Electric / Gas / Water Information collection, analysis and application Knowledge to Shape Your Future 1 Meter Verification Research Approach –Identify client issues, resources, and needs –List common meter verification problems –Examine the data to identify problems/issues Plot and summarize data Compare with billing loads –Develop data flow chart –Develop software solution

Electric / Gas / Water Information collection, analysis and application Knowledge to Shape Your Future 2 Issue, Resources, and Needs –Issue: New purchase procedures in the wholesale power market in Virginia would soon place increased value in having accurate real time delivery point load data via SCADA. –Resources: Near real time Load data from 225 delivery points, along with monthly hourly billing histories. –Need: SCADA delivery point load data, which was incomplete and often inaccurate, needed to be verified, adjusted, and/or estimated.

Electric / Gas / Water Information collection, analysis and application Knowledge to Shape Your Future 3 Common Meter Verification Problems –Missing data –Meter constant errors –Partial intervals (low usage) –Multiple combined intervals (high usage) –Data “glitches” (very high or very low usage)

Electric / Gas / Water Information collection, analysis and application Knowledge to Shape Your Future 4 Other Problems/Issues Identified in Data Most data was good, but there were: –Zero and negative values (faulty, but not missing) –Repeated values (default inputs, stuck meters) –No metering (not just missing values) –Variable length intervals (20 second – 5 minute) –Limited billing histories (less than 2 years) in some cases –Differing treatment of line and transformer losses SCADA and billing meters sometimes on opposite sides of transformer –Some delivery point combined into billing meters

Electric / Gas / Water Information collection, analysis and application Knowledge to Shape Your Future 5 MVES Data Flows

Electric / Gas / Water Information collection, analysis and application Knowledge to Shape Your Future 6 Approach –Problem: Variable Interval Lengths Solution: Combine into 5 minute intervals. –Problem: Meter Constant and Loss Accounting Errors Solution: Allow user to specify delivery point scale factors. –Problem: Missing Values or Bad Values Solution: Estimate through delivery point regression models of hourly billing data, with weather, day-type, and time-of day, explanatory variables. –Problem: Combined SCADA Meters Solution: Allocation factors available to divide model estimates among related SCADA meters.

Electric / Gas / Water Information collection, analysis and application Knowledge to Shape Your Future 7 Approach –Problem: Out-of-Range Values Solution 1: Check for data hi/lo spikes compared to adjoining intervals Solution 2: Allow user specified high and low limits by delivery point. Solution 3: Estimate where user-specified numbers, by delivery point, of standard deviation of model estimate exceeded. –Problem: Repeated Values (including repeated zeros) Solution: Include a user-specified repeated value check by delivery point. –Problem: Faulty zero and negative values Solution: Include user-specified checks by delivery point.

Electric / Gas / Water Information collection, analysis and application Knowledge to Shape Your Future 8 Reporting –Quality flags allow user to identify treatment of 5 minute data. Actual used Missing – estimate used Repeated too many times – estimate used Etc. –Historic hourly comparisons available through user interface for billing, SCADA metering, and model estimates

Electric / Gas / Water Information collection, analysis and application Knowledge to Shape Your Future 9 Questions? ?