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NERC 2017 Long Term Reliability Assessment Update NERC 2017 Long Term Reliability Assessment Update

NERC 2017 Long Term Reliability Assessment Update - PowerPoint Presentation

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NERC 2017 Long Term Reliability Assessment Update - PPT Presentation

amp Weatherbased Load Shape Weighting Proposal for Reserve Margin Studies Pete Warnken August 18 2017 2017 NERC LTRA Update NERC 2017 LTRA Status Update Latest Preparation Schedule 3 2016 LTRA Summary Data Table ID: 632062

based risk probability load risk based load probability 2017 cdr margin reserve summary weather 2016 probabilities weights resources tier

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Presentation Transcript

Slide1

NERC 2017 Long Term Reliability Assessment Update

&

Weather-based Load Shape Weighting Proposal for Reserve Margin Studies

Pete Warnken

August 18, 2017Slide2

2017 NERC LTRA UpdateSlide3

NERC 2017 LTRA Status Update

Latest Preparation Schedule

3Slide4

2016 LTRA Summary Data Table

4

Comparable to CDR Planned

Existing & Net Transfers plus Tier 1

Projects

not included in Tier 1, but have completed

Screening Studies

Anticipated Resources plus Tier 2 Resources and planned mothballs, minus Unconfirmed Retirements

Aggregate PUN capacity forecast

DC Tie Net Flows to MexicoSlide5

May 2017 CDR

Reserve Margin Comparison

5

Main differences

due to:

Unit

status changes after CDR

release

Planned solar and wind project deferrals (-230 MW for 2018)

Unit retirements (-71 MW)Summer rated capacity changes for several existing units (cumulative impact, approx. -50 MW)Treatment of DC Tie capacity (-97 MW in 2018

)Note that the cutoff date for including resources in a given year is July 15, whereas the date for the CDR report is June 1Slide6

Weather-based Load Shape Weighting

ProposalSlide7

Methodology Summary

Weather Risk Measure

Occurrences

of c

onsecutive days per year with temps greater than 100

F for DFW, Houston, and Austin (1950-2016

), resulting in three

data series

Apply regional load weights to three data series and sum into

composite risk score [0  x  36.3

]Determine risk score frequenciesCreate histogram

with bin range from 0 to 37Calculate normalized relative frequencies [0

 x%  1]

7Slide8

Methodology Summary

Outlier

detection

using Quartile Fence Method

Determine breakpoints for “mild” and “extreme” outliers based on relative frequenciesAssign probabilities to each frequency based on risk ranges

No Risk, Low Risk, Moderate Risk, High Risk (mild outliers), Extremely High Risk (extreme outliers)

8Slide9

Methodology Summary

Load Profile Probability Assignment

Assign a probability to each load profile based on its

composite risk

scoreNormalize probabilities based on the load profiles used

9Slide10

Sample Calculation

Results

10

Walk-through of CalculationsSlide11

How Does

C

hanging Sample Size Affect Results?

Conducted analysis for years 1980-2016

Normalized probability weights are less variable:

11Slide12

Reserve Margin Impact

As a test, replaced original weather-year probability weights used for Jan. 2015

PUCT’s Reserve Margin

analysis

with values based on the proposed approach

RM Study: assumed 1% probability of occurrence for 2011 and other annual probabilities set to 9.9%

12