NEEA DEI Study Data Analysis Plan October 28, 2005 RLW Analytics, Inc. Roger L. Wright, Chairman,...
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Transcript of NEEA DEI Study Data Analysis Plan October 28, 2005 RLW Analytics, Inc. Roger L. Wright, Chairman,...
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
dic
ato
rA
ctu
al V
olt
age
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