Electric / Gas / WaterInformation 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 / WaterInformation 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 / WaterInformation 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 / WaterInformation 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 / WaterInformation collection, analysis and application

Knowledge to Shape Your Future 5

MVES Data Flows

Electric / Gas / WaterInformation 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 / WaterInformation 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 / WaterInformation 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 / WaterInformation collection, analysis and application

Knowledge to Shape Your Future 9

Questions?

?