National Renewable Energy Laboratory 

Innovation for Our Energy Future 

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

Office of Energy Efficiency & Renewable Energy

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

Grid Impacts of Wind Power 

Variability: Recent Assessments 

from a Variety of Utilities in the 

United States 

Preprint 

B. Parsons and M. Milligan (Consultant) 

National Renewable Energy Laboratory 

J.C. Smith 

Utility Wind Integration Group 

E. DeMeo 

Renewable Energy Consulting Services, Inc. 

B. Oakleaf 

Excel Energy 

K. Wolf 

Minnesota Public Utilities Commission 

M. Schuerger 

Energy Systems Consulting Services, LLC 

R. Zavadil 

EnerNex Corporation 

M. Ahlstrom 

WindLogics 

D. Yen Nakafuji 

California Energy Commission 

Presented at the European Wind Energy Conference 

Athens, Greece 

February 27–March 2, 2006 

Conference Paper 

NREL/CP-500-39955 

July 2006 
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Grid Impacts of Wind Power Variability: Recent Assessments from a Variety of Utilities in the United States*

Brian Parsons and Michael Milligan**, Consultant 

National Renewable Energy Laboratory 

J Charles Smith 

Utility Wind Integration Group 

Edgar DeMeo 

Renewable Energy Consulting Services, Inc. 

Brett Oakleaf 

Xcel Energy 

Kenneth Wolf 

Minnesota Public Utilities Commission 

Matt Schuerger 

Energy Systems Consulting Services, LLC 

Robert Zavadil 

EnerNex Corporation 

Mark Ahlstrom 

WindLogics 

Dora Yen Nakafuji 

California Energy Commission 

Introduction and Background 

During 2005 approximately 2,500 MW of wind capacity was added in the United States, which brought installed wind 

capacity to about 9,150 MW. Although the total wind capacity in the United States is less than in some countries, wind 

energy has caught the attention of some utilities that depend on natural gas to generate power. There is evidence that 

wind development will continue at significant levels in the United States for the next several years, although it may be 

sensitive to a number of factors that include transmission availability, wind turbine availability, prices of wind 

technology and competing fuels, production tax credit availability, and states’ renewable portfolio standards (RPSs). 

This trend has helped induce electricity providers to investigate the potential impact of wind on the power system. 

Because of wind power’s unique characteristics, many concerns are based on the increased variability that wind 

contributes to the grid, and most U.S. studies have focused on this aspect of wind generation. Grid operators are also 

concerned about the ability to predict wind generation over several time scales. 

In this report we discuss some recent studies that have occurred in the United States since our previous work [2, 3]. The 

key objectives of these studies were to quantify the physical impacts and costs of wind generation on grid operations 

and the associated costs. Examples of these costs are (a) committing unneeded generation, (b) allocating more loadfollowing capability to account for wind variability, and (c) allocating more regulation capacity. These are referred to 

as “ancillary service” costs, and are based on the physical system and operating characteristics and procedures. This 

topic is covered in more detail by Zavadil et al. [4]. 

Time Frames of Wind’s Impact 

Wind can have an impact on several time scales that correspond to grid operations. The shortest is generally in the 

range of milliseconds to seconds, and is the domain of system dynamic stability studies. Most wind integration studies 

 

*

 Portions of this paper have been adapted from IEEE Power & Energy, November/December 2005 [1]. 

** Employees of the Midwest Research Institute under Contract No. DE-AC36-99GO10337 with the U.S. Dept. of 

Energy have authored this work. The United States Government retains, and the publisher, by accepting the article for 

publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide 

license to publish or reproduce the published form of this work, or allow others to do so, for the United States 

Government purposes. 

1
focus on longer time scales, but the stability time frame is a concern and recent developments in the United States have 

addressed this issue. The most important is the Federal Energy Regulatory Commission (FERC) limited grid code for 

wind plants, contained in FERC Order 661A, issued in December 2005 [5]. This ruling addresses the issues of lowvoltage ride-through, reactive power supply, and Supervisory Control and Data Acquisition (SCADA) system 

requirements 

Figure 1 illustrates the key time frames that correspond to utility/grid operations and that have been the focus of most 

integration studies. In the United States, the regulation time frame is the period during which generation automatically 

responds to deviations in load or load net wind. This capability is typically provided via automatic generation control, 

and is a capacity service generally covering seconds to several minutes. Integrating wind into the system would have an 

impact on regulation requirements for the system, and might require additional regulation capability. In the United 

States there are two controlled performance standards, CPS-1 and CPS-2*

, control area operators/balancing authorities 

follow. 

Time (hour of day)

048 12 16 20 24

System Load (MW)

seconds to minutes

Regulation

tens of minutes to hours

Load

Following

day

Scheduling

Days

Unit

Commitment

Figure 1. Time frames for wind impacts 

The second time frame is load following. This is a longer period during which generating units are moved to different 

set points of capacity, subject to various operational and cost constraints. Load following involves capacity and energy, 

and corresponds to time scales that may range from 10 minutes to a few hours. Loads can typically be forecast with 

reasonable accuracy and overall correlation between individual loads tends to be high in this time frame. Generating 

units that have been previously committed, or can be started quickly, can provide this service, subject to physical 

constraints. Beyond the maximum and minimum generation constraints, the ramping constraint (ability to move in 

MW/minute) may be affected by significant wind generation on the system. In systems with little or no wind, the 

changes in load in this time frame can be predicted with varying degrees of accuracy. To the extent that forecasts are 

wrong, the system operator must deal with the resulting system imbalance. Significant wind capacity can increase the 

uncertainty and cost in this time frame. 

 

* These control performance standard cover short-term frequency variations (CPS-1) and longer term imbalance limits 

(CPS-2) on a statistical basis. 

2
Planning for the required quantity of generation and load following capability involves the unit commitment time 

frame, which can range from several hours to a few days. Scheduling too much generation can increase costs 

needlessly, whereas insufficient generation could have a cost component (buying at high market prices or running 

expensive quick-start units) and a reliability component (if sufficient generation has not been started and is not 

available on short notice). 

Most of the studies described here estimate the increased cost of managing a system with significant wind generation. 

The studies approach the cost question by starting with the physical behavior of the system without wind, then detailing 

how that physical behavior is affected by wind power plants. The primary objective of the studies is to take the view of 

the system operator, whose goal is to obtain system balance within required limits. Although U.S. terminology differs 

somewhat from that in Europe, the key physical issues and time frames are very similar. The imbalance impacts of 

wind are seen as unscheduled interchanges or frequency changes on the system when the balancing area cannot respond 

quickly enough to changes in load or wind. The impacts of wind on conventional generation are best analyzed over 

several time scales that correspond to system operation, ranging from automatic response (regulation in the United 

States) of units on automatic generation control, to spinning or standing reserve response (load-following in the United 

States). From the control room, wind variability is combined with load variability over these time scales, along with 

unscheduled deviations from some conventional generators. This net load is seen by the operator and must be balanced. 

Although the analytical tools differ somewhat, several common elements in the analyses have taken place in the United 

States. 

Most of the studies we summarize here are cost-of-service studies that examine the cost of wind in the context of 

regulated utilities. Other studies, such as the one carried out in New York (discussed below) are market studies that do 

not directly calculate cost impacts. Because of this approach, the results of the market-based studies cannot be directly 

compared with ancillary cost studies. 

Xcel Energy North (Minnesota) 

Xcel Energy North serves parts of North Dakota, South Dakota, Minnesota, Michigan, and Wisconsin. The power 

system is summer peaking with a peak demand of approximately 8,000 MW in 2002 projected to rise to approximately 

10,000 MW by 2010. Total system generation is approximately 7,500 MW with the difference made up by power 

purchases. 

Minnesota Department of Commerce Study (September 2004) 

In 2004, a follow-up to an earlier study of the Xcel North system was completed by EnerNex Corporation on behalf of 

Xcel Energy and the Minnesota Department of Commerce. This study also focused on operating impacts but at the 

higher level of 1,500 MW of wind generation (15% penetration in 2010). It determined the incremental costs that 

resulted from plans and procedures needed to accommodate wind generation and maintain the reliability and security of 

the power system. 

Meteorological simulations were carried out by WindLogics, then combined with archived weather data to recreate the 

weather for use in the study analysis. Benefits of geographic dispersion of the wind plants and of wind forecasting were 

also demonstrated. Figure 2 illustrates the area of meteorological modeling that was used to simulate 3 years of 10-

minute wind speed data, subsequently converted to wind power output for the system simulations. 

The costs of integrating 1,500 MW of wind generation into the Xcel North control area in 2010 are no higher than 

$4.60/MWh of wind generation and are dominated by costs incurred by Xcel Energy in the day-ahead time frame to 

accommodate the variability of wind generation and associated wind-generation forecast errors. The total costs include 

about $0.23/MWh resulting from an 8-MW increase in regulation requirements and $4.37/MWh resulting from 

scheduling and unit commitment costs. The study characterized these results as conservative, since improved strategies 

for short-term planning and scheduling and the full impact of new regional markets were not considered. Load 

following impacts were calculated, but because they were quite small, the cost was judged to be insignificant. Figure 3 

shows the impact of wind on morning load pickup and evening ramp-down. 

This study also calculated wind capacity credit as a percentage of installed wind. Several modeling approaches and 

different wind configurations were used to determine the capacity values, which were 26%−34%. 

3
Figure 2. Area of meteorological modeling for Xcel 

Energy North study

Ramp up 

requirement 

increased by 

wind

Ramp down 

requirement 

increased by 

wind

Figure 3. Load following impact on morning/evening ramps for Xcel 

Energy North 

4
California Independent System Operator (CAISO)

In response to legislation in California that established an RPS, the California Energy Commission (CEC) and 

California Public Utilities Commission established a team to examine the integration costs of all existing renewable 

power sources in the state. The analysis of wind generation was based on the three main California wind resource 

areas—Altamont, San Gorgonio, and Tehachapi—for 2002. The contribution that wind (and the other renewables) 

makes to system variability was estimated and CAISO regulation prices were used to estimate the cost of wind’s 

regulation impact. The maximum regulation costs were $0.46/MWh of wind generation, but varied somewhat 

depending on the resource area. 

To estimate the impact on the load-following time scale, data on system load and renewable generation were analyzed. 

The energy market operated on a 10-minute interval during the study period. The analysis focused on potential 

distortions to the dispatch stack that would result from swings in renewable generation. However, because of the 

numerous conventional generators available for redispatch, no measurable impact was found. 

Unit commitment is not the responsibility of the CAISO. Once bids have been accepted, generators assume this 

responsibility, and associated costs are assumed to be reflected in bids. Hence, the impact of wind variability on costs 

in the unit-commitment time frame was not assessed. 

Capacity value for wind was 23%−25% of rated capacity. However, because discrepancies surrounded the actual 

installed capacity, these values are felt to be somewhat imprecise. Capacity value was sensitive to hydro dispatch, 

interchange schedules, and conventional unit maintenance schedules. The Phase I and Phase III reports discuss these 

and other results. 

During the analysis, several data anomalies were uncovered. Most data were obtained from the CASIO plant 

information (PI) database, which records massive quantities of power system data from various metering systems. 

Because of the large volume of data, the PI data are fed through a compression algorithm to save storage space. Some 

irregularities in the system data suggested that the compression algorithm may have artificially smoothed some of the 

high-rate (1-second) data. During early parts of Phase III, some additional anomalies appeared in some data sets during 

data dropouts. The automatic data correction algorithms appeared to interpolate between good data points even if the 

dropout period spanned long periods of time (in some cases, several months). Additional data were obtained from the 

utilities to address these issues and were incorporated in a subsequent multi-year study (below). The Phase III report 

made specific recommendations for quality assurance and testing of data that would be critical to assessing the impact 

of wind and other renewable energy technologies as penetration continues to increase on the CAISO system. 

A multiyear study of the RPS integration cost that covers 2002−2004 is complete and is presently under review by the 

CEC. This final project report will be released very soon. An additional, separate study is also underway to analyze the 

operational issues that would be posed by higher penetrations of wind than are on the California system. This study is 

on behalf of the CEC, managed by Kevin Porter, Exeter Associates, with principal analytic work by GE Energy, Davis 

Power Consultants and AWS TrueWind. Results are anticipated in late 2006. This new study will analyze the impact of 

wind from a market-based approach, and is anticipated to be similar to the NYISO study that was carried out by GE 

Energy in New York, as discussed below. 

New York Independent System Operator (NYISO)

This work, completed in early 2005, was conducted by GE Energy for the NYISO with primary support from the New 

York State Energy Research and Development Authority (NYSERDA). Wind resource projections were provided by 

AWS TrueWind. The project was motivated by an RPS that may result in some 3,000 MW of new wind generation in 

New York within the next ten years. In light of wind’s natural variability, the NYISO wanted to understand the impacts 

of a substantial amount of wind generation on the operation of the New York electric power network. The study 

addressed 3,300 MW of wind in a system that serves a customer load projected at about 34,000 MW in the 2008 study 

year. The key question was whether the system would be able to handle 10% wind penetration without major 

difficulties. Figure 4 shows the relative locations of wind plants used in the analysis. 

This study is the most comprehensive U.S. wind integration assessment conducted to date. It encompassed all the time 

frames discussed above, and estimated system operating costs, impacts on customer payments, reductions in emissions 

from conventional power plants, and the impacts of wind forecasting. The New York system is operated as a single 

large balancing authority, and has well-functioning hour-ahead and day-ahead wholesale markets into which generators 

bid energy. Bids are accepted until projected demand is met on an hour-by-hour basis, and all accepted bidders—

including wind plants, which bid at zero price—are paid the highest accepted bid price. 

This study has estimated wind’s total cost impact on the operation of the system. Increases in costs associated with 

regulation, load following, and generation scheduling that stem from wind’s variability are combined with savings 

5
resulting from fossil fuel displacement. The wind resource was modeled from weather data for the period 2001 and 

2002, and was combined hourly with corresponding coincident load and generation data scaled to the projected 2008 

peak demand. Geographic diversity of the wind was captured by using wind data that corresponded to a number of 

locations. Figure 5 shows an hourly trace of wind generation and load for one week. 

684

358

570

322

400 261

105

600

684

358

570

322

400 261

105

600

Figure 4. Location of NY wind plants from NYISO/GE Study

The overall conclusion from the study was that the New York State power system can reliably accommodate at least 

3,300 MW (10%) of wind generation with only minor adjustments to its planning, operating, and reliability practices. 

No increase in spinning reserve would be required, and 36 MW of additional regulation would be needed to maintain 

frequency at the no-wind level. The total impact on variable operating costs for the study year—including impacts of 

wind variability and fuel savings—was a reduction of $335 million. Fuel displaced by wind was primarily natural gas, 

which was conservatively priced at $6.50−6.80/MMBtu. Total system variable cost savings increase from $335 million 

to $430 million when state of the art forecasting is considered in unit commitment. Perfect forecasting provided an 

additional benefit of about $25 million. 

Reductions in load payments ranged from $515 million to $720 million, with higher savings resulting from state of the 

art forecasts. Revenue paid to the wind generators was $305 million, or about $0.035/kWh. This amount is consistent 

with the terms of typical power purchase agreements between wind plant owners and purchasing utilities that were in 

effect during the study period. This indicates that wind offers a viable business opportunity in New York. 

A loss of load probability approach was used to calculate the capacity credit of wind. A unique feature of the analysis 

was the recognition of the transmission constraint between some wind areas and load areas. Average on-shore capacity 

value was about 9% of rated capacity, and off-shore was 36%. 

Xcel Energy West (Colorado)

The EnerNex-WindLogics team is completing this study for Xcel Energy’s Public Service of Colorado unit. Wind 

penetrations of 10% and 15% have been studied, and a 20% case was being finalized as this paper was submitted. The 

methodology is similar to that employed in the MN DOC study, although an additional element was required to assess 

the impacts on gas purchases, consumption, and storage. Traditionally, gas decisions must be made—and lived with—

every day. As a result, higher penetrations of wind are likely to require additional gas storage, which results in an 

additional cost impact from wind’s variability. As in the Minnesota study, the intra-hour load-following cost was 

6
negligible, and the major impact was related to differences between the hour-by-hour commitment schedule and the net 

of load and wind. Another interesting aspect of this study is the 300 MW pumped-storage unit in Xcel’s service 

territory. At 10% wind penetration, the flexibility offered by the pumped storage unit reduced the integration cost by 

$1.30/MWh. 

0

5000

10000

15000

20000

25000

30000

35000

1 25 49 73 97 121 145

Hour of Week

NYISO Load (MW)

0

500

1000

1500

2000

2500

3000

Wind Output (MW)

NY Loads

WIND

Figure 5. One week of load and wind generation from NY study 

0

200

400

600

800

1000

15000 20000 25000 30000 35000

Peak Load for Corresponding Month (MW)

S ig ma (M W )

Load Wind Load - Wind January 2001

800 MW 

Without 

Wind

950 

MW

With 

Wind

0

200

400

600

800

1000

15000 20000 25000 30000 35000

Peak Load for Corresponding Month (MW)

S ig ma (M W )

Load Wind Load - Wind January 2001

800 MW 

Without 

Wind

950 

MW

With 

Wind

Figure 6. Standard deviation of day ahead forecast errors in NY 

7
Figure 7 shows the region in Colorado that was used for prospective wind plant locations, and Table 1 illustrates some 

of the results from the integration cost study. 

Ponnequin Peetz

Figure 7. Region for wind plants in Xcel Energy West study 

8
Penetration 

Level 10% 15%

Hourly Analysis $2.26/MWh $3.32/MWh 

Regulation $0.20/MWh $0.20/MWh 

Gas Supply (1) $1.26/MWh $1.45/MWh 

Total $3.72/MWh $4.97/MWh

Table 1. Results from Xcel Energy West integration study 

Table Notes: 

(1) Costs include the benefit of additional gas storage 

(2) 20% penetration results were not available at publication, but should be determined in the mid- to late May’06 time 

frame at www.xcelenergy.com

The Xcel Energy West study provides additional useful insights relative to natural gas supply and management. The 

additional gas storage required to accommodate wind’s variability and uncertainty would provide a winter-summer 

seasonal hedging benefit to the system of about $1.00/MWh of wind energy at 15% penetration. And in a much more 

extensive assessment of wind’s role in hedging against swings and spikes in natural gas prices, researchers at Lawrence 

Berkeley National Laboratory find wind energy hedge values of about $5.00/MWh of wind [11]. 

Results Summary and Discussion 

Key results from these and other studies are summarized in Table 2: 

Table 2. Wind impacts on system operating costs 

Date Study Wind 

Capacity 

Penetration 

(%) 

Regulation 

Cost 

($/MWh) 

Load 

Following 

Cost 

($/MWh) 

Unit Commitment Cost 

($/MWh) 

Gas 

Supply 

Cost 

($/MWh) 

Total 

Operating 

Cost Impact 

($/MWh) 

May 03 Xcel-UWIG 3.5 0 0.41 1.44 na 1.85 

Sep 04 Xcel-MNDOC 15 0.23 na 4.37 na 4.60 

July 04 CA RPS Phase III 4 0.46 (1) na Na na na 

June 03 We Energies 4 1.12 0.09 0.69 na 1.90 

June 03 We Energies 29 1.02 0.15 1.75 na 2.92 

2005 PacifiCorp 20 0 1.6 3.0 na 4.6 

April 06 Xcel-PSCo 10 0.20 na 2.26 1.26 3.72 

April 06 Xcel-PSCo 15 0.20 na 3.32 1.45 4.97 

Table Notes: 

(1) Represents maximum regulation cost for all wind resource areas 

9
The results in Table 2 show that the ancillary service impacts of wind from the recent studies are in line with studies 

that we have previously examined [6]. The Xcel studies represent significant steps forward in the analysis, by using 

detailed wind profiles developed to represent the wind behavior coincident with load. The Xcel Energy West study 

illustrates that there is not a one-size-fits-all answer to the wind integration question, and applies a method to analyze 

the impacts on a gas-constrained system where gas purchases are made in advance. The California multiyear study 

applies the methods to three years of data that were collected by the ISO and utilities that purchase the wind output. All 

this recent work points to the desirability of using multiple years of time-synchronized wind and load data to obtain 

more robust results. 

Capturing the spatial variations of wind—both within an individual wind plant and across the entire region 

considered—is also important, since these variations significantly mitigate impacts. 

Conclusions and Insights 

Given the work that has been done, several conclusions are emerging. Although wind imposes additional operating 

costs on the system, these costs are moderate at penetrations expected over the next 5−10 years. These results are 

expected to apply as additional wind generation is developed in the next few years in response to state government 

RPSs, although wind integration costs will increase with penetration. 

Large, diverse balancing areas with robust transmission tend to reduce wind’s impact and ancillary service cost. At 

current U.S. levels, the impact on regulation and load following appear to be modest, and the unit commitment time 

scale appears to be more important. In this time scale wind forecasts can play a more prominent role, and 

improvements in forecasting technology will certainly mitigate wind’s integration costs. As wind penetration increases 

in the United States, better forecasting is expected to play a more important role. To be effective, forecasts do not need 

to be perfect, although increasing accuracy tends to reduce costs. It is possible that at some point the incremental cost 

of forecast improvements will outweigh the incremental benefits that accrue from increased accuracy. 

Aside from large balancing areas, other factors can mitigate wind impacts. If several adjacent balancing areas can 

develop cooperative arrangements or markets for ancillary services, larger quantities of wind could be absorbed 

because of the greater load and wind diversity that would be expected across broader regions. This could be captured 

by larger balancing areas, but other means of tapping this potential can be used. This is discussed further by Kirby and 

Milligan [7]. 

There is also some evidence that system operators will become more familiar with wind after working with it. For 

example, The Western Farmers Electric Cooperative (WFEC) in Oklahoma recently performed an analysis with the 

National Renewable Energy Laboratory of the operational impact of wind on its system. WFEC has a peak load of 

1,400 MW and installed wind capacity of 74 MW. Initially the system operators could not maintain the CPS-1 

frequency standard at its pre-wind level. With experience they became familiar with the wind system and brought CPS1 into its pre-wind range [13]. 

Emerging Best Practices and Methods 

Although there are differences between studies, there appears to be some convergence on techniques and methods used 

to analyze wind’s ancillary service impacts. A key point is to recognize that the entire system—not individual loads or 

generators—need to be balanced. In the United States, this balance does not need to be perfect, but is required to fall 

within the statistical limits defined by CPS-1 and CPS-2. The implication for wind integration is profound: not every 

movement in wind generation needs to be matched one-to-one by another conventional generator. 

The approaches used in recent studies generally capture the important system characteristics through detailed modeling 

of the relevant grid and operational practices. These representations of the system can then be simulated in a 

chronological environment that can observe the detailed constraints on the system that are imposed by loads and 

generators. 

Because wind impacts occur throughout the time domain, the coincidence of loads and wind generation must be 

captured. Because wind speed and wind generation data are often difficult or impossible to obtain for desired time 

periods, an emerging approach is to construct the wind data from detailed time-calibrated mesoscale meteorological 

modeling for the desired time period. Normally this is accomplished by selecting load data for the study period based 

on recent historical data. Wind data sets can then be constructed to match the historical load period. And because wind 

impacts on some longer time scales may differ from year to year, the best approach is to extract multiple years of wind 

data that correspond to the loads in a multiyear study period, and complete several years of detailed simulations. This 

10
picks up any correlation (which may be highly nonlinear with significant phase shifts) between wind and load, and 

improves confidence that the results are meaningful. 

Detailed meteorological modeling also allows the geographic impacts of wind to be represented as the turbines are 

spread over small areas (within a wind plant) or large areas (several wind plants) and picks up the impact of prevailing 

weather patterns that drive the wind generation and influences load. 

The short-term behavior of wind power plants has been quantified by Wan [8]. The data sets indicate that wind power 

variability is quite low at fast time frames, and increases at progressively longer time frames. As a practical matter, this 

implies that wind’s impacts will be relatively small in the regulation time scale, increase at the load following time 

scale, and become even more significant at the unit commitment/scheduling time scale. The U.S. studies broadly 

support this conclusion, and as more wind operating data become available, a more realistic representation of wind in 

the analytic models can be captured so the results are more accurate. 

Within the modeling frameworks used in the U.S. studies, the variability of wind generation is added to the already 

considerable variation in load. The analytic tools approximate the view of the system as seen by the operator. This 

implies that the statistical treatment of the wind and load time series is important and provides a realistic representation 

of wind’s impact on the regulation and load following time frames. 

To better understand the role of forecasting, some studies have constructed wind forecasts and run the analysis with and 

without the forecasts. Clearly forecasting can play an important role in mitigating wind’s impacts on system operations 

and costs, but only if the forecast is used appropriately in the control room. 

Remaining Questions and Future/Ongoing Work 

In spite of significant progress in understanding wind’s impact on the grid, questions remain. Current systems can 

apparently handle wind penetrations up to 10%−20% based on capacity, but the costs appear to increase with 

penetration. Models, analytic tools, and practices have generally not been adapted to extensive experience with large 

quantities of wind. As wind penetration increases over the next several years in the United States, this increasing cost 

will provide an increasing economic incentive to investigate cost-mitigation approaches. 

Several possibilities for these strategies appear promising, but all require further quantification: 

• Dynamic scheduling 

• Consolidation of balancing areas 

• New operational practices and economic curtailment 

• Better use of flexible resources, including dispatchable hydro and pumped storage 

• Plug-hybrid electric vehicles with smart-charge controllers that can provide demand and supply to the grid 

• Hydrogen and other forms of energy storage 

• Aero derivative gas (jet) engines with quick start capability and good heat rates 

• Price-responsive load 

• Integration of wind forecasting into the control room 

• Learning how to best operate the system with large wind power plants 

This list is not exhaustive, nor are the items on the list mutually exclusive. Some combination of these items may 

significantly increase the ability of the grid to absorb increasing quantities of wind generation. 

Future and Ongoing Work in the United States 

In Minnesota a project to evaluate the grid impacts of 20% wind by energy (5 GW of wind) has recently begun. This 

project resulted from legislation, and is on behalf of the Minnesota Public Utilities Commission. EnerNex is the 

contractor; WindLogics provides the meteorological data foundation. The study will also examine the new MISO 

market structure, examine transmission and mitigation strategies, and compare market and reliability rules. Anticipated 

completion date is November 2006. 

In response to the 2004 Xcel Renewable Development Fund solicitation in Minnesota, a team led by WindLogics, 

including EnerNex, AREVA T&D, and the Utility Wind Integration Group, was awarded a grant of nearly $1 million 

to research and demonstrate a utility-scale wind power forecasting system for the Xcel North system. The goal of this 

project is to define, design, build, and demonstrate a complete wind power forecasting system for use by Xcel system 

11
operators. This project will begin in 2006 and builds on other studies that the development team has performed for Xcel 

to quantify the cost of ancillary services for wind plants on the Xcel system. 

 

Key objectives will be to optimize the way wind forecast information is integrated into the control room environment 

(for both load-following and unit commitment time scales) and to evaluate the impact of the wind forecast on control 

room operations. A critical part of the process will be to define the types of wind forecasts, delivery mechanisms, and 

method of control room integration that will be most useful in day-to-day activity. 

In California the Intermittency Analysis Project is evaluating the system impacts of 5 GW wind by 2010, possibly up to 

15% (rated capacity to peak) or greater by 2020. Some items to evaluate include periods of high wind and low load, 

and the study may develop a scenario that aggressively pushes the amount of wind on the system to higher levels. The 

study primary contractors are Davis Power Consultants, GE Energy and AWS Truewind. The study will be completed 

by the end of 2006. 

There are also several projects that involve smaller systems. The Sacramento Municipal Utility District is embarking on 

a study of high wind penetration and will investigate the role of hydro pumped storage. The analysis framework will be 

the Areva Dispatch Training Simulator (DTS), a software platform that mimics the control room environment of the 

system operator. Another project that will use the DTS is at the Public Service Company of New Mexico, which has a 

wind plant that is built along a ridge top. The limited import/export capabilities, the relatively high and increasing wind 

penetration, and ramping impacts provide an interesting look at mitigation strategies, particularly during minimum 

load/maximum wind time periods. Idaho Power and Grant County Public Utilities Department also have projects to 

evaluate wind integration in systems with constrained hydro resources. 

Other larger scale studies are also underway in the United States. Because of limited transmission interconnections in 

parts of the Midwest and West, several transmission organizations have begun to analyze wind scenarios in the 

framework of subregional and regional reliability areas. These studies generally collaborate with the utilities and loadserving entities in the region, and with other stakeholders. Example studies are underway at the Seams Steering Group 

of the Western Interconnection (in process of transferring to the Western Electricity Coordinating Council), Northwest 

Transmission Assessment Committee, Southwest Area Transmission, and MISO. The Rocky Mountain Area 

Transmission Study (RMATS) completed Phase I of a similar project in 2005. There has also been a high level of 

interest in examining transmission tariffs to assess the role of tariff reform, partly growing from the RMATS work, and 

partly from interest in the Northwest by PacifiCorp, Bonneville Power Administration, and the Renewable Northwest 

Project. The FERC has indicated interest in this topic, and we expect further activity in the near future. 

References 

Most of the studies discussed in this article are available on the Utility Wind Integration Group website, 

www.uwig.org. 

1. DeMeo E, Grant W, Milligan M, Schuerger M. Wind Plant Integration. IEEE Power & Energy, Nov/Dec 2005. 

2. Parsons B, Milligan M, Zavadil R, Brooks D, Kirby B, Dragoon K, Caldwell J. Grid Impacts of Wind Power: A 

Summary of Recent Studies in the United States. European Wind Energy Conference, June 2003. Available at 

www.nrel.gov/docs/fy03osti/34318.pdf. Accessed Feb 24, 2006. 

3. Smith J, DeMeo E, Parsons B, Milligan M. Wind Power Impacts on Electric-Power-System Operating Costs: 

Summary and Perspective on Work to Date. American Wind Energy Conference, March 2004. Available at 

www.nrel.gov/docs/fy04osti/35946.pdf. Accessed Feb 24, 2006. 

4. Zavadil R, Miller N, Ellis A, Muljadi E, Making Connections, IEEE Power & Energy, Nov/Dec 2005. 

5. Federal Energy Regulatory Commission. Docket No. RM05-4-001; Order No. 661-A. www.ferc.gov 

6. Smith J, DeMeo E, Parsons B, Milligan M. Wind Power Impacts on Electric-Power-System Operating Costs: 

Summary and Perspective on Work to Date. American Wind Energy Conference, March 2004. Available at 

www.nrel.gov/docs/fy04osti/35946.pdf. Accessed Feb 24, 2006. 

7. Kirby B, Milligan M. A method and case study for estimating the ramping capability of a control area or balancing 

authority and implications for moderate or high wind penetration. American Wind Energy Conference, May 2005. 

Available at www.nrel.gov/docs/fy05osti/38153.pdf. Accessed Feb 24, 2006. 

8. Wan Y. Wind Power Plant Behaviors: Analysis of Long-Term Wind Power Data; National Renewable Energy 

Laboratory Report NREL/TP-500-36651, 2004. Available at www.nrel.gov/docs/fy04osti/36551.pdf. Accessed 

Feb 24, 2006. 

9. Zavadil R, King J, Xiadong L, Ahlstrom, M, Lee B, Moon D, Finley C, Alnes L, Jones L, Hudry F, Monstream M, 

Lai S, Smith J. Xcel Energy and the Minnesota Department of Commerce, Wind Integration Study - Final Report. 

EnerNex Corporation and Wind Logics, Inc., September 2004. 

12
10. GE Energy. 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, 

March 2005. 

11. Bolinger M, Wiser R, Golove W. Quantifying the value that wind power provides as a hedge against volatile 

natural gas prices, Proceedings of WindPower 2002, June 2002. 

12. California RPS Integration Cost reports (Phases I-III) available at 

http://www.energy.ca.gov/reports/reports_500.html. Accessed Feb 24, 2006. 

13. Wan, Y, Liao, J, Analyses of Wind Energy Impact on WFEC System Operations; National Renewable Energy 

Laboratory Report NREL/TP-500-37851, June 2005. 

Acknowledgments 

Thanks to Kevin Porter, Exeter Associates, Henry Shiu, California Wind Energy Collaborative/University of California 

at Davis, Nick Miller and Richard Piwko, GE Energy for their assistance with and comments on this report. 

13
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4. TITLE AND SUBTITLE

Grid Impacts of Wind Power Variability: Recent Assessments from a 

Variety of Utilities in the United States; Preprint 

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B. Parsons, M. Milligan, J.C. Smith, E. DeMeo, B. Oakleaf, K. Wolf, 

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Because of wind power's unique characteristics, many concerns are based on the increased variability that wind 

contributes to the grid, and most U.S. studies have focused on this aspect of wind generation. Grid operators are also 

concerned about the ability to predict wind generation over several time scales. In this report, we quantify the 

physical impacts and costs of wind generation on grid operations and the associated costs. 

15. SUBJECT TERMS

wind energy; wind turbines; grid; variability; utility; utilities; wind generation; wind power variability; grid integration; 

wind energy cost analysis 

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