Systems – IEEE Std. 1366-2003. Presented at the NARUC. Staff Subcommittee
on Electric Reliability in Washington, DC. February 13, 2005 by Cheryl A. Warren
...
Measuring Performance Of Electric Power Distribution Systems – IEEE Std. 1366-2003 Presented at the NARUC Staff Subcommittee on Electric Reliability in Washington, DC February 13, 2005 by Cheryl A. Warren (
[email protected]) Chair, IEEE WG on System Design
Topics for Today
What changed between 2001 and 2003?
Why use 1366-2003 - Guide for Electric Power Distribution Reliability Indices
The Major Event Day Definition
Summary
Potential Impact on Regulation
Benchmarking
2
Industry Guidelines Developed IEEE
Std 1366, 2001 Edition, Revised in 2003
Refines
definitions
Created
the Major Event Day concept
Ballot
Approved
by IEEE REVCOM in December 2003
Approved
by ANSI April 2004
Published
by IEEE SA June 2004
–
3
passed w/98% affirmative
Available at www.ieee.org
Major changes between 2001 & 2003
Tightened definitions.
Created the Major Event Day Concept.
A tool that can assist company decisions and regulatory policy making by standardizing engineering metrics and approaches.
Standard definitions are offered that will lead to better comparability. –
4
Still need to address data collection methods.
Policy decisions are left to the regulatory community.
All Events are reviewed including Major Events.
Why Use 1366-2003
Sound Basis for Measuring Performance.
A clearer view of performance, both on a
Daily basis and
During Major Events
Can form a solid basis for review of operational effectiveness, decision making and policy making.
5
More consistent benchmarking.
States are Moving Toward Reliability Regulation BC
OR
ND
MT
WA ID
WY
MN WI
SD NE
NV
UT
CO
MI
IA IL
KS
IN
MO
OK AZ
NM
WV
TN
AR
MS AL LA
TX
PA
OH
KY
CA
ME
VT NH NY
MA,CT,RI VA
MD,DE,NJ
NC GA
SC
FL
Alaska Hawaii
Considering Adoption of MED Reliability Standards in place
Adopted MED Reliability Standards being considered
*source NRRI 2001 survey & other regulatory documents 6
Three State’s Rules
State 1
State 2
A Major Storm is a period of adverse weather during which service interruptions affect at least 10 percent of the customers in an operating area and/or result in customers being without electric service for durations of at least 24 hours.
State 3
7
Major Storms excluded if 15% of customers served are interrupted over the duration of the event.
It was the result of a major weather event which caused more than 10% or a district or total company customers to be without service at a given time.
SAIDI Performance Different Measurement Methods - Same Company 225 200
SAIDI (min)
175 150
Method 1
125
Method 2
100
Method 3
75 50 25 0 1997
1998
1999
2000 Year
8
2001
2002
2003
Methodology Development IEEE
WG on System Design, that has over 130 members, developed the “2.5 Beta methodology” in IEEE Std 1366 - 2003. Members
include utility employees, regulatory staff, employees from manufacturers, consultants and academics. Seven
9
members stepped up to perform the analysis.
Foundations of the Process
Definition must be understandable by all and easy to apply.
Definition must be specific and calculated using the same process for all utilities.
Must be fair to all utilities.
SAIDI was chosen as the indicator…
10
Large and small, urban and rural….
because it is size independent and it is the best indicator of system stresses beyond those that utility’s staff, build, design and operate to minimize.
Foundations of the MED Definition
To allow appropriate review of both operating conditions partition the data into
daily performance and
major event day performance.
Major event days are days where the system operational and/or design limits are exceeded.
We suggest:
11
Using day-to-day events for trending, internal goal setting, and Commission mandated targets.
Separately report performance during major events (no exclusions).
Methodology Development
Several methods were tested and rejected because they did not meet the basic requirements stated in foundations of the process.
Epiphanies
12
SAIDI is a good indicator of major events.
Interruption data is most closely represented by the log normal distribution.
Log-normal nature of the data 68.3% of population is covered by the mean + 1 std. dev.; 95.4% of population is covered by the mean + 2Log-normal std. dev.; SAIDI per Day SAIDI per Day Histogram 99.7% of population is covered by the mean + 3 std. dev.; 60
100 80 60 40 20 0 0
1
2
-20
3
Annualized # of Occurances over 8 years
Annualized # of Occurances over 8 years
120
50 40 30 20 10 0 -10
-5
0 -10
SAIDI
natural log of SAIDI
Allows Mathematicians to use their statistical toolkits. 13
5
10
Two Categories for Measurement
14
The 2.5 Beta Methodology allows segmentation of reliability data into two distinct sets for review.
One set represents those events of such a reliability magnitude that a crisis mode of operation is required to adequately respond. (major events).
The other set represents the reliability impact of those events that a company has built the system to withstand and staffed to respond to in a manner that does not require a crisis mode of operation. (day-to-day operation).
Major Events versus Day to Day Operations All Sustained Interruptions Recommend that targets be set on Day-to-Day Operations.
Including: Transmission, Planned, Etc.
Day-to-Day Operations
15
Report on both data sets separately.
Major Event Days
Results from One Company SAIDI 167.3
SAIDI (Minutes
180 160 140 120 100 80 60 40 20 0
102.64 64.66 May 3 July 9 July 23 Sept. 11 Nov 17-18 Dec 25
Major Event Days 16
365 Days
358 Days
Day to Day
All
Seven Simple Steps 1.
Collect values of daily SAIDI for five sequential years ending on the last day of the last complete reporting period. If fewer than five years of historical data are available, use all available historical data
2.
If any day in the data set has a value of zero for SAIDI, do not include that day in the analysis.
3.
Take the natural logarithm (ln) of each daily SAIDI value in the data set.
4.
Find α (Alpha), the average of the logarithms (also known as the log-average) of the data set.
5.
Find β (Beta), the standard deviation of the logarithms (also known as the log-standard deviation) of the data set.
6.
Compute the major event day threshold, TMED, using the equation:
TMED = e (α +2.5 β ) 7.
17
Any day with daily SAIDI greater than the threshold value TMED that occurs during the subsequent reporting period is classified as a major event day.
Can be calculated in Excel or an OMS program
Major Event Days – A few facts
A day in which the daily system SAIDI exceeds a threshold value, TMED that is determined by using the 2.5 beta method.
18
For example, if TMED = 3 minutes, than any day where more than 3 minutes of SAIDI is accrued is declared a major event day
Activities that occur on major event days should be separately analyzed and reported. Nothing is “Excluded”!!
Benefits of the Approach
19
Adoption of the 2.5 Beta methodology
will allow for consistent calculation of reliability metrics,
provide companies and commissions with a more accurate indication of a Company’s controllable service quality results,
allow a clear review of company response to crisis mode events, and
provide a less distorted indication of the reliability results for companies of all sizes.
Summary of IEEE 2.5 Beta Methodology
Improves the ability to view system reliability performance, thereby making goal setting and trending more meaningful.
Provides a mechanism for reporting on both day-to-day performance and performance during major events. A mechanism that:
20
allows for review of day-to-day performance without considering the outliers that often mask it.
AND, meaningfully focuses on major event performance in its own right to give a clear view of this very different operating condition.
Consistent method that can be applied by all.
Beyond the Standard… PBR and Benchmarking
Ramifications on Commission Mandated Targets One Company's Performance Exclusion Criteria: Commision- 15% System,
200 175
SAIDI Commission Commission Max Penalty Commission Min Penalty Start of Deadband
SAIDI (min)
150 125 100 75 50 25 0 1997
1998
1999
2000
2001 Year
22
2002
2003
2004
Ramifications on Commission Mandated Targets One Company's Performance Major Event Criteria: IEEE2.5β Method
200 175
SAIDI IEEE
SAIDI (min)
150 Lognormal Max Penalty
125 100
Lognormal Min Penalty Start of Deadband
75 50 25 0 1997
1998
1999
2000
2001 Year
23
2002
2003
2004
Ramifications on Commission Mandated Targets
The existing PBR targets will require adjustment if lognormal nature of data is considered.
The variability in year to year performance should be significantly reduced by using the IEEE methodology, therefore performance bands should be adjusted. –
24
Minor weather events will be captured in the day-to-day performance that will still lead to some variability.
Since we know that reliability data is most closely represented by the log-normal distribution, performance bands should be developed using the log-normal data.
Targets should also be reviewed if OMS systems/processes are introduced.
Basis for Target Setting
It’s appealing to use an industry average (benchmark data) to set targets for utilities in your state.
It’s not practical to do so because:
Each utility has historical rates, maintenance and operating practices, system design, customer density, etc. –
Forcing utilities to change to a radically different performance level has serious rate implications.
Using the companies own historical performance is a reasonable approach.
25
These factors set the underlying performance level of the system.
Issues: Introduction of an OMS, changing weather patterns, mergers and acquisitions.
Benchmarking
Data is Never exactly the same!
Two main reasons for differences:
26
Data Collection Process/System Differences
Exclusion Criteria Differences (Basis)
IEEE 1366-2003
addresses data basis issues by clearly defining the rules.
It DOES NOT address the data collection issues
Benchmark We have anonymously analyzed data for 95 companies throughout the US & Canada for companies that range from rural electric co-ops to IOUs.
Basic
Results for 2003.
Quartile SAIDI IEEE 1 83.59 2 110.99 3 156.10 4 401.55
All Respondents SAIDI All SAIFI IEEE SAIFI All CAIDI IEEE CAIDI All 119.45 0.94 1.29 71.04 84.77 223.68 1.23 1.59 93.69 146.73 400.24 1.50 2.14 121.77 211.73 2352.29 3.15 5.53 424.74 1154.85
*includes results from 70 respondents.
Performing another benchmark in 2005. 27
28
IEEE - SAIDI
440 420 400 380 360 340 320 300 280 260 240 220 200 180 160 140 120 100 80 60 40 20 0
Benchmarking data cannot be taken at face value…performance is a function of many things such as rates, service territory, maintenance/operating practices, etc. Q4 may be an acceptable level of performance for some utilities
Q4
Q3 Q2 Q1
93 72 91 3 82 90 71 50 92 89 21 55 24 2 56 81 77 66 69 38 63 25 67 7 65 1 17 22 6 75 88 23 41 49 13 45 29 14 53 48 74 86 54 78 84 52 58 4 20 60 19 64 57 80 12 85 44 16 73 59 51 47 68 15 61 83 62 70 76 79
SAIDI (min)
(2003)
Utility
29
SAIDI IEEE Trends 160 140 120
Minutes
100 80
•OMS Systems
60
•Weather Changes
40 20 0 1999
2000
2001
2002
2003
2004
Year All Companies
30
Small Companies
Medium Companies
Large Companies
Questions…
31
