NEEA DEI StudyNEEA DEI StudyData Analysis PlanData Analysis PlanOctober 28, 2005October 28, 2005

RLW Analytics, Inc.Roger L. Wright, Chairman, and

Principal Consultant

OutlineOutline

Review our Clatskanie Substation Analysis

Highlight issues in future analysis of CVR substation pilots

Review HVR statusDiscuss plans for analysis of HVR

studies

Clatskanie Substation Clatskanie Substation AnalysisAnalysis

We used the Clatskanie data to test our analysis methodology

We did not have information on the control status each day

Our first attempt was to regress kWh on voltage, • Was not successful• Problem traced to simultaneity of relationship

between voltage and kWDeveloped an algorithm to classify each day as

a control or comparison dayThis gave more plausible results – but the data

are still preliminary

Initial AnalysisInitial Analysis

Initial model: ln(kWh) = β0 + β1 ln(V )

where V = Voltage

Equivalent to assuming a 1% drop in voltage yields a β1 drop in kWh

The Observed DataThe Observed Data

Initial ResultsInitial Results

We were hoping for positive betas!

Simultaneity of Voltage and Simultaneity of Voltage and kWhkWh

CVR effect: A drop in voltage is expected to yield a drop in kWh => + association.

Load effect: An increase in kWh may cause the voltage to fall => - association

A simple regression of kWh on voltage will reflect both effects and give an erroneous estimate of the CVR effect.

RemedyRemedy

Let C = voltage control status, 0 = off or 1 = onOr C = quantitative level of control variable

Record the control status day by day and hour by hour

Study the effect of control status on both kWh and Voltage

Identifying the Control Identifying the Control StatusStatus

Control alternating off and on

No clear control

Energy Print of Control Energy Print of Control StatusStatus

The energy print of voltage revealed periods of good control, periods of poor control, and periods of missing data

Classification of Control Classification of Control StatusStatus

When the circuit was in control the step function was set to 118; otherwise 122

Used to validate the classification visually

Verification of Control Verification of Control StatusStatus

Con

trol In

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ato

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ctu

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Effect of Control on Voltage Effect of Control on Voltage (mnv)(mnv)

Figure 1: Change in Average Voltage

Figure 2: Change in Average kWh

Effect of Control on kWhEffect of Control on kWh

ββ ( (BetaBeta)= )= ΔΔkWh/kWh/ΔΔMNVMNV

For Phase A Feeder A - Divide the - 4% change in kWh by the - 3.2%

change in MNV to obtain a Beta of 1.2

Across All Feeders and Phases - Divide the - .5% change in kWh by the - 3.1% change in MNV to obtain a Beta of .2

-1

0

1

2A

.A

A.B

A.C

A2

.A

B.A

B.B

B.C

BB

.A

BB

.B

BB

.C

C.A

C.B

C.C

D.A

D.B

D.C

E.A

E.B

E.C

Figure 3: Beta, the Change in kWh for a 1% Change in Voltage

Estimated Beta Estimated Beta by Feeder and Phaseby Feeder and Phase

Erratic Stable

Impact By SeasonImpact By Season

Summer Smaller Loads Negligible Cooling Loads Loads are mostly Lights and Plugs

Winter Heating load increases the overall load Voltage control expected to have little or no

effect on Electric Heating Voltage Control, therefore should have

Modest Effect on Lights and Plugs Smaller percentage effect in winter than

summer

Figures 5 and 6Figures 5 and 6

Summarize results for the Winter periodOverall Beta was only 0.1

Figures 7 and 8Figures 7 and 8

Summarize results for the Summer periodOverall Beta was 0.3

Figure 5: Winter Change in Average Voltage

Figure 6: Winter Change in Average kWh

Figure 7: Summer Change in Average Voltage

Figure 8: Summer Change in Average kWh

Effect of TemperatureEffect of Temperature Fit a regression model of the form

kWh = β0 + β1 C + β2 T + ε

kWh – Observed Energy Use of the feeder and phase in any hour of any Control period

C – Indicator Variable that is equal to 1 if control was on in the hour, 0 otherwise

T – Heating degreesIf temperature < 650 then T = 650 –

temperature T = 0 otherwise

Interpretation of the Interpretation of the CoefficientsCoefficients β0 = Least Squares Estimate of the

expected kWh use in an hour with Control Off and with 0 Heating Degrees

β1 = Least Squares Estimate of the change in kWh use in an hour with Control On vs. Control Off

β2 = Least Squares Estimate of the change in kWh use in an hour per unit increase in heating degrees

Figures 9 and 10Figures 9 and 10

Separate Winter and Summer regressions for each combination of feeder, and phase

kWh_off = Estimated value of β0

del_kWh = Estimated value of β1

pct_kWh = del_kWh/kWh_off

Finally, used change in voltage from Figures 5 and 7 to calculate the Beta as pct_kWh / pct_MNV

Figure 9: Winter Change in Average kWh

Winter resultsWinter results

Summer resultsSummer results

Figure 10: Summer Change in Average kWh

Figures 9 and 10Figures 9 and 10 Support the hypothesis that voltage

control has little or no effect on the heating component of the feeder load

Indicate that the average value of Beta is about 0.3 in both the winter and summer, once the heating load has been excluded

A 1% reduction in voltage appears to reduce the non-heating kWh load on the feeder by 0.3% on average across these feeders regardless of the season

Effect By HourEffect By Hour

Repeat this analysis for each hour of the day, from 1 to 24

For each combination of feeder, and phase, and each of the 24 hours, estimate a separate regression model of the form

kWh = β0 + β1 C + β2 T + ε Combined Winter and Summer seasons

into a single regression – as model captured effect of winter heating

Figure 11: Hourly Load Profile of Base Load with Voltage Control Off (0) and On (1),

Feeder A Phase A

Hourly results for Feeder A Hourly results for Feeder A Phase APhase A

Figure 13: Hourly Load Profile of Base Load with Voltage Control Off (0) and On (1)

Average of all Feeders and Phases

Average hourly resultsAverage hourly results

Figure 13Figure 13Provides graphs of average non-heating

hourly load profile of all combinations of feeder and phase with and without voltage control

Voltage regulation has on average a very small effect

Effect is most consistent in the early morning hours when the load is smallest

During peak load effect is negligible

Lessons Learned from Lessons Learned from Clatskanie Clatskanie

The importance of clean voltage and kwh data and accurate information about the status of experimental control

Naive regression analysis can lead to biased findings

Beta seems to vary by end use and seasonCareful regression analysis can ferret out

effects (betas) by season or end use

Unresolved QuestionUnresolved Question

How do the three phases of a feeder interact?

Is it best to analyze each phase separately or can they be combined?

HVR Studies - ObjectivesHVR Studies - Objectives

Estimate the customer-side portion of the CVR effect

Help estimate how the CVR effect varies with end use

Help adapt the findings to various utiities and service areas

Targeted End Use Targeted End Use CategoriesCategories

Effects shown are from prior BPA end use studyWe want to estimate the betas for these four

end use categories

ApproachApproachInstall HVR devices in a stratified

sample of homes to control the voltage (off or on) on a known schedule

Do an onsite audit of each sample homeCollect whole-house load data on hourly

kWh and voltageAnalyze the resulting data much like

substation data, but rolling in the end use information to estimate the end-use effects

Foundations for the HVR Foundations for the HVR AnalysisAnalysisβ = ∆ kWh / kWh

Total House kWh = Sum of kWh by EndUse, i.e. kWh = Σ kWhEU

Similarly ∆ kWh = Σ ∆ kWhEU

where ∆ kWhEU = βEU kWhEU

So β = ∆ kWh / kWh = Σ βEU (kWhEU / kWh)

ApproachApproach

1. the overall β of the house2. The end use energy share of the

house kWhEU / kWh for each of the four end uses

A) Analyze each home’s data to estimate

B) Regress the overall β on the four end use energy shares to estimate the four

end-use betas

Results will be developed byResults will be developed by

1. Western region, all electric2. Western region with gas service3. Eastern region all electric4. Eastern region with gas service

Market segments:

Measures of energy and demand:1. Annual kWh2. Seasonal kWh3. Class peak kW

The Keys to SuccessThe Keys to Success

Reliable estimates of the whole-house betas for most of the sample homes.

Accurate estimates of the end use energy shares.

Substantial variation in the end use energy shares from home to home in the sample

Whole-house BetasWhole-house Betas

Our Clatskanie analysis indicates that we must have accurate information on HVR control status

Each house can be on a different control schedule, but we must know the schedule

End-use Energy SharesEnd-use Energy Shares

We will integrate the information from the onsite audits and whole premise load data

Space heating, water heating, and AC have recognizable energy prints

Must rely on the audits for – Resistance space heating vs. heat pumps– Incandescent vs fluorescent lamps

Other plug loads will generally not be identifiable Probably will have to settle for annual or seasonal

end use shares but not hourly

Variation in End-use SharesVariation in End-use Sharesfrom Home to Homefrom Home to Home

Expect variation due to availability of natural gas, vintage of home, climate zone and service area

Will need to combine all sample homes across utilities

Can hope to borrow strength using seasonal analysis

ConcernsConcerns

Limited time and money for the analysis

Uncharted territoryCVR effects are relatively small and

hard to detectMay depend on severity of weather

during study period