5.2a ERMUSR 04-11-2023Elk River Municipal Utilities
ANNUAL
BENCHMARKING
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REPORT TRRA,CBKER
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PUBLIC
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RESEARCH & DEVELOPMENT
American Public Power Association
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183
1. About This Report
This report focuses on distribution system reliability across the country and is
customized to each utility that participates in the American Public Power Association's
eReliability Tracker service. APPA created the eReliability Tracker Annual Report to assist
utilities in their efforts to understand and analyze their electric system. In 2012, APPA
developed the eReliability Tracker thanks to a grant from the Demonstration of Energy
& Efficiency Developments (DEED) program.
This report reflects data in the eReliability Tracker from January 1, 2022 to December
31, 2022. This analysis might not properly reflect your utility's statistics if you do not
have a full year of data in the system. The report includes data recorded as of March
14, 2023.
Reliability reflects both historic and ongoing engineering investment decisions within a
utility. Proper use of reliability metrics ensures that a utility is performing its intended
function and is providing service in a consistent and effective manner.
While the primary use of reliability statistics is for self -evaluation, you can use these
statistics to compare your utility with similar utilities. However, differences such as
electrical network configuration, ambient environment, weather conditions, and
number of customers served typically limit most utility -to -utility comparisons. Due to
the diverse range of utilities that use the eReliability Tracker, this report endeavors to
improve comparative analyses by grouping utilities by size and region.
Since this report contains data for all utilities that use the eReliability Tracker, it is
important to consider how a particularly large or small utility can affect comparative
benchmarks. To ease the issues associated with comparability, each utility's reliability
statistics are weighted based on customer count when aggregated. This means that all
utilities are equally weighted, and all individual statistics are developed on a per
customer basis.
The aggregate statistics in this report are calculated from the 285 utilities with verified
2022 outage data. Utilities that experienced no outages in 2022, or did not upload any
data, will have NULL, None, or "0" values in their report for utility -specific data and
were not included in the aggregate analysis. Also note that log -normal data with a z-
score"' greater than 3.25 may be excluded if it significantly distorts the aggregate
statistics.
1. A z-score indicates how much a data point differs from the mean. For instance, a z-score of 3.25 indicates
that the data point is three and one -quarter standard deviations from the mean. A z-score of 0 indicates
that the data point is identical to the mean. ,
2
Im
Utility Classifications
This report separates utilities into groups according to geographic region and the
number of customers served. Table 1 shows the range of customer counts for utilities
that use the eReliability Tracker by five distinct groups of approximately 105 utilities
per group.
Your utility is in size class and region >.
Table 1. Customer count range per size class
Utility Size Class
Customer Count Range
Class 1
[0, 1481)
Class 2
[1481, 3239)
Class 3
[3239, 7154)
Class 4
[7154, 13594)
Class 5
[13594, 503649)
Each utility is also grouped with all other participating utilities within their region.
Figure 1 shows the number of utilities using the eReliability Tracker in each region and
Figure 2 shows the states and territories included in each region.
120
Ln
N 100
D 80
4-
0
v 60
40
20
0
Regions
Figure 1. Number of utilities subscribed to the eReliability Tracker by region
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Figure 2. Regions
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11. IEEE Statistics
When it comes to reliability, the industry standard metrics are defined in the Institute
for Electrical and Electronics Engineers' Guide for Electric Power Distribution Reliability
Indices, or IEEE 1366 guidelines. For each utility, the eReliability Tracker performs IEEE
1366 calculations for System Average Interruption Duration Index (SAIDI), System
Average Interruption Frequency Index (SAIFI), Customer Average Interruption Duration
Index (CAIDI), Momentary Average Interruption Frequency Index (MAIFI) and Average
Service Availability Index (ASAI).
It is important to note how major events (MEs) are calculated and used in this report.
An example of a ME includes severe weather, such as a tornado or hurricane, that leads
to unusually long outages in comparison to your distribution system's typical outage.
This report uses the APPA ME threshold, which is based directly on the SAIDI for
specific outage events, rather than a daily SAIDI. The APPA ME threshold allows a utility
to remove outages that exceed the IEEE 2.5 beta threshold for outage events, which
considers up to 10 years of the utility's outage history. In the eReliability Tracker, if a
utility does not have at least 36 outage events prior to the year being analyzed, then
no threshold is calculated. If this is the case for your utility, then you will have a NULL
value in the following field and the calculations without MEs in the SAIDI, SAIFI, CAIDI,
and ASAI sections of this report will be the same as the calculations with MEs for your
utility. More outage history will provide a better threshold for your utility.
Your utility's APPA major event threshold is 98 minutes.
For each of the reliability indices, this report displays your utility's metrics alongside the
mean values for all utilities using the eReliability Tracker and within the same calss and
region as your utility. The first table within each of the following subsections allows you
to better understand the performance of your electric system relative to other utilities
nationwide and to those within your same region or size class. The second table breaks
down the national data into quartile ranges, a minimum value, and a maximum value.
All indices, except MAIFI, are calculated for outages with and without MEs. Furthermore,
the tables show indices for scheduled and unscheduled outages. Note that scheduled
and unscheduled calculations include MEs. Also note that wherever MEs are excluded,
the exclusion is based on the APPA ME threshold for your system.
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11.1. System Average Interruption Duration Index
SAIDI is the average duration (in minutes) of an interruption per customer served by
the utility during a specific time frame.
Since SAIDI is a sustained interruption index, only outages lasting longer than five
minutes are included in the calculations. SAIDI is calculated by dividing the sum of all
customer minutes of interruption"' within the specified time frame by the average
number of customers served during that period. For example, a utility with 100
customer minutes of interruption and 100 customers would have a SAIDI of 1.
Note that in the tables below, scheduled and unscheduled calculations include MEs.
Also note that wherever MEs are excluded, the exclusion is based on the APPA ME
threshold for your system.
Table 2. Average SAIDI with and without MEs
In minutes
Your utility
Utilities that use the eReliability Tracker
Utilities in your region
Utilities in your size class
All
No MEs
Unscheduled
Scheduled
13.29
13.29
10.47
2.81
115.7
67.27
112.51
5.31
98.67
85.01
97.42
1.79
179.211
40.741
76.261
4.22
Table 3. Summary SAIDI data from the eReliability Tracker
In minutes
All
No MEs
Unscheduled
Scheduled
Minimum
0
0
0
0
First Quartile
18.81
11.32
16.84
0.2
Median
47.3
26.17
46.43
1.1
Third Quartile
1 118.671
53.611
113.371
4.12
Maximum
13365.0813365.081
3365.081
122.86
Figure 3. Average SAIDI by region
250
LA
v
C 200
E
Q 50
Regions
6
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1. Customer minutes of interruption is calculated by multiplying total customers interrupted and total minutes
of interruption. ,
11.2. System Average Interruption Frequency Index
SAIFI is the average instances a customer on the utility system will experience a
sustained interruption during a specific time frame.
Since SAIFI is a sustained interruption index, only outages lasting longer than five
minutes are included in the calculations. SAIFI is calculated by dividing the total
number of customers that experienced sustained interruptions by the average number
of customers served during that period. For example, a utility with 150 customer
interruptions and 200 customers would have a SAIFI of 0.75.
Note that in the tables below, scheduled and unscheduled calculations include MEs.
Also note that wherever MEs are excluded, the exclusion is based on the APPA ME
threshold for your system.
Table 4. Average SAIFI with and without MEs
In interruptions
Your utility
Utilities that use the eReliability Tracker
Utilities in your region
Utilities in your size class
All
No MEs
Unscheduled
Scheduled
0.15
0.15
0.1
0.04
0.77
0.52
0.74
0.05
0.51
0.41
0.49
0.03
10.631
0.411
0.611
0.03
Table 5. Summary SAIFI data from the eReliability Tracker
In interruptions
All
I No MEs
Unscheduled
Scheduled
Minimum
0
0
0
0
First Quartile
0.21
0.15
0.2
0
Median
0.59
0.37
0.55
0.01
Third Quartile
1.08
0.721
1.051
0.04
Maximum
13.441
2.921
3.44
1.04
Figure 4. Average SAIFI by region
1.4
0 1.2
a
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v
c 0.6
LL
Q 0.6
N
Q 0.4
j 0.2
Q
0.0
Regions
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11.3. Customer Average Interruption Duration Index
CAIDI is the average duration (in minutes) of an interruption experienced by customers
during a specific time frame.
Since CAIDI is a sustained interruption index, only outages lasting longer than five
minutes are included in the calculations. CAIDI is calculated by dividing the sum of all
customer minutes of interruption by the number of customers that experienced one or
more interruptions during that period. This metric reflects the average customer
experience (minutes of duration) during an outage.
Note that in the tables below, scheduled and unscheduled calculations include MEs.
Also note that wherever MEs are excluded, the exclusion is based on the APPA ME
threshold for your system.
Table 6. Average CAIDI with and without MEs
In minutes
Your utility
Utilities that use the eReliability Tracker
Utilities in your region
Utilities in your size class
All
No MEs
Unscheduled
Scheduled
90.15
90.15
102.31
62.52
126.53
93.65
126.95
112.97
97.72
91.77
98.34
87.71
97.171
86.091
96.161
112.35
Table 7. Summary CAIDI data from the eReliability Tracker
In minutes
All
No MEs
Unscheduled
Scheduled
Minimum
0
0
0
0
First Quartile
64.21
57.12
61.19
53.43
Median
88.09
75.62
86.11
83
Third Quartile
1 121.481
105.31
122.981
128.43
Maximum
14365.9411382.291
4504.811
747.38
Figure 5. Average CAIDI by region
175
Q 75
U
4J 50
01
f0
CU 25
a
Regions
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11.4. Momentary Average Interruption Frequency Index
MAIFI is the average number of momentary interruptions a utility customer will
experience during a specific time frame.
In this report, an outage with a duration of five minutes or less is classified as
momentary. MAIFI is calculated by dividing the total number of customers that
experienced momentary interruptions by the total number of customers served by the
utility. For example, a utility with 20 momentary customer interruptions and 100
customers would have a MAIFI of 0.20.
Momentary interruptions can be more difficult to track and utilities without an
automated outage management system might not log these interruptions; therefore,
some utilities have a MAIFI of zero.
Table 8. Average MAIFI
In interruptions
MAIFI
Your utility NULL
Utilities that use the eReliability Tracker 0.48
Utilities in your region 0.69
Utilities in your size class 0.52
Table 9. Summary MAIFI data from the eReliability Tracker
In interruptions
MAIFI
Minimum
0
First Quartile
0.01
Median
0.12
Third Quartile
0.57
Maximum
5.03
Figure 6. Average MAIFI by region
0.7
p 0.6
Q
0.5
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C 0.4
Q 0.3
0.2
M
L
0.1
Q
0.0
Regions
192
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11.5. Average Service Availability Index
ASAI is the percentage of time the sub -transmission and distribution systems are
available to serve customers during a specific time frame.
This load -based index represents the percentage availability of electric service to
customers within the period analyzed. It is calculated by dividing the total hours in
which service is available to customers by the total hours that service is demanded by
the customers. For example, an ASAI of 99.99% means that electric service was
available for 99.99% of the time during the given period. Note that the higher your
ASAI value, the better the performance.
In the tables below, scheduled and unscheduled calculations include MEs. Also note
that wherever MEs are excluded, the exclusion is based on the APPA ME threshold for
your system.
Table 10. Average ASAI with and without MEs
In percentage
Your utility
Utilities that use the eReliability Tracker
Utilities in your region
Utilities in your size class
All
No MEs
Unscheduled
Scheduled
99.9974
99.9974
99.998
99.9994
99.9783
99.9872
99.9789
99.999
99.9812
99.9838
99.9814
99.9996
199.9849199.99221
99.98551
99.9991
Table 11. Summary ASAI data from the eReliability Tracker
In percentage
FAH I No MEs l Unscheduled I Scheduled
Maximum
100
100
100
100
First Quartile
99.9964
99.9978
99.9967
99.9999
Median
99.991
99.995
99.9914
99.9997
Third Quartile
99.9779
99.9898
99.98
99.9992
Minimum
199.3597199.35971
99.35971
99.9766
Figure 7. Average ASAI by region
100.000
99.975
99.950
0
Q 99.925
Q 99.900
N
a
99.875
N
Q 99.850
99.825
99.800
1 2 3 4 5 6 7 8 9
Regions
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11.6. Energy Information Administration Form 861 Data
Form EIA-861 collects annual information on electric power industry participants
involved in the generation, transmission, distribution, and sale of electric energy in the
United States and its territories.
In 2014, EIA began publishing reliability statistics in Form EIA-861; therefore, APPA
included these statistics in this report for informational purposes. Please note that the
following data includes 175 investor -owned, 464 rural cooperative, and 323 public
power utilities that were large enough to be required to fill out the full EIA-861 form.
The statistics do not include data from utilities that complete the EIA 861-5 form, which
smaller entities complete. Note that the 323 participating public power utilities include
entities classified by EIA as municipal, political subdivision, and state. In addition, since
the collection and release of EIA form data lags by a year, the data is based on 2021
data that was published October 6, 2022. Therefore, we suggest you only use the
aggregate statistics contained herein as an informational tool for further comparison of
reliability statistics.
In Form EIA-861, an entity provides SAIDI and SAIFI including and excluding ME days in
accordance with the IEEE 1366-2003 or IEEE 1366-2012 standard.
Although EIA collected other reliability -related data, the tables below only include SAIDI
and SAIFI data including and excluding ME days. You can download the full set of data
at: www.eia.gov/electricity/data/eia861/.
Table 12. Your utility's SAIDI and SAIFI with and without IEEE ME days
SAIDI with IEEE
ME days
(minutes)
13.29
SAIDI without IEEE SAIFI with IEEE ME SAIFI without IEEE ME
ME days (minutes) days (interruptions) days (interruptions)
13.29
Table 13. Summary SAIDI data from Form EIA-861, 2021
In minutes
Average
All
No MEs
464
140.45
Minimum
0.31
0
First Quartile
84.75
53.07
Median
162.2
97.17
Third Quartile
1 327
164.26
Maximum
31626
3148
Table 14. Summary SAIFI data from Form EIA-861, 2021
In interruptions
All
No MEs
Average
1.71
1.21
Minimum
0
0
First Quartile
0.85
0.63
0.15
0.15
194
12
All
I No MEs
Median
1.32
1
Third Quartile
2.06
1.52
Maximum
19.09
7.77
195
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11.7. Miles of Line and Interruptions
Analyzing interruptions by miles of line can help utilities explore the relationship
between outages, distribution line exposure, and customer density. This analysis
separates utilities into groups of similar average customer density (customers served
per mile). As shown in Table 16, utilities that use the eReliability Tracker were split into
five customer density groups of approximately 46 utilities each. Note that customer
density classes include utilities that either provided their miles of line data in the 2022
eReliability Tracker data verification survey or recorded their data in the eReliability
Tracker. You can use the miles of line -related metrics shown in Table 15 and Table 16 as
an additional benchmark for your utility's reliability along with the customer
normalized -metrics included in the rest of the report. These metrics can be helpful in
understanding, for example, utility reliability against weather and animal -related
outages relative to similarly dense and exposed utilities.
Your utility's total miles of line: 595.68
Your utility's overhead miles of line: 244.84
Your utility's underground miles of line: 350.84
Table 15. Total miles of line and interruptions
Customers
Interruptions
Minutes of
Interrupted per Mile
per Mile
Interruption per Mile
3.25
0.08
9.59
Your utility
Utilities that use the
eReliability Tracker
31.29
0.5
126
Utilities in vour region
19.11
0.341
256.52
Your utility's customer density (customers per mile): 22.23
Your utility belongs to customer density class
Table 16. Miles of line -related metrics by customer density class
Customer Density Customer
AverageAverage
Customers
Average Minutes
Class (Customers Density
Interruptions
Interrupted per
of Interruption
per Mile) Range
per Mile
Mile
per Mile
Class 1 0.0 - 20.85
18.25 0.29
60.47
Class 2 20.85
35.27
21.74
0.38
89.12
_
Class 3 35.27
46.79
25.28
0.42
267.1
Class 4 46.79
38.43
0.62 114.39
63.41
j 0.77 83.08
Class 5 63.41 -
54.95
917.68
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M. Outage Causes
Equipment failure, extreme weather events, wildlife, and vegetation are some of the
most common causes of electric system outages.The following pie chart shows the
percentages of the primary causes of outages for all utilities using the eReliability
Tracker in 2022.
lent
Vegetation
Utility Human Error
Power Supply
Public
1q-
Unknown
Figure 8. Primary causes of outages in 2022
Certain factors, such as regional weather and animal/vegetation patterns, can make
some causes more prevalent for a specific group of utilities. The following section
includes graphs depicting common causes of outages for your utility, all utilities in your
region, and all utilities using the eReliability Tracker.
Charts containing aggregate information are customer -weighted to account for
differences in utility size for a better analytical comparison. For example, a particularly
large utility may have a large number of outages compared to a small utility. To avoid
skewing the data toward large utilities, the number of cause occurrences is divided by
customer size to account for the differences. In Figures 9 -14, the data represent the
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197
number of occurrences for each group of 1,000 customers. A customer -weighted
occurrence rate of "1" means an average of one outage from that cause occurred per
1,000 customers in 2022.
Note that the sustained outage cause analysis is more comprehensive than the
momentary outage cause analysis due to a larger and more robust sample size for
sustained outages. Regardless, tracking both sustained and momentary outages helps
utilities understand and reduce outages. To successfully use the outage information
tracked by your utility, it is imperative to classify and record outages in detail. The
more information provided per outage, the more conclusive and practical your analyses
will be.
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111.1. Sustained Outage Causes
In general, sustained outages are the most commonly tracked outage type. In analyses
of sustained outages, utilities tend to exclude scheduled outages, partial power,
customer -related problems, and qualifying major events from their reliability indices
calculations. While this is a valid method for reporting, these outages should be
included for internal review to make utility -level decisions. In this section, we evaluate
common causes of sustained outages for your utility, corresponding region, and for all
utilities that use the eReliability Tracker. It is important to note that sustained outages
are classified in this report as outages that last longer than five minutes, as defined by
IEEE 1366.
Figure 9. Top five causes of sustained outages for all utilities that use the eReliability
Tracker
O 2.0
0
0
1.5
a
c 1.0
v
U
U
0 0.5
0.0
Outage Cause Types
Figure 10. Top five causes of sustained outages for your utility"'
1.66
1.6
p 1.4
O
1.2
L
d 1.0
0.8
r-
0.6
V
0.45 0.45
U 0.4
0
0.23
0.2
0.0
Tree Squirrel Equipment Electrical Failure
Outage Cause Types
0.23
Vehicle Accident
1. The number of occurrences for each cause is divided by the utility's customer count (in thousands) to
create an occurrence rate that can be compared across different utility sizes.
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199
Figure 11. Top five causes of sustained outages in your region
2.56
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a 1.5
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7
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0.0
0.69
0.27
Utility Maintenance and Repairs Unscheduled Scheduled
Outage Cause Types
200
lu
111.2. Momentary Outage Causes
The ability to track momentary outages can be difficult or unavailable on some
systems, but due to the hazard they pose for electronic equipment, it is important to
track and analyze the causes of momentary outages. This section evaluates the
common causes of momentary outages for your utility, region, and size class as well as
common causes for all utilities that use the eReliability Tracker. Please note that only
outages lasting less than five minutes are classified as momentary, as defined by IEEE
1366. In Figures 12-14, for each utility, the number of occurrences for each cause is
divided by that utility's customer count (in thousands) to create an occurrence rate that
can be compared across different utility sizes.
Figure 12. Top five causes of momentary outages for all utilities that use the
eReliability Tracker
1.2
0 1.0
0
0
L 0.8
U1
a
N 0.6
V
C
N
0.4
u
u
O
0.2
0.0
Equipment Replacement Utility Maintenance and Repairs Other - Vegetation Non -Payment Vegetation
Outage Cause Types
Figure 13. Top five causes of momentary outages for your utility
0.05
0
0 0.04
L
a 0.03
v
V
C
U1 0.02
U
V
0 0.01
0.00 1 0.00 0.00 0.00 0.00 0.00
None None None None None
Outage Cause Types
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201
Figure 14. Top five causes of momentary outages in your region
1.0
O
00 0.8
r-I
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d 0.6
O 0.2
0.0
1.06
0.13
0.05
0.01 0.01
Unknown Storm Squirrel Bird
Outage Cause Types
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202
Thank you for your active participation in the eReliability Tracker service. We hope this
report is useful to your utility in analyzing your system. If you have any questions
regarding the material provided in this report, please contact:
APPA's Reliability Team
Paul Zummo
Ji Yoon Lee
Matthew Atienza
Reliability .PublicPower.org,
American Public Power Association
2451 Crystal Drive, Suite 1000
Arlington, VA 22202
For more information on reliability, visit www.PublicPower.org/Reliability_.
AMERICAN
P U r
�116,11
.r
ASSOCIATION
Powering Strong Communities
2451 Crystal Drive
Suite l000
Arlington, VA 22202-4804
www.PublicPower.org
Copyright 2023 by the American Public Power Association. All rights reserved.
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