy markets and does not engage in sub-hourly 

schedule adjustments with neighbors so it receives especially low marks. With less 

regional focus on transmission planning and greater congestion this ISO receives a lower 

score though offering conditional firm and re-dispatch gets a high score for transmission 

flexibility. This ISO primarily has large base-load generators with little flexibility and, 

like most, has yet to fully develop responsive load. Overall this smaller ISO receives a 

lower score than the large, progressive RTO.

Small independent BAs would typically receive low scores because they do not have 

sufficient load diversity or wind diversity to reduce the relative variability. They may not 

use wind forecasts in the operations center. Typically, they do not operate sub-hourly 

markets or adjust schedules with neighbors more frequently than each hour. They may 

not engage in regional transmission planning to alleviate economic as well as reliability 

concerns and they may not offer effective re-dispatch or conditional firm transmission 

service. Generation is likely inflexible because of the smaller number of generators 

resulting more difficult minimum load problems. 

Large, vertically integrated utilities can have an electrically and geographically large 

footprint though typically not as large as the largest RTOs. Wind may or may not be 

geographically dispersed; in this example we assume that it is somewhat concentrated. 

We also assumed that a persistence forecast was available in the control room. With no 

sub-hourly markets and no sub-hourly coordination with neighbors the BA receives low 

marks. Regional transmission planning and transmission adequacy need improvement. 

Effective re-dispatch and conditional-firm transmission service are not offered. 

Generation is moderately flexible but load is not. Overall this large vertically integrated 

utility receives marks that are similar to those given to the hypothetical smaller ISO. 

Lastly, the figure compares an unconstrained hydro system with a constrained hydro 

system in the generation flexibility category. Some hydro generation can be very flexible 

with fast ramping capability, rapid cycling, and excellent control. An unconstrained 

hydro system would receive high marks for flexibility and its ability to accommodate 

load and wind variability. Biological constraints can remove much of that flexibility, 

however. Still, constrained hydro systems should be carefully examined to determine 

how their flexibility can best be used to maximize their profit and to help integrate wind. 

Wind’s fuel saving capability should be able to maximize the capacity value of a hydro 

system by saving water.

These examples are all hypothetical. The value of the tool is that it supports the 

evaluation of individual BAs as well as comparing the relative capabilities of several 

BAs. A regulator could use the tool as part of a process designed to identify needed 

improvements in market structures and reliability rules as well as in transmission and 

generation facilities.
25 

Conclusions 

Wind power has emerged from being a technical curiosity to being a serious participant 

in the U.S electric power supply mix, offering environmental, economic, security, and 

reliability benefits. With over 25,170 MW of wind capacity currently operating and 8,358 

MW installed in 2008, power system operators are learning how to exploit the benefits of 

wind and to accommodate its limitations. Experience from the United States and Europe, 

which has significantly higher penetration rates than currently seen in the United States, 

indicates that larger markets and balancing areas that are a central feature of ISOs and 

RTOs, can improve the physical conditions needed to integrate large amounts of wind 

energy. ISOs and RTOs, with their day-ahead and real-time markets, large geographies to 

aggregate diverse wind resources, large loads to aggregate with wind, large generation 

pools that tap conventional generator flexibility, and regional transmission planning 

efforts, offer the best environments for wind generation to develop. It is not surprising 

that ISOs and RTOs host a disproportionate amount of wind generation. Recent efforts at 

large scale transmission planning such as the Joint Coordinated System Plan, the Eastern 

Wind Integration and Transmission Study, the Western Wind and Solar Integration 

Study, and the ERCOT Competitive Renewable Energy Zone process all show the 

benefits of removing congestion on a regional bases, both for wind integration and for 

reducing overall power costs for consumers. The evaluation tool we describe in this paper 

provides a systematic way to assess how well a BA is positioned to integrate wind and to 

identify where improvements are needed. 

Acknowledgements 

The authors thank the National Renewable Energy Laboratory for supporting this effort. 

Further Reading and Resources

J. Smith, M. Milligan, E. DeMeo, B. Parsons, “Utility Wind Integration and Operating 

Impact State of the Art,” http://www.nrel.gov/docs/fy07osti/41329.pdf. 

Utility Wind Integration Group web site: www.uwig.org, and the joint statement by 

UWIG, EEI and NRECA (http://www.uwig.org/UWIGIntSummary.pdf). 

References

Zavadil, R. 2009, Eastern Interconnection Wind Integration & Transmission Study, TRC 

Meeting #3, Peabody Little Rock Hotel, Little Rock, Arkansas, February 11-12. 

Gramlich, R. and Goggin, M. 2008, The Ability of Current U.S. Electric Industry 

Structure and Transmission Rules to Accommodate High Wind Energy Penetration, 7th 

International Workshop on Large Scale Integration of Wind Power and on Transmission 

Networks for Offshore Wind Farms. 
26 

Ahlstrom, M, 2008. Short-term Forecasting: Integration of Forecast Data into Utility 

Operations Planning Tools, presented at the Utility Wind Integration Group/National 

Renewable Energy Laboratory Wind Forecasting Applications to Utility Planning and 

Operations, St. Paul, MN. Feb 21-22. 

M. Milligan and B. Kirby 2007, The Impact of Balancing Areas Size, Obligation Sharing, 

and Ramping Capability on Wind Integration, WindPower 2007. 

(CAISO 2007, Integration of Renewables, 

http://www.caiso.com/1ca5/1ca5a7a026270.pdf, November). 

(ERCOT, 2006, Analysis of Transmission Alternatives for Competitive Renewable 

Energy Zones in Texas). 

GE Energy, 2005, The Effects of Integrating Wind Power on Transmission System 

Planning, Reliability, and Operations: Report on Phase 2, Prepared for The New York 

State Energy Research and Development Authority. 

ISO/RTO Council, 2007, Increasing Renewable Resources: How ISOs and RTOs are 

Helping Meet This Public Policy Objective, ISO/RTO Council, October 16. 

Holttinen H., et al, 2007, Design and Operation of Power Systems with Large Amounts of 

Wind Power: State of the Art Report, VTT Working Paper 82, IEA Wind. 

Rohrig, K. (ed.), 2005. Entwicklung eines Rechenmodells zur Windleistungsprognose 

für das Gebiet des deutschen Verbundnetzes. Abschlussbericht Forschungsvorhaben Nr. 

0329915A, gefördert durch Bundesministeriums für Umwelt, Naturschutz und 

Reaktorsicherheit (BMU). Kassel, Germany.

Spees, K. and Lave, L., 2007, Do RTOs Promote Renewables? A Study of State-Level 

Data over Time, Carnegie Mellon Electricity Industry Center Working Paper CEIC-07-

14, December 3. 

U.S. Department of Energy, 2002, National Transmission Grid Study, May. 

F. Van Hulle, 2005, Large Scale Integration of Wind Energy in the European Power 

Supply: Analysis, Issues and Recommendations, European Wind Energy Association, 

www.ewea.org, December. 

R. Zavadil, et al, 2006, Final Report - 2006 Minnesota Wind Integration Study 

Volume I, the Minnesota Public Utilities Commission, November. 

 

i

A balancing area (formerly called control area) is the basic administrative unit within the power system. It 

can be very loosely thought of as a utility. There are about 130 balancing areas in North America ranging in 

size from the 476-MW Turlock Irrigation District to the 150,000 MW PJM.
27 

 ii There are several ways to allocate the combined ACE among the participating balancing areas but in all 

cases the required control actions are smaller than if the ACE signals were not combined. iii Hourly load variability shows the smallest reduction in variability (5%) when state-wide operations are 

compared with zonal operations because loads are highly correlated on an hourly basis. Most loads increase 

in the morning and decrease in the evening. State wide wind does not show that same similar pattern.

iv Kelly, K., The Value of Large Regional Transmission Organizations, presentation at the IEEE 2004 

Power Systems Conference and Exposition, New York, NY. October 12, 2004. Available at 

http://www.ieee.org/portal/cms_docs_pes/pes/subpages/meetingsfolder/PSCE/2004Presentations/474/IEEE-2004-RTO-Benefits-K-Kelly-no-notes.pdfiv

The quoted integration costs are actually the operating cost impacts. Some studies quantify additional wind 

related costs.

v J. Smith, M. Milligan, E. DeMeo, B. Parsons, “Utility Wind Integration and Operating Impact State of the 

Art,” http://www.nrel.gov/docs/fy07osti/41329.pdf. 
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