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Impact of Distributed Energy 

Resources on the Reliability of 

Critical Telecommunications 

Facilities 

Preprint 

D.G. Robinson 

Sandia National Laboratories 

D.J. Arent 

National Renewable Energy Laboratory

L. Johnson 

Sprint Power Standards 

To be presented at INTELEC 2006 (International 

Telecommunications Energy Conference) 

Providence, Rhode Island 

September 10–14, 2006 

Conference Paper 

NREL/CP-620-40220 

June 2006 
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Impact of Distributed Energy Resources 

on the Reliability of Critical 

Telecommunications Facilities 

David G. Robinson 

Sandia National Laboratories 

Albuquerque, NM 87111 

drobin@sandia.gov 

Douglas J. Arent 

National Renewable Energy Laboratory 

Golden, CO 80401 

douglas_arent@nrel.gov 

Larry Johnson 

Sprint Nextel 

Overland Park, KS 66251 

Larry.Johnson@sprint.com 

Abstract - This paper documents a probabilistic risk 

assessment of existing and alternative power supply systems at a 

large telecommunications office. The analysis characterizes the 

increase in the reliability of power supply through the use of two 

alternative power configurations. Failures in the power systems 

supporting major telecommunications service nodes are a main 

contributor to significant telecommunications outages. A logical 

approach to improving the robustness of telecommunication 

facilities is to increase the depth and breadth of technologies 

available to restore power during power outages. Distributed 

energy resources such as fuel cells and gas turbines could 

provide additional on-site electric power sources to provide 

backup power, if batteries and diesel generators fail. The 

analysis is based on a hierarchical Bayesian approach and 

focuses on the failure probability associated with each of three 

possible facility configurations, along with assessment of the 

uncertainty or confidence level in the probability of failure. A 

risk-based characterization of final best configuration is 

presented. 

I. BACKGROUND

Telecommunications has been identified by the Department 

of Homeland Security as a critical infrastructure for the 

United States. Failures in the power systems supporting major 

telecommunications service nodes are a main contributor to 

major telecommunications outages, as documented by 

analyses of Federal Communications Commission (FCC) 

outage reports by the National Reliability Steering Committee 

(under auspices of the Alliance for Telecommunications 

Industry Solutions). There are two major issues that have 

increasing impact on the sensitivity of the power distribution 

to telecommunication facilities: deregulation of the power 

industry and changing weather patterns. 

There is no incentive for the power utilities to make their 

systems more robust to these disturbances; rather, the industry 

has moved to change the reliability-reporting requirements to 

avoid financial penalties [1]. As noted by Sandia researchers 

in 2000, telecommunication companies, emergency services, 

etc. – which are dependent on a reliable source of power – 

need to be prepared for increased uncertainty in the operation 

of the national electrical infrastructure [2]. 

One approach toward improving the robustness of the 

power systems supporting telecommunications offices is to 

improve the reliability of the necessary supply of power. 

Current best practices involve a combination of on-site 

battery backup (for short, intermittent power interruptions) 

and diesel generators (for longer-term interruptions). 

Occasionally, universal power system (UPS) technologies are 

also used for specific data communications equipment 

backup. 

A logical approach to improving reliability would be to 

increase the depth and breadth of technologies available to 

restore power in the face of power outages. Distributed 

energy resources such as photovoltaic (solar) cells, wind 

generators, fuel cells, and gas turbines could provide an 

additional on-site electric power source for backup power, if 

batteries and diesel generators fail. But does the diversity in 

power sources actually increase the reliability of offered 

power to the office equipment, or does the complexity of 

installing and managing the extended power system induce 

more potential faults and higher failure rates? 

1
The goal of this research was to perform probabilistic risk 

assessments (PRAs) on an existing power configuration for a 

large telecommunications center with diesel generator and 

battery backup, and for two alternative power configurations 

adding natural gas turbines as a primary power source. The 

product from the study was an analysis of probability of 

failure associated with each of the three facility 

configurations, along with an assessment of the uncertainty or 

confidence level in the failure probability estimate. 

II. OPERATIONAL SCENARIO

The location of interest for this effort was assumed to be a 

remote switch facility with limited physical access via roads. 

The utility power at this location is particularly unreliable – in 

the most recent four-year period, there were 35 utility outages 

(compared with a national average of approximately three 

outages per year for 2001 and 2002). 

The telecommunication center consists of a main facility 

supported by two switch bays. Power is supplied to the 

facility primarily through a traditional utility drop. In the 

event that utility power is lost, at least two of the three onlocation 1.5MW diesel generators must function. In addition 

to the two generators, the battery system must be available to 

provide minimal support for an additional 4 hours in the event 

that the generators fail. 

For this analysis, an (almost) worst-case scenario was 

assumed. This scenario represents a documented situation that 

will likely exist after a severe-weather event. If utility power 

is lost, restoration of power will not occur within a time 

period that will have a significant impact. Similarly, if a diesel 

generator or gas turbine fails, minor repair is possible, but 

replacement of the entire generator (or turbine) is not an 

option. The only available fuel for the generators is what is 

currently stored on-site (assumed to be 72 hours worth for 

each generator); fuel lost through consumption or 

contamination cannot be replaced within 4 hours. The 4hour 

limit is used because the batteries are required to provide 

minimum support for the plants for an additional 4 hours, in 

the event that power is lost from the generators. 

Figure 1 depicts the time line of events for the Base Case 

scenario: utility power fails, generators start with some 

probability, fail with some likelihood, and run out of fuel after 

72 hours. Batteries provide backup emergency power for a 

maximum of 4 hours (or until they fail during operation). 

III. ANALYSIS APPROACH

The standard methods and data used to estimate the 

reliability of power systems are documented in the IEEE 

Standard 493-1997 IEEE Recommended Practice for the 

Design of Reliable Industrial and Commercial Power Systems 

[3]. However, a traditional approach is not applied because of 

the sequential, time-dependent nature of how the system is 

operated. 

Figure 1 depicts a typical sequence of events where the xaxis is a timeline. Each event logically follows at the 

conclusion of the previous event. For example, for the Base 

Case, the time that the utility is available TU is a random 

variable described by an exponential distribution conditioned 

on the rate of utility power failure: f (T | λU ) = exp[−λU t]. 

The rate at which the utility power fails, λU, is not known with 

certainty; therefore this parameter is characterized as a 

random variable: λ ~ g(λ |α,θ). Because of the conditional 

structure of the distributions for T and λU Gibbs, sampling 

must be used when simulating the power availability. The 

WinBugs computer software was used for all simulations 

conducted in this report [4] 

Similar to the utility reliability, values for diesel generator 

operation reliability, TG, and battery reliability, TB, are 

simulated. The values are then combined to get the time until 

system failure, Tbase=TU+TG+TB. Figure 1 depicts the typical 

time line of events associated with what is referred to in the 

following discussion as the Base Case. 

The following sections address each of the elements of the 

various scenarios. The Base Case is a function of the 

availability of power from the utility, the backup diesel 

generators, and the batteries. The additional alternatives 

explored include the addition of natural gas turbines from 

Capstone and Kawasaki as described in more detail below. 

IV. ANALYSIS ELEMENTS

The following sub-analyses are based on actual data related 

to characteristics of the individual elements. 

A. Utility Reliability 

As suggested earlier, it is assumed that the length of time 

that a utility power is up/operating, TU, is an exponentially 

distributed random variable with parameter λU: 

f (T | λU ) = exp[−λU t]. The parameter λU represents the 

number of utility interruptions per year. However, the 

estimate ˆ λ = 8.75 represents a simple average rate at which 

events occurred during the four years; in reality, there will be 

a great deal of variation or uncertainty about the rate, λU, at 

which events might occur each year. 

Figure 1. Base Case Time Line of Events. The figure shows the event 

sequence that is analyzed for the Base Case, where the x-axis is time. 

 Fitting a distribution to utility interruption rates resulted in 

 λU being a gamma distributed random variable: 

f (λU |α,β) = 1

βα Γ(α)

λU

α−1 exp − λU

β

⎡

⎣ ⎢ 

⎤ 

⎦

⎥ with parameters 

α = 19.14 and β = 0.457. The resulting probability density 

function (PDF), describing the uncertainty in the number of 

utility outages each year, is presented in Figure 2. 

2
Figure 3. depicts the resulting PDF and cumulative 

distribution function (CDF) of the distribution for utility 

reliability. 

B. Generator Reliability 

Failure of a diesel generator is defined as a malfunction of 

the generator or associated support subsystems that prevent 

the generator from starting and running when a demand has 

occurred. Failures can occur in two modes: 

• Failure to start (FTS) – A failure of the generator to 

either manually or automatically start on a bus undervoltage condition, reach rated voltage and speed, close 

the output breaker, or sequence safety-related 

electrical loads onto the respective safety-related bus. 

• Failure to run (FTR) – A failure of the generator to 

continue to supply power to its respective safetyrelated electrical bus given the generator successfully 

started. 

Using IEEE Std 493 [3], an estimated mean failure rate for 

diesel generators is 0.0001 failure/hour with a mean time to 

repair of 3.9 hours. However, the expected failure rate for 

diesel generators used in nuclear power plants is 

approximately 0.0223 failures/hour. In addition, for diesel 

generators at nuclear power plants the Pr{failure to start} = 

0.0241 per demand [5,6]. 

Figure 2. PDF (bars, left axis) and CDF (line, right axis) of the Number 

of Utility Outages per Year 

Using the NUREG/CR-5500 data and the Gold Book as 

starting points, the uncertainty in the generator failure rate can 

be characterized. 

Figure 4. PDF (bars, left axis) and CDF (line, right axis) for Generator 

Reliability (TG) 

Figure 3. PDF (bars, left axis) and CDF (line, right axis) for Utility 

Reliability (TU) 

Figure 5. Battery Reliability Summary (TB) 

This can then be coupled with the failure-to-start likelihood 

and the probability of available fuel supply. Finally, 

considering that two of the three generators must be 

operational, a simulation was performed – the results are 

depicted in Figure 4. The sharp increase at 72 hours is due to 

the limited source of on-site fuel. 

C. Battery Reliability 

Battery backup consists of a bank of valve-regulated leadacid (VRLA) batteries. VRLA batteries are a wellestablished technology used as a backup power source for 

short periods of time. The facility has two switch plants where 

Plant 1 has a 10,000 amp shunt and has 28 strings of C&D 

HD-1300; and Plant 2 has a 15,000 amp shunt and has 8 

strings of C&D HD-1300 and 8 strings of East Penn AVR95-

33. 

A reliability assessment for the batteries was performed in 

a manner similar to that accomplished for the utility and 

generator reliability assessments. The results are depicted in 

Figure 5. 

3
D. Base Case Summary 

Table 1 summarizes the results for the Base Case 

configuration; recall that this represents a summary of the 

reliability characteristics of the current configuration at the 

telecommunications facility under the operational scenario 

assumed. Figure 6 depicts the results graphically. 

III. DISTRIBUTED ENERGY RESOURCES

The following sections discuss the analysis of two 

alternatives to traditional utility power. These alternatives are 

based on the use of natural gas turbines to provide generation 

of on-site power at the telecommunications facility. Two 

configurations of interest were a single Kawasaki turbine and 

an arrangement of four pallets, each with six Capstone natural 

gas microturbines. Both of these configurations depend on a 

supply of natural gas; therefore, the reliable supply of the 

natural gas is critical. Section A addresses the supply of 

natural gas, and Sections B and C document research into the 

two turbine configurations. 

The analysis of the operational scenario proposed involves 

relying on the turbine as the primary facility power source and 

the use of the Base Case configuration as the backup power 

supply. This is depicted in Figure 7. 

A. Natural Gas Pipeline Reliability 

The Office of Pipeline Safety (OPS), within the U. S. 

Department of Transportation, Pipeline and Hazardous 

Materials Safety Administration (PHMSA), has overall 

regulatory responsibility for hazardous liquid and gas 

pipelines under its jurisdiction in the United States. Federal 

safety standards are described in the U.S. Code of Federal 

Regulations (CFR), Title 49 Transportation, Parts 190 – 199. 

There are more than 2 million miles of pipelines that support 

the movement of hazardous liquids, natural gas, and propane. 

The two types of natural gas pipelines are transmission and 

distribution. The focus of this effort involves the 1.86 million 

miles of pipeline involved with distribution of natural gas. 

There are a number of failure modes for pipelines, most of 

which are unique to the area where the pipeline is located – 

including corrosion, external forces (such as excavation or 

natural forces), and material failure. Characterization of the 

probability of failure of a particular distribution line can be 

extremely complicated. For example, failure due to corrosion 

is dependent on factors such as the type and condition of the 

pipe’s coating, the effectiveness of corrosion control 

equipment, and the soil conditions surrounding the pipe. 

Alternatively, the probability of pipeline damage as the result 

of third-party damage depends on, for example, the extent and 

type of excavation or agricultural activity along the pipeline 

right-of-way and the depth of cover over the pipeline. 

According to industry input, natural gas distribution system 

pipeline could be extended about 4 miles (6.437 km), 

utilizing a 4" (101.6 mm) diameter feeder line connected to 

an on-site turbine compressor station, with direct connection 

to the power systems. 

Historically, the dominant failure mode for natural gas 

distribution pipeline is a result of external factors involving 

third parties, e.g., excavation. The rate at which these failures 

occur are a function of the buried depth, how well the pipeline 

is marked, the density of the population, and the land use of 

the area of interest. The factors can be used to augment the 

basic failure rate established for pipes of a particular diameter 

[7]. 

Contribution of external factors to the failure rate of the 

pipeline: λ Ext = λ dKdcKwtKpdKpm d where λ d is the basic 

failure rate for pipes of diameter d, and the correction factors 

 account for failure due to third-party 

activities: buried depth, wall thickness, population density, 

and prevention method. Assuming a distribution pipe 

diameter of 102 mm, an estimate of the basic failure rate is 

Kdc ,Kwt ,Kpd ,Kpm

λ d = 0.218 failures/1,000 km-year (Table 2, [7]). 

It is assumed that, for the facility in question, the area is 

rural; Kpd = 0.81 and best protection method is employed 

and the length of pipe is 6.44 km. Estimates of the failure 

rates are from [8] and [7]. 

Specifically, the following assumptions are made in the 

analysis: 

Figure 6. PDF (bars, left axis) and CDF (line, right axis) for Base Case 

TABLE I.

BASE CASE RELIABILITY SUMMARY (TG) 

Figure 7. Time Line for DER Analyses 

4
• Kdc - worst case is to assume depth of cover is less 

than 0.91 m: Kdc = 2.54 ; best case is to assume that 

depth of cover is greater than 1.22 m: Kdc = 0.54 . 

• Kwt - worst case is to presume that the wall 

thickness of pipe will be no more than the minimum 

of 4.8 mm: Kwt = 1.0 ; and best case assumes that the 

thickness is greater than 4.8 mm: Kwt = 0.2 

• - the location of the telecommunication center 

appears to be rather remote (7 miles of dirt road). 

Assume that the area is rural (best case): 

; and the worst-case assumption is that 

the distribution pipe is laid through a densely 

populated area: . 

Kpd

Kpd = 0.81

Kpd = 18.77

• - worst- case situation implies that there are 

only marker posts to delineate the location of the 

distribution line: ; while for best- case, 

additional methods are used: . 

Kpm

Kpm = 1.03

Kpm = 0.91

Best/worst case (failures/year): 

λ best = λ dKdcKwtKpdKpm d

= (0.000218)(0.54)(0.2)(0.81)(6.44)(6.44)

= 0.000112

λ worst = λ dKdcKwtKpdKpmd

= (0.000218)(2.54)(1.0)(18.77)(1.03)(6.44)

= 0.0.06894

The probability of failure in one year is then assumed to be 

in the range: 

 Fng = {1− exp[−0.000112] = 0.00012,

1− exp[−0.068984] = 0.06662}

The failure rate of pipeline is assumed to be a random 

variable, characterized by a lognormal distribution, with a 5% 

lower limit of 0.000112 and an upper 95% limit of 0.06894. 

Figure 8 depicts the final distribution for the time to failure of 

the natural gas pipeline. As with other elements in the system, 

the time to failure for the pipeline is assumed to be an 

exponentially distributed random variable: 

TNG ~ exp[−λ NG t], where λ NG represents the rate at which 

pipeline failures occur. 

B. Capstone Microturbine Analysis 

The Capstone microturbines were investigated as one 

possible alternative-power generation source. Emissions are 

low: approximately 2.3 parts per million on dry volume basis 

(ppmvd) NOx per generator for 75% loading, and about 2.0 

ppmvd NOx for 100% loading. 

The configuration of Capstone turbines consisted of four 

pallets of six turbines for a total of 24 turbines. Each turbine 

is capable of 60 kW of output; and, for efficiency purposes, 

the turbines are exercised at 90% of their capacity. The total 

available power is then 1.296 MW. The current maximum 

demand is approximately 1.1 MW, leaving an excess capacity 

of 196 kW. At the peak level of loading, only 21 of the 24 

Capstone turbines are needed to supply power for the facility. 

The facility can be supported at a minimum level, if even if 

seven of the microturbines were unavailable. 

According to Capstone engineers, these turbines are 

currently in-place at a variety of locations and have attained 

95% availability. The overall design life is 40,000 hours, and 

Capstone engineers have estimated the mean time to failure 

for an individual microturbine to be 8,000 hours and noted – 

that they expect this to double over during the next few years. 

Scheduled maintenance activities are recommended at 8,000 

and 20,000 hours. The 8,000-hour maintenance is to change 

air and some other filters, and for replacing the igniter. The 

downtime for this service is 3 hours. The 20,000- hour 

service includes the 8,000- hour actions, plus changing of fuel 

injectors. The downtime for this service is 12 hours. 

To characterize the uncertainty in the failure rate for a 

single Capstone microturbine, it was assumed that there was a 

20% chance that the failure rate would be greater than 

λ = 1/8000. Further, it was assumed that there was a 90% 

probability that the failure rate would be less 

than λ =1/16000. Failure probability of the Capstone 

microturbine is assumed to be an exponentially distributed 

random variable: TC1 ~ exp[−λ C1t], where λC1 represents the 

rate at which turbine failures occur. 

Figure 8. PDF (bars, left axis) and CDF (line, right axis) for Natural 

Gas Time to Failure (TNG) 

Figure 9. PDF (bars, left axis) and CDF (line, right axis) for alternative 

configuration w/Capstone Configuration 

5
Using this information and the operational configuration of 

the microturbine array, a cumulative distribution function was 

constructed assuming the Capstone microturbine 

configuration above. Note that these results are sensitive to 

the operation and maintenance philosophy adopted; it is 

possible that more reliable configurations could have been 

found with proper application of optimization algorithms. 

C. Kawasaki Turbine Analysis 

A second power-system configuration involved the use of a 

single Kawasaki natural gas turbine. A typical turbine for this 

application is the GPB 15X 1.5 MW turbine. With the 

addition of a combustion/catalyst system, the emissions of the 

Kawasaki turbine are low: approximately 3 ppmvd NOx (15% 

O2) over a broad range of power. 

Kawasaki provided a mean time to failure (MTTF) = 

200,974 hours; and a mean time to repair (MTTR) = 3.1 

hours; based on a sample of 150 installed units. To 

characterize the uncertainty in the failure rate for a Kawasaki 

turbine, it is assumed that there was a 10% chance that the 

failure rate would be greater than λ K = 1 / 200974 . This 

number is exceptionally high, and to be conservative it is 

assumed that the median failure rate was twice the value 

reported by Kawasaki. This implies that there is a 50% 

chance that the failure rate might be as high as 2 × λ K

As with the Capstone microturbine, the failure rate is 

assumed to be a random variable characterized with a 

lognormal distribution, and this distribution is fully 

characterized by the two failure rates assumed above. Finally, 

the time to failure for a single Kawasaki turbine is assumed to 

be an exponentially distributed random variable: 

TK ~ exp[−λ K t], where λ K represents the rate at which 

turbine failures occur. 

Figure 10 depicts the time to failure of the facility, given 

the installation of a single Kawasaki natural gas turbine. 

VI. RESULTS 

Figure 11 summarizes the results of the three analyses: 

Base Case, alternative configurations that included the 

Capstone microturbine array, and the single Kawasaki 

turbine. The overlay provides the capability to make a riskbased decision of the relative reliability benefits of the three 

alternatives through a comparison of the credibility limits for 

each alternative. 

Since there is no overlap between the credibility limits, a 

comparison is relatively straightforward. Consider a 

comparison between the reliability of the current power 

supply (i.e. Base Case) and the array configuration of 

Capstone microturbines investigated. There is a 90% chance 

that the utility power will fail before 2,529 hours, while there 

is a 90% chance that the Capstone array will provide power 

for at least 2,698 hours. 

Finally, consider that there is a 90% probability that the 

Capstone array will fail to provide power for less than 

approximately 8,706 hours, while there is a 90% probability 

that the single Kawasaki turbine will provide power for at 

least 9,048 hours. 

A significant, but not decisive, element in the lower 

reliability estimate for the Capstone was the configuration and 

operational plan investigated. Other configurations could 

have quite different reliability characteristics and warrant 

further investigation. However, installation issues associated 

with the Capstone, e.g. special enclosure, could be a factor in 

the final decision. 

Figure 10. PDF (bars, left axis) and CDF (line, right axis) for the 

alternative power system configuration with a Kawaski Turbine. 

Not considered in the analysis for either the Kawasaki or 

Capstone configurations is the probability that the turbines 

will fail to start. Just as with the diesel generators, the 

Capstone microturbines can be characterized by a probability 

to start from a cold-standby. This is less of a consideration 

for the Kawasaki since this turbine would be operating 24/7. 

Also not considered in the analyses is consideration of the 

differences in the control systems for the two turbines. 

In conclusion, probabilistic risk assessments (PRAs) on 

alternative-power configurations have been shown to be an 

informative approach to evaluating risk mitigation for power 

disruption. Methodologies have been developed and applied 

to a case study for a large telecom site. For the case-study 

configurations and operating assumptions, a PRA indicates 

that significant increased reliability can be achieved – 

including likelihood of natural gas delivery failure – by 

incorporating a natural gas turbine as primary power with 

utility grid, diesel, and batteries as the backup systems. 

VII. ACKNOWLEDGMENTS 

This project was funded by the U.S. Department of Energy 

through the Office of Planning, Budget, and Analysis. The 

DOE Project Manager is Dr. Allan Hoffman. 

Sandia is a multiprogram laboratory operated by Sandia 

Corporation (a Lockheed Martin Company) for the United 

States Department of Energy’s National Nuclear Security 

Administration, under Contract DE-AC04-94-AL85000. 

NREL is operated for the U.S. Department of Energy, 

Office of Energy Efficiency and Renewable Energy, by 

Midwest Research Institute and Battelle, under Contract No. 

DE-AC36-99-GO103 

6
Figure 11. Comparison CDFs for the three system configurations with 10%, median, and 90% probability points delineated for 

comparison. 

 

VIII. REFERENCES 

1. C. Warren, J. Bouford, “Classification of Major Event Days,” 

Transactions IEEE T&D Conference, IEEE Working Group on System 

Design , 2003. 

2. M. Ivey, A. Ahkil, D. Robinson, K. Stamber, J. Stamp, and K. Chu, 

"Accomadating Uncertainty in the Planning and Operations of Electric 

Power Systems," Sandia National Laboratories, Albuquerque, NM 

SAND2000-1681, July 2000. 

3. IEEE recommended practice for the design of reliable industrial and 

commercial power systems. 1998, USA : 1998.

4. D. Spiegelhalter, A. Thomas, and N. Best, "WinBUGS Version 1.4 " 

2006. 

5. G. Grant, J. Poloski, A. Luptak, C. Gentillon, and W. Galyean, 

"Emergency Diesel Generator Power System (1987-1993)," U.S. 

Nuclear Regulatory Commission NUREG/CR-5500, 1996. 

6. G. Grant, J. Poloski, A. Luptak, C. Gentillon, and W. Galyean, 

"Emergency Diesel Generator Power System (1997-2003)," U.S. 

Nuclear Regulatory Commission NUREG/CR-5500U, 2005. 

7. Y. D. Jo and B. J. Ahn, "A method of quantitative risk assessment for 

transmission pipeline carrying natural gas," Journal of Hazardous 

Materials, vol. 123, pp. 1-12, 2005. 

8. G. A. Papadakis, "Major hazard pipelines: a comparative study of 

onshore transmission accidents," Journal of Loss Prevention in the 

Process Industries, vol. 12, pp. 91-107, 1999. 

7
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Impact of Distributed Energy Resources on the Reliability of Critical 

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14. ABSTRACT (Maximum 200 Words)

This paper documents a probabilistic risk assessment of existing and alternative power supply systems at a large 

telecommunications office. The analysis characterizes the increase in the reliability of power supply through the use 

of two alternative power configurations. Failures in the power systems supporting major telecommunications service 

nodes are a main contributor to significant telecommunications outages. A logical approach to improving the 

robustness of telecommunication facilities is to increase the depth and breadth of technologies available to restore 

power during power outages. Distributed energy resources such as fuel cells and gas turbines could provide 

additional on-site electric power sources to provide backup power, if batteries and diesel generators fail. The 

analysis is based on a hierarchical Bayesian approach and focuses on the failure probability associated with each of 

three possible facility configurations, along with assessment of the uncertainty or confidence level in the probability of 

failure. A risk-based characterization of final best configuration is presented. 

15. SUBJECT TERMS

Distributed energy resources; telecommunications facilities; telecommunications; Federal Communications 

Commission; FCC; universal power systems; electrical infrastructure; power systems; National Renewable Energy 

Laboratory; Doug Arent; Sandia National Laboratory; David Robinson; Sprint Power Standards; Larry Johnson 

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