Methodologies for CVR Impact Measurement

A. Thomas Bozzo Christensen Associates Energy

Consulting April 16, 2014

1

Overview

• Challenges

• Test design

• Estimation approach

• Load model

• Data issues

• Estimation implementation

2

Challenges

• CVR impacts small vs. load variation

• CVRf may vary with end-use load mix

– CVR load impacts may vary by date, time, season

• Routine data issues can swamp CVR effects

– Large customers

– Data dropouts, outages

• Assess different operational models

– “Always on” vs. specific hours

3

Example: Substation Hourly Load

4

Small CVR Load Differences

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Small CVR Load Differences

6

Test Design

• Testing can try to do too much

– Variable voltage reductions (“notch tests”)

– Variable CVR-on test hours

– Testing multiple factors

– Reduced sample sizes, possible confounding

• Also can do too little

– Partial-day testing

– Can’t measure what you don’t test

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Test Design

• Ideally—keep it simple

• Test CVR on/off only

• Single-level treatment (@ max voltage drop)

• Randomize where possible:

– Feeder/substation selection, N >> 2

– Prescreen for large customers, “episodic” loads

– CVR-on test day selection

• Test all hours of potential interest consistently

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Estimation Approach

• Ordinary least squares vs. “robust” regression

• OLS computationally simple, but susceptible to outliers

• Robust regression computationally complex, algorithms can fail to reach estimates

• Results should be similar with clean data

– Robust w/r/t data outliers

– Robust estimates still have sampling variability

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Estimating Equation

• Single hourly time series linear regression model for each tested substation

– Hourly frequency driven by weather data

– Sub load(MW)=f(weather variables, time-of-day, day-of-week, weather-time interaction terms, solar index, CVR on/off state)

• CVR impacts allowed to vary by hour and between weekdays/weekends

– Similar to running regressions for each hour

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Estimating Equation

• Evaluated several weather specifications

– Current weather and moving averages (MA)

– Different thresholds for cooling degree-hours (CDH; summer model)

• Evaluated for prediction error and fit

– The final weather specification (using current, 3-hour MA, and 24-hour MA of cooling degree hours) generally performed well with either 65- or 70-degree CDH thresholds

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Data Issues

• Test schedule validation

– Generally can visually ID CVR on/off transitions

– Unreported exceptions to test schedule

– Periods w/ ambiguous CVR on/off states

• Reviewed loads for outages, data dropouts, other unusual data patterns

• Corrected CVR on/off state by hour as needed

• Dropped periods with gross anomalies

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Example: Voltage Transitions

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Estimation

• We used OLS on screened substation data

– Screen before estimation as much as possible

– Ran corresponding median regression to check adequacy of data screening

• Models explain most variation in sub load

• Average CVRf ~0.5, 90% margin of error 0.3

– Outliers often cases with worse model fit (R-squared <90%; >97% in best cases)

– Results by hour much more variable

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Example 1: CVRf By Hour

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OLS vs Median Regression CVRf by Hour, Test Substation A

Median OLS

Example 2: CVRf By Hour

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Summary

• Weather-sensitive model explains most cooling-season substation load

• Most tested substations have statistically significant impacts, average CVRf is around 0.5

• Similar impacts from median regression

• No clear relationship between substation characteristics and CVRf based on limited classification data

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