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

Valuing Energy Security: 

Customer Damage Function 

Methodology and Case Studies 

at DoD Installations

J. Giraldez, S. Booth, K. Anderson, and 

K. Massey

Produced under direction of the Department of Defense by the National 

Renewable Energy Laboratory (NREL) under Interagency Agreement 

IAG 10-1788 and Task No WFH9.1009. 

Technical Report

NREL/ TP-7A30-55913

October 2012
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.

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Valuing Energy Security: 

Customer Damage Function 

Methodology and Case Studies 

at DoD Installations 

J. Giraldez, S. Booth, K. Anderson, and 

K. Massey

Prepared under Task No. WFH9.1009

Technical Report

NREL/ TP-7A30-55913

October 2012
NOTICE

This manuscript has been authored by employees of the Alliance for Sustainable Energy, LLC (“Alliance”) under 

Contract No. DE-AC36-08GO28308 with the U.S. Department of Energy (“DOE”).

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

Cover Photos: (left to right) PIX 16416, PIX 17423, PIX 16560, PIX 17613, PIX 17436, PIX 17721

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
i

Acknowledgments 

The authors gratefully acknowledge the assistance of Randy Monohan and the rest of the staff at 

Marine Corps Air Station Miramar; and William Elliott, Night Vision & Electronic Sensors 

Directorate, Fort Belvoir, for their participation, time, and support of this work. 
ii

List of Acronyms and Abbreviations

ASAI average system availability index

CAISO California Independent System Operator

CDF customer damage function

CPUC California Public Utility Commission

DoD Department of Defense

FY fiscal year

GDP gross domestic product

GNP gross national product

ISO independent system operator

kW kilowatt

kWh kilowatt-hour

MCAS Marine Corps Air Station

NREL National Renewable Energy Laboratory

NVESD Night Vision and Electronic Systems Directorate

PUC public utility commission

SAIDI system average interruption duration index

SAIFI system average interruption frequency index

SDG&E San Diego Gas and Electric

VEES value of electrical energy security
iii

Executive Summary

This report describes a methodology to value non-energy savings that can be attributed to energy 

security projects. The methodology is based on: (1) completing a site survey to collect data on 

two hypothetical outage scenarios lasting for different durations to compute a customer damage 

function (CDF), which represents interruption costs as a function of the outage duration; (2) 

characterizing the reliability of the commercial power supply at the site; and (3) estimating the 

value of electrical energy security (VEES), which is a metric that characterizes on an annual 

basis the cost of utility outages. The avoided cost of commercial power outages can be used as 

ancillary non-energy savings to increase the return on investment of energy security projects.

For the purposes of this report, the general reach of energy security is focused on electrical 

energy security concerns, in particular, to the provision of electrical power during commercial 

electric power outages. Valuing energy security is an increasingly important component for 

financing energy projects. In the military sector, the ability to assess the value of energy security 

will play a critical role in prioritizing mission critical energy projects. 

The CDF concept and energy security valuation have a variety of applications. An established 

CDF military function could be used as a tool when making investment decisions regarding 

energy security projects. It will assist with justifying project financing and/or comparing the 

relative benefits of several projects. As an example, the VEES, which is the annual avoided cost 

of commercial power outages, can be included as “non-energy” savings in the solicitation for 

Energy Conservation Investment Program funds, and used to prioritize investment and tradeoffs 

of energy security projects at installations with critical missions. Additionally, the process of 

working with the CDF questionnaire and establishing all of the assumptions necessary to develop 

the CDF curve can expose critical vulnerabilities at an installation that might otherwise only be 

detected through an outage. 

The CDF function has direct applications for the Department of Defense (DoD). The National 

Renewable Energy Laboratory’s proposed energy security evaluation process is relatively easy to 

populate and could be adopted as a standard against which current and future installation energy 

security projects are prioritized. The methodology also could be developed into a tool for facility 

and regional energy managers, and/or DoD portfolio and enterprise managers to inform their 

decisions related to energy security projects.

The methodology presented in this paper can and will continue to evolve as lessons are learned 

from its application. Some of the next steps needed to refine and improve this methodology for 

application in the DoD would be to: (1) develop further processing of the surveyed data to refine 

both probabilities of events occurring and their associated cost with DoD personnel; (2) complete 

an extensive survey in strategically selected military installations; and (3) create a tool to 

compute the potential annual savings from increasing energy security, including survey 

processing capabilities, and CDF curve and commercial power reliability databases. 

Summary of Results

The survey and CDF methodology were completed at two DoD installations: Marine Corps Air 

Station (MCAS) Miramar in San Diego, California, and the 300 Compound Area at Fort Belvoir

(Army), Virginia . The VEES value was calculated using equation (ES.1) below:
iv

VEES = Annual # outages ∗ CDF(Duration) � $

kWpeak� ∗ Peak[kW] (ES.1)

Where Annual # outages is the annual number of outages, CDF(Duration) is the value in 

$/kilowatt (kW) peak of an outage cost obtained from the CDF curve for a specified Duration of 

the interruption, and peak is the peak-demand of the installation. 

The CDF for MCAS Miramar is shown in Figure ES-1. Table ES-1 below shows a range of

VEES numbers at MCAS Miramar using Scenario A that could be used to support an energy 

security project.

Figure ES-1. CDF for MCAS Miramar

Table ES-1. Annual VEES Values at MCAS Miramar Under a Non-Emergency Scenario

Case number Annual number of 

outages 

Annual outage 

duration

VEES in $/year

1. SDG&E 10 year 

average SAIFI and 

SAIDI

0.75 2.6 hours $ 390,000

2. SDGE 2011 SAIFI 

and SAIDI

1.5 9.5 hours $ 2,925,000

3. MCAS Miramar 

experiencing a 5 hour 

outage every 2 years

0.5 5 hours $ 487,500

0

50

100

150

200

250

300

350

1 4 7 10 13 16 19 22

SCENARIO A

(non-emergency

situation)

SCENARIO B

(emergency

situation)

$/kW peak

hours

MCAS Miramar Customer Damage Function
v

The first case uses the average SAIDI and SAIFI values over the last ten years, Case 2 uses 

SAIDI and SAIFI values in 2011, and finally the third case is calculated assuming that the base 

experiences a large 5 hour outage every two years. The annual losses due to outages at MCAS 

Miramar depending on the number and duration of outages range from $390,000 to $2.92 

million.

The survey was also completed at the 300 Compound Area of the Fort Belvoir Army base, and 

the data were processed to compute the CDF shown in Figure ES-2. Table ES-2 shows annual 

VEES numbers at the 300 Compound Area. 

Figure ES-2. CDF for the 300 Compound Area, Fort Belvoir, Virginia

Table ES-2. Annual VEES Values at 300 Compound Area (Fort Belvoir) Under a Non-Emergency 

Scenario

Case number Annual number of 

outages 

Annual outage 

duration

VEES in $/year

1. Dominion Virginia 

Power 10 year average 

SAIFI and SAIDI

2.2 6.6 hours $2,227,500

2. Dominion Virginia 

Power 2011 SAIFI and 

SAIDI

2.4 24.8 hours $7,560,000

3. 300 Compound Area 

actual number and 

duration of outages in 

2010 

8 2 hours $3,960,000

0

100

200

300

400

500

600

700

800

1 4 7 10 13 16 19 22

SCENARIO A

(non emergency

situation)

SCENARIO B

(emergency

situation)

$/kW peak

hours

Fort Belvoir 300 Compound Area - Customer Damage Function
vi

Using the CDF function in Figure ES-2 and utility 10 year average metrics in Case 1, the 

expected annual losses are approximately $2.23 million per year. In case 2, Dominion Virginia 

Power 2011 reliability metrics are used and the annual losses add up to $7.56 million. Finally, in 

Case 3, the actual number and duration of outages are used at the 300 Compound Area, from 

reference [26] and assuming that in 2010 the installation experienced eight 2-hour long outages, 

the annual losses for FY 2010 were $3.96 million. 

The VEES numbers in Table ES-1 and ES-2 represent the potential value of an energy security 

project that would provide 100% reliable power in case of commercial power interruption. A derating factor to the VEES number should be applied depending on the degree of reliability 

provided by the energy security project.
vii

Table of Contents

ACKNOWLEDGMENTS ......................................................................................................................................... I

LIST OF ACRONYMS AND ABBREVIATIONS.......................................................................................................... II

EXECUTIVE SUMMARY ...................................................................................................................................... III

SUMMARY OF RESULTS................................................................................................................................................... III

TABLE OF CONTENTS........................................................................................................................................ VII

LIST OF FIGURES.............................................................................................................................................. VIII

LIST OF TABLES................................................................................................................................................ VIII

1 INTRODUCTION ......................................................................................................................................... 1

2 METHODOLOGIES FOR VALUING ENERGY SECURITY ................................................................................... 5

3 CUSTOMER DAMAGE FUNCTION................................................................................................................ 6

4 RELIABILITY IN ELECTRIC SYSTEMS.............................................................................................................. 9

5 CASE STUDIES: MCAS MIRAMAR AND 300 COMPOUND AREA AT FORT BELVOIR........................................11

5.1 MCAS MIRAMAR CDF AND GRID RELIABILITY......................................................................................................11

5.1.1 MCAS Miramar CDF ...............................................................................................................................11

5.1.2 Reliability of Miramar’s Electric Utility (San Diego Gas & Electric)........................................................13

5.1.3 CDF to Support Financing Energy Security Projects at MCAS Miramar .................................................17

5.2 300 COMPOUND AREA – FORT BELVOIR – CDF AND GRID RELIABILITY.....................................................................18

5.2.1 300 Compound Area CDF .......................................................................................................................18

5.2.2 Reliability of Fort Belvoir’s Utility (Dominion Virginia Power) ...............................................................19

5.2.3 CDF to Support Financing Energy Security Projects at the 300 Compound Area ..................................21

5.3 COMPARISON OF THE TWO CASE STUDIES’ CDF CURVES ........................................................................................23

6 CONCLUSION AND FUTURE WORK ............................................................................................................24

REFERENCES......................................................................................................................................................26
viii

List of Figures

Figure ES-1. CDF for MCAS Miramar ......................................................................................... iv

Figure ES-2. CDF for the 300 Compound Area, Fort Belvoir, Virginia ........................................ v

Figure 1. Major power disturbances in North America from 1992 to 2011 ................................... 2

Figure 2. Literature research on cost of outages (in $/kWpeak) for four sectors: industrial, 

commercial, governmental and institutional and residential ................................................. 7

Figure 3. CDF for MCAS Miramar .............................................................................................. 11

Figure 4. Loss of productivity in $ in Scenarios A and B for 4, 8, and 24 hours of lack of power 

supply................................................................................................................................... 12

Figure 5. Cost of risk associated to an outage in Scenarios A and B and lasting for 1, 4, 8 and 

24 hours................................................................................................................................ 13

Figure 6. SDG&E’s SAIDI and SAIFI values from 2000 to 2011 ............................................... 15

Figure 7. Average customers affected and duration of interruption of the largest annual 10 

events from 2001 to 2010 .................................................................................................... 16

Figure 8. Frequency distribution of outage duration of 100 largest events from 2001 to 2010 ... 16

Figure 9. Number of 10 largest outage events by cause ............................................................... 17

Figure 10. CDF for the 300 Compound Area, Fort Belvoir, Virginia .......................................... 19

Figure 11. Dominion Virginia Power SAIDI and SAIFI values from 2000 to 2011.................... 20

Figure 12. Percentage of customers affected by more than 10, 4-6, and 1-3 outages .................. 21

Figure 13. Percentage customers by outage cause........................................................................ 21

List of Tables 

Table ES-1. Annual VEES Values at MCAS Miramar Under a Non-Emergency Scenario ......... iv

Table ES-2. Annual VEES Values at 300 Compound Area (Fort Belvoir) Under a NonEmergency Scenario .............................................................................................................. v

Table 1. SDG&E Reliability Indices in 2011 ............................................................................... 14

Table 2. Annual VEES Values at MCAS Miramar Under a Non-Emergency Scenario .............. 18

Table 3. Annual VEES Values at 300 Compound Area (Fort Belvoir) Under a Non-Emergency 

Scenario................................................................................................................................ 22
1

1 Introduction

“Google the term ‘energy security’ and you’ll get 92 million hits. Say energy security to five 

different people, and I bet you’ll get 10 different answers,” said Michael A. Aimone, director of 

Business Enterprise Integration, Office of the Deputy Undersecretary of Defense (Installations 

and Environment) in a blog post about energy security in March 2009. [1]

In the 2010 Quadrennial Defense Review Report, there is a definition of energy security for the 

Department of Defense (DoD): “[Energy security] means having assured access to reliable 

supplies of energy and the ability to protect and deliver sufficient energy to meet operational 

needs.” [2] However, this definition is very generic and does not define what “reliable supplies 

of energy” or “sufficient energy” means. Is a reliable supply of energy a power supply that can 

guarantee 100% delivery? Does 100% delivery assume a certain level of redundancy in power 

supply? Are operational needs defined in order to guarantee sufficient energy? Is sufficient 

energy expected to meet operational loads for an indefinite amount of time? These are just some 

of the questions that illustrate the complexity of defining energy security for DoD. 

The Navy’s definition of the term is “...having assured access to reliable and sustainable 

supplies of energy and the ability to protect and deliver sufficient energy to meet operational 

needs,” [3] which is very similar to the definition included in the 2010 Quadrennial Defense 

Review Report. The Army takes it to the next level in the Army Energy Security Implementation 

Strategy by defining more precisely the following three terms: sufficiency, surety, and 

sustainability: 

“Above all, energy security means having adequate power to conduct critical 

missions for the duration of that mission (sufficiency). Secondarily, and leading 

to sufficiency, is ensuring resilient and redundant energy supplies that are 

accessible when needed (surety). Finally, the energy supplies must present the 

lowest life cycle cost, while considering all statutory and executive order 

requirements, as well as the impact to mission, community, and environment 

(sustainability).” [4] 

Here the definition of sufficiency includes the duration for which the reliable power supply is 

expected, and the definition of surety affirms that the reliability of the power supply is expected 

to be resilient and redundant. 

For the purposes of this report, the general reach of energy security is focused on electrical 

energy security concerns, in particular, to the provision of electrical power during commercial 

electric power outages. One of the reasons to increase the sufficiency of energy security in DoD 

installations is that they are almost completely dependent upon the commercial power grid. 

The report of the 2008 Defense Science Board Task Force on DoD Energy Strategy, More 

Fight—Less Fuel, [5] called on the department to initiate aggressive energy innovations aimed at 

reducing risk to soldiers and enhancing the military’s long-term energy security. A key thread in 

the report was the vulnerability of the nation’s electric power grid. At fixed installations in the 

United States, utility providers have become increasingly efficient following deregulation, in an 

attempt to lower costs, but also less resilient and more susceptible to outages. They are 
2

vulnerable to extreme weather, cyberattack, physical attack, and an aging infrastructure already 

operating beyond its design life. Large blackouts at a national level are growing in number and 

severity as shown in Figure 1.

Figure 1. Major power disturbances in North America from 1992 to 2011

The number of outages affecting 50,000 or more people has risen from a total of 41 during 1991–

1995 to 58 during 1996- 2000 to 92 during 2001–2005. The trend is similar for occurrences over 

100 megawatts, going from 66 to 76 to 140 in the same time periods. [6] The latest data 

published in Eaton Corporation’s Blackout Tracker United States Annual Report for 2011

confirm an upward trend with the following results for a number of reported outages affecting 

50,000 or more people: 42 outages in 2009, 52 outages in 2010, and 109 outages in 2011. [7] In 

all, 3,071 reported outages were tabulated and used as the basis for the 2011 report. The number 

of people affected by outages jumped from 17.5 million in 2010 to 41.8 million in 2011 due to 

multiple, massive power failures. Reliability metrics for individual utilities vary and are

discussed more in section 4 of this report. In section 5 of this report, metrics from the utilities 

that serve the facilities discussed in the case studies are presented.

Solutions for backup power in DoD installations have not kept pace with the growing frequency 

and duration of commercial utility outages. [8] Most DoD installations have contingency plans in 

the event of a grid failure that can provide backup power to critical loads using, in most cases, 

diesel-powered building generators. However, the expected duration for these contingency plans 

is not well defined; diesel generators are not designed to run for weeks at a time, and fuel storage 

capacities vary widely. Additionally, the preventative maintenance for these diesel generators 

does not always prepare the generators for 100% availability; they have a low probability of 60% 

to start when needed. [9] 
3

Microgrids are a primary solution to address the need for greater energy security in military 

installations. In a statement to Congress in March 2012, Dr. Dorothy Robyn, deputy 

undersecretary of Defense (Installations and Environment) states that:

“[Microgrids]... will reduce installation energy costs on a day-to-day basis by 

allowing for load balancing and demand response—i.e., the ability to curtail load 

or increase on-site generation in response to a request from the grid operator. 

Most important, the combination of on-site energy and storage, together with the 

microgrid’s ability to manage local energy supply and demand, will allow an 

installation to shed non-essential loads and maintain mission-critical loads if the 

grid goes down.“ [10] 

Due to an increasingly stressed grid, ancillary services–such as operating and regulating 

reserves–are an essential component of grid reliability and stability. Operating reserves can be 

called upon quickly when a transmission line or generating station becomes unavailable or when 

local area distribution problems arise. Regulating reserves help balance the power system and 

maintain a steady frequency given the constant fluctuations in demand and intermittent 

generation sources. Due to the general decrease in reliability of the commercial power grid, it is 

expected that energy security projects at the customer level, such as a military installation, will 

benefit the local utility. In the new developing market systems, energy security projects such as 

microgrids can be paid for by providing demand-response services; and/or supplying a number 

of ancillary services or reliability services such as voltage regulation, reserve power, black start, 

controlled islanding, etc. Thus, it is expected that microgrids can both improve electrical energy 

security at DoD and provide increasingly highly valued reliability services to utilities and 

regional markets. 

The scale and type of benefits created from microgrids will vary depending on customer and 

location-specific circumstances, including the thermal and electric demands of interconnected 

loads, the configuration of the local distribution system, the ability of existing legacy grid

infrastructure to meet load growth, the local utility’s generation mix, the retail cost of energy, 

and the energy market structure among others. The article “Microgrids: An Assessment of the 

Value, Opprotunities and Barriers to Deploymnet in New York State” [11] describes in depth the

economic value streams of microgrids, which depend on the generating technologies deployed. 

Some of the benefits include:

• Reduced overall energy costs by reducing energy consumption and utility 

transmission and distribution services charges

• Exported power to the grid 

• Participation in demand-response programs 

• Participation in distribution and transmission ancillary services

• Greater use of renewable generation through the use of advanced control systems, 

demand response, and dispatchable generating sources

• Increased power quality

• Reduced power interruptions. [11]
4

Contingency plan expenditures traditionally have been reviewed and approved based solely on 

initial capital cost. However, efforts toward life cycle costing or performance parameters are now 

being discussed. Drexel Kleber, the director of the DoD Strategic Operations Power Surety Task 

Force, describes it in an article published in 2009: 

“Today, more than ever, the Department of Defense is learning to accept and 

create new business models, where fiscal decisions are made with a greater 

understanding of US national security vulnerabilities, mission requirements and 

long-term Energy security objectives. It is expected that some critical 

infrastructure investments will be made based on their contribution to DoD’s 

Energy security objectives but which do not meet traditional life cycle cost 

analysis objectives or which don’t provide a return on investment. This process 

will become easier with the development of an ‘energy key performance 

parameter’ as recommended by the Defense Science Board Task Force and 

metrics to quantify the ‘fully burdened cost of fuel’.” [8]

Although there are references to the need for metrics, as of today, the authors have not been able 

to find a metric that justifies the investment in energy projects that contribute to increased energy 

security. This paper uses the customer damage function (CDF) that has been used in non-military 

scenarios to develop a methodology to quantify the non-energy savings or avoided costs of 

energy security projects on DoD installations. Following this introduction, the paper is organized 

and presents information as follows: 

• Section 2- Discusses methodologies for valuing energy

• Section 3- Describes CDF in detail 

• Section 4- Discusses how to characterize the reliability of the commercial power grid

• Section 5- Presents two CDF case studies and the respective utility reliability metrics

• Section 6 –Presents conclusions and future work. 
5

2 Methodologies for Valuing Energy Security

New efforts are underway to place a value on energy security as a way to justify any additional

costs to enhancing energy security. Investments to enhance energy security should result in the 

reduction or elimination of outages. Outages have very real costs to DoD, both direct and 

indirect, so the avoided cost of outages can be viewed as a legitimate part of the return on those 

investments. There are two ways of quantifying outage costs described below: macroscopic and

microscopic. 

The macroscopic method to valuing energy security would use the costs of energy not supplied 

[$/kilowatt hour (kWh)] and/or a cost per interrupted power [$/kilowatt (kW)] as an adjustable 

measure for interruption severity. This approach attempts to relate the outage costs to a national 

level by estimating the total cost of outages over a long (i.e., annual) period of time. It is difficult 

to accurately assess the value of unserved energy for every customer due to the potentially large 

volume of data. The gross national product (GNP) and gross domestic product (GDP) estimation 

methods have been developed to approximate long-term outage costs. The GNP (or GDP) outage 

cost method is designed to estimate the outage cost based on the ratio of GNP (or GDP, 

respectively) to the total energy consumption. In a macroscopic approach, the outage cost is 

calculated using equation (1) by:

Outage Cost � $

kWh

� = GNP (or GDP)in $

Total Annual Energy Consumption in kWh (1)

The macroscopic approach is based on economic productivity parameters and is particularly 

difficult to apply to a military installation whose primary function is national security rather than 

economic productivity. 

A microscopic approach uses costs for the energy not supplied and requires a large-scale survey 

to collect the outage cost information. Interruption costs are a function of both the outage and 

user impacts, and can be presented as a function of outage duration. This approach is known as a 

CDF. Actual interruption damage may depend on the type of interrupted customers, duration of 

the power outage, and the situation in which the interruption occurs: day of week, time of day, 

customer’s activity at the moment of interruption, etc. This microscopic approach is better suited 

to the mission of DoD and the actual impacts on an installation than the macroscopic approach.

The CDF is typically computed based on customer survey data. The survey must include direct 

and indirect losses to the customer. Direct impacts are those resulting directly from cessation of 

supply while indirect impacts result from a response to an interruption. Hence, direct economic 

impacts include loss of production, paid-for resources (material, labor, etc.), process restart costs, 

spoilage of raw material or food, equipment damaged as a direct result of the power outage, costs 

associated with human health and safety, etc. Direct social impacts include any inconvenience 

due to lack of transportation, and economic personal injury or fear. Indirect losses usually rise as 

consequences and may be difficult to categorize. Examples of such costs are civil disturbances or 

disobedience, failure in providing an essential service, etc. 

Once the survey is completed, direct and indirect economic impact data are processed and 

transformed in order to create a CDF curve.
6

3 Customer Damage Function

CDF customer surveys can be found in the literature for industrial, commercial, and residential

sectors of the economy. However, the authors found no published survey that addressed the 

impact of utility interruptions on DoD bases. A military base is a unique area that includes 

residential, commercial, and mission critical activities. The National Renewable Energy 

Laboratory (NREL) has prepared a survey specific to military bases, building on previous 

surveys with questions directed toward the military sector.

Surveys are associated with hypothetical scenarios that may vary in several ways (duration, 

time/type of day, season, etc.). For this survey, two hypothetical scenarios were developed that 

vary in duration and emergency level:

1. Scenario A- There is a power outage and the lack of power supply lasts for 1, 4, 8, or 24 

hours. The base is not in an emergency situation.

2. Scenario B- There is an outage and a natural disaster, or a terrorist attack event is 

simultaneously going on in the area. The lack of power supply lasts for 1, 4, 8, or 24 

hours, and the base is under an emergency situation and has to remain operational.

1

Hypothetical scenarios can be further developed, and different outage duration times may be 

selected depending on the mission type of the installation. For instance, R&D installations may 

be severely affected by short outage durations (less than 1 hour) versus main operational bases 

that may be affected by longer outage durations (longer than 1 day).

The NREL-designed survey assesses direct and indirect costs of power failure due to:

• Suspended operations, personnel sent home from work, overtime for emergency 

workers

• Base security impacts and infrastructure impacts (heat, water, sewer, traffic lights)

• Curtailing commercial facility operating hours, food spoilage

• Delays or failures in backup generation, fuel for backup generators

• Equipment damage

• Human lives put in danger

• Residential impacts

• For disaster/attack, impact on relief and rescue missions.

Once the survey has been completed by a facility, the data are processed and dollar values are 

assigned to the items such as:

• Loss of productivity ($/hour/person)

 1 “Operational base” refers to DoD bases’ mission to utilize an installation’s military assets for protection, defense, 

or force projection. or for humanitarian relief and rescue missions using military assets as the central command and 

control hubs to coordinate the work of other deployed national resources; and as a source of skilled personnel to 

provide rescue, recovery, medical and other emergency services required by survivors. 
7

• Cost of equipment damaged and repairs ($/equipment)

• Cost of restarting equipment ($/hour/person)

• Cost of human life/lives put to risk (probability x $/person) – mission critical

• Food spoilage [$/kilowatt (kW) peak]

• Backup generator fuel use ($/gallon)

• Loss of ability to communicate (probability x $).

The results of surveys completed at two DoD installations are described in section 5 of this 

report. A literature review was performed and used to create the plots in Figure 2. [12-18] The 

values from the research were converted to U.S. dollar values in 2010.

2 From the literature 

research, it can be observed that the cost ranges for each of the categories are very wide. An 

Electric Power Research Institute report also outlines “...the tremendous variability in costs 

across business establishments.” [19] It is natural that not every sector has similar costs due to 

outage events; and within each sector, not every company, campus, or agency will have similar

costs. However, the CDF methodology is based on customer surveys and hypothetical scenarios, 

and therefore is subjective. It is expected that the larger the database to create sector CDFs, the 

more accurate they will be. 

Figure 2. Literature research on cost of outages (in $/kWpeak) for four sectors: industrial, 

commercial, governmental and institutional and residential

 2 The consumer price index annual average number published by the U.S. Department Of Labor Bureau of Labor 

Statistics, Washington, D.C, was used to compute the inflation rate factor.
8

For that reason, completing an extended outage survey at strategically selected military bases 

would be a logical next step. As more data are compiled, the following questions should be 

considered: 

• Is there an intuitive form for an installation’s CDF curve that varies by mission type?

• How should the military sector CDF curve compare with other sector CDF curves?
9

4 Reliability in Electric Systems

The utility reliability indices described in this section are consumer or end-user oriented indices, 

and depend to different degrees on the number of consumers affected, the total number of 

consumers, the average load connected, and the duration of interruption [20]. These values often 

depend on utility reporting procedures, such as how an end-user is defined, whether by a single 

physical location or the number of meters. 

The three indices described here are the three most popular customer-oriented indices. The 

system average interruption duration index (SAIDI) is defined in equation (2) as the average 

interruption time per customer:

SAIDI = sum of customer interruption durations

total number of customers (2)

The system average interruption frequency index (SAIFI) is defined in equation (3) similarly to 

SAIDI, but describes how often interruptions occur per customer, on average:

SAIFI = total number of customer interruptions

total number of customers served (3)

The average system availability index (ASAI) is defined in equation (4) as the average system 

availability:

ASAI = customer hours of available service

customer hours demanded (4)

The ASAI index describes the time, as a fraction of a year, for which the system is available for 

customer use.

Measures of reliability, such as the metrics described in equations (2)-(4), have been reported by 

electric utilities to public utility commissions (PUCs) for many years. In some cases, utilities also 

report on reliability metrics of the largest annual outage events (i.e., large as defined by number 

of customers affected, duration or both) and their cause, which raises the following questions: 

• Do average metrics, such as SAIDI and SAIFI, capture key criteria for energy 

security planning at DoD installations? 

• What is the risk that DoD wants to assume and plan for? (Section 5 in this report 

includes a review and analysis on reliability metrics for the utilities providing 

electrical power to the case study installations discussed in section 4 of this report.)]

An analytical approach could also be followed to characterize the reliability of an area’s electric 

power system. This approach is more comprehensive and requires generation and load models to 

compute reliability metrics such as expected energy not supplied or loss of load probability. The 

generation model includes the capacities, forced outage rates, failure rates, and repair rates. The 
10

load model includes the probability of load changes and demand. This approach would require 

an extensive data gathering phase to model all the generators and loads in an area. 
11

5 Case Studies: MCAS Miramar and 300 Compound Area at Fort 

Belvoir 

The survey and CDF methodology were completed at two DoD installations: Marine Corps Air 

Station (MCAS) Miramar in San Diego, California, and the 300 Compound Area at Fort 

Belvoir(Army), Virginia. The two military installations have radically different missions and are 

served by different commercial electric utilities. 

5.1 MCAS Miramar CDF and Grid Reliability

In this section, the results from the survey and the CDF for MCAS Miramar are presented first, 

followed by the analysis of its utility power supplier’s reliability. It concludes by calculating the 

value of energy security for Miramar.

MCAS Miramar’s mission is to maintain and operate facilities and provide services and material 

support to the 3rd Marine Aircraft Wing and other tenant organizations. [21] Miramar provides 

world-class facilities with operational and service support excellence to facilitate combat 

readiness and training objectives. Miramar’s vision is that its strategic location in the 

southwestern United States and mission are essential elements in the preparation of Marines and 

sailors for combat.

5.1.1 MCAS Miramar CDF

The survey was completed at MCAS Miramar, and the data were processed to compute the CDF 

shown in Figure 3. 

Figure 3. CDF for MCAS Miramar

0

50

100

150

200

250

300

350

1 4 7 10 13 16 19 22

SCENARIO A

(non-emergency

situation)

SCENARIO B

(emergency

situation)

$/kW peak

hours

MCAS Miramar Customer Damage Function
12

The results may appear to be counterintuitive because the cost of an outage at MCAS Miramar is 

greater under nonemergency conditions, with the exception of an outage lasting 4 hours. As a 

matter of fact, the main driver of an outage cost is the loss of productivity at the base resulting 

from sending personnel home, and this happens to a greater extent under nonemergency 

conditions. In Scenario A, 80% of the personnel at the base are sent home if the outage lasts for 

more than 1 or 2 hours versus 30% sent home during emergency conditions in Scenario B. 

The loss of productivity cost is shown in Figure 4. The cost corresponds to the product of the 

number of people sent home and the average federal salary per hour in San Diego ($25.5/hourperson pre-tax)[22] and the time of unproductiveness. The latter is obtained by calculating the 

probability that an outage occurs during working hours, which is 17%, 33%, and 100% for 4, 8,

and 24 hours respectively. The probability of an outage occurring is multiplied by the duration of 

the outage, for outages lasting 4 and 8 hours, to obtain the unproductive time. For an outage 

lasting 24 hours, the probability is multiplied by 10 hours of working time because a long outage 

can cause delays and further complications. 

The second driver and main difference in cost between scenarios A and B are the risks associated 

with the outage. The risk cost was calculated by estimating the probability of losing a human life 

times the cost associated with a human life. The probability of losing a human life is a subjective 

value determined by MCAS Miramar after several discussions with NREL and the flight-line 

operations team. The cost of a human life used in this study is $6.3 million and was taken from a 

report published by the Department of Transportation and used for statistical analysis of security 

measures. [23]

Figure 4. Loss of productivity in $ in Scenarios A and B for 4, 8, and 24 hours of lack of power 

supply

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

4500000

Loss of productivity

SCENARIO A, 4h

SCENARIO B, 4h

SCENARIO A, 8h

SCENARIO B, 8h

SCENARIO A, 24 h

SCENARIO B, 24 h

$
13

Figure 5 shows the final costs associated with the risks in scenarios A and B for different 

durations of the outage. The risk associated with an outage lasting 1 hour is the same in scenarios 

A and B under the assumption that in the first hour of an outage, there is the same lack of 

information and the probability of risk is relatively high (0.1% of losing a life). However, if the 

outage lasts for 4 hours, the risk increases in Scenario B due to the fact that the base is under an 

emergency condition (10% probability of losing a life). Chaos and riskier decisions can occur in 

the first 4 hours of an outage under a natural disaster or terrorist attack event in the proximities of 

the base. On the contrary, a nonemergency (Scenario A) outage lasting 4 hours would have 

decreased risk, as it is assumed that the base has had the time to resolve the situation (0.01% of 

losing a life). Finally, for outages longer than 4 hours, it is assumed that for both scenarios A and 

B, the probability of risk decreases when compared to the 4-hour outage because the base has 

had the time to get informed and lay out a plan of operation minimizing the risk. However, it is 

still considered that Scenario B will have a higher risk than Scenario A due to the fact that more 

people remain or have to evacuate the base under an emergency situation.

Figure 5. Cost of risk associated to an outage in Scenarios A and B and lasting for 1, 4, 8 and 24 

hours

5.1.2 Reliability of Miramar’s Electric Utility (San Diego Gas & Electric)

An overview and analysis of reliability metrics and utility outage events for San Diego Gas & 

Electric (SDG&E) are discussed next to characterize the reliability of commercial power supply 

at MCAS Miramar. 

The SDG&E Electric System Reliability Annual Report for 2011 was prepared in response to 

California PUC (CPUC) Decision 96-09-045. [24] The CPUC decision established additional 

reliability recording, calculation, and reporting requirements for SDG&E. All statistics and 

$0.00

$100,000.00

$200,000.00

$300,000.00

$400,000.00

$500,000.00

$600,000.00

$700,000.00

$800,000.00

$900,000.00

$1,000,000.00

Risk

SCENARIO A, 1h

SCENARIO B, 1h

SCENARIO A, 4h

SCENARIO B, 4h

SCENARIO A, 8h

SCENARIO B, 8h

SCENARIO A, 24 h

SCENARIO B, 24 h
14

calculations include forced transmission, substation, and distribution outages, and exclude 

planned outages. Forced outages are those that are not prearranged, and sustained outages are 

those outages that lasted 5 minutes or more in duration, while momentary outages are those 

outages that lasted less than 5 minutes in duration.

The measurement of each reliability performance indicator excludes CPUC major events and 

events that are the direct result of failures in the independent system operator (ISO)-controlled 

bulk power market, or non-SDG&E owned transmission and distribution facilities. A major 

event is defined in CPUC Decision 96-09-045 as an event that meets at least one of the following 

criteria:

• The event is caused by earthquake, fire, or storms of sufficient intensity to give rise to 

a state of emergency being declared by the government.

• Any other disaster not in the previous bullet that affects more than 15% of the system 

facilities or 10% of the utility’s customers, whichever is less for each event.

A summary of 2011 performance is presented in Table 1, where SAIDI is given in minutes and 

SAIFI in customer interruptions, both per customer per year. [24] Including major events, in 

2011, the average annual outage interruption per customer is approximately 9 hours and 28

minutes, and the average annual number of interruptions per customer is 1.5. The 10-year 

average reliability metrics, including major events, is 2 hours and 35 minutes for outage 

interruption per customer (SAIDI) and 0.75 for the number of interruptions per customer 

(SAIFI).

Table 1. SDG&E Reliability Indices in 2011

Figure 6 shows SDG&E’s SAIDI and SAIFI values from 2000 to 2011 and shows that there is no 

trend in values increasing or decreasing over time. Note that the 2003 and 2007 peaks in SAIDI 

values correspond to the firestorms that occurred in the state of California, and the peak in 2011 

corresponds to the Pacific southwest electrical outage of September 8. The average number of 

customers affected and average duration of interruption of the annual 10 largest annual events 

from 2001 to 2010 are shown in Figure 7; and once more, the data show no clear trend in the 

number of customers affected or duration of the outages in the last 10 years to conclude that the 

numbers are increasing or decreasing over time. However, the data show that large events can 

last between 10 and 90 hours. 
15

Figure 6. SDG&E’s SAIDI and SAIFI values from 2000 to 2011

Average metrics, such as SAIDI and SAIFI, may not capture key criteria for energy security 

planning in DoD installations and the question becomes: what is the risk that DoD installations 

want to assume? Figure 8 shows the statistical frequency distribution of the duration of the 100 

largest events over the past 10 years and could help an installation assess its energy security 

requirements. For example, is a 5% chance of having an outage event that lasts for 90 hours 

acceptable? Is more than a 40% chance of having an outage event lasting between 10 and 20 

hours acceptable? Other historical data that would help answer these questions would include the 

number of large outage events that an installation experiences a year. This information could be 

used to compute a facility’s probability of experiencing a large outage event. Unfortunately, 

military installations typically do not keep records of outage events.

Ten years of data for the 10 largest outages per year by cause are summarized in Figure 9. The 

statistics show that the average interruptions by cause are, in a 10-year average, six interruptions 

per year due to non-utility causes and three interruptions per year due to utility-related events. 

Non-utility causes include weather related issues such as rain, wind, and lightning storms; 

external factors causing equipment failures such as vehicle, vegetation, or animal contact; and 

California ISO (CAISO) related issues including load curtailment and increases in peak load that 

are expected to keep occurring in following years. These causes do not entirely depend on the 

utility’s reliability of power supply. Utility-related events include SDG&E equipment failure 

outage events that might be prevented or mitigated by increasing preventive maintenance on 

utility-owned equipment. 

To make matters worse, CAISO plans to take the San Onofre nuclear power plant generating 

units offline for maintenance, which, according to the current planning standards, would put the 

San Diego area at risk of outages. [25]

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0

100

200

300

400

500

600

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

SAIDI (min) SAIFI (outage)

min # outages
16

Figure 7. Average customers affected and duration of interruption of the largest annual 10 events 

from 2001 to 2010

Figure 8. Frequency distribution of outage duration of 100 largest events from 2001 to 2010

0

10

20

30

40

50

60

70

80

90

100

0

10000

20000

30000

40000

50000

60000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Average customers affected Average customer interruption (hours)

# customers hours

0%

5%

10%

15%

20%

25%

Frequency in %

Duration of interruption in hours
17

Figure 9. Number of 10 largest outage events by cause

5.1.3 CDF to Support Financing Energy Security Projects at MCAS Miramar

The previous discussion on utility reliability metrics and outage events is useful to come up with 

a representative annual outage duration to be used to compute “energy savings” from increasing 

the energy security at MCAS Miramar. The Value of Electrical Energy Security (VEES ) values 

are obtained as follows in equation (5) below:

VEES = Annual # outages ∗ CDF(Duration) � $

kWpeak� ∗ Peak[kW] (5)

Where Annual # outages is the annual number of outages, CDF(Duration) is the value in 

$/kWpeak of an outage cost obtained from the CDF curve for a specified Duration of the 

interruption, and Peak is the peak-demand of the installation. 

Table 2 below shows annual VEES values at MCAS Miramar using Scenario A in the CDF 

curve in Figure 3 that could be used to support an energy security project depending on the 

annual number and duration of outages. The first case uses the average SAIDI and SAIFI values 

over the last ten years, Case 2 uses SAIDI and SAIFI values in 2011, and finally the third case is 

calculated assuming that the base experiences a large 5 hour outage every two years. An 

example of using equation (5) to obtain the VEES value in Case 3 in Table 2 is shown in 

equation (6) below:

0.5 ∗ 75[ $

kWpeak] ∗ 13000[kW] = $487,500 (6)

0

1

2

3

4

5

6

7

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Weather SDG&E equipment failure External factors ISO related
18

Table 2. Annual VEES Values at MCAS Miramar Under a Non-Emergency Scenario

Case number Annual number of 

outages 

Annual outage 

duration

VEES in $/year

1. SDG&E 10 year 

average SAIFI and 

SAIDI

0.75 2.6 hours $ 390,000

2. SDGE 2011 SAIFI 

and SAIDI

1.5 9.5 hours $ 2,925,000

3. MCAS Miramar 

experiencing a 5 hour 

outage every 2 years

0.5 5 hours $ 487,500

The VEES numbers above in Table 2 represent the potential value of an energy security project

that would provide 100% reliable power in case of commercial power interruption. A de-rating 

factor to the VEES number should be applied depending on the degree of reliability provided by 

the energy security project.

Note that in the computation of the CDF, microgrid capabilities are not taken into consideration. 

For instance, if MCAS would have the capability to island during a commercial power outage, 

the installation could become a secure regional operational center for the Marine Corps. The 

value to an energy security project under that assumption would increase considerably, 

particularly under an emergency situation (Scenario B). The final decision on the value of energy 

security needs to come from MCAS Miramar. It needs to set its acceptable level of reliability. 

The statistics show that outage events can be long in duration, and the base needs to assess 

acceptable risk. 

5.2 300 Compound Area – Fort Belvoir – CDF and Grid Reliability

The 300 Compound Area in Fort Belvoir is mainly dedicated to research and development. Due 

to the sensitivity of its testing equipment, the Night Vision & Electronic Sensors Directorate 

(NVESD) is particularly impacted by power outages. Any electrical disturbance can destroy 

sensitive testing equipment, nullify tests being performed over long periods, and/or substantially 

delay the deployment of new technology to the warfighter. In addition to the NVESD facilities, 

there are tenants in the 300 Compound Area that directly support the warfighter and for which 

power outages can cause a risk to critical operations. 

5.2.1 300 Compound Area CDF

The survey was also completed at the 300 Compound Area of the Army base of Fort Belvoir, and 

the data were processed to compute the CDF shown in Figure 10. Once again, the results may 

appear to be counterintuitive because the cost of an outage is greater under nonemergency

conditions, with the exception of an outage lasting less than 3 hours. The main driver of an 

outage cost is the loss of productivity at the base based on personnel being sent home. Under 

non-emergency conditions in Scenario A, almost 100% of the personnel at the base are sent 

home if the outage lasts for more than 1 or 2 hours versus 67% in Scenario B. 
19 

Figure 10. CDF for the 300 Compound Area, Fort Belvoir, Virginia 

Among the main items contributing to the cost of an outage at the NVESD facilities is the 

probability of interrupting experiments in their state-of-the-art laboratories. For example, 

experiments requiring the vacuum chamber in operation are interrupted by the loss of 

commercial power supply, and the time wasted to restart the process, as well as data losses, has a 

substantial adverse impact on the tenants. 

The loss of productivity cost is a product of the number of people sent home, their average 

hourly salary (300 Compound Area- $80/hour-person3

), and the time they are unproductive. The 

latter is obtained by calculating the probability that an outage occurs during working hours, 

which is 17%, 33%, and 100% for 4, 8, and 24 hours respectively. The probability of an outage 

occurring is multiplied by the duration of the outage—for outages lasting 4 and 8 hours—to 

obtain the unproductive time. For an outage lasting 24 hours, the probability is multiplied by 10 

hours of working time because a long outage can cause delays and further complications. In 

addition, there is an added cost due to the time wasted to restart the process as well as data losses 

for the tenants of the NVESD. It was estimated that on average, an outage would interrupt an 

experiment equivalent to 1 month of five researchers’ time. 

The difference between scenarios A and B is due to fewer personnel sent home in Scenario A 

and a higher risk of putting a human life in danger in Scenario B. The risk in the 300 Compound 

Area is increased because the tenants support critical life action overseas, and an outage may 

compromise critical missions due to disrupted communications. 

5.2.2 Reliability of Fort Belvoir’s Utility (Dominion Virginia Power) 

An overview and analysis of reliability metrics and utility outage events for Dominion Virginia 

Power are discussed next. The reliability metrics and data presented were provided by the 

 

3

 Fully burdened federal cost of employment after-tax 

0

100

200

300

400

500

600

700

800

1 4 7 10 13 16 19 22

SCENARIO A

(non emergency

situation)

SCENARIO B

(emergency

situation)

$/kW peak

hours

Fort Belvoir 300 Compoun Area - Customer Damage Function
20

Division of Energy Regulation of the Virginia Commonwealth State Corporation Commission. 

Customer interruptions include all service interruptions greater than 2 minutes, and momentary 

outages are not included. 

Figure 11 shows Dominion Virginia Power SAIDI and SAIFI values from 2000 to 2011 

including major storms. SAIFI values on range from 1.5 to 3.7 outages per customer. As seen in 

Figure 11, SAIFI has no clear tendency to increase or decrease over time. (The major blackout in 

2003 shows a large aberration to this trend line.) The average value of approximately two

outages per customer for this metric is considered a high value when compared to other utilities. 

With regards to SAIDI, the average duration of outages per customer range from 180 minutes to 

1,500 minutes, with an average over the last ten years of 4 hours and 48 minutes excluding the 

spike in 2011 and 6 hours and 33 minutes including the spike in 2011. Note that the dramatic 

increase in SAIDI in 2011 may be due to the outage cause by Hurricane Irene. 

Figure 11. Dominion Virginia Power SAIDI and SAIFI values from 2000 to 2011

Figure 12 shows the percentage of customers affected by number of outages. The percentage of

customers affected by more than 10 outages is very low, between 0.1% and 0.4 %. The 

percentage of customers affected becomes more significant within four-to six outages, and 

noticeably increases for customers affected between one to three outages per year. Figure 13 

shows the percentage of customers affected by outage cause.

0

0.5

1

1.5

2

2.5

3

3.5

4

0

200

400

600

800

1000

1200

1400

1600

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

SAIDI SAIFI

min # outages
21

Figure 12. Percentage of customers affected by more than 10, 4-6, and 1-3 outages

Figure 13. Percentage customers by outage cause

5.2.3 CDF to Support Financing Energy Security Projects at the 300 Compound

Area

Average metrics in this case show the unreliability of commercial power supply to the 300 

Compound Area in Fort Belvoir. In a report, “Achieving Energy Security Utilizing a Microgrid in 

Tri-generation Mode at Fort Belvoir 300 Area Compound” [26], the U.S. Army Research 

Development and Engineering Command’s Communications-Electronics Research, documents 

the ongoing disruptions in electricity supply in the 300 Compound Area. From 2006 to 2010, the 

300 Compound Area has experienced 37 electrical outage events between 6 a.m. and 6 p.m., 

0.0000%

10.0000%

20.0000%

30.0000%

40.0000%

50.0000%

60.0000%

70.0000%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Percent of Customers with > 10 outages Percent of Customers with 4-6 Outages

Percent of Customers with 1-3 Outages

0%

5%

10%

15%

20%

25%

30%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

% Customer Hours Out – Trees % Customer Hours Out – OH Mat’l % Customer Hours Out – UG Mat’l

% Customer Hours Out – Public % Customer Hours Out – Weather % Customer Hours Out – Misc.

% Customer Hours Out – Company % Customer Hours Out – Bulk Pwr.
22

lasting a total of 115 hours. In addition, there were 34 outages totaling 152 hours between 6 p.m.

and 6 a.m. The financial impact in “Achieving Energy Security Utilizing a Microgrid in Trigeneration Mode at Fort Belvoir 300 Area Compound” was calculated based on the cost of all 

employees and contractors in the 300 Compound Area during the time of the outages. [26] The 

calculation takes into account that a certain percentage (20%) of all personnel would be offsite 

during any outage and reduces the calculation accordingly. 

Table 3 shows annual values of energy security at the 300 Compound Area. Using the CDF 

function in Figure 10 and utility 10 year average metrics in Case 1, the expected annual losses 

are approximately $2.23 million per year. In Case 2, Dominion Virginia Power 2011 reliability 

metrics are used and the annual losses add up to $7.56 million. Finally, in Case 3, the actual 

number and duration of outages are used at the 300 Compound Area, from “Achieving Energy 

Security Utilizing a Microgrid in Tri-generation Mode at Fort Belvoir 300 Area Compound,”

[26] and assuming that in 2010 the installation experienced eight 2-hour long outages, the annual 

losses for FY 2010 are $3.96 million. 

An example of using equation (5) to obtain the VEES value in Case 1 is shown below in 

equation (7):

2.2 ∗ 225[ $

kWpeak] ∗ 4500[kW] = $2,227,500 (7)

Table 3. Annual VEES Values at 300 Compound Area (Fort Belvoir) Under a Non-Emergency 

Scenario

Case number Annual number of 

outages 

Annual outage 

duration

VEES in $/year

1. Dominion Virginia 

Power 10 year average 

SAIFI and SAIDI

2.2 6.6 hours $2,227,500

2. Dominion Virginia 

Power 2011 SAIFI and 

SAIDI

2.4 24.8 hours $7,560,000

3. 300 Compound Area 

actual number and 

duration of outages in 

2010 

8 2 hours $3,960,000

Once again, a de-rating factor to the VEES number should be applied depending on the degree of 

reliability provided by the energy security project. The final decision on the value of energy 

security needs to come from the installation itself. It needs to set an acceptable level of 

reliability. The statistics show that outage events can be long in duration, and the base should 

assess if it would be acceptable to be exposed to such risk.
23

5.3 Comparison of the Two Case Studies’ CDF Curves

Below is a comparison of MCAS Miramar’s CDF with the 300 Compound Area’s CDF:

• Outage cost:

o Scenario A– The outage cost in $/kW peak is higher for the 300 Compound Area

at Fort Belvoir than for MCAS Miramar. The main driver of the outage cost for 

both sites is the loss of productivity. The average salary used at the 300 

Compound Area is higher than the average government salary in San Diego used 

at MCAS Miramar. Secondly, the fully burdened federal cost of employment after 

tax was used at the 300 Compound Area versus the pre-tax wage employment 

cost at MCAS Miramar. In addition, the 300 Compound Area is a smaller site 

than MCAS Miramar, and experiences utility outages regularly, making data 

collection an easier task. The personnel at the 300 Compound Area had more 

detailed data on utility outage costs.

o Scenario B – The outage cost in $/kW peak is higher for the 300 Compound Area

at Fort Belvoir than for MCAS Miramar. Once more, the loss of productivity cost 

used for the 300 Compound Area is higher than for MCAS Miramar.

• CDF form: 

o Scenario A – The cost of a 1 hour or less outage at MCAS Miramar has little 

economic impact versus at the 300 Compound Area where a short interruption of 

power causes significant power losses. The outage cost for both sites trend to be 

linear with respect the duration of the outage.

o Scenario B – The form of the MCAS Miramar CDF curve is different from the 

linear trend of the 300 Compound Area CDF curve. Under an emergency situation 

at MCAS Miramar, critical personnel and aircraft would be evacuated to an 

alternate base. For that, it was assumed that the risk increases in the first 4 hours 

of an outage when the chaos and lack of information types of situation may affect 

the safety of the evacuation procedure of the base. After 8 hours, it is assumed 

that MCAS Miramar personnel and operations would have been evacuated.
24

6 Conclusion and Future Work

This paper describes a methodology to identify and value non-energy savings from energy 

security projects. The methodology is based on: (1) completing a site survey to collect data on 

two hypothetical outage scenarios lasting for different durations to compute a CDF, (2) 

characterizing the reliability of the commercial power supply at the site, and (3) estimating on an 

annual basis the cost of utility outages. The annual cost of a commercial power supply that has 

low reliability can be used as non-energy savings provided by energy security projects to 

increase the return on investment of energy security projects such as microgrids. 

Valuing energy security is an increasingly important component for financing energy projects. In 

the military sector, the ability to assess the value of energy security could play a critical role in 

prioritizing mission critical energy projects. The CDF concept and energy security valuation 

have a variety of applications. An established CDF military function could be used as a tool 

when making investment decisions regarding energy security projects. It might assist with 

justifying project financing, and/or comparing the relative benefits of several projects. 

Additionally, the process of working with the CDF questionnaire and establishing all of the 

assumptions necessary to develop the CDF curve can expose critical vulnerabilities at an 

installation that might otherwise only be detected through an outage. 

Should the CDF military function be of interest to the DoD, NREL’s proposed energy security 

evaluation process could be refined and adopted as a method to value energy security. The 

methodology also could be developed into a tool for facility and regional energy managers, DoD 

portfolio and enterprise managers, or both to inform their decisions related to energy security 

projects.

The methodology presented in this paper will continue to evolve as there are lessons learned 

from its application. It is recommended that military installations consistently record outage 

events and their consequences to the mission. The next steps needed to refine and improve this 

methodology for application in the DoD would be to:

• Develop further processing of the survey data to refine probabilities of events 

occurring and their associated cost with DoD personnel

• Extensively survey in strategically selected bases and analyze, compare CDF curves 

and adjustment

• Create a tool to compute the potential annual savings from increasing energy security. 

The tool would include survey processing capabilities, a CDF curve database, and a 

utility and regional reliability database among others. 

• Explore further methodologies, such as the probabilistic risk assessment used by the 

U.S. Nuclear Regulatory Commission, to estimate risk by computing real numbers to 

determine what can go wrong, how likely is it, and what are its consequences.

• Work with DoD, the Office of Management and Budget, and the National Institute of 

Standards and Technology to address how energy security valuation techniques could 

ultimately be used for the economic justification of energy security projects, both 

government funded and third-party financed.
25

• Research the added value of site-specific energy security projects to the surrounding 

community such as other DoD installations and/or critical civilian sites. 
26

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