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CHAPTER 7. ENERGY USE AND END-USE LOAD CHARACTERIZATION

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 LOAD MODEL FOR LIQUID-IMMERSED TRANSFORMERS ................................ 7-2

7.2.1 Inputs to Hourly Load Model .......................................................................................... 7-3

7.2.1.1 Utility Information......................................................................................... 7-3

7.2.1.2 Initial Peak Transformer Loading.................................................................. 7-4

7.2.1.3 Hourly Price-Load Model.............................................................................. 7-5

7.2.1.4 Transformer Load Simulation........................................................................ 7-7

7.3 MODEL FOR DRY-TYPE TRANSFORMER LOADS................................................. 7-8

7.3.1 Overview of Monthly Load Model.................................................................................. 7-8

7.3.2 Monthly Load Simulation................................................................................................ 7-9

7.3.3 Inputs to Monthly Load Model........................................................................................ 7-9

7.3.3.1 Customer Data ............................................................................................... 7-9

7.3.3.2 Initial Peak Transformer Load ..................................................................... 7-10

7.3.3.3 Transformer Loss Factor.............................................................................. 7-11

7.3.3.4 Coincident Peak Load.................................................................................. 7-11

LIST OF TABLES

Table 7.2.1 Definition of EMM Regions in NEMS................................................................ 7-4

LIST OF FIGURES

Figure 7.2.1 Electricity Market Module Regions in NEMS..................................................... 7-4

Figure 7.2.2 Distribution of Initial Peak Loading Used in the Hourly Load Analysis ............ 7-5

Figure 7.2.3 Binned Load Versus Marginal Price for EMM Region SERC............................ 7-6

Figure 7.2.4 Average Joint Probability Distribution for Commercial Customers, 1998-

2000...................................................................................................................... 7-7

Figure 7.2.5 Average Joint Probability Distribution for Industrial Customers, 1998-

2000...................................................................................................................... 7-8
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CHAPTER 7. ENERGY USE AND END-USE LOAD CHARACTERIZATION

7.1 INTRODUCTION

The U.S. Department of Energy (DOE) characterized energy use and end-use load for 

distribution transformers. Estimates of energy use enabled evaluation of energy savings 

associated with operating distribution transformers at various efficiency levels. The 

characterization of end-use load enabled evaluation of the impact of load on electricity demand. 

DOE’s analysis produced a distribution of results for a range of installation types, operating 

conditions, and climate locations intended to represent the diversity of the application and 

performance of distribution transformers.

Distribution transformers consume energy via both no-load losses and load losses. Noload losses, which are constant over time, occur whenever a transformer is energized by power 

lines. Load losses vary with the square of the load the transformer is serving. There are two types 

of distribution transformers: liquid-immersed and dry. Liquid-immersed transformers are owned 

primarily by electric utilities. Utilities pay marginal costs for the power used to generate 

electricity, costs that can vary by the hour. DOE therefore developed a statistical simulation 

model to estimate the hourly load characteristics of liquid-immersed transformers and to develop 

a correlation between hourly loads and system loads. Dry-type transformers are owned by 

commercial and industrial (C&I) establishments, which are billed for electricity according to a 

tariff. For dry-type distribution transformers, DOE used empirical estimates of load 

characteristics to estimate monthly average (root mean square) loads and peak coincident loads.

This chapter first describes transformer losses, then presents the details of the load 

characterization models DOE developed for liquid-immersed and dry-type transformers.

The no-load losses experienced by distribution transformers arise primarily from the 

switching of the magnetic field in the transformer core material. Those losses, which are roughly 

constant, occur whenever the transformer is energized (i.e., connected to a live power line). Load 

losses, also known as resistance or I2

R losses, vary in response to the changing load on the 

transformer. Load losses are proportional to the load squared plus a relatively small temperature 

correction (<15 percent for loads less than the rated load). DOE uses the following formula, 

which incorporates both load and no-load losses, to estimate the energy used by a distribution 

transformer.

ET =∈NLL+ E +∈LL �E

Emax � �

2

Where:

∈NLL = the no-load loss rate,

E = the total energy used by a transformer experiencing instantaneous load,

∈LL = the load loss rate, and

Emax = the expected peak load on the transformer.
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The characteristics of transformer loads required for DOE’s life-cycle cost (LCC) 

analysis also depend on the way the user’s electricity is priced. Because approximately 95

percent of liquid-immersed transformers are owned by electric utilities, the appropriate 

electricity price for those transformers is the cost of production, which varies hourly. For those 

types of transformers, DOE’s analysis was based on hourly load and price data. The electricity 

use of dry-type transformers, which are installed primarily in commercial and industrial 

buildings, is billed monthly. For those types of transformers, DOE developed an analysis based 

on monthly, building-level data.

7.2 LOAD MODEL FOR LIQUID-IMMERSED TRANSFORMERS

This section describes the hourly load model DOE developed in support of its LCC 

analysis for liquid-immersed transformers. 

The operating cost savings associated with improved transformer efficiency are equal to 

the energy savings (reduction in losses) times the price of energy. For liquid-immersed 

transformers, the appropriate price is the marginal production cost of electricity. This production 

cost, which varies regionally and temporally, correlates strongly with the magnitude of the total 

electric system load. Because the load on an individual transformer also correlates somewhat

with system load, there is some correlation between transformer load losses and the price of 

electricity. To capture those correlations, DOE developed a statistical model based on hourly 

electric system load data, marginal hourly electric system production prices, a joint probability 

distribution between transformer and system load levels. The steps in the operation of the hourly 

load simulation program are described below.

1. The program selects a transformer owner from a list of utilities that own electricity 

distribution equipment. 

2. The program determines a sample weight for the selected utility, based on total 

kilowatt-hours sold. 

3. The program selects the customer type (residential or C&I) served by the transformer 

and the appropriate weight for that customer type. The weight is assigned based on 

the fraction of that utility’s electricity sales to that customer type. 

4. The program goes through a loop to calculate the hourly transformer loads and 

system marginal prices for the selected transformer. System prices, and their 

dependence on system load, are determined from historical data. Prices differ by 

region and season. The transformer load is estimated based on a joint distribution 

function that predicts the transformer load for a given system load. The individual 

steps in the loop are as follows.

a. Choose a system load value from the system load distribution function.

b. Estimate the system price for that system load.

c. Estimate the transformer load for that system load.

5. For each simulation, the program provides output to be used in calculating the LCC. 

The output includes a transformer identification (ID), the utility ID, customer 

category, and transformer load losses and operating costs. 
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7.2.1 Inputs to Hourly Load Model 

The following sections describe the inputs used in simulating the hourly load for liquidimmersed distribution transformers.

7.2.1.1 Utility Information

The LCC analysis for liquid-immersed transformers uses two types of information related 

to electric utilities. The first is drawn from the Energy Information Administration’s (EIA’s)

Form 861 database.

1 Form 861 provides, through its Form 2, the annual sales in megawatt-hours

for each utility to the residential, commercial, and industrial sectors. Form 861’s Form 4 lists all 

the utilities that own electricity distribution equipment and the county in which that equipment is 

located. Based on those data, DOE created a list of utilities that own transformers and assigned a 

weight to each based on the electricity sales of that utility. 

The second type of utility information used in the LCC analysis is hourly system loads 

and prices. DOE developed regional system loads and prices for the set of regions defined in the 

EIA National Energy Modeling System (NEMS) Electricity Market Module (EMM).2 The 

regions represent both national reliability regions and, where they exist, integrated wholesale 

electricity markets, as illustrated in Figure 7.2.1. Each region in turn comprises a number of 

electric utility control area operators (CAOs), some of which may also be utility companies.

DOE obtained hourly load and system lambda data (for regions without wholesale markets) or 

day-ahead market price data (for market regions) from the Federal Energy Regulatory 

Commission (FERC) Form 714 database.

3 DOE aggregated the hourly data to produce regional 

time series for the EMM regions. Appendix 7-B contains the list of entities, along with their 

designated CAO and EMM regions, for which DOE obtained the FERC data used to create the 

hourly time series. 
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Figure 7.2.1 Electricity Market Module Regions in NEMS

The numbered regions in Figure 7.2.1 are described in Table 7.2.1.

Table 7.2.1 Definition of EMM Regions in NEMS

Index Abbreviation Definition

1 ECAR East Central Area Reliability Coordination Agreement

2 ERCOT Electric Reliability Council of Texas 

3 MAAC Mid-Atlantic Area Council 

4 MAIN Mid-America Interconnected Network 

5 MAPP Mid-Continent Area Power Pool

6 NY New York

7 NE New England 

8 FL Florida Reliability Coordinating Council

9 SERC Southeastern Electric Reliability Council

10 SPP Southwest Power Pool 

11 NPP Northwest Power Pool 

12 RA Rocky Mountain Power Area

13 CA California

7.2.1.2 Initial Peak Transformer Loading

DOE used a distribution of values for initial peak loading to characterize the annual peak 

load served by each transformer in its simulation. The initial peak loading is the ratio of the 
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transformer’s peak load in the first year of operation to the transformer’s rated load, before 

accounting for any new load growth that occurs later.

4 DOE selected a distribution of initial peak 

loadings that had a median of 85 percent, a minimum of 50 percent, and a maximum of 130 

percent. Standard engineering practice for sizing distribution transformers selects a transformer 

based on the expected annual peak of the load being served, with some provision for load 

growth. Given the provision for future growth, initial peak loading usually is less than 100 

percent. In practice, however, there usually is some error in estimating the peak load that will be 

served, and engineers generally use a discrete set of transformer ratings that are imperfectly 

matched with the expected peak load. Distribution transformers generally are manufactured in 

discrete kilovolt-ampere (kVA) ratings and, on average, the next-larger kVA rating is 50 percent 

larger than the next-lower kVA rating (measured relative to the smaller size). Therefore, the

initial peak loading can be as high as 130 percent, because for short periods a transformer can be 

loaded to more than 130 percent of nameplate capacity.5

 Figure 7.2.2 illustrates the distribution 

of initial peak loading that DOE used.

Figure 7.2.2 Distribution of Initial Peak Loading Used in the 

Hourly Load Analysis

7.2.1.3 Hourly Price-Load Model

The price-load model relates the marginal cost of meeting the next load increment to the 

current system load. The marginal cost is interpreted as the time-varying marginal price of 

electricity for a system. The Department estimated the relationship between system loads and 

system marginal prices for each region based on hourly data collected by FERC Form 7143 and, 

where appropriate, day-ahead market data from independent system operators. FERC data 

provide hourly system load and lambda values, where the system lambda is defined 

approximately as the operating cost of the generating unit on the dispatch margin. For regions 

that have integrated wholesale electricity markets, DOE used the day-ahead market data that

include the hourly system load and the market-clearing price. DOE used data for 2008, the most 

recent year for which data from all sources were available. 

0.00

0.02

0.04

0.06

0.08

0.10

0.12

50% 60% 70% 80% 90% 100% 110% 120% 130%

Peak Load Probability

Transformer Peak Loading
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The correlation between hourly system prices and loads is illustrated in Figure 7.2.3. This 

figure shows a scatter plot of price versus load for the SERC region. The data pairs (price and 

load values in each hour) are sorted into bins based on load level. Those bins are represented by 

different colors in the figure. 

Figure 7.2.3 Binned Load Versus Marginal Price for EMM

Region SERC

DOE estimated the marginal system price within each bin as follows.

pj = p̅

j + δj

Where:

j = the bin index,

p̅

j = the average value of the prices in bin j and,

δj = a random increment within bin j.

In general, both the average price and the range of hourly prices increases with system 

load. To capture the increase in price volatility as a function of system load, DOE added a 

random increment δ to the average marginal price p̅for each load bin j. To estimate the

increment δj, DOE used a probability distribution function (PDF) calculated independently for 

each bin j. The PDF for the increment is assumed to be triangular and centered at zero, with the 

distribution parameters for each bin determined by the data. The approach is described in more 

detail in Appendix 7-A.

0

50

100

150

200

250

0 20000 40000 60000 80000 100000 120000 140000 160000

Lambda $/MWh

Load MWh
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Within the LCC spreadsheet, system loads are represented using a load distribution 

function. This function is calculated by counting the number of times the load level falls inside 

each load bin. The bin sizes are variable and depend on region; the number of bins is constant 

and equal to fifteen.

7.2.1.4 Transformer Load Simulation

DOE estimated the loads on individual liquid-immersed transformers for both residential 

and non-residential customers by creating hourly proxy transformer loads. The important 

quantities for the LCC analysis are the number of hours the transformer is subject to a given load 

level and the correlation between transformer loads and system loads. The first is important for 

determining the total load losses, and the second for accurately estimating the economic value of 

load losses. To estimate the coincident between peak transformer load and system load, DOE 

constructed a joint probability distribution function (JPDF) based on a dataset consisting of 

several hundred hourly whole-building loads. For this analysis, a proxy system load was 

constructed by summing all the available building loads. DOE then estimated the JPDF by

defining a set of bins for both the proxy system load and individual load time series, and 

counting the number of values that fell into each bin. The system load bins used in the JPDF are 

the same as the load bins used in the hourly price-load model. DOE created a separate JPDF for 

residential, commercial and industrial customers. Figure 7.2.4 and figure 7.2.5 show separate 

color plots of the JPDF for commercial, and industrial customers. The figure shows the system 

load bins on the horizontal axis and the transformer load bins on the vertical axis, with different 

colors representing the probability that, in a given hour, the system load and transformer loads 

will fall into the given bin. The figure shows that, for low system loads, transformer loads are 

distributed broadly, whereas for higher system loads transformer loads are more tightly 

correlated with system load.

Figure 7.2.4 Average Joint Probability Distribution for 

Commercial Customers, 1998-2000

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Transformer Load Bins

Proxy System Load Bins

0.020-0.025

0.015-0.020

0.010-0.015

0.005-0.010

0.000-0.005
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Figure 7.2.5 Average Joint Probability Distribution for 

Industrial Customers, 1998-2000

7.3 MODEL FOR DRY-TYPE TRANSFORMER LOADS

This section describes the modeling approach DOE used to estimate the loading for drytype distribution transformers. Given that this type of equipment is owned primarily by C&I

entities, which are billed monthly for electricity, DOE developed appropriate methods to 

estimate the impacts of higher transformer efficiency on monthly energy losses and demand.

7.3.1 Overview of Monthly Load Model

DOE defined a customer sample for dry-type distribution transformers based on buildinglevel data drawn from the EIA’s Commercial Buildings Energy Consumption Surveys (CBECS) 

for 1992, 1995, and 2003. In its analysis, DOE assumed that each building has a transformer, and 

used building monthly electricity consumption and demand data as inputs to a statistical model 

that estimates the transformer-level data. DOE determined the economic value of no-load and 

load losses by the marginal price of electricity for each building, as determined by the electricity 

tariff. In this analysis, DOE used a previous, detailed study of commercial building energy 

prices,

6 which showed that every building’s electricity costs can be represented as a marginal 

price for energy and a marginal price for demand. Both prices vary by region and season.

Transformer losses contain a constant component (the no-load or core losses) and a 

component that depends on the square of the load on the transformer (the load or coil losses). 

The economic value of transformer losses is a function of the load on the transformer and the 

timing of that load with respect to variable energy costs and building peak demand. To the extent 

that there is a correlation between transformer losses and variable energy costs, the cost of the 

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Transformer Load Bins

Proxy System Load Bins

0.030-0.040

0.020-0.030

0.010-0.020

0.000-0.010
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electricity supplying the transformer losses will be different from the average cost of electricity. 

The LCC analysis for dry-type transformers uses a statistical model of the monthly transformer 

loss factors, along with a correlation between individual transformer and whole-building loads, 

to estimate changes in monthly electricity consumption and peak demand and the corresponding 

electricity cost savings for C&I customers.

7.3.2 Monthly Load Simulation

The monthly load simulation model embedded in the LCC spreadsheet proceeds as 

follows.

1. A customer (building) is selected from the sample in the spreadsheet; if the building’s

annual peak load is smaller than the rated capacity of the transformer design under 

consideration, the building is dropped from the sample.

2. An initial peak loading is assigned to the transformer.

3. The program begins a loop on the monthly electricity consumption and demand data 

for the building. For each month, the program:

a. calculates the load factor (LF), which is equal to the ratio of the average load 

to the peak load for that month;

b. estimates the transformer loss factor (LSF) as a function of the LF; and

c. estimates the transformer coincident peak load (CPL) as a function of the LF.

4. The monthly load data are passed to the controlling loop and used in the LCC

analysis to calculate the operating cost savings from reduced load losses.

7.3.3 Inputs to Monthly Load Model 

The following sections describe the inputs to DOE’s monthly load model, which include 

customer data, initial peak transformer load, transformer loss factor, and coincident peak 

transformer load.

7.3.3.1 Customer Data

The customer sample for the dry-type transformer LCC analysis was drawn from the

1992 and 1995 CBECS.7,8 Those survey years were used because they include data on monthly 

building-level electricity consumption and demand. All 1992 and 1995 samples that provided a 

complete year of monthly data were combined into a single sample. Weights for the full sample 

were determined by scaling the original building weights to match the floorspace for the 

corresponding building categories given in the most recent CBECS (from 2008). The building 

categories used to define the weights were based on building activity, census division, and

building size.

DOE had no comparable sample to provide monthly data for industrial customers. To 

represent the fraction of transformers that are installed in industrial buildings, DOE assumed that 

(1) industrial buildings share the load characteristics of the large buildings defined in CBECS, 

and (2) industrial buildings utilize transformers in a way that is comparable to similarly sized 

warehouse-type buildings. In the previous final rule for distribution transformers,

9 DOE assumed 
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that monthly demand and usage for large industrial and commercial customers are similar. It 

verified this assumption by comparing load factor distributions of C&I customers for a utility in 

the southeastern United States. DOE found that the differences among customer classes were 

much smaller than those within each class.

9

DOE used floorspace data from the EIA’s 2006 Manufacturing Energy Consumption 

Survey10 to estimate the total floorspace of industrial buildings that would contain transformers 

covered by this rulemaking. This floorspace was added to the CBECS-based floorspace for large 

commercial buildings to determine total weights for each building in the customer sample. 

Buildings having annual peak loads less than the transformer capacity specified in the 

design under consideration were screened out of the sample. The customer sample contains a 

range of building sizes having a wide range of annual peak loads. Although larger buildings 

undoubtedly contain multiple transformers, DOE currently has no quantitative information on 

how the number of transformers in a building scales with either the building floorspace or the 

building annual peak load. Thus, to account for the effect of multiple transformers in a single 

building, DOE used a simple approach whereby it multiplied the building sample weight by the 

number of floors in the building.

7.3.3.2 Initial Peak Transformer Load

Initial peak load is the annual peak load on the transformer in the first year of operation

divided by its rated capacity. The Institute of Electrical and Electronics Engineers (IEEE) has a

Draft Guide for Distribution Transformer Loss Evaluation11 that defines a similar measure of 

peak transformer loading called an “equivalent annual peak load,” which accounts for changes in 

peak load throughout the life of a transformer. IEEE’s Draft Guide, which refers to the initial 

peak loading as “initial transformer loading,” uses values of 0.9 and 0.95 in its example 

calculations. Rather than applying the IEEE’s equivalent annual peak load, DOE accounted for 

annual changes in peak load in the LCC calculation by applying an annual rate of change in 

transformer load. DOE characterized a range of initial peak loads by defining a distribution of 

initial peak loads. 

Distribution transformers generally are manufactured in discrete kVA ratings that 

represent their power-handling capacity. On average, each higher kVA rating is 50 percent larger 

than the previous kVA rating (measured relative to the smaller rating). Transformers can be 

loaded above their kVA rating (or nameplate capacity) for short periods. However, transformers 

are often sized conservatively to avoid the possibility of an overload, especially for low-voltage, 

dry-type transformers. DOE received stakeholder comments that medium-voltage, dry-type 

transformers are loaded more heavily than are low-voltage, dry-type transformers.

12 DOE 

therefore selected higher initial peak loading for medium-voltage, dry-type transformers than for

low-voltage dry-types. If electrical engineers accurately size dry-type transformers 

conservatively with a 10-percent safety margin relative to the nameplate capacity, initial peak 

load ranges from 60 percent to 90 percent. The high end of the range is the maximum initial peak 

load that allows for a 10-percent margin of safety. The low end of the range reflects the threshold 

peak load at which the next-smaller kVA rating will provide 90 percent peak loading. In 

response to stakeholder comments, DOE adjusted this assumption for medium-voltage, dry-type 
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transformers, selecting initial peak loadings that were 10 percent higher than for the low-voltage, 

dry-type transformers. Thus the distribution of initial peak load for low-voltage, dry-type 

transformers has a constant probability between 60 percent and 90 percent of nameplate 

capacity; the distribution for medium-voltage, dry-type transformers has a constant probability 

between 70 percent and 100 percent of nameplate capacity.

DOE believes that, in selecting an appropriate kVA rating for an application, engineers 

choosing dry-type transformers are conservative and do not take advantage of the fact that 

transformers can be safely overloaded for short periods. The National Electric Code13 encourages 

conservative transformer sizing by requiring a transformer that is serving a secondary circuit of 

less than 600 volts to be rated at not less than 80 percent of the total amperage of the secondary 

circuit protection (table 450.3(A) of the code).

7.3.3.3 Transformer Loss Factor

For a distribution transformer, the loss factor (LSF) is the ratio of the annual average load 

losses to the peak value of load losses. The LSF is equal to the average of the square of the 

transformer load divided by the square of the peak transformer load.

The characteristics of transformer load DOE needed for C&I building owners are the 

energy and demand savings associated with load losses. The energy savings depend on the LSF, 

which is proportional to the average value of the squared load. To estimate the load loss factor 

for each building, DOE used an expression that relates LSF to load factor (LF):

LSF = α*LF + (1-α)*LF2

where α is a parameter with α < 0.5. The LF, which is available from the CBECS data, is equal to 

the ratio of the average hourly load to the peak load. DOE estimated a probability distribution for 

the parameter α based on hourly building load data from the End-Use Load and Consumer 

Assessment Program (ELCAP)

6 survey and additional confidential data from stakeholders.

7.3.3.4 Coincident Peak Load

Coincident peak load (CPL) captures the coincidence between a transformer’s load and 

the building’s peak load. For a building that has a single transformer, the coincidence would be

perfect, and the CPL would equal one. In practice, the degree of coincidence depends on how 

transformers are installed in the building. To model the diversity within transformer loads and 

total building loads, DOE constructed a statistical model that predicts the CPL as a function of a 

building’s load factor. The statistical model is based on data for monthly LFs and LSFs

calculated using hourly building load data from the ELCAP dataset6 and other data. The 

modeling approach is discussed in more detail in Appendix 7-A.
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REFRENCES

1. U.S. Department of Energy-Energy Information Administration Form EIA-861: Annual 

Electric Power Industry Database. (2008).at 

<http://www.eia.doe.gov/cneaf/electricity/page/eia861.html>

2. Energy Information Administration - Office of Integrated Analysis and Forecasting The 

National Energy Modeling System. (U.S. Department of Energy: Washington DC, 2009).at 

<http://www.eia.doe.gov/oiaf/aeo/overview/>

3. U.S. Department of Energy-Federal Energy Regulatory Commission Form No. 714 - Annual 

Electric Control and Planning Area Report. (U.S. Department of Energy-Federal Energy 

Regulatory Commission: 2008).at <http://www.ferc.gov/docs-filing/forms/form714/overview.asp>

4. M. E. de Oliveira, A. Padilha-Feltrin & F. J. Candian Investigation of the Relationship 

between Load and Loss Factors for a Brazilian Electric Utility. 

(2006).DOI:10.1109/TDCLA.2006.311390

5. U.S. Department of the Interior - Bureau of Reclamation Permissible Loading of OilImmersed Transformers and Regulators. (1991).

6. Pratt, R., Williamson, M., Richman, E. & Miller, N. Commercial Equipment Loads: End-Use 

Load and Consumer Assessment Program (ELCAP). (1990).

7. U.S. Department of Energy-Energy Information Administration 1992 Commercial Buildings 

Energy Consumption Survey (CBECS). (1992).at 

<http://www.eia.doe.gov/emeu/cbecs/92microdat.html>

8. U.S. Department of Energy-Energy Information Administration 1995 Commercial Building 

Energy Consumption and Expenditures (CBECS). (1995).

9. U.S. Department of Energy-Office of Building Technologies Chapter 6. Energy Use and 

End-Use Load Characterization. Distribution Transformers Energy Conservation Standard

Technical Support Document (2007).

10. Institute of Electrical and Electronics Engineers Inc. Draft Guide for Distribution 

Transformer Loss Evaluation. (2001).

11. P.J. Hopkinson HVOLT Consultants Inc. ANOPR for Distribution Transformers, 

EERM/STD-00-550, SNOPR and ANOPR Comments, comment No. 65, p. 3. (2004).

12. National Fire Protection Association National Fuel Gas Code - 2002 Edition. (National Fire 

Protection Association: 1 Batterymarch Park, P.O. Box 9101, Quincy MA, 2002).