Bill Effects of Demand- Based
Rates on C ommonwealth
Edison Residential C ustomers
A frequent rationale for demand-based rates is the utility
assertion that they should reflect customer cost-causation. More
analysis is needed to test this assertion, incorporating data from
utilities’ cost of service studies and comparing it to individual
usage and bill effects.
By Jeff Zethmayr
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Bill Effects of Demand-Based
Rates on Commonwealth
Edison Residential Customers
A frequent rationale for demand-based rates is the utility
assertion that they should reflect customer cost-causation. More
analysis is needed to test this assertion, incorporating data from
utilities’ cost of service studies and comparing it to individual
usage and bill effects.
By Jeff Zethmayr
The tension between revenue security,
fairness of cost allocation, and consumers’
fixed charges increase bills for lower-use
customers, while lower SFV volumetric
control over their bills has long dominated
the utility rate design policy discussion.
charges reduce incentives for energy
efficiency measures.
Nationwide, many utilities have pushed for
straight-fixed-variable (“SFV”) rate designs,1
which increase the fixed portion of
customers’ delivery bills. Consumer,
environmental, and low-income advocates
J eff Ze th may r is a Senior Policy Analyst
with the Citizens Utility Board of Illinois,
where he studies rate design from a
have resisted this push because higher
consumer advocacy perspective. Previously,
he consulted on Corporate Social
Responsibility issues in the utility industry.
He holds Masters in Public Administration
SFV rate designs assign levels of fixed and variable
rate recovery equal to a utility’s respective
proportions of fixed and variable costs. For a
distribution-only utility, the proportion of costs that
are fixed is higher than the historical proportion of
fixed delivery charges to total rate recovery.
1
from Columbia University's School of
International and Public Affairs and a BA in
English from Macalester College.
July 2016 / 1
A residential rate design anchored neither
higher fixed charges – a reasonable
to high fixed fees nor volumetric charges
would be made possible with the advent of
concern given the lack of data and analysis.
advanced metering infrastructure (“AMI”.)
“Smart meters” capture the rate at which a
customer is using electricity (in kW) at any
given time, allowing various measures of
maximum monthly usage (“MMU”) to be
used for billing.
Billing customers based on a measure of
their maximum usage is attractive to utilities
on practical and conceptual levels.
Depending on the measure of MMU used,
customers show lower variation compared
to projections. This allows utilities to more
In this paper we use actual historical data
from Commonwealth Edison (“ComEd”)
customers to measure the effects of a
revenue-neutral shift to demand-based
distribution rates. To qualify the results, we
set forth the following criteria for a
potentially successful demand charge
scenario:
•
an annual basis, for a majority of
customers;
•
requirement. It also may align delivery
charges more closely with utility costcausation; utility costs are partially driven
by the capacity of the delivery grid, which
in turn is driven by peak load rather than
the ultimate volume of kWh delivered. In
theory, then, instituting a demand charge
could potentially provide a middle-ground
between higher fixed charges and pure
kWh-volumetric pricing, satisfying the
utility’s desire for more stability while still
providing customers with some incentive to
lower peak usage in order to lower their
demand charge. But there is great
uncertainty about the actual effects of
demand charges on residential customers.
Many advocates are concerned that a
demand charge would be no better than
The charge would limit the annual bill
increase for those customers who did
accurately forecast billing determinants in
the rate design process, and go into a
billing year with a higher degree of
confidence they will realize their revenue
The charge would not increase bills on
realize an increase;
•
The charge would not
disproportionately increase bills for lowincome or low-use customers; and
•
The charge would reward customers
with less “peak-y” usage patterns.
We constructed a model to determine the
annual bill impact of a switch to demand
based rates for customers across the usage
spectrum and from the different residential
classes. We obtained a year’s worth of
anonymous usage data for a large subset of
ComEd delivery customers, including
monthly kWh delivered and values for four
alternative MMU measures. We calculated
customers’ monthly bills under all four
MMU measures and compared them to bills
using the 2014 ComEd actual rate design
to ascertain the bill impacts that would
have occurred from a switch to demandbased rates.
July 2016 / 2
An additional aspect of this analysis was to
smart meters, as they formed the testing
investigate the effects of lowering the level
of the fixed monthly customer charge and
group for the company’s AMI pilot and
were chosen in a geographic area found to
collecting a higher percentage of revenue
based on demand. Given that demand-
be most representative of the overall
ComEd system.
based rates provide the utility greater
revenue security, they should also provide
The dataset also tagged customers as
an opportunity to lower customer charges,
a primary goal of most consumer
advocates. The model allows for analysis at
any level of fixed customer charge
recovery.
“Low-Income” who were participants in
Illinois’ Low Income Home Energy
Assistance Program (“LIHEAP”) or were on
a Percentage of Income Payment Plan
(“PIPP”) during the year. This is an
imperfect marker of low-income status,
Our analysis shows that, at the current level
of customer charge recovery, demand
because only a subset of low-income
households enrolls in these programs, and
charges based on three of the four MMU
those that do may not be representative of
measures satisfy all four of the above
criteria, and that lowering the level of
the overall low-income population. To
expand our definition, we also included
customer charge recovery increases the
proportion of customers who see savings
customers likely to be low-income based on
where they live. ComEd provided us with
from the change, while magnifying the bill
impact on those who see bill increases. Our
ZIP+4 codes for most of the anonymous
customers in the dataset, minus the last two
conclusion is that an appropriate demand
digits.2 We compiled a separate list of
charge would use one of these three
measures, and would lower the customer
Cook County ZIP+2 codes that correlated
with high-poverty census tracts,3 and added
charge to a level that includes more
customers in the savings while minimizing
customers whose ZIP+2 code appeared on
this second list to the low-income
bill increases on the rest of the customer
rate class.
population.
Data
This analysis is based on a dataset provided
by ComEd of anonymized 2014 usage data
from all 106,054 customers served by the
company’s Maywood Operations Center,
which covers an area in Chicago’s Western
suburbs and two neighborhoods in the City
of Chicago. These customers were the first
in ComEd’s service territory to receive
Demand-based customer bills were
calculated using the actual rates charged
during 2014, from the rate design
approved by the Illinois Commerce
Commission (“ICC”) in Docket 14-0312.
ZIP+2 codes that contained fewer than fifteen
people were not provided, for privacy reasons.
2
Greater than 50% of households below poverty
level
3
July 2016 / 3
Usage Data
Rate Design
The data includes monthly kWh usage,
three monthly MMU measures based on the
The analysis was performed using the ICC
approved rate design for 2014. This rate
Maximum Kilowatt Delivered (“MKD”)
design provides different monthly customer
methodology, and an annual MMU
measure using the Network Service Peak
charges and per kWh distribution charges
for four distinct residential classes, and a
Load (“NSPL”) methodology. The MKD
values measure a customer’s highest rate of
uniform metering charge and per kWh
distribution tax recovery charge.4 (Table 1.)
electricity consumption during a given
month, at any point during a set number of
The four classes are Single Family No Space
Heat (“SFNH”), Multi-Family No Space
hours of the day. The data includes MKD-
Heat (“MFNH”), Single Family with Space
9, MKD-16, and MKD-24 values, looking at
electricity usage during a set nine hours,
Heat (“SFH”), and Multi-Family with Space
Heat (“MFH”).
sixteen hours, and all day, respectively. The
NSPL value measures
a customer’s average
usage rate during the
delivery system’s five
annual peak hours,
resulting in a monthly
demand charge that
remains constant
throughout the
subsequent delivery
year.
A critical distinction between the MKD and
NSPL methodologies is whether they
measure coincident peak (“CP”) or noncoincident peak (“NCP”) usage. MKD
values measure NCP usage, meaning each
customer’s MMU may occur on a different
Analysis
Our model used the above inputs to
generate 12 months of simulated bills for
each customer under five separate rate
day, and during a different hour. NSPL is a
designs: the current volumetric rate design,
and a demand-based rate design using
CP value, meaning it measures each
customer’s usage during the same peak
each of the four MMU measures. Annual
amounts billed to each customer under the
period.
The State of Illinois assesses a per-kWh tax on
electricity distribution, which is a pass-through charge
that is collected through delivery bills.
4
July 2016 / 4
different demand-based rate designs were
distribution charge recovery, which is
then compared to the annual total from the
current rate design to determine the annual
divided by the number of kW billing
determinants for each of the new rate
bill effect.
designs.
The model assigns the revenue
The end result is a distinct rate design that
requirement for each class according to the
replaces each class’s $/kWh distribution
ICC approved 2014 rate design. This
analysis assumes the shift to demand rates
charge with a $/kW charge based on each
of the four MMU values, at the set level of
is revenue neutral, reflecting no change in
either the utility’s approved revenue
fixed customer charge recovery, allowing
for a direct comparison for each customer
requirement or the proportion assigned to
residential customers, to allow for a direct
against what their monthly bills were under
the historically charged rates for 2014.
comparison to the status quo rate design.
The total amount of revenue collected from
Results
each residential rate class is held constant
Save/Loss Percentage
for each demand-based design, equal to
the amount collected under the actual 2014
rates.
With the customer charge set at the status
quo level of 30%, just over half of ComEd
The second step of the model reassigns
customers would have seen annual bill
savings under all four demand charge
customer charge recovery and distribution
scenarios. This proportion varies between
charge recovery between individual
customers, according to the variable level
the different residential rate classes, with
the MFNH class seeing the highest
of customer charge recovery. Metering and
tax charge recovery amounts are subtracted
proportion of savers (58.5% in the NSPL
scenario, and ~55% in the different MKD
from each class’ revenue requirement, as
these charges are not affected by a switch
scenarios), and the SFH and MFH classes
seeing lower proportions (~54% in the
to demand based rates. The new customer
NSPL scenario, and between 43% and 48%
in the different MKD scenarios). As the
charge recovery level, according to a
variable percentage of total recovery, is
customer charge recovery level decreases,
then divided by the number of bills (equal
to the number of customers in each class
the number of customers seeing annual bill
savings increases. (Table 2.) Save
times twelve). The remaining revenue
requirement represents the new level of
Percentages at Customer Charge Levels –
All Customer Classes.
July 2016 / 5
The results show a similar pattern of save
percentages among low-income customers,
with higher proportions of low-income
customers seeing annual savings than the
general population at every level of
customer charge recovery. This pattern of
higher win ratios was observed using both
the LIHEAP-PIPP participation proxy and
the geographic methods for low-income
customer identification. (Tables 3 and 4.)
M agnitude of Bill Effects
As the percentage of revenue recovered
through fixed charges decreases, the
magnitude of annual savings increases for
customers who benefit. At the status-quo
level of 30%, the median annual bill
decrease for SFNH customers (who see
decreases) ranges from $58.78 in the MKD-
July 2016 / 6
9 scenario to $84.11 in the NSPL scenario.
profiles. At the 30% status-quo level of
If the customer charge is eliminated, this
range increases to $68.20 - $131.40. This
customer charge recovery, the median
annual bill increase for SFNH customers
increase in savings with lower customer
charges is less visible at the high end of the
(who see higher bills) ranges from $37.20 in
the MKD-9 scenario to $73.21 in the NSPL
bill effect spectrum: customers in the 90th
percentile of annual savings for SFNH
scenario. If the customer charge is
eliminated altogether, the range increases
customers actually see lower savings with
to $66.23 - $122.40.
no customer charge under MKD scenarios,
with higher savings using the NSPL
This effect is magnified at the higher end of
the loss spectrum: the 90th percentile of
measure, demonstrating that further
decreases in the customer charge have little
annual bill increases for SFNH customers
(who see higher bills) ranges from $171.35 -
impact on these customers. Figures 1 and 2
(pages 9-10) demonstrate the median and
$184.21 under the current customer
charge, and jumps to $309.61 - $321.90
90th percentile levels of annual savings,
respectively, at variable levels of customer
with no customer charge.
charge recovery.
At every level of customer charge recovery,
the top one percent of bill effects for SFNH
Because the demand charge scenarios in
this analysis are revenue-neutral, a direct
customers is significant, ranging between
$684.69 and $921.99 annually under the
consequence of expanding the pool of
customers seeing annual savings is growth
current customer charge, and between
$1,214.91 and $1,406.04 annually with no
in the per-customer bill increases for those
customer charge. These effects reflect a
on the other side of the spectrum:
customers with relatively peaky usage
steep increase for high users with extremely
low load factors. Customers in this range of
July 2016 / 7
bill effects have both very high demand and
highly peaky usage, suggesting they reflect
very large dwellings with central airconditioning and very low usage in off-peak
hours, or perhaps contain businesses that
are mistakenly characterized as residential
spaces. The trend of larger bill increases for
affected customers at lower levels of
•
A majority of customers would not see
an increase in their bills
•
The charge would limit the annual bill
increase for customers who realized one
•
Low-income customers would not be
disproportionately affected
•
Customers with higher load factors
would see bill decreases
customer charge recovery is consistent
across residential customer classes. Figures
The first and third of these criteria are met
3 and 4 (pages 10-11) illustrate the median
and 90th percentile annual bill increases at
by all four of the demand-based rate
scenarios. Almost every scenario we
variable customer charge recovery levels.
calculated produced at least a majority of
consumers who would see annual bill
Load Factor Correlation
Under most demand charge scenarios,
customer usage with a higher load factor is
associated with lower annual bills. All of the
MKD methodologies showed inverse
correlations between annual bill impacts
and high load factor at every level of
customer charge. The NSPL methodology
showed a weaker correlation than MKD
rates at all levels of customer charges, with
a slightly positive correlation for MultiFamily customers when the customer
charge is zero. Figures 5-7 (page 12-13)
plot the relationship, for each MMU
measure, between customer load factor
and annual bill impact at the 30%, 15%,
and 0% customer charge recovery levels.
savings from a switch to demand charges,
and every scenario showed that low-income
customers, using both PIPP and LIHEAP
program enrollments and geographic
estimates as proxies, are more likely than
not to be among those seeing savings.
Variation in revenue percentage recovered
through the monthly fixed customer charge
turns out to be a powerful lever in
determining these proportions, including
more customers in the population of annual
savers the lower it gets, especially those in
the low-income subset. However, this
increase is not consistent; decreasing the
charge from the status quo of 30%
produces steady increases in the savings
percentage until it reaches around 15%,
Discussion
and then every scenario shows only
marginal savings percentage increases from
At the outset of this investigation, we
going lower.
suggested four criteria for a potentially
Our analysis also shows that demand
appropriate demand-based delivery rate
design:
charges do appear to associate higher bills
with peakier usage, while benefitting
customers with better load shapes. This
July 2016 / 8
identifies a potential cross-subsidization
ComEd residential customers would cut the
embedded in ComEd’s current approved
rate design. Customers with higher than
customer charge in half (15% recovery
level). This would achieve an appropriate
average peaks relative to their usage
volume place a higher stress on the delivery
balance between including as many
customers in the saving population as
system than they are being charged for on
their bill, while customers with lower peaks
possible without continuing to drive-up the
transfer of revenue responsibility onto
relative to their usage are paying more
those with less than ideal load shapes.
under volumetric billing than perhaps they
should. This correlation between load
Further Analysis
shape and bill effects is helpful for lowincome customers, as the data shows they
tend to have better load shapes.
The final question becomes limiting the
magnitude of annual bill increases for
customers with peaky load shapes. For
every scenario we analyzed, the per-
Further analysis on the bill effects of a shift
to demand-based rates is needed in the
areas of the second-order effects of such a
shift on usage and energy efficiency, yearto-year variability in demand, and the
impact on distributed energy resource
payback.
customer increase rose at lower levels of
customer charge
recovery. At very low
levels of customer
charge, this trend
continues even while
the savings
percentage shows
only marginal
improvement. The
data suggests, then,
that an appropriate
demand charge for
July 2016 / 9
studies should also
analyze and compare
bill effects from TimeOf-Use pricing5 plans to
demand-based rates,
both in conjunction and
as a replacement rate
design, as well as
alternative methods for
measuring demand.
One such alternative
that is already being
studied is a rate based
on the average of a
One criticism leveled at demand-based
rates is that they could potentially reduce
the incentive for energy efficiency by
focusing price signals on peak rather than
overall energy usage. Further analysis will
customer’s weekday
peaks for the month, rather than the
customer’s single highest peak. Early
results indicate this method would
moderate some of the extreme bill effects
be needed to compare
bill impacts of specific
energy efficiency
measures under both
volumetric and
demand-based rate
regimes, and to
examine changes in
total bills, not solely
delivery charges.
Preliminary analysis
suggests minimal effect
on several energy
efficiency measures,
but further analysis is
warranted. Further
Time-Of-Use pricing assigns a different per-kWh
usage rate for different blocks of hours in the day.
5
July 2016 / 10
at the high tail of the usage and load factor spectrum, suggesting it would produce a fairer
outcome overall.
This analysis was performed on only one year’s worth of usage data. A useful next step would
be to look at 2015 usage data to see how much demand usage varies from year to year in the
absence of rate design intervention. Combined with assumptions on price-elasticity of power
demand for customers, this could lead to lower utility projections for system peak demand in
the long run, leading to lower total system costs over time.
Finally, a frequent rationale for the implementation of demand-based rates is the utility
assertion that they better reflect customer cost-causation. Further analysis should test this
assertion by incorporating data from utilities’ itemized cost of service studies, and comparing it
to individual usage and bill effects.
July 2016 / 11
July 2016 / 12
July 2016 / 13