Presentation on theme: "Working with AMI Data Eric Jung SouthEastern Illinois Electric Cooperative."— Presentation transcript:
Working with AMI Data Eric Jung SouthEastern Illinois Electric Cooperative
Load Modeling Hourly data advantages Accuracy! (less than 5% variance) Fast load allocation Hourly data disadvantages File verification & estimation difficult and time consuming Must mix with traditional load allocation if AMI not 100% deployed
File Setup Interval files Desired intervalDi Desired interval -1Di-1 Desired interval +1Di+1 Desired interval, different day (similar load characteristics) Desired interval, different day 2 Repeat until all meters have a reading
File Setup Continued Outage file (fast ping) for desired day CIS data Multiplier Billing rate (optional) Reactive load Matching field to outage table Location number link to Premise ID
Validation Pulse count to KW multiplier: (≠ Kh) Verify meter type, multiplier and module match Multiplier = (Kw * 1000) / Pulse count
Estimation If valid read from desired interval (Di) use it directly If not use Di-1 or Di+1 If all three invalid use same interval from different day Repeat until valid reads for all meters
Reading Modification Sum total load by interval Apply adjustment factor by percentage difference between intervals. Ie. Di-1 total load is 5% < Di. Divide all Di- 1 reads by 95% Suggest applying adjustment factors by billing rate.
Additional Load Info Take phasing from Outage file on single phase meters Three phase loads must come from another source: CIS Mapping Reactive load must come from CIS
Outage Data Location # Meter # Module # Phase Substation Master Table Location # Meter # Module # Phase Substation Multiplier Load Interval 1 Load Interval 2 KVAR Interval Data 1 Module # Meter # Meter Type Pulse Count Kw (converted pulse) Table links - Blue Data source - Green CIS Data Location # PF% Multiplier
Load Application for 100% AMI One load group Set sources to swing Set CF% to 100% PF % only applies to those without KVAR Apply load and save errors!
Load Application for < 100% AMI All AMI data in one load group Settings for this load group will be as for 100% AMI Remainder will be as traditional Run load allocation and save errors!
Phasing Correction Match load file phasing with error file from load application Use “re-phase elements in file” updateable utility to phase according to load file Re-run load application and view errors Errors will be connectivity errors
Accuracy Absolute: 3.3% (average deviation) Individual phase variation >10A indicates phasing or loading errors. Normal < 5 A error per phase at feeder level
Two Feeder Examples Johnston City NW (average feeder) Shell East (very accurate) ModeledActual% Diff KW3098.23007.73.01% KVAR608.9635.74.22% A Amps148.5139.86.19% B Amps133.9134.60.49% C Amps138.7133.53.86% ModeledActual% Diff KW3959.03964.80.15% KVAR A Amps209.1211.10.93% B Amps151.2149.11.44% C Amps165.4160.03.34%
Lessons learned Check large industrial loads If load down during peak, consider adjusting to realistic level for analysis Trust the Twacs phasing, but check for phase rolls in software Scrutinize the pulse count multipliers! There will be errors!
Blink File Import Setup blink file using AMI momentary outage data Suggest weekly or monthly intervals Use “apply reliability indexes” utility Element name,saidi,saifi,caidi… Element name,blink week 1,blink week 2…
Blink Analysis Set “color by custom” Graphical indication of blinking line sections
Single Outage File Import Similar to blink file import Leave only location and on/off status in file Convert outage status into 1 (on) or 0 (off) Save as CSV and load as “reliability.txt” Provides a snapshot of system status
Multiple Outage File Import Link several outage files together based on location Create one master database with several on/off entries (maximum of 6) i.e. Element name,2pm result, 4pm result… Provides progress view of system restoration
Outage Analysis Single outage file: Color by custom based on phase Highlights line section outages Multiple outage file Color by custom based on status change
Conclusions AMI data can bring load model accuracy to the next level Apply reliability indexes utility is an extremely flexible tool AMI data is not likely to save time on load allocation
Contact Info Eric Jung Engineering and Purchasing Manager SouthEastern Illinois Electric Cooperative email@example.com