Conference Paper

NREL/CP-550-46505

September 2009

Eastern Wind Integration 

and Transmission Study –

Preliminary Findings

Preprint

D. Corbus, M. Milligan, and E. Ela

National Renewable Energy Laboratory

M. Schuerger

Energy Systems Consulting Services

B. Zavadil

EnerNex Corp.

To be presented at the 8th International Workshop on Large Scale 

Integration of Wind Power and on Transmission Networks for 

Offshore Wind Farms

Bremen, Germany

October 14–15, 2009
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Abstract — The Eastern Wind Integration and 

Transmission Study is one of the largest regional wind 

integration studies to date, examining the operational impact 

of 20% and 30% wind energy in the Eastern Interconnect and 

the transmission expansion needed to achieve those wind 

energy penetrations. This paper reviews the scope of the 

study, the development of wind datasets, the transmission 

expansion analysis, and the results to date of the wind 

integration analysis for the four scenarios. 

Index Terms — grid integration, wind power, transmission, 

power system operation, reserve requirements.

I. Introduction

The Eastern Wind Integration and Transmission Study 

(EWITS) is one of the largest regional wind integration 

studies to date [1]. It was initiated in 2008 to examine the 

operational impact of up to 20-30% energy penetration of 

wind on the power system in the Eastern Interconnect of 

the United States. This study was set up to answer 

questions that utilities, regional transmission operators, and 

planning organizations had about wind energy and 

transmission development in the east. 

Previous studies have laid the groundwork for wind 

integration studies (e.g., New York, 2005, and Minnesota, 

2006) [2, 3], and these focused on specific states or 

utilities. The EWITS regional study approach allows 

additional questions to be answered including: 

• How do local wind resources compare with higher 

capacity-factor wind power that requires more 

transmission?

• How does geographic diversity of wind reduce wind 

integration costs (i.e., spreading the wind power over a 

larger region and thereby “smoothing” out some of the 

variability)?

• How does offshore wind power compare with onshore 

wind power?

• What transmission is needed to facilitate higher 

penetrations of wind power?

• What is the role and value of wind forecasting?

• How are wind integration costs spread over large 

market footprints and regions?

• What additional operating reserves are needed for large 

wind power deployments?

The EWITS is sponsored by the U.S. Department of 

Energy (DOE) with project coordination by the National 

Renewable Energy Laboratory (NREL). The wind 

integration and transmission analysis is being lead by 

Enernex, with support from Ventyx and the Midwest 

Independent System Operator (MISO). In addition, Risoe 

National Laboratory, Denmark, and Institut für 

Energiewirtschaft und Rationelle (IER), Germany, are 

working on a sensitivity analysis for EWITS using the 

WILMAR stochastic planning model. Wind inputs for 

EWITS were developed by AWS Truewind. This study and 

its partner study, the Western Wind and Solar Integration 

Study, are conducting an operating impact analysis to see if 

20-30% wind energy is feasible from an operational level. 

II. Major Tasks

In this paper, we focus on the three important parts of 

EWITS: 1) wind data development, 2) transmission analysis, 

and 3) wind integration analysis. As with any analysis, the 

results are only as good as the analysis inputs. Careful 

development and validation of wind data inputs for EWITS 

is essential to yielding meaningful results. New transmission 

will be required for much of the future wind development in 

the eastern interconnect; hence, it is imperative to plan for 

this transmission because it takes longer to build new 

transmission than it does to build new wind plants. The 

transmission analysis for EWITS includes an expansion plan 

that is used in the wind integration analysis. Scenario 

modeling investigates the operational impacts of wind 

variability and uncertainty. For the EWITS analysis, we used 

a deterministic production cost model, but also include 

stochastic modeling as a sensitivity tool to look at different 

unit commitment strategies. Sensitivity analyses are also 

conducted using the deterministic model to analyze unit 

commitment and operating reserve considerations, different 

market scenarios, and carbon pricing.

A. Wind Data Development

It is important in wind integration studies to use 

concurrent load and wind data to capture the correlations 

between load and wind (i.e., weather). The use of 

numerical weather prediction models is an accepted method 

to produce a time-series of wind plant output data. 

Essentially, physics-based, numerical simulations on 

supercomputers, integrated with observational datasets, 

recreate the weather of historical years and generate a fourdimensional gridded wind-speed dataset. A wind speed 

time-series dataset can be extracted and converted to wind 

power output. This approach produces a temporally, 

spatially, and physically consistent wind dataset. 

Wind forecasting is an increasingly common tool for

utilities and independent system operators (ISOs) in 

scheduling generation units. Wind integration studies 

typically include the impact of wind forecast errors on 

integration costs. Wind forecast data must also be modeled, 

and it is important that there be no systematic biases in the 

wind forecast data (e.g., using the same model to generate 

wind plant ‘actuals’ and ‘forecasts’ can result in forecasts 

that are too accurate).

For EWITS, AWS Truewind used the Mesoscale 

Atmospheric Simulation System [4] to generate wind 

resource and power output at potential onshore and 

offshore wind plant sites in the eastern United States. 

Study – Preliminary Findings

Authors Dave Corbus1), Michael Milligan1), Erik Ela1), Matt Schuerger2), Bob Zavadil3), ) National Renewable Energy 

Laboratory, Golden, CO, USA. +1 303 384 6966, e-mail David.Corbus@nrel.gov, 2) Energy Systems Consulting 

Services, St. Paul, MN, 3) EnerNex Corp. Knoxville, TN.

1
MASS was initialized using the National Centers for 

Environmental Prediction/ National Center for 

Atmospheric Research Global Reanalysis (NNGR) data). 

The NNGR data provide a snapshot of atmospheric 

conditions around the world every six hours at 19 levels in 

the troposphere and lower stratosphere on a 210-km grid. 

Weather observations were assimilated every 12 hours, 

introducing a discontinuity in the data, which was then 

smoothed out.

The MASS simulations for EWITS cover a three-year 

period from 1 January 2004 through 31 December 2006. 

For each month during the three-year period, MASS was 

initialized from the NNGR data on the first and fifteenth or 

sixteenth day of the month followed by a 15- or 16-day 

sequence of 12-hour simulations. Rawinsonde and surface 

observations of temperature, dew point temperature, wind 

velocity, and pressure were assimilated every 12 hours on 

the 30-km and 8-km grids using an objective analysis 

procedure. Except for the initial run, all subsequent 

simulations used the previous MASS fields as the starting 

point (“first guess”) for the objective analysis. The NNGR 

provided lateral boundary conditions for the 30-km grid 

throughout all of simulations, but the inner grids always 

derived boundary conditions from the outer MASS grids.

Once the MASS simulations were completed, data were 

extracted at ten-minute intervals for all 2-km grid cells 

associated with the more than 13,000 project sites. The 

meteorological data generated by MASS were scaled to 

long-term wind speeds, bias corrected to match diurnal 

patterns from tall tower observations, adjusted for wake 

and other losses, time-filtered to account for observed 

variability, and converted to plant output with densityadjusted generic composite power curves for each IEC 

class. The resulting plant output files contain simulated 

speeds at 80- and 100-m hub heights along with the power 

output for all IEC classes every ten minutes for the threeyear simulation period.

Fig. 1. 579 GWs of onshore wind sites for EWITS. 

There were 589 GWs of wind sites available for the 

onshore sites and over 100 GWs for the offshore sites. 

Modeling scenarios were developed, as discussed below, 

using this “superset” of wind data sites (less than half of the 

wind data was needed for any one scenario). Fig. 1 shows 

the locations of the 579 GWs of onshore sites.

B. Scenario Analysis

EWITS evaluates the operational impact of large 

amounts of wind power on the eastern interconnect and 

coincides with transmission analysis being conducted by 

several regional grid operators including the Pennsylvania 

New Jersey Maryland Interconnection (PJM), Midwest 

Independent System Operator (MISO), Southwest Power 

Pool (SPP), Tennessee Valley Authority (TVA), New York 

Independent System Operator (NYISO), and other related 

entities. In this study, we modeled the year 2024 three 

times, with the historical load and weather patterns from 

2004, 2005, and 2006, allowing for some analysis of inter 

annual variability, which was not insignificant. 

EWITS has four different scenarios of future wind 

penetrations (three 20% wind energy scenarios and one 

30% wind scenario) ranging from utilization of higher 

capacity-factor wind power in the Midwest with more 

transmission build-out to more emphasis on “local wind.” 

The high capacity-factor wind power scenario requires a 

significant build-out of high-voltage direct current (HVDC) 

transmission with high-voltage AC “collector” systems to 

deliver the wind power from the Midwest to the load 

centers on the east coast. The "local wind" scenario shows 

that offshore wind is needed for a 20% wind penetration 

and reveals what future onshore transmission might look 

like for the east coast. Scenario four, with 30% wind 

power, requires the large transmission build-out, use of 

lower capacity-factor “local” wind power, and significant 

offshore wind power. Table 1 shows a summary of the 

EWITS scenarios.

TABLE I. EWITS SCENARIOS 1-4.

C. Transmission Modeling

Transmission flows between regions in EWITS are 

determined in part by the difference between PROMOD®

model transmission simulations using a “copper sheet” (i.e., 

no transmission constraints, no congestion) versus the 

existing transmission system. Transmission capacity is 

designed to deliver 80% of the desired energy flow. Figs. 2 

and 3 show the transmission overlays for scenarios 1 and 3 

respectively. Fig. 2 shows the scenario 1 overlay with the 

high-voltage AC collector systems that are used to feed the 

HVDC transmission that carry the high capacity factor 

wind energy west to east. Fig. 3 shows the overlay for 

Scenario 3 with high offshore wind and the AC collector 

systems for offshore wind feeding HVDC systems going 

2
from the east to the south, as well as from west to east. The 

large difference in the locational marginal price (LMP) of 

generation from the Midwest region to the East region 

results in an economic benefit that can be used to justify the 

build-out of transmission from the Midwest to the East. 

Fig. 2. EWITS Transmission Overlay for Scenario 1.

 

Fig. 3. EWITS Transmission Overlay for Scenario 3.

D. Wind Integration Analysis

EWITS uses the PROMOD® model for hourly 

production cost simulations using the transmission overlays 

for each scenario, the wind plant outputs described 

previously for 2004-2006, and the actual load data for 2004-

2006. PROMOD® takes the wind generation at each 

“injection” bus and dispatches conventional generation units 

accordingly for each market region while solving at the node 

for the LMP price. The tool will simulate actual operations 

by solving the unit commitment problem assuming the wind 

power and load forecasts, and then dispatching the units 

based on the actual modeled wind and load data. This is 

necessary to get realistic results because in actuality unit 

commitment decisions must be made well in advance in 

order to allow generators sufficient time to start up and 

synchronize to the grid. There is a hurdle rate that accounts 

for hourly transactions between eight different market 

regions. The simulation is done over the entire study region 

and the wind plant and load time-series data capture the 

geographic diversity. 

Operating and contingency reserves are calculated 

outside the PROMOD model and input to the model for 

each region. One of the interesting results to date from 

EWITS is that wind integration costs are driven primarily 

by reserve costs, and less by forecast error. Therefore, the 

methodology for calculating reserves is a very important 

part of the integration study. Fig. 4 shows the average 

annual incremental variable spinning 

Fig. 4. Annual Average Incremental Variable Spinning reserve (MW). 

reserves for the different regions. The Southwest Power 

Pool (SPP) region has a small load and lots of wind, and 

results in the highest reserves. The methodology used to 

calculate the reserve requirements was very detailed and 

used sub-hourly wind data. The study team analyzed the 

variability and short-term uncertainty impacts and assumed 

a typical number of standard deviations of these impacts 

should be covered by spinning reserve.

E. General Results 

The study is not yet complete, but there has been a 

tremendous amount of data development and analysis 

conducted to date. Some of the preliminary results are 

presented below for 2024 based on the wind inputs for 2004. 

EWITS, like many previous integration studies, 

calculates a wind integration cost that reflects the additional 

cost of integrating the wind energy into the system due to 

the variability (due to the changing wind resource) and the 

uncertainty (inability to perfectly forecast the weather). The 

integration costs are used to evaluate different wind 

scenarios on a regional basis. Lately, there has been an 

increasing move away from a simple determination of wind 

integration costs towards a balanced view of both 

integration costs and operational savings due to displaced 

fuel and emissions, and these are also addressed in EWITS, 

but not discussed in this paper. 

Previous wind studies that were focused on lower 

penetration levels of wind power often compared wind to a 

daily flat block of energy that delivered the same amount of 

energy as the wind each day (i.e., a “proxy” resource). This 

gave a conservative estimate of comparing wind power to a 

perfectly reliable energy source. This methodology no longer 

worked in EWITS because it resulted in large wind power 

3
deviations from one day to the next. EWITS addresses this 

issue by using an hourly flat block comparison instead, using 

the wind power as it is actually delivered, based on the wind 

data inputs. Although there are still shortcomings with the 

hourly flat block proxy, they are beyond the scope of this 

paper to discuss and it should be noted that this area is an ongoing research and discussion.

Wind integration costs and adjusted production costs 

from the PROMOD® modeling are shown in Table II for 

the different EWITS scenarios for the 2004 wind data. 

These integration costs are driven primarily by the need for 

additional reserves but also include the costs for uncertainty 

(i.e., not being able to accurately predict the wind).

Scenario 3, with more local wind (i.e., wind sited closer to 

the load) results in slightly lower integration costs than the 

other two 20% scenarios, and Scenario 4 with 30% wind 

results in significantly higher wind integration costs. Note 

that the integration costs do not reflect the higher capital 

costs (typically in the range of 50%) for the offshore wind 

in Scenarios 3 and 4. 

TABLE II. Wind Integration Costs

Operational analysis of specific EWITS regions based 

on the PROMOD® production cost simulations for high 

wind power and low load periods reveals interesting results 

and lend insight into understanding operation of the system 

over the entire region. Fig. 5 shows the MISO load and net 

load, generation, and wind curtailment during a high wind 

week in spring for Scenario 1. 

Fig. 5. MISO Generation for High Wind Week. 

Wind curtailment is one way of evaluating how much 

wind the system can accommodate. Scenarios that result in 

high wind curtailments will negatively impact the value of 

the wind. Mitigation measures for reducing these 

curtailments (e.g., short-term storage) would come at a 

cost. Fig. 6 shows the wind curtailment summary for the 

EWITS scenarios.

Fig. 6. Wind Curtailment for EWITS Scenarios. 

Wind curtailment for the 20% wind scenarios is highest in 

the SPP region for Scenarios 1 and 2, and is high in the New 

York Independent System Operator (NY-ISO) and 

Independent System Operator- New England (ISO-NE) 

regions for Scenario 3. Both Southeast Reliability Council 

(SERC) and Tennessee Valley Authority (TVA) have very 

little wind generation, so the megawatt quantities of wind 

curtailments are not that high. The wind curtailments 

include both transmission constraints and minimum 

generation events, but are predominantly due to transmission 

constraints. This was concluded by doing additional 

PROMOD runs for Scenario 2, isolating the effects of 

transmission constraints and minimum generation events and 

comparing the curtailments between the two runs.

Capturing the geographical diversity of wind power for 

the large study region is an important part of the modeling. 

Fig. 7 shows a plot of the normalized standard deviation 

measured at varying production levels for 5 different size 

regions, from an 85-GW full market (e.g., MISO) down to a 

single 500-MW plant. Each smaller group is a subset of the 

larger one. The plot shows how the variability of the wind 

reduces significantly with expanding wind capacity. Another 

interesting aspect of the plot is that the peak of the variability 

corresponds to a production output of about 50%.

Fig. 7. Comparison of 10-minute variability as diversity increases – 10-

minute changes as a function of production.

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

Region

Wind Curtailment (%)

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 1 4.05% 10.52% 2.56% 0.03% 4.22% 0.04% 6.89% 6.38%

Scenario 2 2.32% 11.49% 1.43% 0.59% 11.00% 7.92% 3.75% 6.25%

Scenario 3 2.26% 2.58% 1.38% 7.76% 9.72% 0.53% 0.52% 3.26%

Scenario 4 7.81% 11.28% 6.70% 10.57% 9.36% 8.03% 10.71% 8.72%

MISO/MAPP SPP PJM ISONE NYISO SERCNI TVASUB Total 

Footprint

4
F. Reliability Analysis

Each different scenario studied results in a different 

effective load carrying capability (ELCC) of wind, based on 

that scenario’s unique characteristics and where the wind is 

located. The loss of load expectation (LOLE) analysis is 

based upon a sequential Monte Carlo simulation using GE’s 

Multi-Area Reliability Simulation (GE-MARS) tool. The 

analysis is benchmarked to a reliability of one day in ten 

years. The wind generation is incorporated through the three 

year (2004, 2005, and 2006) 8760 hourly profiles (wind 

generation is effectively a load modifier). Each of the three 

years of LOLE analysis is run 6,000 times to achieve 

accurate convergence (non-wind generation is probabilistic 

based on forced outage ratings; the load model is not 

probabilistic). Each zone (market footprint) is modeled 

without internal constraints. Sharing of generation between 

zones is limited to excess generation only (up to the tie 

limits). The interface limits between zones are developed 

from a series of PROMOD® simulations.

The different transmission sensitivities studied are:

Stand-Alone (Isolated) Zone

• No zone to zone interfaces modeled

• Provides a base point for calculating ELCC 

before introducing Interfaces to the LOLE 

modeling 

Without Transmission Overlay

• Constrained case

• Includes only existing transmission and 

interface limits

With Conceptual Transmission Overlay

• Increased zone to zone interface limits

• Increased ability to move wind energy across 

the system

Infinite Tie Limits (Copper-Sheet)

• The highest level of transfer capability

Fig. 8 shows the ELCC for the pre- and post overlay (i.e., 

the transmission system with and without the overlays 

shown in Figs. 2 and 3). The shaded area shows the 

increased ELCC of the transmission overlay.

Fig. 8. ELCC for Pre- and Post Overlay. 

G. Sensitivity Analysis

EWITS includes two sensitivity analyses for Scenario 2: 

a carbon sensitivity case using PROMOD® to model 

carbon emissions at a price that causes a shift to gas 

generation from coal generation, and a modeling sensitivity 

case using the WILMAR model from Europe to look at 

“rolling” unit commitment (e.g., generator dispatch at 3-

hour intervals) and stochastic unit commitment. 

Some of the key assumptions for the carbon sensitivity 

case are: natural gas at a nominal $8, 15% capacity credit 

for wind, nuclear capital costs are $6000/kW, no 

cycling/ramping of the nuclear, and no increases in demand 

response or efficiency. The Electric Generation Expansion 

Analysis System (EGEAS) model was used to identify the 

carbon price required to drive a change from new coal in 

the expansion model. Wind was not a variable in the 

expansion, but was forced into the mixed at 20% 

penetration. The result was that a significant change from 

coal to gas did not occur until a carbon price of nearly

$100/ton CO2. It should be noted that a change from coal 

to nuclear occurred at a much lower price. The next step in 

this analysis is to run a full year of PROMOD® simulations 

for Scenario 2.

Because of the uncertainty of wind power prediction, 

much work has been performed in evaluating different ways 

of solving the unit commitment problem. In EWITS, a 

sensitivity study was performed using the WILMAR model 

[5] to evaluate the different impacts that would occur by 

performing a stochastic unit commitment problem and with 

performing a continuous rolling unit commitment update. 

The stochastic unit commitment solves a number of different 

wind power and load scenarios and minimized the expected 

cost. Fig. 9, for example, shows multiple scenarios of 

residual load (load minus wind) in the PJM area. The 

stochastic unit commitment solves to ensure all scenarios can 

be met in a least cost manner so that the system is robust and 

ensures reliable and cost effective solutions despite many 

uncertain outcomes. Rolling unit commitments are 

performed to support the theory that forecasts become better 

as the time horizon becomes closer and so the unit 

commitment decisions should be updated continuously. 

Fig. 9. Multiple residual load scenarios for PJM. 

5
III. Summary and Future Work

Some of the preliminary key findings to date in EWITS 

are [6]: 

• For all three 20% wind scenarios, a significant 

transmission build-out is required. 

• Modeling results to date show that 20% wind energy is 

possible on the eastern interconnect, and future work 

should be done to further quantify the mitigation 

measures needed to accommodate 30% wind power.

• Variable reserves have more impact on the integration 

cost compared to day-ahead wind forecast errors. 

• The closer the wind plant locations are to load centers, 

the cheaper the integration cost. 

• For 20% wind, Scenario 1 has the highest wind 

integration cost, while Scenario 3 has the lowest cost. 

• Zero hurdle rates between regions have minimal impact 

on total footprint integration costs. 

• Year-to-year variation in ELCC results are significant, 

and the ELCC analysis should be carried out for 

additional years

It is anticipated that EWITS will continue to build on the 

body of knowledge accumulated in Phase I, and look at 

additional modeling sensitivities in Phase II including 

sensitivities to load (e.g., demand side management), 

market size, and reserve modeling.

IV. REFERENCES

[1] Corbus, D., Lew, D., Jordan, G., Winters, W., Van 

Hulle, F., Manobianco, J., and Zavadil, B.; Grid Interation 

and Transmission: Getting to Higher Levels of Wind”, to 

be published in IEEE Power and Energy, Nov/Dec 2009.

[2] 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, City, State, 

March 2005.

[3] EnerNex Corp. and Windlogics Inc. for the Minnesota 

Public Utilities Commission, “Final Report – 2006 

Minnesota Wind Integration Study, Volume I”, Minnesota 

Public Utilities Commission, St. Paul, MN, November 30, 

2006. Available at

http://www.puc.state.mn.us/docs/windrpt_vol%201.pdf. 

Accessed April 26, 2007.

[4] Kaplan, M. L., J. W. Zack, V. C. Wong and J. J. 

Tuccillo, 1982: Initial results from a mesoscale atmospheric 

simulation system and comparisons with an AVESESAME I data set. Monthly Weather Review, 110, 1564-

1590.

[5] R. Barth, H. Brand, P. Meibom, and C. Weber, “A 

stochastic unit commitment model for the evaluation of the 

impacts of the integration of large amounts of wind power,” 

in Proc. 9th International Conference on Probabilistic 

Methods Applied to Power Systems, Stockholm, 2006. 

[6] Recent analysis results can be found at: 

http://wind.nrel.gov/public/EWITS/

6
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Eastern Wind Integration and Transmission Study – Preliminary 

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14. ABSTRACT (Maximum 200 Words)

This paper reviews the scope of the Eastern Wind Integration and Transmission Study (EWITS), the development of 

wind datasets, the transmission expansion analysis, and the results to date of the wind integration analysis for the 

four scenarios.

15. SUBJECT TERMS

Wind; solar; resource data; integration; grid studies; WWSIS; EWITS; transmission; power system operation; reserve 

requirements.

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