Coolidge Connector Need and NTA
Analysis
Prepared for the Vermont Department of Public Service
by Energy and Environmental Economics, Inc.
6/6/2008
Brian Horii
Doug Allen
Dr. CK Woo
Eli Kollman
Michele Chait
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Table of Contents
INTRODUCTION ..............................................................................................3
BACKGROUND ..................................................................................................3
SUMMARY OF FINDINGS ....................................................................................4
PEAK DEMAND FORECAST .........................................................................8
BACKGROUND ..................................................................................................8
LOAD FORECAST PROCESS................................................................................9
INCORPORATING ENERGY EFFICIENCY ........................................................... 10
LOAD FORECAST RESULTS.............................................................................. 13
WEATHER SENSITIVITY OF VERMONT LOADS ................................................. 16
COMPARISON TO LA CAPRA AND ISO-NE FORECASTS ................... 18
LA CAPRA FORECAST ..................................................................................... 18
ISO-NE FORECAST ......................................................................................... 19
ISO-NE 2007 VERSUS 2008 CELT FORECAST ................................................ 22
ENERGY EFFICIENCY ................................................................................. 24
STATUS QUO EE ............................................................................................. 24
INCREMENTAL EE ........................................................................................... 24
MEASURE PENETRATIONS FOR INCREMENTAL EE .......................................... 26
POTENTIAL IMPACT OF THE INCREMENTAL EE ............................................... 26
DEMAND RESPONSE .................................................................................... 28
LA CAPRA ESTIMATE ...................................................................................... 28
CURRENT DEMAND RESPONSE ENROLLMENT ................................................. 29
DEMAND RESPONSE IN ISO-NE FORWARD CAPACITY MARKET..................... 30
E3 DR FORECAST ........................................................................................... 32
PEAKER UNITS IN VERMONT ................................................................... 34
GENERATION UNITS ........................................................................................ 34
RELIABILITY METRICS .................................................................................... 35
MODELING ASSUMPTIONS AND RESULTS ........................................................ 37
GENERATOR SIMULATION ............................................................................... 37
CAPACITY DISTRIBUTION APPROACH ............................................................. 40
INCREMENTAL PEAKING UNIT CAPACITY ....................................................... 42
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COMBINED HEAT AND POWER (CHP) .................................................... 43
ECONOMIC FEASIBILITY.................................................................................. 43
POWER AND GAS PRICE PROJECTIONS ............................................................ 43
CHP UNIT CONFIGURATION ........................................................................... 44
CHP COST EFFECTIVENESS UNDER ALTERNATE ELECTRICITY COST
SCENARIOS ..................................................................................................... 45
TRANSMISSION NEED DATE ..................................................................... 47
COST OF DELAY OR DEFERRAL .............................................................. 50
BASE CASE ..................................................................................................... 50
DELAY CASES ................................................................................................. 50
E3’S DEFERRED CONSTRUCTION CASE ........................................................... 52
CONCLUSION ................................................................................................. 54
APPENDIX A: 1-IN-10 VERSUS 90TH PERCENTILE PEAK LOAD
FORECAST ...................................................................................................... 55
APPENDIX B: PEAKING GENERATION ANALYSIS .............................. 57
APPENDIX C: GENERATOR SYSTEM CAPACITY DISTRIBUTION .. 58
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Introduction
Background
On November 7, 2007, Vermont Transco, LLC, and Vermont Electric
Company, Inc. (collectively, “VELCO”) and Central Vermont Public
Service Corporation (“CVPS”, together with VELCO, “Petitioners”)
filed an application with the Vermont Department of Public Service
(“Vermont DPS”) for a Certificate of Public Good for the “Southern
Loop Project.” The Petitioners claim that this project is needed to
address reliability concerns arising in Vermont in the near future due to
load growth in Vermont and the greater New England region.
The filing indicates that under VELCO’s projections, the Vermont
transmission system will not satisfy mandatory NERC reliability criteria
in 2009 under certain contingencies, putting the system at risk for voltage
collapses and thermal overloads that have the potential to affect service
not only in Vermont, but in New York and the rest of New England as
well.
The VELCO transmission system relies heavily on imports from outside
the state, through major ties with Quebec, New York, and New
Hampshire, to serve Vermont load and the Connecticut Valley Electricity
Company (“CVEC”) service territory in New Hampshire. Within the
state, as much as 45% of the state’s power is carried on Line 340 from
Vernon to Coolidge, even in the absence of outages. The heavy
dependence on this line puts VELCO at risk for reliability problems if it
fails, especially when either the Highgate Converter (200 MW of import
from Canada) or the PV20 line (an underwater line providing imports
from New York) is out of service.
Load flow models prepared by VELCO show that their system is at risk
at load levels as low as 1,155 MW in 2010, only 37 MW higher than the
all-time peak on the VELCO system, which occurred on August 2, 2006.
As load outside of Vermont grows, however, the threshold load level at
which the system is at risk in the event of a contingency decreases. To
solve these reliability issues, the Petitioners have proposed a second 345-
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kV line running parallel to Line 340 from Vernon to Coolidge, along
with a substation into which that line will connect and other upgrades to
solve local reliability problems.
La Capra Associates, Inc. was hired by VELCO to examine feasible nontransmission alternatives (“NTAs”) that could act as cost-effective
alternatives to the Southern Loop Project in the appropriate time frame.
In looking at these NTAs, La Capra performed analyses of the potential
for energy efficiency, distributed generation, and demand response to
achieve reductions in peak load and mitigate the reliability problem.
The La Capra analysis identified four NTAs consisting of demand
response, energy efficiency, and distributed generation schemes, as well
as a fifth hybrid alternative that combined the proposed line with
achievable energy efficiency. These alternatives were compared to
VELCO’s proposal in terms of societal cost, capital cost, capacity
provided, and risk level. Overall, the La Capra analysis concluded that
the hybrid option, combining the aggressive pursuit of peak reductions
through energy efficiency with the Southern Loop Project, had the most
favorable tradeoff between risk and cost.
Summary of
Findings
Energy and Environmental Economics, Inc. (“E3”) was hired by the
Vermont Department of Public Service to “provide a detailed review of
petitioner’s analysis of the feasibility and cost-effectiveness of targeted
non-transmission alternatives to the transmission proposal.”1 E3 divided
this analysis into four areas: (1) load forecasting; (2) distributed
generation resources (including combined heat and power); (3) energy
efficiency; and (4) other demand-side resources. In each category, E3
analyzed the information presented by VELCO and La Capra for any
deficiencies, and produced alternate estimates of resource availability
where appropriate. The report examines the effect of any alternate
1
Vermont Department of Public Service Request for Proposals
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estimates of resource availability on the need for the project, and presents
E3’s findings on the value of deferring the need for the line.
E3’s biggest concern regarding the La Capra analysis is their load
forecasting methodology and projected load levels. La Capra’s
projections for future load are based on adjustments made to a DPS load
forecast produced in the 2006 Draft Update to the Vermont Electricity
Plan. When compared to forecasts produced independently by E3 and
ISO-NE, the La Capra forecast predicts peak load to be 32 MW higher in
2011, when the proposed line is projected to be in service. Due to the
relatively small Vermont load, a difference of 32 MW represents about
two years of load growth. The difference in the load forecasts leads to a
difference of a few years in the timing of the problem; this, in turn,
affects the analysis of NTAs.
La Capra estimates that demand resources will be able to reduce the
summer peak load by 14 MW in 2010, based on adjustments to FERC
and ISO-NE estimates of achievable peak reductions as a percentage of
the summer peak. However, since the La Capra report was released,
ISO-NE has conducted the first of their Forward Capacity Auctions as
part of their Forward Capacity Market, which contracted resources for
the 2010 – 2011 delivery year. E3 incorporates the results of that
auction, and finds that the Forward Capacity Auction attracted less
demand response for the summer of 2010 (after adjustments for response
rate, location, etc.) than was assumed in the La Capra analysis.
The analysis of generation resources centers on issues regarding the
availability of peaking generation resources. In their analyses, VELCO
and La Capra assumed that many of the smaller diesel and distilled fuel
oil generators in Vermont would be unavailable to run during capacity
emergencies, and so were “held in reserve for loss of McNeil,”2 the
largest generator in Vermont. However, many of these same generators
bid into the Forward Capacity Auction held in February, which indicates
2
Prefiled Testimony of Dean L. LaForest and Christopher Diebold, p. 44 at 8.
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that they intend to be available at least until the summer of 2010.
Though these units are unreliable when taken individually, E3 believes
that the aggregate dependable capacity of the generators should be
counted toward meeting peak demands and lowering the net peak loads.
E3 estimates that the small generators plus the two planned 20MW
Swanton peaker units can firm-up McNeil and provide an additional
45MW of dependable summer peak capacity with a high level of
reliability.
Under the assumptions used by La Capra in their analysis of combined
heat and power (“CHP”) options, E3 agrees that there are no costeffective opportunities for CHP installations in the relevant study area.
However, this result is driven by La Capra’s natural gas and electricity
price forecasts, which project that nominal electricity prices will decrease
while nominal gas prices remain at or near their current level. E3
expects that as the current long-term contracts with Entergy and Hydro
Quebec begin to expire in 2012, electricity prices will increase as those
contracts are replaced with new generation or market-priced power.
While higher electricity prices could result in cost-effective CHP
applications in the future, the amounts are small and the Coolidge
Connector project would need to be deferred several years before one
could confidently forecast CHP adoptions.
With regards to the testimony of Mr. Ralph Roam on the costs of the
project, E3 disagrees with Mr. Roam’s finding that delays of the project
will necessarily result in high inflation in the real cost of the project.
While E3 agrees with Mr. Roam’s finding that starting the project on
time and delaying or suspending it mid-construction will lead to
significant cost increases, E3 also believes that there is value to deferring
the start date of the project. Though the deferral value calculated by E3
is small due to the high rate of inflation in the costs of materials, it
implies that there is no increase in the present value cost of the project if
it is deferred, which is contrary to Witness Roam’s findings because he
did not examine a straight deferral case.
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E3 conservatively estimates that using the load thresholds provided in the
La Capra report the transmission project is needed in 2012, but could be
pushed out to 2014 or 2015 with aggressive incremental energy
efficiency activities. However, if the PSB determines that the
Department’s lower peak load thresholds are the appropriate levels to use
for planning purposes, then the transmission project cannot be deferred
and should proceed on its current schedule. Department witness Smith
discusses the reasoning and analysis behind the lowering of the 2011
peak load threshold from 1155MW to 945MW..
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Peak Demand Forecast
Background
This section presents E3’s assessment of the reasonableness of the 1-in10 peak demand forecast used by La Capra to assess NTAs. Based on
the forecast described below and supported by a comparison to the ISONE 2008 CELT report, E3 concludes that La Capra’s summer peak3 load
forecast for planning purposes overstates the peak demand for Vermont
and CVEC4 by approximately two years of demand growth.
E3 has prepared a summer peak demand (MW) forecast for Vermont for
2008-2017. The forecast includes the CVEC load served by the Vermont
transmission infrastructure, and is at the transmission level (i.e. includes
losses). E3 used two forecast methods The first follows the 1-in-10
extreme weather methodology used by ISO-NE, and the second is a 90th
percentile forecast that is more conservative (results in a higher peak
forecast) than the 1-in-10 method. E3’s analysis utilizes the second
(higher) forecast as a conservative assumption.
In general terms, E3’s forecast is an ordinary least squares (OLS)
regression using weather, a trend variable for the year, and dummy
variables for day of the week and month of the year. The regression is
performed using daily summer peak data from 1991 through 2007, and
captures the increasing sensitivity of peak demands to weather that
Vermont has been experiencing due to increased air conditioning usage
in the state. The forecast also includes the demand reductions that are
expected to occur due to continued energy efficiency activities by
Efficiency Vermont (EVt) and the Burlington Electric Department
(BED).
3
The southern portion of Vermont experiences its peak loads in the winter.
However, the service territory as a whole has evolved over the past ten years
from winter peaking to summer peaking. Accordingly, this forecast focuses on
the summer peak demands for Vermont.
4
The transmission system serving the state of Vermont is a network of 345kVa
and 115kVa VELCO infrastructure that also serves a small section of Central
Valley Electric Cooperative (CVEC). Unless otherwise noted, any references to
Vermont load include the portion of CVEC load served by VELCO.
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Load Forecast
Process
Exhibit BKH-2
To make the net peak demand forecast, E3 used the following steps:
1. Apply ordinary least squares (OLS) to a sample of 799 daily peaks
recorded on weekdays with average weighted temperature humidity
index (WTHI) at or above 65 in the summer months of June –
September during 1991-2007.5
2. Use the estimated regression to make a 1-in-10 forecast that reflects
the 90th percentile of average WTHI recorded on Vermont’s annual
summer peak days during 1965-2007, see Table 1 below. This
forecast is a conditional forecast, aiming to capture an extreme
weather event of 76.9 average WTHI6 expected to occur with a 10%
probability. This 1-in-10 methodology is consistent with the ISONE Extreme Weather Peaks and La Capra 90/10 forecasts. The
forecast also includes projected levels of energy efficiency activities
at current levels (status quo EE) as describe below.
Table 1: Annual maximum Average WTHI for Vermont 1965-2007
Summary statistic
Average WTHI
Number of observations
43
Mean
74.9
Variance
2.71
Median
74.9
90th percentile
76.9
3. Use the estimated regression to establish the distribution of a peak
MW forecast based on the mean and variance of average WTHI
shown in Table 1. The 90th percentile forecast is then defined as the
MW level that an actual peak may exceed with only a 10%
5
The analysis uses 1991 through 2007 daily peak loads for Vermont plus the
portion of CVEC load served by VELCO (VT+CVEC) from VELCO’s power
accounting data. E3 removed four days where the reported daily peak loads
were in excess of the annual peak values, and one day (6/11/99) where the peak
load was 200 MW lower than the peak load on the prior or subsequent day . E3
also excluded all non-summer days, weekends, holidays (including days just
prior to or following July 4th where a visual inspection of the data indicated
extended holiday usage on those days), and days where the average WTHI was
below 65 degrees. This left 799 peak demands on days with temperatures high
enough to reflect weather sensitive usage.
6
Average Weighted Temperature Humidity Index (WTHI) is the average of the
WTHI values for the 24 hourly values for the day. Each hourly WTHI value is
the temperature humidity index weighted 59% for that day’s value, 29% for the
hourly value in the prior day, and 12% for the hourly value two days prior.
(Temperature Humidity Index = 0.5* dry bulb temperature + 0.3 * wet bulb
temperature + 15)
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probability. The 90th percentile forecast recognizes multiple sources
of forecast uncertainty, including the regression’s coefficient
estimates, weather, and forecast error. Like the 1-in10 forecast, the
90th percentile forecast also includes projected levels of energy
efficiency activities at current levels (status quo EE) as described
below.
Table 2 presents the two peak demand forecasts. The table shows that
the 90th percentile forecast is slightly higher, and therefore more
conservative for purposes of estimating transmission capacity needs.
The two forecasts are very similar, however, indicating that the forecasts
are insensitive to how the forecasting process accounts for uncertainty.
Note that the forecasts are based on VELCO power accounting data that
does not include losses. The actual peak load, including losses, would be
about 12.4MW higher than what is shown in the table7.
Table 2: Peak demand forecasts (net of status quo energy efficiency, but
unadjusted for losses)
Year
Peak demand 1-in-10 weather Peak demand 90th percentile
forecast
forecast
2008
1124.24
1126.02
2009
1135.88
1137.50
2010
1148.34
1149.84
2011
1161.49
1162.90
2012
1175.62
1176.94
2013
1190.78
1192.02
2014
1206.15
1207.33
2015
1221.73
1222.86
2016
1237.51
1238.61
2017
1253.58
1254.65
Losses would add 12.4MW to each year.
Incorporating Energy
Efficiency
E3’s load forecast reflects load reductions associated with historic and
projected energy efficiency (EE) activities. Figure 1 below reports the
estimated MW size of EE programs for the historic period of 1995-2007
and forecast period of 2008-2017. The historic EE data is used to inform
7
12.4 MW is the difference between the Vermont peak load value for 2007
quoted in A.DPS:PET.1-4, less the 2007 peak load value from A.DPS:PET.1-3.
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the regression model (it is a right hand side variable) and the forecast
period EE data is included in the forecast, scaled by the regression
coefficient. To be applicable to the forecasting of summer peak MW, the
EE values should reflect only the reductions from EE in place at the
beginning of summer, rather than the reductions from EE in place at the
end of the calendar year. Accordingly, E3 has adjusted the historical
reported values provided by the Vermont utilities and Efficiency
Vermont to reflect this mid-year convention by including only 50% of
the EE in the year of installation.8 In the year following the installation
year, E3 includes 100% of the EE, and then adjusts the load reductions
by applying the annual decay factors developed by Optimal Energy for
subsequent years. For the forecast status quo EE, E3 started with
Optimal Energy’s estimates, removed 2007 (as that is part of the
historical EE activities) and adjusted the EE reductions for the mid-year
convention and decay factors.
8
Table FN1 uses a stylized three year EE program to illustrate how E3 has
implemented the mid-year EE adjustment described above. Columns A through
D show “end of year” values that are reported by entities like Efficiency
Vermont. The end of year values show the peak reductions attributable to the
EE measures installed in years 1 through 3, regardless of whether the measures
were in place at the time of the summer peak. Each column follows a tranche of
EE measures. The peak load reductions are highest in the year of installation,
then decline each year due to performance decay and measure persistence. The
summer peak reductions derived by the mid-year adjustment are shown in
columns E through H. The adjustments are only applied in the year when the
measures are installed. The adjusted values are highlighted in bold. For
example, looking at line 2 shows that the end of year value for measures
installed in year 2 in column B is 80MW, and the mid-year corrected value in
column F is only 40MW (half of the end-of-year value). After year 2, the
measures installed in year 2 are not adjusted. This is clearly illustrated by rows
4 and 5 where the total mid-year values in column H and the end of year values
in column D are the same.
Table FN1 Stylized Mid-Year EE Adjustment
A
1
2
3
4
5
Yr1
Yr2
Yr3
Yr4
Yr5
B
C
End of Year Values
Yr1
Yr2
Yr3
100
95
80
90
80
110
89
78
105
85
75
100
D
Total
100
175
280
272
260
E
F
G
H
Mid-Year Values
Yr1
Yr2
Yr3 Total
50
50
95
40
135
90
80
55
225
89
78
105
272
85
75
100
260
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Figure 1: Status Quo EE Inputs (reductions counted in the forecast are
lower)
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
180
Summer Peak Reductions (MW)
160
140
120
100
80
60
40
20
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
0
EE Smr
Peak
Reduction
(MW)
0.9
2.0
2.6
3.5
4.1
5.2
8.7
13.6
19.6
27.1
35.3
44.6
56.2
70.3
84.4
97.3
109.2
119.7
128.7
137.4
145.8
153.9
In the regression analysis, the Status Quo EE input has a coefficient of 0.69. This indicates that each 1.0 MW of Status Quo EE reduces the
summer peak by 0.69 MW. Applying that factor to EE values from
Figure 1 yields the de-rated EE that is included in the E3 forecasts. The
de-rated values are indicated by the triangle marker in Figure 2.
Figure 2: De-rated EE reductions included in E3 peak forecast
Unadjusted
EE
Derated
EE
2001
9
6
2002
14
9
2003
20
14
2004
27
19
2005
35
24
2006
45
31
2007
56
39
2008
70
49
2009
84
58
2010
97
67
2011
109
75
2012
120
83
2013
129
89
2014
137
95
2015
146
101
2016
154
106
2017
162
112
180
160
22
23
Total EE in Place Mid-Year (MW)
Derated EE included in E3 Forecast
120
100
80
61.0%
60.3%
60
40
20
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2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
0
1995
Summer Peak MW
140
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Using de-rated EE peak reductions results in a higher load forecast than
would be obtained from using an unadjusted Status Quo EE forecast. E3
has not investigated the reasons for the difference between EE reported
and verified savings, and the regression results, but believes that it is
appropriate to use de-rated EE peak reductions. Possible explanations
for the differences include the fact that the peak reductions verified and
reported by Efficiency Vermont are based on the timing of the ISO-NE
summer peak. Vermont experiences its peak demand earlier in the day
than ISO-NE, so the peak reduction of residential EE measures in
particular, would likely be lower at the time of the Vermont peak. The
less than 100% regression coefficient factor may also reflect the lower
contribution to peak load reductions provided by some types of DR
measures at times of extreme weather9 .
Load Forecast
Results
E3’s summer peak load forecast is listed in Table 2 above and shown
graphically in Figure 3 below. The 2008 forecast is only slightly above
the 2006 recorded peak demand, which is likely the result of the ramp-up
of energy efficiency activities since 2006 and the extreme weather
experienced at the time of the 2006 summer peak.
9
A two-stage air conditioner is an example of a device that would provide
large energy savings and reasonable load reductions during time of moderate
temperatures, but would have higher peak demand during times of extreme
weather.
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Exhibit BKH-2
Figure 3: Recorded and Forecast Vermont + CVEC Summer Peak
Demand (with Status Quo EE and losses)
Summer Peak MW (VT + CVEC)
1,300
1,250
1,200
1,150
1,100
1,050
1,000
950
900
850
Load with losses (VT+CVEC)
2016
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
800
E3 90th %tile Forecast w/ Losses & SQ EE
Table 3 reports E3’s daily peak demand regression model. The data
sample contains 799 daily observations of peak MW recorded on hot
summer (June – September) weekdays with average weighted
temperature humidity index (WTHI) at or above 65 in 1991-2007.10 E3
tested other weather metrics, such as the maximum WTHI value for each
day, but found that the average WTHI provided the best statistical fit and
provides the best indication of a multiple day heat storm.
The regression is statistically significant (as shown by the F-statistic of
1163), has high adjusted R2 of 94%, and has statistically significant
coefficient estimates at the 5% level, except for those of the June, July
and Wednesday binary indicators. The interpretation of the coefficient
estimates is as follows:
The 06/11/09 observation is 598.65 MW, well below the prior day’s 806.62
MW. As a result, it is deleted from the estimation sample by reason of data
error.
10
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
Exhibit BKH-2
The coefficient estimates for the month-of-year and day-of-week
binary indicators show that August Mondays tend to have the highest
daily peak demands when compared to other weekdays in the
summer.

The coefficient estimate for Mid-year EE is -0.69 and has a standard
error of 0.08. The estimate’s 95% confidence interval is (-0.53, 0.85) (= -0.69 ± 1.96 * 0.08), rejecting the hypothesis that each midyear EE MW reduces daily peaks by 1 MW. E3’s forecast counts
69% of the estimated mid-year EE for peak reductions.

For the average WTHI of 75, Vermont’s peak demand will grow at
19.7 (= -43.9 + 0.847 * 75) MW per year.

For year 2007 (t = 17), the weather response is 19.8 MW (= 5.39 +
0.847 * 17) for each increment of average WTHI. This weather
response grows at a rate of 0.847 MW per year.
Table 3: Peak demand regression for 799 summer (June-September)
weekdays with average WTHI ≥ 65 in 1991-2007
Variable
Coefficient estimate with
standard errors in ( )
p-value
Intercept
351.0 (35.76)
<.0001
= 1 if June, 0 otherwise
-5.02 (2.67)
0.0607
= 1 if July, 0 otherwise
-2.99 (2.53)
0.2369
= 1 if August, 0 otherwise
13.90 (2.54)
<.0001
= 1 if Monday, 0 otherwise
7.78 (2.16)
0.0003
= 1 if Tuesday, 0 otherwise
5.96 (2.09)
0.0045
= 1 if Wednesday, 0 otherwise
2.46 (2.13)
0.2476
= 1 if Thursday, 0 otherwise
5.54 (2.15)
0.0101
EE size (MW)
-0.692 (0.08)
<.0001
Time trend t
-43.86 (3.40)
<.0001
Average WTHI
5.39 (0.52)
<.0001
Time trend * Average WTHI
0.847 (0.049)
<.0001
F-statistic with (11, 787)
degrees of freedom
1164
< .0001
Adjusted R2
0.9413
Mean square error
347.2
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E3’s regression equation contains the variable (Time trend * Average
WTHI). E3 has included that variable in the model specification to
reflect the increasing importance of weather sensitive loads such as air
conditioners in driving the Vermont system peak. Figure 4 shows the
summer load factor for Vermont plus CVEC from 1991 through 2007,
based on VELCO power accounting data. The figure shows a clear
downward trend in load factors, which indicates that the loads are
becoming more peaked, which is consistent with weather sensitive end
uses.
Figure 4: Annual Summer Peak Load Factors
85%
Annual Load Factor (Smr Peak)
Weather Sensitivity
of Vermont loads
Exhibit BKH-2
80%
75%
70%
65%
y = -0.0065x + 13.762
R2 = 0.6905
60%
55%
50%
1990
1995
2000
2005
2010
This increasing level of weather sensitive loads is further confirmed by
an examination of the daily summer peak loads and temperatures. The
two figures below compare the sensitivity of Vermont plus CVEC
summer loads in 1996 and 2006. The trend line in each figure shows the
average relationship between average WTHI and summer peak demands
for summer weekdays with average WTHI temperatures above 65
degrees. The steeper the slope, the more the loads vary with weather.
The figures clearly demonstrate that over the past ten years, the Vermont
plus CVEC loads have become about twice as sensitive to weather.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Figure 5: Vermont plus CVEC daily peak response to temperature (1996)
1200
y = 9.9359x + 126.39
R2 = 0.6874
1100
Daily Peak MW
1000
Weekday Load
Weekend Load
900
Weekday above 65
Linear (Weekday above 65)
800
700
600
50
55
60
65
70
75
Avg WTHI
Figure 6: Vermont plus CVEC daily peak response to temperature (2006)
1200
y = 19.436x - 414.52
R2 = 0.8357
1100
Daily Peak MW
1000
Weekday Load
Weekend Load
900
Weekday above 65
Linear (Weekday above 65)
800
700
600
50
55
60
65
70
75
80
Avg WTHI
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Comparison to La Capra and ISO-NE Forecasts
Figure 7 compares E3’s 90th percentile forecast with La Capra’s 90/10
La Capra Forecast
forecast. Both forecasts reflect status quo energy efficiency reductions.
The E3 forecast is lower than the La Capra forecast by about two years
of growth, and would be even lower had E3 included the full EE
reductions that La Capra included in their forecast.
Figure 7: Comparison of E3 and La Capra forecasts11
1,250
1,200
1,150
1,100
1,050
1,000
950
900
850
2016
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
800
1990
Summer Peak MW (VT + CVEC)
1,300
Load with losses (VT+CVEC)
E3 90th %tile Forecast w/ Losses & SQ EE
La Capra 90/10 w/ SQ EE
E3’s forecast includes less that half of the EE peak reduction used by La
Capra. Figure 8 shows the Status Quo EE included in the La Capra
forecast (diamond marker),12 the Status Quo EE numbers used as inputs
to the E3 load forecast (square marker), and the de-rated EE reflected in
the peak loads estimated by the E3 forecast (triangle marker). Absent
E3’s conservative treatment of EE reductions, E3’s forecast would easily
11
La Capra 90/10 w/ SQ EE is from Reliability Needs Assessment table on page
25 of RSH-2, "Total VT."
12
The values are the difference between the La Capra 90/10 with CVEC
forecasts that (a) include SQ EE and (b) exclude future EE. See Exhibit
Petitioners RSH-2, p. 24.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
be below La Capra’s forecast by an additional two years’ growth in
2016, shown by the 30 MW gap between the middle and lowest lines.
Figure 8: EE Peak Reductions Included in the Forecasts for Status Quo EE
installed in 2007 or later (reductions at the generator level)
160
Summer Peak Reductions (MW)
La Capra SQ EE
140
E3 SQ EE Inputs
120
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Derated SQ EE in E3 Forecast
100
80
60
40
La Capra
SQ EE
14.0
33.0
51.0
69.0
87.0
100.0
112.0
124.0
136.0
147.0
Derated
E3 SQ EE in
SQ EE
E3
Inputs Forecast
11.6
8.0
25.7
17.7
39.8
27.5
52.8
36.4
64.6
44.6
75.2
51.9
84.1
58.0
92.8
64.1
101.2
69.9
109.3
75.4
20
ISO-NE Forecast
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
0
A comparison of the E3 and La Capra forecasts with ISO-NE’s most
recent forecast for Vermont plus CVEC from their 2008 CELT is shown
below. At first glance, the ISO-NE forecast seems to confirm the La
Capra forecast. However, the ISO-NE forecast reflects a level of EE
activity consistent with past levels, and does not reflect the higher status
quo EE activity levels assumed in the La Capra analysis.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Figure 9: Comparison of E3 and La Capra Forecasts with Unadjusted ISONE 2008 CELT
1,250
1,200
1,150
1,100
1,050
1,000
950
900
850
2016
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
800
1990
Summer Peak MW (VT + CVEC)
1,300
Load with losses (VT+CVEC)
E3 90th %tile Forecast w/ Losses & SQ EE
ISO NE 2008 CELT 1-in-10 Weather (Tab 7)
La Capra 90/10 w/ SQ EE
Table 4: E3, La Capra, and Unadjusted ISO-NE 2008 CELT Forecasts
(MW)
Year
2008
2009
2010
2011
2012
2013
2014
2015
2016
E3 Forecast SQ EE and
losses
1,138
1,150
1,162
1,175
1,189
1,204
1,220
1,235
1,251
La Capra SQ EE
1,161
1,180
1,192
1,207
1,224
1,240
1,251
1,260
1,266
ISO-NE
(unadjusted
for SQ EE)
1,140
1,165
1,185
1,200
1,220
1,235
1,245
1,255
1,265
The ISO-NE ignores energy efficiency in their analysis, allowing it to
affect projections only inasmuch as it has affected peak loads in the
recorded data. Thus, the ISO-NE forecast incorporates a trend of energy
efficiency, which assumes that energy efficiency efforts continue along
the trend seen in the past.
To make an apples-to-apples comparison of the ISO-NE forecast to those
prepared by La Capra and E3, E3 adjusted the ISO-NE forecast to reflect
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
status quo EE activity levels. The adjustment involves first determining
the amount of EE implicitly included in the ISO-NE forecast, and then
adjusting the ISO-NE forecast downward by the difference between the
SQ EE forecast and the implicit EE in the ISO-NE forecast.
To estimate the amount of energy efficiency captured in the ISO-NE
forecast, E3 trended the historical mid-year EE values for the years 1991
through 2007. E3 used the derated EE values because E3’s regression
analysis indicates that the de-rated values reflect the EE reductions that
are observed in the recorded peak loads. E3’s estimate of the de-rated
peak reductions from EE are indicated by the pink boxes. The trended
amounts that E3 believes are implicitly included in the ISO-NE forecast
are shown in by the dashed line with the diamond markers, and the
difference is indicated by the green triangles.
120
100
80
Trend of Derated
Pre-2008 EE (MW)
60
Derated average
EE in E3 Forecast
Difference
40
20
2016
2014
2012
2010
2008
2006
2004
2002
2000
EE Summer Peak Reductions (MW)
Figure 10: Comparison of E3 EE Forecast, and EE Implicit in ISO-NE
Forecast
The ISO-NE forecast net of status quo energy efficiency is derived by
subtracting the difference from the ISO-NE 90/10 forecast. This is
shown as the solid circles in Figure 11 below. The figure clearly
demonstrates that after adjusting for status quo EE, the E3 and ISO-NE
peak forecasts are very close. Table 6 shows the projected load levels
under the three forecasts, and the MW difference relative to the E3
forecast for both the La Capra and ISO-NE forecasts.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Figure 11: A Comparison of EE included in the E3 and ISO-NE models
Summer Peak MW (VT + CVEC)
1,300
1,250
1,200
1,150
1,100
1,050
1,000
950
900
850
2016
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
800
Load with losses (VT+CVEC)
E3 90th %tile Forecast w/ Losses & SQ EE
ISO NE 2008 CELT 1-in-10 Weather (Tab 7)
La Capra 90/10 w/ SQ EE
ISO-NE less New EE above the trend
Table 5: 90/10 forecasts from E3, La Capra, and ISO-NE (MW)
Year
2008
2009
2010
2011
2012
2013
2014
2015
2016
ISO-NE 2007 versus
2008 CELT forecast
E3 Forecast
1138
1150
1162
1175
1189
1204
1220
1235
1251
La Capra - SQ EE ISO-NE 2008 net of
(Difference)
EE (Difference)
1161 (+23)
1132 (-6)
1180 (+30)
1152 (+2)
1192 (+30)
1168 (+6)
1207 (+32)
1180 (+5)
1224 (+35)
1198 (+9)
1240 (+36)
1212 (+7)
1251 (+31)
1221 (+1)
1260 (+25)
1230 (-5)
1266 (+15)
1239 (-12)
The testimony of Dean L. LaForest and Christopher Diebold discusses
VELCO’s belief that the ISO-NE 2007 forecast “is lower than it should
be by at least 30 to 35 MW per year over the first few years” (p. 37 at
16). This conclusion is based on the fact that the ISO-NE system peak
load in 2007 corresponded to the forecasted 30/70 peak value forecasted
in the ISO-NE 2007 CELT, which LaForest and Diebold claim indicates
that the weather in New England at the time was 30/70 weather. ISONE’s 30/70 forecast for Vermont’s 2007 peak load was 1045 MW, 35
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
MW lower than VELCO’s recorded value of 1080 MW, prompting
VELCO’s concern. However, in their response to DPS:PET.2-24.a,
VELCO states that the WTHI on the day of the 2007 Summer Peak “fell
just below the expected value of 80.1.” Weather data from the Vermont
DPS indicates that the WTHI was actually above the expected value, not
below, at roughly 80.2. Rather than corresponding to VELCO’s
assumption that the weather during the 2007 peak was indicative of
30/70 weather, a WTHI of 80.2 indicates that the weather was closer to
50/50 or even 60/40 weather. For this weather, ISO-NE forecasted a
Vermont peak load of between 1070 MW and 1075 MW. Rather than
being 35 MW low, as LaForest and Diebold claimed, ISO-NE’s forecast
appears to have been low by about 5-10 MW.
The fact that ISO-NE and E3, using more recent data, independently
generated consistent forecasts indicates that the E3 forecast may be more
accurate than the forecast used by La Capra. This is supported by the
fact that La Capra did not itself generate a forecast, but rather made a
series of adjustments to an existing forecast. The existing forecast that
they modified was developed in mid-2006, and appears to be based on
data that did not include 2006. Thus, E3 believes that the La Capra load
forecast is too high and that making decisions based on this load forecast
would be inappropriate.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Energy Efficiency
VELCO retained Optimal Energy, Inc. to develop and coordinate the
study of energy efficiency to be included in the analysis of NTAs for the
Southern Loop and Coolidge Connector. As part of this work, Optimal
Energy developed forecasts of (1) the summer peak demand reductions
that would occur under status quo EE funding comparable to Efficiency
Vermont 2008 levels, and (2) the incremental cost-effective EE peak
reductions that could be achieved in the Vermont Central and Northwest
zones.
Status Quo EE
E3 reviewed Optimal Energy’s estimates of status quo energy efficiency
reductions and considers them to be reasonable estimates of summer
peak reductions as reported for the tracking of Efficiency Vermont goals.
Note that this assessment is based on the kW claims, and not the actual
impact on the Vermont peak. As discussed above, the actual reduction
reflected in the load forecast is about 70% of the EE input values. E3
assessed the reasonableness of the status quo estimates by comparing the
forecast budgets and reductions to past EE budgets and reported program
achievements. Using the 2005 through 2007 average summer kW
reduction per dollar of expenditures, E3’s estimate of annual summer
peak reductions for reporting purposes is within one percent of Optimal
Energy’s forecast. Using the 2007 average summer kW reduction per
dollar of expenditure, E3’s estimate is within 10% of the Optimal Energy
summer demand reduction estimate. Given the uncertainly surrounding
future budgets, potential declining effectiveness in attaining summer
peak kW reductions, and variations in decay rates, E3 does not consider
this difference in peak kW estimates to be an issue.
Incremental EE
Optimal Energy also estimated an additional achievable potential of
88MW of cost-effective EE summer peak reductions in Vermont’s
Central and Northwest regions. Optimal Energy estimates that it would
cost $256 million (2007 dollars) over ten years to attain this additional
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
88MW. 13 This would result in net savings for customers, however, as
the EE measures are cost-effective. While it is true that average rates
could increase if the additional EE were implemented, the average
customer’s energy expenditures would likely decrease.
Table 6 compares Optimal Energy’s estimate of achievable potential for
Vermont with estimates for New Jersey, Connecticut, and California,
three states with relatively aggressive efficiency programs. The potential
MW demand savings projected by Optimal Energy for VT as a
percentage of the forecast load are higher (but not dramatically so) than
those projected for NJ and CA, while they are lower than the estimates
for CT. This is the case despite Optimal Energy’s somewhat conservative
assumptions regarding market penetration (see next section).
Table 6: EE Potential Estimates in High EE Jurisdictions
VT
NJ
CT
CA
Author
Optimal
KEMA14
GDS15
Itron16
Year
2016
2020
2012
2020
MW Savings
8.0%
5.9%
13.4%
7.0%
12.5%
3.4%
GWh Savings
4.5%
10.5%
Savings as percent of forecasted load
13
Prefiled Testimony of Jonathan Kleinman, Page 7 at 1.
KEMA (2004). “New Jersey Energy Efficiency and Distributed Generation
Market Assessment.” Final Report to Rutgers University Center for Energy,
Economic and Environmental Policy. August 2004.
14
GDS Associates (2004). “Independent Assessment of Conservation and
Energy Efficiency Potential for Connecticut and the Southwest Connecticut
Region.” Prepared for the Energy Conservation Management Board. June
2004.
15
Itron (2007). “Assistance in Updating the Energy Efficiency Savings Goals
for 2012 and Beyond. Task A4.1 Final Report: Scenario Analysis to Support
Updates to the CPUC Savings Goals.” Prepared for the California Public Utility
Commission. March, 24, 2007.
16
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Coolidge Connector Need and NTA Analysis
Measure
Penetrations for
Incremental EE
Exhibit BKH-2
For the Commercial and Industrial sectors, the Optimal Energy study
assumed penetration rates of 50% for lighting and 25-65% for other end
uses. The report acknowledges that these penetration rates are lower
than those presented in other studies and reports, which have targeted
penetration goals as high as 85%. The Northwest Power and
Conservation Council (NWPCC) calls for a target penetration of 85% for
retrofit measures and 65% for lost-opportunity measures over 12 years.
In supporting these targets, the NWPCC cites a targeted Hood River
Conservation Project which achieved 83% penetration in two years with
100% incentives.
In California, the IOU’s have targets set based on an 80% penetration
rate over 10 years. In a 2007 study supporting the CPUC’s update of
energy efficiency goals, Itron assumed penetration rates similar to those
used by Optimal Energy, though Itron’s low end penetration rates were
somewhat higher. For the base case, Itron assumed penetration rates of
40-45% for the lighting, commercial cooling and commercial
refrigeration measures that represented the majority of the potential
savings. Penetration for other end uses was estimated in the 50-60%
range. For the more aggressive “Full Market” scenario, the penetration
rates were 60-70% for lighting, commercial cooling and commercial
refrigeration, and 70-80% for other end uses.
Potential Impact of
the Incremental EE
If one assumes that the annual incremental peak kW reductions could be
obtained in proportion to the status quo EE reductions (that is, no
significant additional ramp-up lag for the incremental EE), 46MW would
be achieved by 2011. Factoring in the EE de-rating factor of 69% from
E3’s regression analysis would reduce the reductions to about 32MW.
The annual peak reductions from incremental EE are shown in Table 7.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Table 7: Annual Incremental EE Peak Impacts (MW)
A
Year
2008
2009
2010
2011
2012
2013
2014
2015
2016
B
C
Expected peak
Status Quo EE Incremental EE
impacts
Inputs
(Col A * 88/107.7) (Col B * 69%)
8.7
7.1
4.9
25.3
20.7
14.3
41.2
33.6
23.2
56.6
46.3
31.9
71.6
58.5
40.4
81.2
66.3
45.8
90.3
73.7
50.9
99.1
81.0
55.9
107.7
88.0
60.7
88MW is the incremental EE potential from Optimal Energy.
While this is a significant reduction in peak demand, E3 is concerned
that it is not clear that Efficiency Vermont could scale up implementation
efforts quickly enough to attain this level of savings. This would require
a doubling of the expected Efficiency Vermont effort in 2008.
In addition, E3 is concerned that it will become increasingly difficult to
achieve the full level of peak reductions as saturation increases. Program
implementations typically follow an S-curve where adoptions are slow to
start, then hit a period of sustained success before hitting the period of
diminishing returns. It is unclear when those diminishing returns would
significantly hamper the peak kW reduction effort.
Because of these concerns, E3 has included incremental EE as the last
load reduction option in its forecast of peak loads. To the extent that the
Coolidge Connector can be deferred by other means, Vermont will gain
more experience and confidence with implementing sustained high effort
EE programs in the state. This would inform future expectations of the
ability of Vermont to cost-effectively attain the incremental EE
reductions and further defer the need for the Coolidge Connector.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Demand Response
La Capra Estimate
The discussion of demand response (DR) in the VELCO filing is found
mostly within Appendix B to the La Capra Report. The information
presented there is derived primarily from two sources: (1) “Assessment
of Demand Response and Advance Metering,” a FERC staff report from
August 2006; and (2) ISO-NE’s 2004 Demand Response Evaluation
Report. La Capra made adjustments to the DR estimates based on two
assumptions. First, they assumed that most residential DR would come
from a reduction in air conditioner use. Since Vermont has very little
penetration of residential air conditioners, they assumed that residential
DR would not be available in Vermont. Second, they excluded all
generation DR resources, deeming them ineligible for consideration
based on a belief that DR generators would not be able to obtain the air
permits necessary to run when called upon. La Capra estimated that
generation made up 50% of the non-residential DR for each of the
studies.
Applying these assumptions to the 6% DR estimate made by FERC and
the 4.3% estimate made by ISO-NE results in estimates for available DR
in Vermont of 2.4% and 1.1% for the FERC and ISO-NE studies,
respectively.17 La Capra concluded that a DR potential of 2%, or 21
MW, represented a reasonable estimate for the VT load area. This 21
MW number was then shared to the different zones proportional to load,
reducing it to 17 MW of DR in areas where load reductions would
decrease the loading on Line 340. Finally, the 17 MW was scaled by the
Subscriber Performance Index, which represents the proportion of load
enrolled that typically responds to calls to reduce demand. Overall, this
resulted in an estimate of 14 MW of DR for the relevant areas of VT.
17
The FERC study assumes 20% residential DR, so La Capra counted 40% of
the FERC estimate (half of the 80% non-residential is assumed to be emergency
generation) for 40% * 6% = 2.4% available DR in Vermont. The ISO-NE study
assumes 50% residential DR, so La Capra counted 25% of the ISO-NE estimate
for 25% * 4.3% = 1.1%.
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Coolidge Connector Need and NTA Analysis
Current Demand
Response
Enrollment
Exhibit BKH-2
The following table shows the demand response resources enrolled with
ISO-NE at month’s end for the past year, classified as “Ready to
Respond” or “Approved.”18
Table 8: VT Demand Response Resources Enrolled with ISO-NE
Ready to Respond
Approved
March 30, 2007
32.6 MW
0.2 MW
May 1, 2007
26.6 MW
0.0 MW
June 1, 2007
27.2 MW
0.0 MW
July 2, 2007
22.4 MW
2.6 MW
July 31, 2007
22.5 MW
5.0 MW
August 31, 2007
29.7 MW
0.2 MW
October 1, 2007
32.1 MW
0.0 MW
November 1, 2007
33.4 MW
0.0 MW
November 30, 2007
54.6 MW
8.1 MW
December 31, 2007
62.6 MW
0.5 MW
January 31, 2008
64.3 MW
0.7 MW
February 29, 2008
60.3 MW
0.0 MW
March 31, 2008
41.4 MW
0.5 MW
Though there is a significant amount of load available to respond in the
winter months, this amount drops sharply (by over 60% from the
maximum to the minimum) during the summer months. This is
consistent with the discussion of Demand Response in Appendix B to the
La Capra report on page 2. This section cites the 34 MW that ISO-NE
listed as either “ready to respond” or “approved” as of December 31,
2006, but points out that the 34 MW value “represents both generating
and non-generating assets and assets that are likely capable of responding
in only one season such as ski area snowmaking.” These single season
“Ready to Respond” means the registration process is complete and the
resource is eligible to participate in an Event, while “Approved” means the
application for registration has been approved by ISO-NE. Source:
http://iso-ne.com/genrtion_resrcs/dr/stats/enroll_sum/index.html
18
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
assets are likely a reason for the decrease in available demand response
between the winter and summer months.
However, ISO-NE has also recently changed the way that they calculate
payments in their Day-Ahead Load Response Program (DALRP), which
also may have caused a significant decline in participation. In February,
ISO-NE filed a proposal with the Federal Energy Regulatory
Commission (FERC) to raise the price threshold at which demand
response providers were paid to reduce load, which was approved by
FERC on April 4, 2008. Before this proposal, the Locational Marginal
Prices were so high relative to the baseline that demand response
resources could collect money for reducing load 80% of the time, which
was preventing the development of accurate baselines from which to
measure the reductions. ISO-NE believed that this was resulting in
substantial payments for load reductions that weren’t actually happening.
Since ISO-NE filed for this change, there has been a significant decline
in the participation of demand response resources in the DALRP, but as
this corresponds with the time of year during which there is normally a
decline in enrollment anyway, it is hard to determine the extent to which
the decline is due to seasonality of demand response resources and the
extent to which it is due to the new payment scheme.
Demand Response in
ISO-NE Forward
Capacity Market
The first Forward Capacity Auction (FCA) for the ISO-NE Forward
Capacity Market (FCM) was held in February 2008, and is described in a
presentation made by the ISO-NE Demand Resources Department to the
Demand Resources Group on April 2, 2008.19 This presentation shows
the resources bid into the first FCA by resource type. The resources for
Vermont are shown below:
19
http://www.isone.com/committees/comm_wkgrps/mrkts_comm/dr_wkgrp/mtrls/2008/apr2200
8/fca-1_results_measure_type_040708.ppt
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Table 9: VT Demand Resources Cleared in the 2010/2011 FCA
Demand Resources in VT
Resource Type
Cleared MW –
Existing20
Cleared MW – New
Total MW
Dist Generation – Fossil Fuel
0
0
0
Dist Generation - Renewable
0
0
0
Energy Efficiency
2.0
55.7
57.7
Real-Time Demand
Response and Load
Management
8.1
15.6
23.6
Real-time Emergency
Generation
20.3
0
20.3
Total
30.4
71.3
101.6
The resources described above are committed for the 2010 – 2011 Power
Year. When considering the DR resources that could serve to defer the
need for the given project, the energy efficiency measures described
above should be excluded, because they are accounted for separately in
the analysis.21 The concern of the La Capra report discussed above
regarding seasonal resources is not relevant for the forward capacity
market because the demand response resources offer capacity bids for a
specific month, and all DR resources are required to “demonstrate or
certify that [they have] the capability to deliver the targeted load
reduction”22.
Though the resources above have bid into the capacity market in ISONE, there is no guarantee that they will drop load when called upon.
Payments in the Forward Capacity Market are based on performance
under shortage events, but the only penalty that the FCM has for a failure
to respond is forfeiture of payment for capacity the resource did not
20
Demand Response resources that are commercial by March 1, 2007 or are
under construction and either (1) have an in-service date before February 1,
2009, (2) are required by a contract, or (3) elect to be treated as such are
considered “Existing.” All other Demand Resources are treated as “New” DR
resources.
21
It is interesting to note, however, that the La Capra report assumes that there
will be 69 MW of Energy Efficiency under the status quo, which seems
reasonable given the amount bid into the first FCM auction.
22
A.DPS:PET.1-111
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
provide. As this is the first Forward Capacity Auction that has taken
place the full three years ahead of the time period for which it is
contracting resources, there is no experience with the auction or the
response rate of enrolled resources.
In performing reliability assessments, ISO-NE does not include real-time
emergency generation resources, which account for about 20 of the 44
MW of non-EE demand resources. Real-time emergency generation is
not included because it is called only under Action 12 of ISO-NE
Operating Procedure 4, and OP-4 actions are only implemented in the
case of a real time capacity deficiency.23 Consistent with ISO-NE’s
treatment, E3 separated real-time emergency generators from the other
demand response resources for the remainder of the analysis.
E3 DR Forecast
From 2010 forward, E3 considers it most appropriate to use the results
from the FCM (excluding energy efficiency) now that they are available.
This results in an available demand response potential in Vermont of
about 24 MW in 2010. When performing reliability assessments, ISONE derates demand response resources in two steps. First, they are
scaled down by a factor of 1.143 to accurately model their system
benefits, and then they are derated by an additional 11% to reflect the
historic failure to respond. Applying this methodology to the 24 MW
listed above results in 18.4 MW of demand response resource availability
for the VELCO system in 2010.
E3 further derated the DR resources to reflect their geographical location
in the state, as load reductions have different levels of effectiveness
depending on where they are located. Both the load forecast and the
peak load thresholds used to determine the need for the project reflect the
total Vermont plus CVEC load levels. VELCO has stated that load
reductions in the Southern and Northern regions would provide little or
no load relief for the Coolidge Connector project. Therefore, to the
23
Direct Testimony of Stephen Rourke, FERC Docket ER08-633-000, page 9 at
4.
Energy and Environmental Economics, Inc.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
extent that the share of load reductions from the Southern and Northern
load regions is greater than the share of total load in those regions, the
DR reductions need to be adjusted downward. E3 assumed that 50% of
DR resources were located in the Southern and Northern regions, as
compared to 21% of the total Vermont load. Accordingly, E3 adjusted
the DR resources counted for load reduction to 63% of the total. This
results in a total DR resource availability of 11.6 MW for the purposes of
E3’s analysis.
Energy and Environmental Economics, Inc.
Page 33 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Peaker Units in Vermont
In their prefiled testimony, LaForest and Diebold stated that for the
generation-dispatch assumptions for all studies,
…Highgate Converter is unavailable when considered
as a long-term outage and modeled with approximately
200 MW of import otherwise; Ryegate is dispatched at
20 MW; McNeil is dispatched at 51 MW; the Coventry
landfill generator is modeled at 5 MW while the other
thermal units are off and assumed held in reserve for
loss of McNeil.24
The ability of “other thermal units” to provide peaking capacity was
studied by E3 in order to determine the potential for these units to
provide capacity beyond their ability to back-up the McNeil generator.
While these peaking resources may be highly unreliable when considered
individually, the aggregate ability of these units to provide peaking
capacity in combination with other generation, import, and demand side
resources should be fully considered in the determination of the need for
additional transmission capacity.
Generation Units
The specific units modeled for reliability purposes and their associated
capacities are listed in Table 8. These FCM listed capacities were
obtained from generator bids submitted into FCM as detailed in ISONE’s March 3, 2008 “Forward Capacity Auction Results Filing” with
FERC.25 The assumed capacities used in this analysis are from Table 2
in the Draft Responses to the DPS Supplemental Informal Discovery
Requests for VELCO filed on May 28, 2008. The assumed capacity
levels are generally lower than the FCM bid capacity levels as they
reflect the contribution of the generator in mitigating the N-1-1
transmission constraints on the VELCO system. In addition the DPS
24
Southern Loop Project, Pre-filed Testimony of Dean L. LaForest and
Christopher Diebold, Section 7.3, page 44.
25
Filed in Docket ER-08-000. Available online at http://www.isone.com/regulatory/ferc/filings/2008/mar/er08-633-000_03-0308_fca_results_filing.pdf
Energy and Environmental Economics, Inc.
Page 34 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
discovery requests did not contain capacity assumptions for the small
diesel units of St. Albans, Barton, and Enosburg. The assumed capacity
numbers for the Enosburg and Barton units were de-rated to 20% of their
capacity based on VELCO reported transmission constraints.26
Table 10: Vermont Generators
Excluded Generators
FCM Capacity
Assumed
Capacity27
McNeil
52
40
Ascutney GT
9
10
St Albans Diesel
1.2
1.2
Berlin A&B GT
37.7
30
Gorge GT
6.6
5
Essex Diesels
2
5
Vergennes Diesels
4
0
Burlington GT
18.6
15
Enosburg Diesel
0.7
0.14
Barton Diesels
0.6
0.12
Rutland 5 GT
10.1
5
Florence Cogen A
3
3
Florence Cogen B
2.9
2
Swanton Peaker 1
20
12.5
Swanton Peaker 2
20
12.5
136.4
101.46
Total excluding McNeil
italics – proposed
Reliability Metrics
The capacity available from the system of generators was modeled based
on three statistics for each generator; the mean time to repair (MTR), the
mean time to failure (MTF), and the Equivalent Demand Forced Outage
Rate (EFORd). The MTF and MTR are approximations of the number of
26
Northern Zone resources contribute 20%, based on A.DPS:PET.1-118
27
The assumed capacities are based on Table 2 from the Draft Responses to the
DPS Supplemental Informal Discovery Requests for VELCO, May 28, 2008.
Energy and Environmental Economics, Inc.
Page 35 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
hours a plant will be in an available or un-available state respectively.
The implied outage frequency from these two rates is simply the ratio of
the MTR to the sum of the MTR and MTF.
Implied Outage Frequency =
MTR
MTR  MTF
The MTFs and MTRs for several of the generators were calculated
directly from historic performance data for the generator. In the cases
where no direct performance data was available generators were assigned
MTFs and MTRs identical to other generators with available data and of
similar fuel source and size. In the case where neither historical data nor
a clear mapping to another generator was available generators were
assigned the average MTR and MTF from A.DPS:PET.1-60.27
The EFORd reliability metric was also used to estimate each generator’s
individual reliability performance. The EFORd metric is a more
appropriate metric for estimating outage rates of peak capacity
generators than the traditional Equivalent Forced Outage Rate (EFOR) as
it accounts specifically for a generator’s expected availability during
demand periods. The EFORd metric was adopted as the reliability metric
for ISO New England capacity planning procedures in the Standard
Market Design for New England (SMD) and re-confirmed in a 2005
FERC Order.28, 29 As with the MTR and MTF, the EFORd value was
calculated directly from generator historical performance data where
possible. In the absence of individual EFORd values the ISO New
England EFORd class averages were used.30
27
This average was calculated excluding unit K
28
See New England Power Pool, 100 FERC ¶ 61,287 at P 12, 96-98 (2002),
order on rehearing, 103 FERC ¶ 61,304 at P 77 & n.29 (2003).
29
Docket Nos. ER05-715-000,ORDER ACCEPTING, AS MODIFIED,
PROPOSED INSTALLED CAPACITY REQUIREMENTS FOR THE
2005/2006 POWER YEAR , May 2005, Analysis 32
30
ISO New England EFORd Class Averages from NERC Brochure, Feb 2008.
http://www.iso-ne.com/genrtion_resrcs/gads/index.html
Energy and Environmental Economics, Inc.
Page 36 of 58
Coolidge Connector Need and NTA Analysis
Modeling
Assumptions and
Results
Exhibit BKH-2
The available capacity from the system of generators was modeled in
two ways. One approach involved a simulation of generator states using
the MTF and MTR statistics for each generator. This approach returned
an empirical distribution, through repeated simulation, for the available
capacity from the system of generators in each hour in a 4-month period.
The other approach used the generator EFORd rates to calculate the
implied distribution of available capacity from the system of generators.
The distribution calculated in this analysis represents the distribution of
capacity in any given peak demand hour.
Generator Simulation
The MTRs and MTFs were used to simulate four months of hourly
availability data for each generator, which was then combined with the
capacity for that generator to determine the available capacity in each
hour. The available hourly capacities for each of the generators listed in
Table 8 were then combined to develop an hourly available capacity for
the entire system of generators. The purpose of this approach was to
estimate available capacity from these generators over the course of a 3
month summer system peak period when they could be used to alleviate
capacity constraints on the Vermont grid. A more detailed description of
this modeling approach can be found in Appendix B.
The analysis looked at several scenarios for the generator MTRs and
MTFs used for modeling purposes. In each scenario the MTR was held
constant at the directly reported and mapped rates, while the MTF was
varied between each scenario. The first “un-adjusted” scenario used the
directly reported and mapped MTFs to simulate generator availability.
The second scenario shifted the MTF in order to calibrate the plant
implied outage frequency to the assumed plant EFORd value. Thus,
given the EFORd rate and holding the MTR rate constant, the following
equality was solved for the MTF:
MTR
 EFORd
MTR  MTF
Energy and Environmental Economics, Inc.
Page 37 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
The EFORd scenario is an approximation of the reliability of the
generators when they are used primarily for capacity demand purposes.
The final “mid-point” scenario used the average MTF rates between the
first two scenarios.
Due to confidentiality agreements the specific plant performance data
cannot be directly reported; however, Table 10 below lists the pertinent
statistics for the EFORd and MTR values as well as the MTF and
implied outage frequency values for each scenario.
Table 11: Generator Statistics
All Scenarios
Maximum
Average
Minimum
Un-Adjusted
Mid-Point
EFORd Matching
EFORd
MTR
(hrs)
MTF
(hrs)
Implied
Outage
Frequency
MTF
(hrs)
Implied
Outage
Frequency
MTF
(hrs)
Implied
Outage
Frequency
25.79%
12.33%
3.47%
83
51
7
366
126
7
73.08%
33.68%
4.69%
515
282
33
36.60%
17.00%
3.99%
931
438
59
25.79%
12.33%
3.47%
The available capacity for the system of generators was simulated over
2,928 hours (24 hrs x 122 days). This process was repeated 1,000 times
in order to approximate the distribution of available capacity in each
hour. This resulted in 1,000 approximations for the available capacity in
each individual hour over the course of three months. From the 1,000
approximations the 1st, 5th and 10th percentiles of available capacity for
each hour were calculated. These represent the minimum capacity levels
that can be expected to be available in each hour with 99%, 95%, and
90% confidence respectively.
The percentiles for each of the 2,928 hours are shown in Figures 12-,14
with each figure representing a different scenario from Table 9.
Energy and Environmental Economics, Inc.
Page 38 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Figure 12: Base Scenario
Capacity Simulation - Un-Adjusted Scenario
120
1st Percentile
5th Percentile
10th Percentile
MW
100
80
60
40
1
241
481
721
961
1201
1441
Hour
1681
1921
2161
2401
2641
2881
Figure 13: Mid-Point Scenario
Capacity Scenario - Mid Point
120
MW
100
80
60
1st Percentile
5th Percentile
10th Percentile
40
1
241
481
721
961
1201
1441
Hour
1681
Energy and Environmental Economics, Inc.
1921
2161
2401
2641
2881
Page 39 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Figure 14: EFORd Adjusted Scenario
Capacity Simulation - EFORd Scenario
120
MW
100
80
1st Percentile
60
5th Percentile
10th Percentile
40
1
241
481
721
961
1201
1441
Hour
1681
1921
2161
2401
2641
2881
The results of these simulations in Figure 12 indicate that the generators
listed in Table 8 can provide over 40 MW of capacity with 99%
reliability over the course of a capacity constrained summer.
Alternatively when the generators are used for capacity purposes as
opposed to base generation, as illustrated by the EFORd case, they can
provide over 70 MW of capacity with 99% reliability.
Capacity Distribution
Approach
The first step in calculating the various capacity probabilities for the
system of generators was to define all possible configurations of the
generators in the system. Using the assumption that all generators were
either on at their full capacity or forced out with zero capacity, the total
available capacity was calculated for each configuration. Finally the
probability of each configuration was calculated based on the individual
EFORd values for the generators under the assumption that generator
forced outages are independent of each other. The details of the assumed
generator configurations and their associated probabilities can be found
in Appendix C. The results of this analysis provided the available
capacity distribution shown in Figure 15.
Energy and Environmental Economics, Inc.
Page 40 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Figure 15: Dependable Capacity based on Probabilistic analysis
9.
5%
10
.0
%
9.
0%
8.
5%
8.
0%
7.
5%
7.
0%
6.
5%
6.
0%
5.
5%
5.
0%
4.
5%
4.
0%
3.
5%
3.
0%
2.
5%
2.
0%
1.
5%
1.
0%
0.
5%
110
100
90
80
70
60
50
40
30
20
10
0
0.
0%
Capacity (MW)
Total Capacity Based on EFORd
Probability of Less than or Equal to this Capacity
The probabilities associated with each capacity represent the probability
that the system capacity will not exceed that particular level in any given
demand hour during the year. These results show the overall capacity at
all levels of reliability under the full system of generators including
McNeil. The probability that the system will provide less than 80 MW of
capacity during demand hours is approximately 1%. Alternatively the
probability that the system will provide less than 60 MW of capacity
during demand hours is approximately less than 0.5%. To redefine these
results from a typical reliability perspective selected available capacity
probabilities are displayed in Table 10.
Table 12: EFORd Probabilistic Capacity
Probability of Capacity Availability
Capacity (MW)
99.99%
99.97%
99.00%
98.00%
97.00%
96.00%
95.00%
94.00%
93.00%
92.00%
91.00%
90.00%
41.32
46.96
80.84
86.46
91.34
93.96
96.46
98.46
100.26
101.46
103.46
106.34
Energy and Environmental Economics, Inc.
Page 41 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
These capacity probabilities can be tied back to the generator simulation
scenario in which the MTF hours were set to match each generators
EFORd rate to the implied outage frequency rate. The mean capacity
levels for the 1st, 5th and 10th percentile simulation graphs should match
closely the capacity levels listed above for the 99%, 95%, and 90%
probability respectively.
Incremental Peaking
Unit Capacity
The generations analyses presented above evaluate the combined
dependable capacity provided by the peaker generators and McNeil.
However, VELCO has already included the capacity from McNeil in
their determination of the peak load thresholds. Therefore, from an NTA
perspective, the incremental dependable capacity provided by the peaker
generators is the totals from above less 40MW. This adjustment is
applied to each of the three generator simulation scenarios as well as the
EFORd distribution capacity results. The incremental dependable
capacity values are summarized in Table 11. The numbers reported for
the generator simulation results are the minimum capacity percentile
amongst all hours in the 4-month simulated period.
Table 13: Dependable Capacity of Peaker Generators by Reliability Level
(MW)
Unadjusted recorded
Mid-point simulation
EFORd-based simulation
EFORd probabilities
99 percent 95 percent 90 percent
3
32
41
13
45
54
28
51
59
41
56
66
The mid-point scenario for the generator simulation provides 45MW of
dependable capacity above the McNeil generator. E3 considers the 95
percent reliability level to be reasonable for the need determination,
because that level of aggregate reliability is comparable to what one
could expect from a “reliable” unit that a transmission company would
include in its reliability analyses. If the system of generators was used
primarily in a peak capacity role then the performance would be closer to
the results for the EFORd simulation scenario or similarly the EFORd
distribution results, which both provide over 50 MW of incremental
capacity above McNeil at a 95% reliability level.
Energy and Environmental Economics, Inc.
Page 42 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Combined Heat and Power (CHP)
E3 also reviewed the CHP data provided in Attachment A.DPS:PET.218c. Based on the review of this data, E3 agrees with La Capra’s finding
that there is no economically feasible CHP in the study area projected to
be available to reduce summer load in 2011. This result is driven largely
by two factors: (1) the relationship between projected electricity and
natural gas prices and (2) the assumption that all CHP units will be sized
only for hot water and heating requirements, with no units configured to
accommodate summer cooling loads. However, if electricity prices
increase substantially in the future (due, for example, to the expiration of
long-term procurement contracts in 2012) the CHP projects could
provide cost-effective summer peak reductions achieved through the
addition of cooling equipment to the CHP unit.
Economic Feasibility
Economic feasibility for CHP installations is determined by a calculation
of the ratio of benefits to costs. In the calculation performed by La
Capra (included in Attachment A.DPS:PET.2-18c), the costs are defined
as participant capital and operating costs relating to the CHP unit, and
the benefits are based on ratepayer savings. The ratio is the NPV of
benefits divided by the NPV of costs at a discount rate of 10%, and a
ratio greater than 1.0 indicates an economically feasible outcome. The
NPVs are based on 10-year projections commencing in 2007. No
incentives or tax credits are assumed to be available to participants. It
should be noted that using 2007 as the first year is not accurate for CHP
projects going into service in later years, however the annual power and
gas price data provided does not vary in ways that would change the
results if this methodology were changed.
Power and Gas Price
Projections
Attachment A.DPS:PET.2-18c provides the power and natural gas price
estimates used in La Capra’s benefit-cost ratio test. These assumptions
are described in the table below.
Energy and Environmental Economics, Inc.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Table 14: Power and Gas Price assumptions
Row
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
New England ICAP forecast, $/kw-yr
NE Hub All-hours Price, $/MWh (from AESC MA Definition1)
VT Price All-hours ($/MWh) (from AESC)
Forecasted Propane Prices, 2005$/mmbtu
Forecasted Propane Prices, nominal$/mmbtu
Exh.ES-2 Commerical/Industrial All ($/mmbtu)
Exh.ES-2 Commerical/Industrial All (2005$/mmbtu)
Ancillary Service Price Forecast ($/MWh)
Wholesale Distillate Fuel 2005$/mmbtu
Wholesale Distillate Fuel 2005$/mmbtu
2007
21.09
78.84
76.54
15.08
15.92
11.99
11.41
2.00
9.25
$ 9.77
$
$
$
$
$
2008
20.80
66.24
64.76
14.90
16.13
10.10
9.38
1.69
9.07
$ 9.82
$
$
$
$
$
2009
19.56
53.28
53.34
15.10
16.75
9.26
8.39
1.39
9.27
$ 10.29
$
$
$
$
$
2010
31.03
46.33
47.79
15.57
17.70
8.63
7.63
1.25
9.74
$ 11.08
$
$
$
$
$
2011
49.22
48.16
51.20
15.27
17.80
8.93
7.70
1.34
9.44
$ 11.01
$
$
$
$
$
2012
78.07
50.07
54.86
14.98
17.90
9.40
7.91
1.43
9.15
$ 10.94
$
$
$
$
$
2013
80.25
50.53
56.54
14.69
17.99
9.89
8.12
1.48
8.86
$ 10.85
$
$
$
$
$
2014
82.49
51.00
58.27
14.40
18.07
10.88
8.71
1.52
8.57
$ 10.76
$
$
$
$
$
2015
84.79
51.47
60.05
14.09
18.13
10.61
8.29
1.57
8.26
$ 10.63
$
$
$
$
$
2016
87.15
51.95
61.89
14.14
18.64
10.90
8.31
1.62
8.31
$ 10.96
$
$
$
$
$
Row (6) provides the nominal natural gas price forecast corresponding
with the nominal AESC power prices in rows (2) and (3). Over the 10year term from 2007 through 2016, nominal power prices are projected
to decrease approximately 34%, with a 2016 nominal price of $51.95 per
MWh, while gas prices are projected to decrease approximately 9%, with
a nominal 2016 price of $10.90 per MMBtu. These projections equate to
a marginal heat rate of 6,575 Btu/kWh in 2007, declining to 4,766
Btu/kWh in 2016. It is not clear what market fundamentals are driving
the dramatic decline in power prices projected through 2016 while
natural gas prices remain relatively constant in nominal terms.
This high cost of natural gas fuel relative to the low value of electricity
savings is a significant factor contributing to benefit-cost ratios of less
than one.
CHP Unit
Configuration
La Capra’s analysis assumes that all units are sized to accommodate the
thermal load from hot water and heating requirements only, despite the
fact that in some cases summer cooling loads are expected to be sizeable.
The La Capra report concludes that out of a potential 54.2 MW of CHP
capacity in the study area, only 4.1 MW would be available in the
summer, all at a benefit-cost ratio of less than 1. The low coincidence
with summer peak results from the assumption that CHP units will only
serve heating and domestic hot water loads, a very small proportion of
which are assumed to be operating during the summer peak.
However, there are several potential additional ways each CHP project
could be configured, which each result in different amounts of generation
available during the summer peak.
Energy and Environmental Economics, Inc.
Page 44 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
The CHP units could be sized to cover the peak summer cooling,
summer heating, and DHW loads. This configuration would require
additional cooling equipment and a potentially substantial resizing of the
CHP unit, both of which would increase the capital cost.
The CHP unit could also be sized to cover the peak summer cooling,
summer heating and DHW loads, up to a maximum equal to the winter
loads. This would increase capital costs due to a reconfiguration of the
CHP unit and the addition of cooling equipment and would result in
maximum available summer capacity in the range of 54.2 MW, assuming
all of the projects were implemented.
The CHP unit could be sized to cover the facility’s electric load, with
waste heat above facility needs not recovered. E3 did not consider this
case because the facilities’ electric loads are quite large and thus this
configuration would result in significant waste heat.
In order to serve additional cooling loads, the CHP would need to be
configured with equipment that can produce air conditioning. This
equipment can comprise a combination of a desiccant dehumidifier and
an absorption chiller, and may require additional or modified HVAC
cooling coils, piping and controls equipment. Installation of this
equipment is anticipated to cost approximately 7% more per kW, and
approximately 50% more in annual O&M costs.
CHP Cost
Effectiveness under
Alternate Electricity
Cost Scenarios
While the economics of the CHP projects improve substantially when the
CHP units are also sized to serve summer thermal cooling loads, the
improvement is not significant enough to achieve economic feasibility
under La Capra’s electricity price forecast. With wholesale power costs
of approximately $75 in 2011, CHP health applications become costeffective, yielding 4.4 MW of summer capacity. With power prices of
about $110 per MWh in 2011, about 32 MW of summer CHP capacity
would be cost-effective.
Energy and Environmental Economics, Inc.
Page 45 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Table 15: Cost Effective CHP Options in Vermont
Summary
Benefit/Cost Ratio - current
Estimated Number of Sites
Summer Peak kW Served
Installed Capacity kW
Total Summer Potential MW
Power Prices for C:B >1 ($/MWh)
Potential MW if C:B>1
College
School
0.78
7
87
87
0.62
$
Grocery
0.82
122
106
106
12.97
125.00
12.97
Health
0.67
29
13
13
0.37
$
0.91
13
328
328
4.40
75.00
4.40
Lodging
$
0.85
28
137
137
3.80
100.00
3.80
Office
$
0.85
137
180
180
24.58
110.00
24.58
Restaurant
Retail
0.66
146
9
9
1.31
$
Warehouse
0.62
393
15
15
5.89
200.00
5.89
0.62
16
14
14
0.23
From the information in Table 13, one can develop a supply curve of
cost-effective CHP, based on increasing levels of electricity power
prices. The supply curve is shown in Table 16 below, but has not been
adjusted for estimates of adoption levels.
Table 16: Cost-Effective CHP Supply Curve
Power
Price
($/MWh)
$
75
$
100
$
110
$
125
$
200
Incremental
Cost Effective
CHP Summer
MW
4.4
3.8
24.6
13.0
5.9
Increase in Cumulative Cost
Power Price
Effective CHP
(%)
Summer MW
46%
4.4
95%
8.2
115%
32.8
144%
45.8
291%
51.6
Care should be exercised in using the values in this table because the values are
based on societal cost test benefit cost ratios of 1.0. The table does not evaluate
payback periods and likely participation rates for the customers.
Retail electricity prices will likely increase significantly in the future
with the expiration of the current long-term contracts with Entergy and
Hydro Quebec starting in 2012. Those retail price increases could
increase customer interest in options such CHP, which could encourage
adoption of CHP. However, we would generally expect CHP adoption
levels to be far lower than EE adoption levels because of the typically
higher complexity of CHP projects.
Because the amount of CHP potential is relatively small under power
price levels that are as much as twice what La Capra has assumed, and
because there is a high level of uncertainty regarding the level of
adoptions of cost-effective CHP by customers, E3 has not included CHP
reductions in its need date analysis below.
Energy and Environmental Economics, Inc.
Page 46 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Transmission Need Date
Figure 16 presents the need analysis performed by E3. The downward
sloping line (vertical line marker) is the peak load threshold from
VELCO’s pre-filed testimony.31 The top line is La Capra’s forecast,32
and the next line is E3’s forecast net of the status quo EE activities
(triangle). Subtracting out peak load reductions for DR located in the
Central and Northwest regions in Vermont yields the next line (solid
circle). This (solid circle) peak load line indicates a need date of 2011,
consistent with the planned in-service date of the Coolidge Connector.
Factoring in the capacity that could be dependably provided by the small
generators not included in the development of VELCO’s peak load
threshold results in either the shaded or empty circle lines. The
difference in the two lines is the level of dependability that one requires
from the generators. The shaded yellow line requires 99% reliability
from the generators, resulting in a need date of 2011. The empty circle
line requires only 95% reliability from the generation, resulting in a 2013
need date. Because of the uncertainty surrounding the performance of
the generators and the small margin in 2013, E3 believes that it is
prudent to assume 2012 rather than a 2013 need date for the 95%
generation scenario.
Finally, the figure shows the potential impact of incremental EE
activities beyond the status quo levels. That incremental EE pushes the
need date out two years to 2014, but is less certain in deliverability than
the status quo EE, DR, or peaker generation capacity.
31
From Reliability Needs Assessment table on page 25 of RSH-2, "target
state-wide loads".
32
La Capra 90/10 w/ SQ EE is from Reliability Needs Assessment table
on page 25 of RSH-2, "Total VT."
Energy and Environmental Economics, Inc.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Figure 16: Project Need Date
1,300
1,200
1,150
1,100
1,050
1,000
950
900
850
2015
2013
2011
2009
800
2007
Summer Peak MW (VT + CVEC)
1,250
E3 90th %tile Forecast w/ Losses & SQ EE
E3 Forecast (w/ Status quo EE and DR)
E3 Forecast (w/ SQ EE, DR, 99th %tile Gen)
E3 Forecast (w/ SQ EE, DR, 95th %tile Gen)
Peak Load Threshold
La Capra 90/10 w/ SQ EE
E3 Forecast (w/ SQ EE, DR, 95th %tile Gen, incr EE)
Table 17: Vermont + CVEC Peak Loads and Peak Load Threshold (MW)
Summer Peak Load Adjustments
Year
2008
2009
2010
2011
2012
2013
2014
2015
2016
Demand
Response 99th %tile 95th %tile
(DR)
Gen
Gen
-11.6
-11.6
-11.6
-11.6
-11.6
-11.6
-11.6
-13
-13
-13
-13
-13
-13
-13
-45
-45
-45
-45
-45
-45
-45
Incr EE
-23.3
-32.0
-40.5
-45.9
-51.0
-56.1
-60.9
Adjusted Summer Peak Loads
E3 90th %tile
Forecast w/
Losses & SQ
EE
1,138
1,150
1,162
1,175
1,189
1,204
1,220
1,235
1,251
E3
forecast
less DR
1,151
1,164
1,178
1,193
1,208
1,224
1,239
Less DR, VELCO PreLess DR Less DR 95th %tile filed Peak
and 99th and 95th Gen, Incr
Load
%tile Gen %tile Gen
EE
Threshold
1,180
1,170
1,138
1,106
1,082
1,155
1,151
1,119
1,087
1,145
1,165
1,133
1,092
1,135
1,180
1,148
1,102
1,115
1,195
1,163
1,112
1,095
1,211
1,179
1,123
1,085
1,226
1,194
1,133
1,075
It is important to note that the load threshold listed in the La Capra report
(and employed in the analysis above) may be too high. The threshold
load depends on a variety of assumptions about generator availability
outside the state, acceptable voltage levels, power factor caps, and a
“droop” in the Essex STATCOM and Granite synchronous connectors.33
Under some combinations of the treatment of these variables, the
threshold load level at which the system is at risk can fall as low as 900
33
These factors are discussed further in the Draft Responses to the DPS
Supplemental Informal Discovery Requests for VELCO, May 28, 2008.
Energy and Environmental Economics, Inc.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
MW, well below the current load levels. The Department’s experts have
determined that 945 MW is the appropriate peak load threshold in 2011.
This is 210 MW lower than what VELCO presented in their pre-filed
testimony, and would indicate that the transmission project should be
constructed according to the current proposed schedule.
Energy and Environmental Economics, Inc.
Page 49 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Cost of Delay or Deferral
The pre-filed testimony of Ralph Roam discusses the projected cost of
building the Coolidge Connector and the impact that specific delays in
the completion of the project would have on the cost. Mr. Roam presents
a base case and two alternative delay cases. The first delay case assumes
that the project starts on time but takes longer than expected, incurring
additional costs through extended hours needed to complete the project,
cost increases due to inflation, and increased capital interests due to the
longer construction time. The second delay case assumes that
construction proceeds normally until the beginning of 2009, at which
point it is indefinitely suspended. Eventually, it is resumed and
completed within the normal time frame for the remaining portion. E3
posits a third deferral case, in which all project delays happen before the
project is begun, and therefore there is no increase in hours or the interest
paid on capital. The only cost increases are the result of inflation in the
cost of transmission materials.
Base Case
In Mr. Roam’s Base Case, the construction term is approximately three
years and the project has a commercial operation date (COD) of
4/1/2011. The total Base Case capital cost is $264.8 million, divided into
categories as described in Table 15 below:
Table 18: Base Case Capital Costs
Material
Labor
Equipment
Indirects
Capital Interests
Contingency
Total
Delay Cases
Base Case
COD 4/1/2011
$84,211,979
$42,030,987
$17,579,760
$54,047,180
$22,807,861
$44,135,553
$264,813,320
The two cases examined by Mr. Roam describe scenarios where the
construction term is of longer duration than the Base Case. In the
Increased Construction Term case, the project is commenced on
schedule, the construction term is longer than expected, and the project
achieves commercial operation later than expected. In the Construction
Energy and Environmental Economics, Inc.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Halted case, the project is commenced on schedule, halted, and then
resumed after a period of months or years. Both cases result in increased
costs for Material, Labor, and Equipment due to general inflation for
these components. Both cases result in increased costs for Indirects
(costs associated with engineering and design, operations, impact studies,
etc.) due to inflation as well as the assumption that more hours related to
Indirect activities will be charged to the project if the construction period
is extended. Capital Interest costs also increase in both cases because of
increased costs and the longer term of the construction period loan. The
Contingency provision is not expected to change. The projected costs for
Mr. Roam’s Increased Construction Term case are described in Table 16
below:
Table 19: Increased Construction Term Case
Material
Labor
Equipment
Indirects
Capital Interests
Contingency
Total
Base Case
Ave Annual
Ave Annual
COD 4/1/2011 COD 1/1/2012 % Increase COD 1/1/2013 % Increase
$84,211,979
$89,477,886
8.42%
$97,525,054
8.75%
$42,030,987
$44,800,437
8.88%
$48,862,704
8.99%
$17,579,760
$18,739,419
8.89%
$20,346,675
8.71%
$54,047,180
$66,092,151
30.77%
$82,610,490
27.44%
$22,807,861
$27,665,873
29.36%
$35,147,752
28.03%
$44,135,553
$44,135,553
0.00%
$44,135,553
0.00%
$264,813,320 $290,911,319
13.35% $328,628,228
13.13%
Because E3’s Deferred Construction case does not assume that the
construction term has increased, the results from this Increased
Construction Term case are not utilized in E3’s Deferred Construction
case analysis.
The projected costs for Mr. Roam’s Construction Halted case are
described in Table 20 below:
Table 20: Construction Halted Case
Material
Labor
Equipment
Indirects
Capital Interests
Contingency
Total
Base Case
Ave Annual
Ave Annual
Ave Annual %
Ave Annual %
COD 4/1/2011 COD 1/1/2014 % Increase COD 1/1/2015 % Increase COD 1/1/2016
Increase
Increase
COD 1/1/2017
$84,211,979 $106,384,456
8.87% $116,141,896
8.95% $126,892,557
9.02%
$138,742,159
9.07%
$42,030,987 $53,331,052
9.04% $58,248,111
9.09% $63,661,166
9.13%
$69,622,683
9.17%
$17,579,760 $22,110,380
8.70% $24,047,338
8.71% $26,176,291
8.74%
$28,518,147
8.78%
$54,047,180 $72,385,314
11.21% $73,795,532
8.66% $75,245,934
7.21%
$76,737,664
6.29%
$22,807,861 $34,916,370
16.75% $34,916,370
12.03% $43,287,704
14.44%
$47,953,737
13.80%
$44,135,553 $44,135,553
0.00% $44,135,553
0.00% $44,135,553
0.00%
$44,135,553
0.00%
$264,813,320 $333,263,125
8.72% $351,284,800
7.83% $379,399,205
7.86%
$405,709,943
7.70%
Energy and Environmental Economics, Inc.
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Coolidge Connector Need and NTA Analysis
E3’s Deferred
Construction Case
Exhibit BKH-2
The Deferred Construction case examines the impacts of delaying
commencement of construction until approximately three years before
the line is needed. It is similar to the base case except (a) capital cost
estimates incorporate inflation rates expected in future years, (b) capital
interests are increased to reflect capital cost increases due to inflation,
and (c) the project achieves commercial operational at a later date. The
construction term remains the same as in the base case. Table 18 shows
the results of the Deferred Construction Case.
The Material, Labor, and Equipment costs in the Deferred Construction
case are identical to those in the Construction Halted case. This is
because cost increases in these categories are the result of inflation only.
The Indirects category in the Construction Halted case includes cost
increases due to additional hours related to Indirects activities, as well as
inflation. Mr. Roam’s testimony does not supply enough information to
determine how to adjust the figure to remove the additional hours. The
Indirects category has therefore been inflated using the minimum
inflation rate of the Labor category and the Indirects category. Capital
Interests line has been increased pro rata with increased construction
costs because the construction term is the same as in the Base Case. The
Contingency line has not been adjusted.
Table 21: Deferred Construction Case
Base Case
Ave Annual
Ave Annual
Ave Annual %
Ave Annual %
COD 4/1/2011 COD 1/1/2014
% Increase COD 1/1/2015 % Increase COD 1/1/2016
Increase
Increase
COD 1/1/2017
Material
$84,211,979 $106,384,456
8.87% $116,141,896
8.95% $126,892,557
9.02%
$138,742,159
9.07%
Labor
$42,030,987
$53,331,052
9.04% $58,248,111
9.09% $63,661,166
9.13%
$69,622,683
9.17%
Equipment
$17,579,760
$22,110,380
8.70% $24,047,338
8.71% $26,176,291
8.74%
$28,518,147
8.78%
Indirects
$54,047,180
$68,577,808
9.04% $73,795,532
8.66% $75,245,934
7.21%
$76,737,664
6.29%
Capital Interests
$22,807,861
$27,758,920
7.41% $29,816,217
7.41% $31,676,907
7.16%
$33,716,817
7.03%
Contingency
$44,135,553
$44,135,553
0.00% $44,135,553
0.00% $44,135,553
0.00%
$44,135,553
0.00%
Total
$264,813,320 $322,298,170
7.41% $346,184,647
7.41% $367,788,408
7.16%
$391,473,023
7.03%
PV 4/1/2011
$264,813,320
$263,698,176
$263,309,131
$260,054,867
$257,322,416
Total PV Savings/(Cost)
$1,115,144
$1,504,189
$4,758,453
$7,490,904
Annual Average PV Savings
$405,507
$401,117
$1,001,780
$1,302,766
Though Mr. Roam found that his delay scenarios resulted in high cost
overruns and cost increases, E3’s deferral case shows a net present value
benefit from delaying commencement of the project. The average annual
project cost increases resulting from E3’s deferred construction case are
less than or equal to 7.41%. Because VELCO’s after-tax WACC is
7.57%, this result means that the present values of the Deferred
Energy and Environmental Economics, Inc.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Construction case capital costs are less than those in the Base Case. In
present value terms, delaying construction to commercial operation dates
in January of 2014, 2015, 2016, or 2017 are all less costly in present
value terms than the Base Case.
For example, the three year deferral case has an average annual savings
of $405 thousand per year. When expressed in revenue requirement
levels, the present value savings would be approximately 40% higher,34
or approximately $567 thousand per year.35
34
Revenue requirements are the total amount of money that the utility collects
from customers to pay for all operating and capital costs, including a fair return
on investment. The present value of revenue requirements (PVRR) is higher
than the present value of the project costs because of additional items that are
typically not including in project cost estimates such as property and income
taxes and O&M. The 40% adder is a typical PVRR adjustment for transmission
projects. For example, the San Diego Gas and Electric Company’s Sunrise
Powerlink transmission project has a 42% adder for a project with a 40 year
expected life. Typical PVRR adders range from 30% to 60%.
35
The testimony of Brian Horii uses a simplified approach to estimating the
deferral value that arrives at a deferral value of $550 thousand per year for this
value.
Energy and Environmental Economics, Inc.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Conclusion
Overall, E3 finds that the VELCO and La Capra analyses were thorough
and fairly complete, especially given the information available to them at
the time the reports were prepared. Incorporating updated information
on peak loads and participation in the ISO-NE Forward Capacity Market,
as well as allowing small generators to contribute to resolving the peak
capacity problem, can push the need date for the Coolidge Connector to
between 2012 and 2014 under the threshold values presented by La
Capra in their report.
E3 disagrees with Mr. Roam’s testimony that delays in the project will
necessarily result in substantial cost increases in present value terms.
While this is true for the delay cases that Mr. Roam showed where the
project was started and then suspended or delayed, this would not be the
case if the start date for the project were delayed and the project
proceeded on a normal schedule once begun. Due to the high rate of
inflation in the cost of transmission elements, the monetary value to
deferral is relatively small. However, deferral of the line has the
advantage of allowing Vermont to gather information and gain
confidence in the new levels of EE activity and the evolving DR market.
In addition, deferral could allow time for new generation to be built in
response to the expiration of the long-term contracts, and reveal
customer responses to increasing electricity prices.
The discussion above is predicated on the peak load threshold levels in
VELCO’s pre-filed testimony. If the Board determines that the
Department’s lower peak load threshold is the appropriate value to use
for planning purposes, then deferral would not be possible and the
transmission project should proceed on its current schedule.
Energy and Environmental Economics, Inc.
Page 54 of 58
Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Appendix A: 1-in-10 versus 90th percentile peak load
forecast
This section explains the two methods of making a 1-in-10 peak demand
forecast. The first method assumes a 1-in-10 weather event (e.g., very
hot day). It captures the extreme weather’s effect on peak demand. The
second method derives a 1-in-10 peak demand forecast for a future year
as the 90-percentile of that year’s forecast distribution.
Suppose the summer peak demand data is generated by a linear
regression:
yt
=
xt  + t
where yt = peak demand in year t; xt = row vector of explanatory
variables (e.g., the intercept, time trend, and average WTHI);  =
column vector of coefficients to be estimated; and t = normally
distributed error with zero mean and finite variance 2. Let b be the
ordinary least squares (OLS) estimate of  .
Conditional on a forecast of the explanatory variables xF in a future year,
the peak demand forecast is xF b.
Suppose xF = x’ captures a 1-in-10 weather event. The forecast based on
the 1-in-10 weather event is simply x’ b.
Suppose (m, v2) are the mean and variance of a peak demand forecast
based on uncertain drivers. The 90-percentile of the forecast’s
distribution is (m + 1.28 v), the 1-in-10 forecast that an actual peak may
exceed with a 10% probability.
To derive (m, v2), assume xF = x”, which captures the median weather
event. The mean forecast is m = x”b, whose conditional variance is:
var(m | xF = x”) =
xF  xFT + s2
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
where  = covariance matrix of b and s2 = OLS estimate of 2. This
conditional variance understates the dispersion of m when xF is uncertain
with covariance matrix An estimate of the unconditional variance of
m is:36
v2
=
(xF  xFT + s2) + bT b + trace ().
Hence, v2 the sum of (a) (xF  xFT + s2), the forecast’s conditional
variance, (b) bT b, the explanatory variables’ dispersion magnified by
the estimated coefficients, and (c) trace (), the explanatory variables’
dispersion magnified by the coefficient estimates’ dispersion.
Feldstein, M. (1971) “The Error of Forecast in Econometric Models when the
Forecast-Period Exogenous Variables are Stochastic.” Econometrica 39(1): 5560.
36
Energy and Environmental Economics, Inc.
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Appendix B: Peaking Generation Analysis
The approach for simulation of hourly available capacity from a system
of generators follows from Mazumdar et al.37 The process for each
simulation of 3 month hourly system capacity starts with individual
simulations for each generator in the system. This is accomplished under
the assumption that the state of each generator in any given hour is
independent of all other generators in the system. For each generator the
only parameters necessary are the Mean Time to Failure (MTR) and
Mean Time to Repair (MTR). The initial state of a generator is simulated
with the following probabilities:
P[up] 
MTF
MTF  MTR
P[ Down] 
MTR
MTF  MTR
Once the initial state has been determined the amount of time in hours
that the generator is in that initial state is assumed to be exponentially
distributed with mean equal to MTF if the generator is in the up state,
and mean equal to MTR if the generator is in the down state.38 Therefore
an exponential random variable with the appropriate mean is simulated
and that number of hours is assigned the current state. Once the hours
for that state have been simulated, the generator is then assumed to
switch to the opposite state and the number of hours that it remains in the
second state is again simulated as an exponential random variable with
mean equal to MTF if the generator is in the up state and MTR is the
generator is in the down state. This process is repeated until it accounts
for all hours in the three month period.
Mazumdar M., Coit D. and Shih FR (1999), “A Highly Efficient Monte Carlo
Method for Assessment of System Reliability Based on a Markov Model”,
American Journal of Mathematical and Management Sciences 19(1-2): 116-133.
37
38
Details of the exponential distribution can be found for example in:
Johnson N., Kotz S., and Balakrishnan N., Continuous Univariate Distributions,
Volume 1 (1994), Wiley Series in Probability
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Coolidge Connector Need and NTA Analysis
Exhibit BKH-2
Appendix C: Generator System Capacity Distribution
We assume for a general system of i = 1,…,n generators and with the
capacity of generator i defined as ci then we can calculate the possible
system configurations and their associate probability as follows.
Assuming each generator is either on at full capacity or off at zero
capacity then there are a total of j = 1, … , 2n possible on/off
configurations for all n generators. If we further define the state of
generator i in configuration j as si,j where si,j is 0 is generator i is out in
configuration j and 1 if generator i is available in configuration j. Our
available system capacity SC j in configuration j is
SC j  i 1 c j  si , j
n
Having the capacity available in each configuration j we must also
calculate the probability of that particular configuration occurring. The
configuration probabilities are calculated assuming independence of the
generator states in each configuration that is whether one generator is
available or out has no effect on the states of the other generators. We
define the probability that a generator i is unavailable during any given
hour of the year as pi, where pi is assumed to be that generators EFORd.
Conversely the probability that the generator is available in any given
hour is 1-
pi. Having these probabilities the probability of any given
generator configuration SP j is the product of the probabilities of each
generators state in that configuration.
SPj   (1  pi )   pi
i on
i off
This leads to a system of j generator configurations with associated
configuration capacities SC j and configuration probabilities SP j. These
configurations represent the possible states of the generator system and
their associated probability in any given demand hour during the year.
Energy and Environmental Economics, Inc.
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