NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Benefit of Regional Energy 

Balancing Service on Wind 

Integration in the Western 

Interconnection of the United 

States

Preprint 

M. Milligan, B. Kirby (Consultant)

and J. King (Consultant)

National Renewable Energy Laboratory

S. Beuning

Xcel Energy

To be presented at the 9th Annual International Workshop on 

Large-Scale Integration of Wind Power into Power Systems as well 

as on Transmission Networks for Offshore Wind Power Plants 

Québec, Canada

October 18-19, 2010

Conference Paper

NREL/CP-5500-49076

October 2010
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1

 Abstract—Interest in various wide-area balancing 

schemes to help integrate wind have generated significant 

interest. As we have shown in past work, large balancing 

areas not only help with wind integration, but can also 

increase the efficiency of operations in systems without 

wind. Recent work on the Western Wind and Solar 

Integration Study (WWSIS) has found that combining 

balancing over the WestConnect footprint will increase the 

efficiency of commitment and dispatch at wind 

penetrations ranging from 10-20% of annual electricity 

demand, and will be essential for high penetrations and 

small balancing areas. In addition the Northwest Wind 

Integration Action Plan recommended balancing area 

cooperation as a method to help integrate the large 

potential wind development. In this paper we investigate 

the potential impact of a proposed Energy Imbalance 

Service on the ability of the non-market portions of 

Western Electricity Coordinating Councils (WECC) 

United States footprint to integrate wind energy. We will 

utilize data adapted from the WWSIS for the Western 

Interconnection. The analysis uses time-synchronized wind 

and load data to evaluate the potential for ramp 

requirement reduction that could be achieved with 

combined operation. Chronological analysis and ramp 

duration analysis quantify the benefit in terms of not only 

the ramp sizes, but the frequency of the potentially 

avoided ramps that must be managed by the non-wind 

generation fleet. Multiple approaches that can be used to 

achieve these benefits will also be suggested in the paper. 

We also suggest other approaches that can help achieve 

much of the benefit of full consolidation without requiring 

the physical consolidation of balancing areas.

Index Terms—Wind energy, balancing area, wind integration

I. INTRODUCTION

he existing state-level renewable portfolio standards in the 

Western Interconnection will require substantial 

renewable resources, expected primarily as wind generation. 

At typical capacity factors and considering annual energy 

consumption, potentially more than 50 GW of wind resource 

installation will be installed by 2020. The associated high 

 M. Milligan is with the National Renewable Energy Laboratory (email: 

michael.milligan@nrel.gov)

B. Kirby is a consultant to NREL (email: kirbybj@ieee.org)

S. Beuning is with Xcel Energy, Inc. 

J. King is with REPAE, consultant to NREL.

level of variability further increases the efficiency benefit over 

the “traditional” economic dispatch benefits of regional 

market operations. This analysis indicates the extent to which 

pooled regional dispatch for matching generation to load 

mitigates the costs and improves associated reliability, 

particularly in scenarios with high penetration of variable 

output resources, such as wind. 

II. DATA

The data used for this analysis was developed for the 

Western Wind and Solar Integration Study. This data consists 

of synchronized chronological load and wind power 

production data from the WWSIS dataset.

The weather data was simulated by 3Tier group using a 

mesoscale Numerical Weather Prediction (NWP) Model to 

recreate the weather across the western U.S. from existing 

atmospheric measurements and archived information about the 

historical weather at many locations. The data is sampled 

from the model and saved at 2km spatial resolution, every 10 

minutes for 3 years. The wind speed data along with other 

atmospheric parameters were then processed to provide wind 

plant output at each of the saved locations.

The 30% “In Footprint” plant selection scenario from the 

WWSIS was used as the basis for wind plant location with 

2006 data chosen for the analysis. Load profile information 

from 2006, derived from Ventyx Velocity Suite, was escalated 

to 2017 using NERC peak load and energy forecast filings 

from the various planning areas in WECC. We aggregated the 

data into regional footprints: Columbia Grid, Northern Tier 

Transmission Group, WestConnect, and California. Because 

Canada did not have a wind build-out in the study, we did not 

include the Canadian provinces here; however we believe that 

this would be a necessary step to fully understand the impact 

of wide-area balancing in the Western Interconnection.

The NWP model of the Western Interconnection contained 

geographic and temporal seams that were not possible to 

resolve. This resulted in unrealistic wind energy ramps near 

the temporal boundaries, which occurred every three days. To 

ensure realistic data and to maintain consistency with the 

statistical analysis of the WWSIS, we eliminated every third 

day, corresponding to the seams identified in the study.

Benefit of Regional Energy Balancing 

Service on Wind Integration in the Western 

Interconnection of the United States

Michael Milligan, National Renewable Energy Laboratory

Brendan Kirby, Consultant, National Renewable Energy Laboratory

Jack King, Consultant, National Renewable Energy Laboratory

Stephen Beuning, Xcel Energy

T
2

III. OVERVIEW OF THE PROPOSED “EFFICIENT DISPATCH 

TOOLKIT”

In the Western Interconnection, areas outside of California and 

Alberta do not presently have a common energy market, 

although there is bilateral transaction activity in the region. 

The Seams Issues Subcommittee of the Western Electricity 

Coordinating Council (WECC) is currently investigating an 

Efficient Dispatch Toolkit (EDT) that would achieve many of 

the benefits of a large-scale energy market, but without a 

coordinated unit commitment or regulation market.

The proposed EDT would use two primary tools: an 

Enhanced Curtailment Calculator (ECC), which can prioritize 

and allocate transmission service curtailments based on 

service priority for power flow impacts on the grid. Both 

tagged and un-tagged flows (most deliveries inside balancing 

areas are not tagged) will be evaluated by the ECC. The ECC 

would pass relevant curtailment information to the second 

tool, the Energy Imbalance Market (EIM).

The EIM uses a security-constrained economic dispatch to 

provide two functions:

• Balancing Service − this service redispatches 

generation to balance maintain balance between 

generation and load. For deliveries scheduled in 

advance, the effect is that deviations from schedules 

in generator output and errors in load schedules are 

supplied by the market.

• Congestion Redispatch Service − this will redispatch 

generation to relieve overload constraints on the grid. 

Information provided to the EIM from the ECC 

ensures correct allocation of the costs of redispatch 

service.

The current approach that is used by WECC BAs for 

balancing services comes from FERC Pro Forma Tariff 

Schedules 4 and 9. The proposed EIM replaces part of the BA 

services and results in a “virtual consolidation” due to a widearea security constrained economic dispatch that covers 

imbalances. The congestion redispatch service is new to the 

non-market portions of WECC. 

The EIM design includes a feature different from most 

regional markets in the US where internal resources are 

subject to a “must offer” requirement. Instead the default 

operating assumption is that each market participant provides 

sufficient resources to cover its own obligations (as is the case 

today) and the regional economic dispatch is provided by any 

resource that voluntarily offered responsive capability, and 

which is cleared by the security-constrained economic 

dispatch process. Most transmission service deliveries would 

continue to use traditional reserved transmission service, but 

the EIM would not use pre-reserved transmission. Instead, the 

EIM flow would receive the lowest transmission service 

curtailment priority. By this mechanism, EIM flows would not 

displace reserved transmission service. 

Unlike other regional markets where transmission service 

for market delivery is provided under a regional network 

service tariff, the EIM flows would pay an imputed service 

compensation after-the-fact to participating transmission 

providers. At this stage of development of the EDT toolkit, the 

specific terms for the transmission service revenue target and 

revenue allocation among participating transmission providers 

have not yet been established. 

The EIM function adds some operational steps to the 

current practices used in the Western Interconnection today. 

Functionally, the operating steps for the proposed EIM track 

closely with the operating process established in the 

Southwest Power Pool (SPP) in their Energy Imbalance 

Service Market. Figure 1 illustrates the timeline for operation 

of the proposed EDT toolkit.

Figure 1. Operation timeline for the EIM toolkit.

The EIM would effectively implement one form of a virtual 

BA across the Western Interconnection (California and 

Alberta would not be included because they already have 

energy markets). Imbalances would be netted out, much as 

they would be in a single BA. As proposed, the EIM does not 

result in a coordinated unit commitment, nor does it pool 

regulation, which remains a service at the local balancing area 

level. However, the netting of energy imbalance, which would 

include impacts of load and wind, are expected to be 

significant. FIGURE 2 illustrates the concept, with each of the 

small bubbles representing a single BA. The arrows between 

the BAs indicate bilateral tagged energy flows that would not 

be precluded in the EIM. However, under the EIM, only the 

footprint net imbalance would need to be managed, resulting 

in less “pent-up” variability within the local balancing areas 

and less required ramping across the footprint.

Schedule Day-Ahead & 

Up-to 30 minutes prior to

Operating Hour 

Real-time 

Dispatch Post-Operating 

time

Forecast and

unit Commit

Generators

self-schedule

Generators 

voluntary submit

offers

DSM resources 

voluntary submit

offers

Prepare and 

finalize predispatch schedules

Perform securityconstrained 

economic dispatch 

to keep balance

Provide redispatch

if any congestion

occurs

Send dispatch

set points to

generators

Coordinate any

contingency

reserve deployments

With EIS dispatch

Market Participant: WECC EIM Market Operator:

Gather meter

data to support

settlements

Provide 

settlement statement

and invoices

Transmission Provider:

Provide meter

data to support

settlements
3

Figure 2. The EIM would effectively pool variability 

within the operating footprint, similar to a single BA.

IV. BASIC SCENARIO DESCRIPTION

In our analysis we examined two basic levels of 

aggregation, representing alternative levels of wide-area 

management and implementation of the EIM. The first 

aggregation level is based on the existing transmission 

planning regions within the U.S. portion of WECC: Columbia 

Grid (CG), Northern Tier Transmission Group (NTTG), 

WestConnect (WC), and California.1 This first level of 

analysis addresses the impact of pooling within each of these 

regions, without considering the impact of a WECC-wide 

balancing market. The second level of analysis examines the 

impact of full pooling in the U.S. portion of WECC, as would 

be experienced with a full implementation of the EIM. We 

note that many other possible configurations are possible, 

based on which entities participate in the EIM.

 1 We consider California as a single area, combining the CAISO region 

with other balancing areas in the state for this analysis.

V. VARIABILITY ANALYSIS

Larger operating footprints improve the ability of the 

system to respond to variability. This occurs for two reasons: 

(1) pooling of variability of loads and wind generation 

increases diversity, which reduces the overall variability perunit, and (2) a broader resource mix increases ramping 

capability linearly. The result is that aggregation provides an 

increased ability to manage variability, which itself is 

somewhat reduced with aggregation. This principle can be 

applied to many facets of power system operation, and is one 

driver for the formation of reserve-sharing pools that reduce 

the total level of contingency reserve needed to maintain 

reliability.

Table 1 shows the peak, average, and minimum load and 

wind for all of WECC and the five regions. Both coincident 

and non-coincident values are shown. Aggregation provides a 

host of benefits for load as well as for wind. Aggregation 

reduces the peak capacity requirements for load alone. 

Coincident peak load is 6.2% lower for WECC than the sum 

of the non-coincident peak loads which each BA must support 

on its own. Minimum loads are also improved (raised) through 

aggregation: 8.6% for WECC and 17.2% for Columbia Grid. 

Load factor is 3.9% better for the aggregated WECC; 62.9% 

vs 59.0%. 

Aggregation also benefits wind. Peak WECC wind is 

reduced by 15.3% through aggregation. WECC aggregated 

minimum wind is 420MW compared with zero to 43MW for 

the individual sub regions. WECC wind capacity factor 

increases by 6.1% with aggregation. Aggregating wind also 

reduces the maximum wind penetration. One BA in 

WestConnect (WAUW) has a maximum ten minute wind 

penetration of 784% which is reduced to a maximum of 95% 

for the aggregated WestConnect and a maximum 62% for 

aggregated WECC.

EIM Footprint

EIM Tool: 

SCED

Intra-hour variability is captured and allocated 

in real-time within the entire region, limited by 

Diversity benefit the physical capability of the wires.

reduces operating 

costs for balancing.

Table 1. Load, wind, and penetration levels for WECC and its five regions.

WECC WestConnect Northern Tier Columbia Grid Canada California

# of BAs 32 14 5 9 2 2

Load

Max Non-Coincident 203,000 62,623 23,979 23,664 24,292 68,442

Max Coincident 190,500 60,925 23,123 23,154 24,067 68,308

Avg 119,783 33,682 15,821 14,811 17,761 37,709

Min Coincident 86,062 22,509 10,613 10,363 13,561 25,722

Min Non-Coincident 79,270 21,056 10,240 8,842 13,469 25,664

Wind

Max Non-Coincident 72,623 26,250 10,997 11,853 0 23,523

Max Coincident 61,508 24,398 10,682 11,702 0 23,523

Avg 24,421 8,624 3,805 4006 0 7,987

Min Coincident 420 124 69 2 0 43

Min Non-Coincident 47 1 3 2 0 43

Non-Coincident CF 33.6% 32.9% 34.6% 33.8% 0% 34.0%

Coincident CF 39.7% 35.3% 35.6% 34.2% 0% 34.0%

Penetration

Max in Area 784% 784% 181% 227% 0% 90%

Max Coincident 62% 95% 81% 106% 0% 81%

Energy 20% 26% 24% 27% 0% 21%
4

VI. RESERVE ANALYSIS

One very important aspect of the variability due to wind is 

the impact on the reserve generation capacity that is necessary 

to cover the variations in the load and wind. A methodology 

was developed to estimate the increased requirements for 

regulation with wind variability in the Eastern Wind and 

Transmission Study (EWITS). 

Short term variability is a problem since we can’t fully 

anticipate the changes with scheduling and must cover those 

fluctuations with reserves. In a system with 10 minute 

markets or schedule updates, the best we can do is forecast a 

flat value for wind output for the next interval based on the 

past 10 to 20 minutes. The wind varies on that time scale and 

need to understand how it will vary during the forecast 

interval. With a statistical approach, we can estimate how 

much reserve is required if we have an estimate of the 

standard deviation of the variability.

The variability is a function of production level. At low 

levels of production, turbines that are part of a wind plant are 

not spinning or at very low output. At high levels of 

production, turbines are in the flat portion of their power 

curves. Changes in the wind tend to yield small changes in 

output. In the middle of the power curve, however, small 

changes in wind can yield large changes in output. The 

EWITS method recognizes that the short term forecast error in 

WTG output and thus short term variability is a normally 

distributed value. Through analysis, an equation can be 

written for the standard deviation (sigma) that variability that 

varies with production level.

The EWITS method determines the equation for sigma by 

analyzing the wind production data over some long period of 

time (a year or more) and calculating the standard deviation 

for the variability in various ranges of wind output. For each 

hour of production data, the standard deviation (sigma) is 

calculated from that equation. A component 3 time sigma is 

combined with a regulation component of 1% of hourly load 

to estimate the total regulating requirement for variability. 

The 3 sigma approach estimates reserve values that will cover 

99.9% of all short term variability.

An additional uncertainty component due to hour-ahead 

wind forecasting error was calculated for the EWITS study. 

This component is calculated in a similar manner to the short 

term forecast error described above, using an equation to 

describe the standard deviation of hour-ahead forecast error. 

With that equation, the expected sigma for the forecast error is 

calculated based on the previous hour’s production 

(persistence forecast). This component helps to insure the 

system is positioned with enough maneuverability to cover 

probable forecast error.

The variability (short term forecast) and uncertainty 

components can be combined to access the total additional 

spinning reserve burden due to load and wind variability. 

Please refer to Section 5 of the EWITS final report for full 

description of the methods employed.

The method described was applied to the WECC system in 

the following way. Reserve components were calculated for 

load serving balancing authority in WECC, organized by the 

regions Columbia Grid, Northern Tier Transmission Group, 

WestConnect and CISO. Those requirements take the form of 

an 8760 hour vector for each BA. The hourly requirement 

vectors were summed across the BAs for the regions and for 

WECC as a whole. These vectors represent the ‘Before 

Consolidation’ data. To calculate the ‘After Consolidation’ 

requirements, the wind production data is aggregated across 

the regions and WECC and the reserve requirements 

calculations repeated. This procedure was repeated for the 

hour-ahead forecast error component.

Figure 3. Impact of pooling on the variability 

component of reserves, using the EWITS approach.

Figure 4. Impact of pooling on the regulation 

reserve.

The greatest benefits for the regional consolidations (virtual 

or otherwise) fall to WestConnect due to the wide territory and 

the resulting high diversity of the region, as was seen in 

Section V. Smaller benefits are seen for Columbia Grid 

where much of the wind is concentrated in one area (Montana) 

thus diluting the effects of consolidation. California (CISO) 

shows very little benefit since it already operates as a single 

market and only one smaller BA (LADWP) for this analysis. 

By far, the greatest benefit is seen for consolidation of the 

entire WECC territory. This is because of the wide diversity 

when all of the BA’s are combined.
5

VII. IMPLICATIONS FOR PLANNING

System planners must assure that there is sufficient ramping 

capability as well as sufficient capacity to meet net load. Table 

1 showed the significant capacity benefits that can be realized 

through BA cooperation or aggregation. Aggregation also 

reduces ramping requirements. Figure 5 shows the maximum 

and average daily peak ramping requirements for 

WestConnect load. Greater capacity is required for longer 

ramps but the increase is not linear. As expected, the peak one 

hour ramp is at a faster rate (greater MW/min) than the peak 

eight hour ramp. Figure 6 shows both the maximum ramping 

capacity required for each ramp duration for the year as well 

as the average of the daily peak ramping requirements.

Interestingly aggregation reduces the maximum annual 

ramping requirement significantly more than it reduces the 

average daily ramping requirement. This is because the daily 

load shape is highly correlated between the BAs in the 

aggregation. Figure 6 shows the same ramping requirements 

for all of WECC.

Figure 5. WestConnect load daily maximum ramping 

requirements.

Figure 6. WECC load daily maximum ramping 

requirements.

Figure 7 and Figure 8 show the wind ramping requirements 

for WestConnect and WECC while Figure 9 and Figure 6 

show the net load ramping requirements. For convenience the 

ramping sign convention for loads is used in all of the figures. 

A positive ramp represents a load increase or a wind decrease 

since both require conventional generation to ramp up in 

response. Several characteristics are apparent in both the 

WestConnect and WECC data. One is that the wind ramping 

requirements are significantly lower than the load ramping 

requirements. Second is that there is a greater ramping 

aggregation benefit for wind than for load indicating greater 

diversity in wind patterns than in load patterns. Third, the 

maximum annual ramping requirements are significantly 

greater for both load and wind than the average daily 

maximum ramping requirements. Finally, aggregation benefits 

remain strong for net load.

Figure 7. WestConnect wind daily maximum ramping 

requirements.

Figure 8. WECC wind daily maximum ramping 

requirements.
6

Figure 9. WestConnect net load daily maximum 

ramping requirement.

Figure 10. EDT Footprint net load daily maximum 

ramping requirement.

Figure 11examines how frequently four hour ramping 

capacity is required in the assumed footprint for EDT 

operations. The aggregated wind fleet, for example, ramps 

down (positive ramp on the graph) at a maximum of 7,913 

MW/4hr or more on half of the days (50%). Without 

aggregation the total maximum daily wind ramp from all the 

BAs in the EDT footprint is 16,451MW/4hr or more on half of 

the days. Load ramps up at 27,013MW/4hr or more on half of 

the days for both the aggregated and unaggregated EDT 

footprint. Net load ramps up at 28,998MW/4hr or more for 

aggregated EDT footprint and 33,130MW/4hr or more for the 

individual BAs for half the days. Four hours is shown as an 

example, ramping requirements of all durations from ten 

minutes to 12 hours were computed. The reduction in ramping 

requirements is greatest for the few days when the highest 

ramps are required. This represents a significant potential 

savings in capacity.

Figure 11. Assumed EDT footprint maximum daily 

four hour ramping requirements.

VIII. CONCLUSIONS

This analysis indicates that the efficient dispatch toolkit 

proposed by the WECC stakeholders holds great potential to 

mitigate the operations impacts associated with integration of 

large amounts of wind generation. The EDT effectively pools 

variability across the interconnection, and although it does not 

result in coordinated unit commitment, energy variability, both 

from load and from wind, is reduced. The impact on 

variability reserve is summarized in Figure 12, and shows a 

significant reduction in maximum, average, and minimum 

dynamic reserve levels.

Although there are a number of mechanisms that may 

achieve a similar reserve reduction, the proposed EDT will 

result in a reduction of an average of 3,101 MW of variability 

reserve in the West.
7

Figure 12. Savings in variability reserve.

IX. REFERENCES

[1] Richard Piwko, Xinggang Bai, Kara Clark, Gary Jordan, Nicholas 

Miller, and Joy Zimberlin. “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, Mar. 2005. Available: 

http://www.nyiso.com/public/webdocs/services/planning/special_studies

/wind_integration_report.pdf.

[2] Enernex Corporation, “2006 Minnesota Wind Integration Study Volume 

I,” Nov. 2006. Available: http://www.uwig.org/windrpt_vol%201.pdf.

[3] J. C. Smith, M. Milligan, E. Demeo, and B. Parsons, “Utility Wind 

Integration and Operating Impact State of the Art,” IEEE Trans. Power 

Syst., vol. 22, pp. 900-908, Aug. 2007.

[4] North American Electric Reliability Corporation, “Reliability Standards 

for the Bulk Electric Systems of North America,” November 2009.

[5] “ERCOT Methodologies for Determining Ancillary Service 

Requirements,” 2009.

[6] B. Kirby, 2006, Demand Response For Power System Reliability: FAQ, 

ORNL/TM 2006/565, Oak Ridge National 

Laboratory, December 2006.

[7] Report: Entsoe, UCTE Operational Handbook Policy 1, [Online] 

http://www.entsoe.eu/index.php?id=57

[8] Report: Eirgrid, All Island Reserve Review, Dublin, Ireland, 2003.

[9] Y. Rebours, D. Kirschen, M. Trotignon, and S. Rossignol, “A Survey of 

Frequency and Voltage Control Ancillary Services – Part I: Technical 

Features,” IEEE Trans. Power Syst., vol. 22, no. 1, pp. 350-, Feb 2007.

[10] E. Ela, M. Milligan, B. Parsons, D. Lew, and D. Corbus, “The evolution 

of wind power integration studies: past, present, and future,” 

Proceedings of IEEE PES General Meeting, Calgary, CA, July 2009.

[11] P. Meibom, R. Barth, H. Brand, B. Hasche, D. Swider, H. Ravn, and C. 

Weber, “All Island Grid Study Workstream 2B: Wind Variability 

Management Studies,” July 2007. Available: 

http://www.uwig.org/Irish_All_Island_Grid_Study/Workstream_2B.pdf

[12] “Wind Power Integration in Liberalised Electricity Markets 

(WILMAR),” www.wilmar.risoe.dk

[13] R. Doherty and M. O’Malley, “New approach to quantify reserve 

demand in systems with significant installed wind capacity,” IEEE 

Trans. Power Syst., vol. 20, no. 2, pp. 587–595, May 2005.

[14] Enernex Corporation, “Eastern Wind Integration and 

Transmission Study,” Prepared for the National Renewable 

Energy Laboratory, January 2010. Available: 

http://www.nrel.gov/wind/systemsintegration/pdfs/2010/ewits_final

_report.pdf.

The Alliance for Sustainable Energy, LLC (Alliance), is the 

manager and operator of the National Renewable Energy 

Laboratory (NREL). Employees of the Alliance, under 

Contract No. DE-AC36-08GO28308 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 United States 

Government purposes.

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F1147-E(10/2008)

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Benefit of Regional Energy Balancing Service on Wind Integration in 

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S. Beuning (Xcel Energy)

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14. ABSTRACT (Maximum 200 Words)

This analysis indicates the extent to which pooled regional dispatch for matching generation to load mitigates the 

costs and improves associated reliability, particularly in scenarios with high penetration of variable output resources, 

such as wind.

15. SUBJECT TERMS

Wind energy; balancing area; wind integration; WECC; Western Interconnection; grid

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT

UL

18. NUMBER 

OF PAGES

19a. NAME OF RESPONSIBLE PERSON

a. REPORT

Unclassified

b. ABSTRACT

Unclassified

c. THIS PAGE

Unclassified 19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98)

Prescribed by ANSI Std. Z39.18