A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Facilitating Wind Development: 

The Importance of Electric 

Industry Structure 

B. Kirby and M. Milligan 

Technical Report 

NREL/TP-500-43251 

May 2008 

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Contract No. DE-AC36-99-GO10337

Technical Report 

NREL/TP-500-43251 

May 2008 

Facilitating Wind Development: 

The Importance of Electric 

Industry Structure 

B. Kirby and M. Milligan 

Prepared under Task No. WER8.5001 
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Contents 

Introduction..........................................................................................................................1 

Relevant Characteristics of Wind Power for System Operators..........................................1 

 System Operation with Wind..........................................................................................2 

 Accommodating Wind....................................................................................................3 

 The Benefits of RTOs and ISOs for Wind...................................................................10 

Some Non-RTOs are Adopting or Evaluating RTO-Like Functions.................................16 

 Statistical Evidence of the Benefits of RTOs in Integrating Wind..............................17 

Conclusions........................................................................................................................20 

Acknowledgements............................................................................................................20 

Further Reading and Resources .........................................................................................20 

References..........................................................................................................................20 

Figures 

Fig. 1. Wind generator variability loses correlation as the distance ...................................4 

Fig. 2. Physical ramping requirements can be reduced .......................................................7 

Fig. 3. Combining balancing areas can reduce ramping requirements ................................8 

Fig. 4. In this example, load following is required from an expensive..............................10 

Fig. 5. ISOs and RTOs in North America..........................................................................14 

Fig. 6. West Denmark can export excess wind power.......................................................15 

Fig. 7. NREL’s Western Wind and Solar Integration Study .............................................17 

Tables 

Table 1. Wind Forecasting Accuracy Improves When Larger Geographic 

Areas are Considered ...........................................................................................................6 

Table 2. At the End of 2007 More Wind was Located in ISO/RTOs than Outside 

ISOs/RTOs.........................................................................................................................12 

Table 3. Wind Integration Cost Study Results...................................................................15 

 iii
Introduction 

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

economic benefits. A record 5,244 MW of new wind generation was installed in the U.S. 

last year, bringing the total wind generation fleet capacity to 16,818 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 market structures that 

provide access to conventional generation flexibility and maneuverability; the ability of a 

generator to ramp up and down quickly and accurately, to turn on and off quickly and at 

low cost, and the ability to operate at low minimum loads. 

This paper attempts to evaluate which wholesale electricity market-structure 

characteristics best accommodate wind energy. Market structures are naturally dependent 

upon the physical and regulatory characteristics of the region they operate in. We find 

that open market structures with large geographic scope, along with day-ahead, hourahead, 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. 

Relevant Characteristics of Wind Power for System 

Operators 

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

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

 1
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 fuelconsuming power plants instead, and generate as much as possible at wind plants. 

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. 

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. 

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. Therefore, wind 

greatly benefits from a long-term, regional approach to transmission planning. 

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 

Reliability Council 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 

 2
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, 

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 

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

when distances between wind farms are large. 

Figure 1

 3
0

0.2

0.4

0.6

0.8

1

0 100 200 300 400 500 600

Distance [km]

Correlation Coefficient

4h Average

12-hour 

1h Average

30min Average

5min Average

2h Average

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

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

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%-50%. Thus, power system 

operators can more accurately predict and plan for changes in wind output when their 

systems are larger. 

 4
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. 

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. 

 5
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 

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. 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 2. 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 2 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 

 6
hour to hour. The lower portion of Figure 2 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. 

-150

-100

-50

0

50

100

150

Excess Ramp (MW/hr): Chronological and Load Duration Curve

0 2000 4000 6000 8000

-150

-100

-50

0

50

100

150 Load Excess Ramp

Figure 2. 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 2. 

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 3, 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 2. What this figure shows 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 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 

 7
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

 

-400

-200

0

200

400

Excess Ramp (MW/hr): Chronological and Load Duration Curve

0 2000 4000 6000 8000

-400

-200

0

200

400

 Load and Wind Excess Ramp

Figure 3. Combining balancing areas can reduce ramping requirements for systems that have 

significant wind and load. 

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 (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 Figure 2 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 

 8
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; 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 U.S. 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. This implies that load following 

services, which has no energy component, can often be extracted at no cost from the 

energy market. This can have important consequences for wind integration, and is 

discussed further in that context below. 

 9
It is possible for a power system to have insufficient ramping capability with 

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

 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 can not 

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 & Kirby, 2007) 

Figure 4

The Benefits of RTOs and ISOs for Wind 

Of the various utility structures operating in the U.S. 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 tap the physical maneuvering capabilities of the conventional 

2500

2600

2700

2800

2900

3000

7:00

7:30

8:00

8:30

9:00

9:30

10:00

10:30

11:00

11:30

12:00

12:30

13:00

13:30

14:00

MW

Figure 4. In this simple example, load following is required from an expensive 

peaking generator, but energy is only an incidental product. 

Base Load - $10/MWh

Peaking - $90/MWh

Total Load

 10
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 hour-ahead 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: 74 

percent of installed wind capacity is now located in ISO and RTO regions even though 

only 44 percent of wind energy potential and only 53 percent of electric demand is in 

these areas. Table 2 shows how wind was distributed inside and outside ISO and RTOs. 

 11
Table 2. At the End of 2007 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

MISO SD 54.00 California 196.40

SPP NM 201.98 Washington 1,163.18

CAISO California 2,242.43 Colorado 1,066.75

ERCOT Texas 4,356.35 Oregon 885.39

MISO Minnesota 1,299.75 New Mexico 294.00

MISO Iowa 1,273.08 Wyoming 288.45

MISO/PJM Illinois 699.36 Montana 145.53

SPP Oklahoma 689.00 South Dakota 44.26

NYISO New York 424.80 Idaho 75.32

SPP Kansas 364.20 Nebraska 73.38

MISO North Dakota 344.77 Hawaii 63.12

PJM Pennsylvania 293.53 Missouri 56.70

PJM West Virginia 66.00 Tennessee 28.98

MISO Wisconsin 53.05 Alaska 1.59

ISONE Maine 42.10 Utah 0.89

PJM New Jersey 7.50 Arkansas 0.10

PJM/MISO Ohio 7.42 Non-RTO Total 4,384.04

ISONE Vermont 6.05

ISONE Massachusetts 4.72

MISO Michigan 2.59

ISONE New Hampshire 1.40

ISONE Rhode Island 0.66

Total RTO/ISO 12,434.74

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

providing 20 percent 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 

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

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

 12
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 5 shows how RTOs and ISOs are distributed 

across the U.S. 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 3 shows results from several 

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

Plan, 2007).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 nonISO/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-hourly balancing from regulating units. Another 

reason for these results is the large size of ISOs and RTOs, which means there is much 

 13
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 5. ISOs and RTOs in North America have broad geographic and electrical reach. 

Table 3. 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. 

 14
Wind Integration is facilitated by Energy Markets in Europe 

The evidence from the U.S. 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 6, 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. 

W e st D e nm ark January 3 -1 5 , 2 0 0 5

0

500

1000

1500

2000

2500

3000

3500

4000

1 49 97 145 193 241 289

hour

MW

W ind

Load

Figure 6. West Denmark can export excess wind power to other energy markets when wind exceeds 

load. 

 15
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) 

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 7 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). 

 16
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). 

Figure 7. 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). 

Statistical Evidence of the Benefits of RTOs in Integrating Wind 

A recent Carnegie Mellon University (CMU) Industry Center Working Paper partly 

funded by the American Public Power Association (Do RTOs Promote Renewables?, by 

Kathleen Spees and Lester Lave, 2007) found a slight negative correlation between wind 

development and RTO membership (“The size of the negative relationship is small”). 

This is a surprising finding given the numerous benefits that RTOs and ISOs provide for 

wind development and integration. The authors were not able to explain the results and 

stated in the conclusions: 

 17
“The statistical analysis indicates that membership in an RTO is negatively 

correlated with wind development, but we have no explanation as to why this 

would be true and so leave this result for further investigation.” (emphasis 

added) 

The authors acknowledge the fact that ISOs and RTOs host a disproportionate amount of 

wind generation. “We estimate that 66% of United States wind generated electricity was 

produced in RTO states in 2005, even though those states represent only 30% of U.S. 

wind resource potential and 48% of national electric generation from all sources.” These 

2005 numbers are a bit lower than the 2006 figures, where ISOs and RTOs hosted 73% of 

wind generation while containing only 44% of the resource potential and 53% of the 

electric demand. The difference shows the trend for wind development within ISO/RTO 

regions. 

Because the CMU study’s conclusion is inconsistent with the fact that ISOs and RTOs 

actually host a disproportionate amount of wind generation, a closer look at the CMU 

study methodology is warranted. The CMU study used a state-by-state statistical model 

to try to separate RTO and ISO membership from a number of other factors promoting 

wind and renewables development. They tried to correct for eight influences that might 

be the “real” reasons why there is a greater proportion of wind developed in RTO regions 

(descriptions below based on Spees-Lave text): 

RTO – The number of years that the state has been at least partly in an RTO is 

included to test whether being a member of an RTO induces more renewables. 

GSP – Gross state product per capita, since wind would be selected on 

environmental sustainability grounds, not because it is the cheapest source 

MWh – The total amount of electricity generated in the state, since larger states 

should find it easier to incorporate wind into their grid. 

Price – The price of electricity in the state, since the higher the price, the more 

attractive wind is. 

Dem – The proportion of the major-party votes won by the Democratic 

presidential candidate in 2004, since liberals are more likely to favor the 

environmentally sustainable electricity source. 

WindPot – The potential wind resource in each state, since the states with the 

greatest wind resource are likely to have the lowest cost. 

PolicyP – The eight transformed variables that characterize state incentives to 

develop wind such as tax benefits and renewable portfolio standards. 

YT – Dummy variables for each year to account for any factor that is common to 

all states in each particular year, such as the expiration of the federal production 

tax credit. 

A closer look at the CMU study methodology is warranted to try to understand why the 

study found that ISO and RTO membership would discourage wind development given 

the acknowledged fact that ISOs and RTOs actually host a disproportionate amount of 

wind generation, and the physical features of RTO and ISO markets discussed in this 

paper promote wind development. When there is a mismatch between what one would 

 18
have expected from conventional economic reasoning and the conclusions drawn from 

statistical models, the mismatch generally reflects one of two problems: either

conventional economic reasoning is too simplistic and a more sophisticated theory is 

needed to explain the counter-intuitive relationship in the data, or there are flaws in the 

statistical methodology that produce spurious results. The latter appears to be the case 

with the CMU study. 

The principal problem with the Spees and Lave study is that the variables they are testing 

for are difficult to quantify. In particular, they defined ISOs and RTOs by state and start 

date, creating significant errors in the resulting variable values: 

• ISOs and RTOs are typically not coincident with state borders, creating estimation 

error – California, for example, was treated “as fully within an RTO even though 

there are actually some sections of California not within California ISO (CAISO) 

territory.” 

• ISOs and RTOs grew substantially over the study period changing whether states 

were in or out of an RTO. PJM, for example, is limited in their study to the size it 

was over ten years ago, before its massive expansion. 

• MISO and SPP are not considered at all because they are too new. Consequently, 

the largest RTOs, with the greatest aggregation benefits, are not considered. 

The result of these variable definitions was that almost half of the nation’s electric 

generation, and a significant portion of its wind development, were systematically 

excluded from recognition in their statistical analysis. 

Attempting to quantify complex state renewables incentives into simple comparative 

variables almost certainly introduces significant errors. Similarly, parsing ISO and RTO 

membership based on the market start date ignores the fact that generation investment is 

forward looking. Investors were aware that markets would be operating and that ISO and 

RTO benefits would apply well before the actual market start dates. 

A purely statistical analysis must be able to explain its conclusions. However, the Spees 

and Lave report cannot explain why ISO/RTO organization has a negative impact on 

wind development, particularly given the advantages that ISOs/RTOs offer for wind 

development and operation. The CMU analysis is correlation without causation, based on 

flawed variable definitions, and has no explanatory power. 

Spees and Lave’s finding of slight negative correlation of wind development and RTO 

membership is not convincing given the judgment required to create the input data. 

Although statistical analysis can be useful, it must either explain reality, or convincing 

evidence must be presented that shows the relevance of the analysis. The Spees and Lave 

results fly in the face of reality, and there is no plausible explanation of their results. 

These points were not challenged by Spees and Lave or any other authors to our 

knowledge. 

 19
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 16,818 MW of wind capacity currently operating and 5,244 

MW installed in 2007, 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 U.S., 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. 

Acknowledgements 

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

and also thank Michael Goggin, American Wind Energy Association, for wind plant 

locations in the U.S., and Bruce Green, National Renewable Energy Laboratory for 

comments and suggestions. 

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 

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. 

 20
(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. 

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. 

 21
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