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NEEA DEI Study
Analysis Plan
August 9, 2005
RLW Analytics, Inc.
Roger L. Wright, Chairman, and Principal
Consultant
Outline
Review our Clatskanie Substation Analysis
Highlight issues in future analysis of CVR
substation pilots
Review HVR status
Discuss plans for analysis of HVR studies
Clatskanie Substation Analysis
 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 kW
 Developed an algorithm to classify each day as a
control or comparison day
 This gave more plausible results – but the data are
still preliminary
Initial 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 Data
Initial Results
We were hoping for positive betas!
Simultaneity of Voltage and kWh
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.
Remedy
Let C = voltage control status,
0 = off or 1 = on
Or 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 Status
Control alternating off and on
No clear control
Energy Print of Control Status
The energy print of voltage revealed periods of
good control, periods of poor control, and periods of
missing data
Classification of Control Status
 When the circuit was in control the step
function was set to 118; otherwise 122
 Used to validate the classification visually
Actual Voltage
Control Indicator
Verification of Control Status
Effect of Control on Voltage (mnv)
Figure 1: Change in Average Voltage
Effect of Control on kWh
Figure 2: Change in Average kWh
β (Beta)= ΔkWh/ΔMNV
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
Estimated Beta
by Feeder and Phase
Erratic
2
Stable
1
0
E.C
E.B
E.A
D.C
D.B
D.A
C.C
C.B
C.A
BB.C
BB.B
BB.A
B.C
B.B
B.A
A2.A
A.C
A.B
A.A
-1
Figure 3: Beta, the Change in kWh for a 1% Change in Voltage
Impact 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 6
Summarize results for the Winter period
Overall Beta was only 0.1
Figures 7 and 8
Summarize results for the Summer period
Overall 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 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 degrees
If temperature < 650 then T = 650 – temperature
T = 0 otherwise
Interpretation of the Coefficients
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 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
Winter results
Figure 9: Winter Change in Average kWh
Summer results
Figure 10: Summer Change in Average kWh
Figures 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 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
Hourly results for Feeder A Phase A
Figure 11: Hourly Load Profile of Base Load with Voltage Control Off (0) and On (1),
Feeder A Phase A
Average hourly results
Figure 13: Hourly Load Profile of Base Load with Voltage Control Off (0) and On (1)
Average of all Feeders and Phases
Figure 13
Provides 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 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 season
 Careful regression analysis can ferret out effects
(betas) by season or end use
Unresolved 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 - 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 Categories
 Effects shown are from prior BPA end use study
 We want to estimate the betas for these four end
use categories
Approach
Install 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 home
Collect whole-house load data on hourly
kWh and voltage
Analyze the resulting data much like
substation data, but rolling in the end use
information to estimate the end-use effects
Foundations for the HVR Analysis
β = ∆ kWh / kWh
Total House kWh = Sum of kWh by End
Use, i.e. kWh = Σ kWhEU
Similarly ∆ kWh = Σ ∆ kWhEU
where ∆ kWhEU = βEU kWhEU
So β = ∆ kWh / kWh = Σ βEU (kWhEU / kWh)
Approach
A) Analyze each home’s data to estimate
1. the overall β of the house
2. The end use energy share of the
house kWhEU / kWh for each of the
four end uses
B) Regress the overall β on the four end
use energy shares to estimate the four
end-use betas
Results will be developed by
Market segments:
1.
2.
3.
4.
Western region, all electric
Western region with gas service
Eastern region all electric
Eastern region with gas service
Measures of energy and demand:
1. Annual kWh
2. Seasonal kWh
3. Class peak kW
The 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 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 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 Shares
from 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
Concerns
Limited time and money for the analysis
Uncharted territory
CVR effects are relatively small and hard
to detect
May depend on severity of weather during
study period