Increasing Variability in SAIDI andImplications for Identifying

Major Events Days

Joseph H. EtoKristina H. LaCommare & Michael D. Sohn

Lawrence Berkeley National LaboratoryJHEto@lbl.gov | (510) 486-7284

IEEE Power & Energy Society General Meeting 2014July 30, 2014

National Harbor, MD

Background

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• IEEE DRWG has begun examining trends in the number ofMajor Event Days identified each year using Standard 1366

• IEEE DRWG provided LBNL with daily SAIDI data from pastbenchmarking analyses to further explore these trends and aswell as possible extensions to the method outlined in Standard1366

• Today, LBNL summarizes its findings and observations for IEEEDRWG’s consideration and discussion

IEEE Standard 1366

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• First developed in 1998 to define reliability indices

• Amended in 2003 to add a consistent approach for segmentingMajor Event Days

• Uses 2.5*beta to estimate a threshold daily SAIDI, Tmed, abovewhich a Major Event Day is identified

• Tmed = exp (α+2.5β)• Beta = log-normal standard deviation• Alpha = log-normal statistical mean

• Amended in 2012 (MED identification unchanged)

Evaluating how 1366 performs

• For a normal distribution:

– Multiplying beta (the standard deviation) by 2.5 covers99.379% of the expected observations (assuming a one-sidedconfidence interval)

– For a year of daily observations, this translates to anexpectation of 2.3 Major Event Days per year

• Using the IEEE DRWG benchmark data, we:

– computed the Tmed for a forthcoming year (using the prior 5years); and

– counted number of MEDs identified (for each year goingforward)

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Questions we sought to address

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• Are the number of MEDs identified by Std 1366 increasingover time?

• Are 5-year sequential groupings of daily SAIDIs normallydistributed?

• How well do extensions to Std 1366 perform?

– Option 1: Use a smaller pool of years for which SAIDIsmay be more normally distributed

– Option 2: Choose a different multiplier on beta

– Option 3: Compute an alternative statisticalrepresentation for the 5-year group of SAIDIs

Are the number of MEDs identified byStd 1366 increasing over time?

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Answer: Yes!

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Are sequential 5-year grouping of dailySAIDIs normally distributed?

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% of Utilities withShapiro-Wilk Test of Normality p-value < α

% SW p-value <0.5

% SW p-value <0.05

Answer: No!

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• In the slides that follow, we consider the effect of differentoptions for selecting Tmed based on direct extensions of themethods and assumptions inherent in Std 1366

• The intent is to evaluate options that may improve our abilityto identify a consistent number of Major Event Days overtime

• None of these options should be confused as potentialrecommendations or suggestions for modifications to Std1366

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Option 1: Use a smaller pool of years, for whichSAIDIs might be more normally distributed

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Reducing the #years does

increase theprobability thedata may be

normallydistributed

But thereare no

noticeablechanges inthe # MEDsover time

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Option 2: Choose a different multiplier on beta

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No. of 2.5 MEDs

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Increasing the betamultiplier can significantly

reduce the # MEDs

Increasing the betamultiplier increases the

share of utilitiesexperiencing 0-3 MEDs

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Efforts to try and fit the data to a normal distribution do not appearto improve the statistical predictability of the data

• S-W test rejected for >95% utilities – not “normal”

• Decreasing the historic number of years to compute Tmeddoesn’t reduce the number of MEDs identified – not “useful”

• Increasing the beta multiplier (variance) does reduce thenumber of MEDs identified but doing so implies the data arenot normally distributed, statistically speaking – not“consistent”

Commentary on Options 1 and 2

Option 3: Compute an alternative statisticalrepresentation for the 5-year group of SAIDIs

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• We examined a mixture modelconsisting of two normal distributions

• Mixture models are popular forrepresenting data withsubpopulations. In this instance: canwe use an automatable approach toidentify and separate extreme events(e.g., owing to weather or plannedoutages) from all other events

• We applied an algorithm to identifythe best fitting combination of twonormal distributions to the data

Determining Tmed using 2 normal distributions

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• Step 1: Compute best fitting mixture of 2 normaldistributions

• Select the Tmed so that the cumulative area underthe left side of the curve adds to 99.379%

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To the left ofthe dashed line:A + B = 99.379%

% Utilities with more than 3, 5, or 7 MEDs

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% IEEE MEDs >3 % Mix MEDs >3

% IEEE MEDs >5 % Mix MEDs >5

% IEEE MEDs >7 % Mix MEDs >7

# MEDsdecreases usinga 2-componentmixture modelcompared to

Std 1366

Comparing the number of MEDs per year

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IEEE Std 1366 vs. LBNL Mixture Model

Mixture model yields fewer MEDs and lower variance

What value would Tmed need to be in order toestimate 2.3 MEDs per year on average?

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Year 2006 Range of Tmed to get ~2.27 MEDs

Distance from 2nd

highest (top of verticalbar) to 3rd highest(bottom of verticalbar) daily SAIDI

Red dash:IEEE Tmed

Blue dash:mixture modelTmed

Identifying 2-3MEDs per year

consistently is stillchallenging

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Summary of our findings to date

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• Major events appear to be increasing over recent years

• Daily SAIDI for IEEE DRWG benchmark utilities are not normallydistributed

• Using fewer historic years to calculate Tmed does not help

• Using higher multiples of beta can reduce the average # MEDsbut is ad hoc; hence, the predictive power for future yearsrequires further study

• The example of a mixture model is promising, but further studyis required

Next steps and food for thought

• Pursue efforts to identify subsets of a utility’s data that follow alog-normal distribution

– Take explicit account of pre-arranged interruptions?

– Segment data within a service territory more finelyaccording to geography?

• Start a discussion on the objective of methods for identifyingMEDs, especially if MEDs are found to be increasing over time

– Consider whether design targets for distribution arechanging over time

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