Application No.:
Exhibit No.:
Witnesses:
A.11-06-007 (Updated)
SCE-02
C. Sorooshian
S. Verdon
P. Nelson
R. Thomas
(U 338-E)
Phase 2 of 2012 General Rate Case
Marginal Cost And Sales Forecast Proposals
Before the
Public Utilities Commission of the State of California
Rosemead, California
October 7, 2011
1812027.v8
Phase 2 of 2012 General Rate Case Marginal Cost and Sales Forecast
Proposals
Table Of Contents
Section
I.
Page
MARGINAL COSTS ........................................................................................1
A.
Introduction And Summary Of Recommendations ...............................1
B.
Methodology Overview .........................................................................4
1.
Marginal Cost Principles............................................................4
2.
Marginal Cost Scope And Application ......................................5
3.
Cost Drivers ...............................................................................6
4.
5.
C.
a)
Electricity Usage Cost Driver ........................................6
b)
Design Demand Cost Driver..........................................6
c)
Customer Cost Driver ....................................................9
Time-Of-Use (TOU) Issues .....................................................10
a)
Generation Marginal Costs ..........................................10
b)
Delivery-Related Marginal Costs ................................11
Annual Cost Of Capital Investments RECC
Methodology ............................................................................12
Marginal Cost Methodology ................................................................15
1.
2.
Witness
R. Thomas
P. Nelson
C. Sorooshian
R. Thomas
P. Nelson
Electricity Usage Marginal Costs ............................................15
a)
Generation Capacity Marginal Cost.............................16
b)
Energy Marginal Cost ..................................................20
c)
Impacts Of Renewable and Greenhouse Gas
Policy on Energy Costs................................................22
d)
Loss Of Load Expectation ...........................................26
Delivery-Related Marginal Costs ............................................28
a)
NERA Regression Methodology .................................29
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R. Thomas
Phase 2 of 2012 General Rate Case Marginal Cost and Sales Forecast
Proposals
Table Of Contents (Continued)
Section
II.
Page
3.
Customer Marginal Costs ........................................................30
4.
Street Lighting and Outdoor Lighting Marginal Cost..............34
SALES AND CUSTOMER FORECAST .......................................................41
1.
Witness
S. Verdon
Billing Determinants And Present Rate Revenue....................42
Appendix A: Glossary.....................................................................................................
R. Thomas
Appendix B: Circuit Analysis For Determination Of Effective Demand
Factors..................................................................................................................
C. Sorooshian
Appendix C: Marginal Energy Cost Analysis ................................................................
P. Nelson
Appendix D: SCE Costing Period Study ........................................................................
C. Sorooshian
Appendix E: NCO Marginal Cost Methodology ............................................................
R. Thomas
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Phase 2 of 2012 General Rate Case Marginal Cost and Sales Forecast
Proposals
List Of Figures
Figure
Page
Figure I-1 Illustration of the RECC Methodology.....................................................................................13
Figure I-2 Annual Payment for $100 Capital Investment 10% Discount Rate and 3%
Annual Inflation ...................................................................................................................................14
Figure I-3 Derivation of Capacity Value – CT Proxy With Energy Rent Adjustment..............................18
Figure I-4 ...................................................................................................................................................24
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Phase 2 of 2012 General Rate Case Marginal Cost and Sales Forecast
Proposals
List Of Tables
Table
Page
Table I-1 Electricity Usage – Related Marginal Costs (2012$, at generation level) ...................................3
Table I-2 Delivery – Related Marginal Costs (2012$, at applicable voltage level) ....................................3
Table I-3 Marginal Customer Costs (In $/Customer, 2012$) ......................................................................4
Table I-4 CT Proxy Cost (2012$) ..............................................................................................................17
Table I-5 Generation Capacity Marginal Cost (2012$) .............................................................................19
Table I-6 Generation Capacity Marginal Costs, 2012-2014 Average (2012$)..........................................20
Table I-7 Energy Marginal Costs, 2012-2014 Average (2012 ¢/kWh) .....................................................22
Table I-8 Comparison Of The Ratio Between of On-Peak of Energy Prices between 2009
and 2012 GRC (nominal $)..................................................................................................................22
Table I-9 Impact of Renewable Policy .....................................................................................................25
Table I-10 Impact of Carbon Prices Associated With GHG Policy .........................................................25
Table I-11 Prices Between 20% RPS-no GHG and 33% RPS with GHG................................................26
Table I-12 Relative LOLE Factors (Sum = 1) ..........................................................................................28
Table I-13 Delivery-Related Marginal Costs (2012$) ...............................................................................30
Table I-14 Customer Marginal Cost Components For GS-1 Customers (In $/CustomerYear, 2012$) ........................................................................................................................................32
Table I-15 Customer Marginal Costs (In $/Customer-Year, 2012)...........Error! Bookmark not defined.
Table I-16 Monthly Street Light Facility Marginal Costs (2012$)............................................................37
Table I-17 Monthly Street Light Facility Marginal Costs (2012$) Continued..........................................38
Table I-18 Monthly Street Light Facility Marginal Costs (2012$) Continued..........................................39
Table I-19 Monthly Street Light Facility Marginal Costs (2012$) Continued..........................................40
Table II-20 Forecast Grid Sales and Customers For Years 2012 Through 2014.......................................42
-iv-
I.
1
MARGINAL COSTS
2
3
A.
Introduction And Summary Of Recommendations
For over twenty years, the Commission has relied on marginal cost principles for assigning
4
5
revenue requirements to customers (by rate group), and as guidance for setting the level of individual
6
rate components.1 This chapter presents SCE’s marginal costs for providing regulated utility services to
7
our customers.2
The starting point for calculating marginal costs is the identification of cost drivers, that is, those
8
9
fundamental aspects of customer electricity requirements that directly cause SCE to incur costs. Next,
10
marginal costs are calculated for small changes in each cost driver, by dividing the change in total cost
11
by the change in the cost driver. Finally, these marginal costs are attributed to measurable aspects of
12
customer requirements such as energy consumption, peak demand, and customer type. This allows the
13
rate components most associated with these measurable customer requirements, specifically energy
14
charges, demand charges and monthly customer charges, to be set based on the corresponding marginal
15
cost components.
Marginal costs are used to calculate marginal cost revenues – that is, the revenues that SCE
16
17
would collect if all of its customers were charged rates that equal marginal costs. Marginal cost
18
revenues are then used to allocate the authorized revenue requirements to rate groups, a process called
19
revenue allocation. Finally, marginal costs are considered when designing rates (for each rate group) to
20
recover the allocated revenue requirements.
In this application, SCE proposes to restructure several rate groups to facilitate the
21
22
implementation of the Commission’s dynamic pricing goals, as ordered in D.09-08-028, and to improve
23
the revenue allocation and rate design associated with standby customers. The current agricultural rate
1
Revenue requirements are the costs of providing utility services that the Commission has determined are appropriate to
recover through customer rates. Rate groups are categories into which similar types of customers are grouped, such as
residential service or small general service. Rates are the regulated (tariffed) prices charged to customers in each rate
group for utility services. These rates typically consist of multiple components, such as energy charges, demand charges
(where metering permits) and a monthly customer charge.
2
Regulated utility services refer to electricity supply (production or procurement of power for customers), electricity
delivery (transmission, subtransmission and distribution) and customer services (interconnection to the delivery system
and managing SCE’s relationship with customers, including handling customer communications, measuring usage,
maintaining records, and billing.)
-1-
1
groups (PA-1, PA-2, TOU-AG, and TOU-PA-5) would be classified into two groups; one for customers
2
with peak demand equal to or above 200 kW, referred to as TOU-PA-3 and the other for customers with
3
peak demands below 200 kW, referred to as TOU-PA-2. Large power standby customers with demands
4
greater than 500°kWwould be classified in three rate groups separate from the large power TOU-8 rate
5
groups in an effort to simplify the rate structure and to reduce a potential cost shift between standby and
6
non-standby customers. The new standby groups will be treated the same as all other rate groups with
7
respect to cost studies and revenue allocation. The standby reclassification aligns SCE’s treatment of
8
standby customers with Pacific Gas and Electric Company’s (PG&E’s) current marginal cost and
9
revenue allocation process for standby customers.
In the testimony which follows, SCE presents marginal costs based upon three cost drivers:
10
11
electricity usage, design demand, and number of customers. The cost of procuring electricity to meet
12
changes in customer electricity usage varies hourly. SCE and other retailers are required to procure
13
dependable generation resources with sufficient capacity to meet 115% to 117% of forecast demand.
14
Marginal generation costs (energy and capacity) are associated with the electricity usage cost driver and
15
are aggregated in time-of-use periods which group together hours with similar loads and costs.
16
SCE’s electric delivery system consists of a network of high-voltage (transmission and
17
subtransmission) and low-voltage (distribution) facilities which connect generation resources to
18
customer facilities. The delivery system is designed and constructed to meet the expected peak demand
19
placed on it, so design demand is the associated cost driver. Design demand is a localized cost driver,
20
since portions of SCE’s delivery system peak at different times depending on the area, and the mix of
21
customers varies by area. In addition to design demand, some of the costs which customers impose on
22
the delivery system are fixed based on the customers’ location, and do not vary with customer electricity
23
usage. That is, a portion of the delivery system represents grid infrastructure which, like streets and
24
roads, is extended to those who live in an area regardless of actual usage.3
3
In general, the underground conduit running through a residential or commercial development and the poles running
through a right-of-way between adjacent rows of homes are not any bigger or more costly depending on how much
electricity they carry. Grid infrastructure is not a new concept. The original marginal cost methodology adopted by the
Commission identified a minimum distribution system component of marginal customer costs which identified the costs
associated with a hypothetical redesigned distribution system capable of delivering only a minimum amount of
electricity but capable of providing service to all customers. Measurement was difficult, and this approach was
eventually abandoned.
-2-
1
Finally, the number of customers is a cost driver, reflecting the marginal costs of customer
2
interconnection to the delivery system and various customer services. Since the marginal costs of
3
customer interconnection and customer services vary by type of customer, there is an individual
4
marginal cost for each customer category. Like generation capacity and delivery marginal costs, SCE’s
5
customer marginal costs are calculated based on the real economic carrying charge (RECC)
6
methodology.4 However, in recognition that the Commission adopted an alternative new customer only
7
(NCO) method in SCE’s 1995 GRC, SCE presents calculations based on the NCO methodology as well
8
in Appendix E.
SCE’s recommended marginal costs are summarized in Table I-1, Table I-2, and Table I-3.
9
Table I-1
Electricity Usage – Related Marginal Costs
(2012$, at generation level)
Annual
Energy
(¢/kWh)
Capacity
($/kW-yr)
Summer
Winter
On-Peak Mid-Peak
Off-Peak
Mid-Peak Off-Peak
4.944
6.156
5.550
4.664
5.183
4.635
125.1
87.70
25.65
1.13
10.13
0.50
Based upon the time periods in the TOU-8 tariff and at the generator level.
Includes GHG valuation. Assumes an average gas price of $5.36/mmBTU.
Table I-2
Delivery – Related Marginal Costs
(2012$, at applicable voltage level)
Delivery-Related Marginal Costs
4
$/kW-year
Marginal Subtransmission Cost (Non-ISO) - 66KVA
35
Marginal Distribution Cost - 12KVA
91
This methodology is also called the rental value method or the economic deferral method.
-3-
Table I-3
Marginal Customer Costs
(In $/Customer, 2012$)
Customer costs (2012$)
$/Customer/year
Domestic
159.61
GS-1
234.60
TC-1
243.46
GS-2
1,886.32
TOU-GS-3
4,080.69
TOU-8
Secondary
5,691.63
Primary
2,873.31
Sub-Trans
19,665.32
AG <= 200
1,248.28
AG > 200
3,680.48
Metered Street Lights
163.02
Unmetered Street Lights*
LS-1
LS-2
OL
DWL
Per Customer
+ Per Lamp
+ Per Lamp
+ Per Lamp
+ Per Lamp
25.86
7.40
8.00
7.64
3.42
*Unmetered Street Lights Customer marginal cost is a per customer cost plus a per lamp cost.
Section B, below, describes the principles and methodological approaches that guided the
1
2
development of SCE’s marginal costs. Finally, Section C presents SCE’s marginal cost study and the
3
derivation of individual marginal cost components. A glossary of terms is provided in Appendix A.
4
Additional information supporting SCE’s marginal cost study is presented in Appendices B through D.
5
B.
6
7
Methodology Overview
1.
Marginal Cost Principles
The Commission’s reliance on marginal cost principles for revenue allocation and rate
8
design is well founded on economic principles. Marginal costs reflects the change in costs incurred, or
9
avoided, to serve a small increment (decrement) in demand for utility services. Setting utility rates equal
10
to marginal costs provides an economically correct price signal, which encourages customers to use
-4-
1
electricity efficiently and to make appropriate choices when purchasing electricity-consuming
2
equipment and appliances. When utility rates are not set equal to marginal cost, users of utility services
3
may over consume or avoid services, depending or whether prices are set less than or greater than
4
marginal cost. Moreover, there is growing interest in customer-site distributed generation and demand
5
response, and increased awareness of distribution competition among utilities, municipalities and other
6
public entities. In this environment, inefficient pricing can lead to uneconomic bypass of utility
7
facilities, resulting in unnecessary investment in duplicative facilities and higher rates for remaining
8
utility service customers.
In practice, the Commission deviates from setting rates equal to marginal costs in order to
9
10
establish overall utility rates that recover a utility’s authorized revenue requirements. The Commission
11
has frequently assigned authorized revenue requirements in proportion to marginal cost revenues by the
12
equal percent of marginal cost (EPMC) method.5
2.
13
Marginal Cost Scope And Application
SCE’s marginal costs reflect the full chain of services required to provide electricity to
14
15
customers, although SCE’s role in the provision of such services remains somewhat unclear. State law
16
allows some customers to directly access markets for electricity supply instead of procuring such service
17
from SCE. This includes community choice aggregation (CCA) customers and existing direct access
18
(DA) customers. DA, which had been suspended from new entry, was reopened in early 2010 on a
19
limited basis. Existing DA customers are permitted to obtain some metering and billing services from
20
their electricity supply provider instead of SCE. For the purpose of this testimony, SCE is assumed to
21
obtain electricity supply either from wholesale market purchases or from its own generating facilities. It
22
is also assumed that SCE will continue to provide metering and billing services. Thus, SCE’s marginal
23
costs only reflect the costs of serving a “bundled service” customer.
24
SCE’s higher voltage transmission facilities are subject to Federal Energy Regulatory
25
Commission (FERC) jurisdiction and are under the operational control of the California Independent
26
System Operator (ISO). FERC-jurisdictional (ISO controlled) assets and activities have not been
27
included in the marginal cost study. Marginal costs associated with the FERC-jurisdictional facilities
5
The Equal Percent of Marginal Cost (EPMC) has been the basis of SCE’s revenue allocation methodology in each of our
last three GRC rate design proceedings.
-5-
1
and activities are excluded from marginal cost revenues and the revenue allocation process, since FERC
2
is responsible for determining revenue requirements and rates associated with these facilities and
3
activities.
SCE’s marginal costs are intended to represent conditions expected to occur during the
4
5
period from 2012 to 2014. In particular, electricity supply marginal costs are based on a three-year
6
forecast (expressed in constant 2012 dollars). Thus, there is no need to true-up SCE’s marginal costs in
7
annual rate design proceedings.
3.
8
Cost Drivers
The cost drivers that SCE has identified and used to determine its marginal costs are
9
10
described below.
11
a)
Electricity Usage Cost Driver
The cost associated with a change in customer electricity usage includes energy-
12
13
related and capacity-related components. Since SCE buys and sells power in the electricity market in
14
which its service area is located, the market clearing price of this power is an appropriate measure of
15
energy-related marginal generation costs. As described further in Section I.C.1., energy-related
16
marginal generation costs are forecast through production simulation model forecasts of market clearing
17
prices. Capacity-related marginal generation costs are measured by annualizing the expected costs of a
18
utility-built combustion turbine (CT) as a proxy. Because CTs operate during periods of high market
19
prices, and are able to earn energy rents (operating profits in excess of variable operating costs) that
20
recover a portion of their fixed costs, these energy rents are deducted from the annualized CT proxy
21
costs to determine capacity-related marginal costs.
Energy-related marginal costs are aggregated into time-of-use (TOU) periods.
22
23
Capacity-related marginal costs are assigned to TOU periods using a loss-of-load expectation6 (LOLE)
24
measure, also derived from production simulation modeling.
b)
25
Design demand is the amount of delivery capacity that T&D planners determine
26
27
Design Demand Cost Driver
to be necessary when planning to serve the additional demand of a customer or group of customers. For
6
Also called loss-of-load probability, or LOLP.
-6-
1
a large customer, planners may investigate the customer’s electrical equipment, and the expected
2
utilization of this equipment (i.e., customer site diversity of use) in order to size the customer’s final line
3
transformer and “upstream” facilities. For smaller customers, planning standards have been developed
4
to identify expected peak demand. Smaller customers frequently share a final line transformer, and
5
design demand takes into consideration diversity of appliance use within the customer’s premise, and
6
diversity between customers served from the same transformer.
In past GRCs, the design demand value was assumed to be equal to the maximum
7
8
amount of demand or usage placed on the system. However, SCE believes that the “planned capacity”
9
amount is a more appropriate measure because planned capacity more accurately reflects the “cost-to-
10
growth” ratio incurred during years when there is negative load growth such as a recession. Negative
11
growth due to the current recession or to dramatic conservation efforts as seen during the energy crisis,
12
distorts the average cost models by inflating the cost-to-growth ratio. SCE is therefore using planned
13
capacity in determining the cost-to-growth ratio for design demand marginal costs.
Design demand is expected to have the greatest impact on the capacity of
14
15
transformers in the delivery system. Power is typically delivered to the transmission system from
16
regional generators or regional interties at 220 kV or higher voltages. This power typically goes through
17
three stages of transformation: from 220 kV to 66 kV (subtransmission voltage), from 66 kV to 12 kV
18
(primary voltage), and from 12 kV to between 120 and 480 volts at the customer premise (secondary
19
voltage). When there is an increase in the planned level of capacity, additional transformer capacity
20
must be added at each of these steps to accommodate the increase. Additional substation facilities are
21
required as a result of increases in transformer capacity. An increase in design demand might also result
22
in an increase in the number of distribution circuits7 serving a local area. The use of planned capacity to
23
set the cost-to-growth ratio recognizes that incremental assets installed to meet load growth are still in
24
place during periods of negative load growth.
25
Design demand, or planned capacity, does not fully reflect the evolution of SCE’s
26
distribution system over time. Design demand is related to a customer’s expected maximum usage at the
27
time of service installation, but maximum usage may vary over time. In older neighborhoods, for
7
Distribution circuits are lines connecting customers in an area to a nearby substation.
-7-
1
example, transformer capacity, and distribution circuit routings may have been reconfigured over time to
2
keep up with increasing demand. Keeping track of the contribution of an individual customer to the
3
delivery capacity built to serve an area would be subjective and unwieldy. In addition, the time in which
4
maximum usage occurs varies by climate zone and by the mix of customers in an area. Thus, system
5
peak demand (a measure appropriate for the capacity component of marginal generation costs) and
6
design demand are not necessarily coincident.
In order to relate design demand to measurable customer attributes, SCE
7
8
developed a measure of peak load diversity, which we call effective demand. Effective demand is
9
expressed as a factor (effective demand factor or EDF), which is the ratio of a customer’s contribution to
10
the peak load on a transmission or distribution circuit to the customer’s annual noncoincident peak
11
demand. EDFs vary by type of customer and by the voltage level of the circuit. Unlike rate group
12
coincident demand, which is measured for customers within a particular rate group, effective demand
13
takes intergroup diversity into account. This is important because the impact of a particular customer on
14
delivery capacity in an area may vary depending on the characteristics of nearby customers. For
15
example, a medium-sized business connecting to a distribution circuit primarily serving other business
16
customers would cause planners to consider the customer’s entire maximum load in circuit design.
17
However, the same business connecting to a distribution circuit in a residential area would not have as
18
great an impact on circuit peak demand since residential customers’ demands tend to peak later in the
19
day than business customers.
SCE has over 4,400 distribution circuits, each of which typically provides service
20
21
to customers in a variety of rate groups. Distribution circuit EDFs are calculated as follows. First, the
22
number of customers by rate group is determined for each circuit, and is used to develop a profile of the
23
number of customers by rate group on a typical distribution circuit. These profiles are calculated for
24
each type of customer, using an average of the circuits weighted by the number of customers of the
25
particular type. For example, the typical TOU-8 (large customer) distribution circuit serves fewer
26
residential and small business customers, since the design demand of the large customer leaves less
27
capacity available for others.8 Next, a Monte Carlo simulation method is used to randomly “populate”
8
Distribution circuits are typically sized to handle about 400 amperes of current flow. At 12 kV, this is adequate to serve
a maximum of 4,800 kW.
-8-
1
each typical circuit type with customers from SCE’s load research samples. This step is performed for
2
each circuit type. Next, individual customers on each simulated circuit are selected, and the contribution
3
of the customer to the circuit peak is determined. For example, if the Monte Carlo simulation is for a
4
typical TOU-8 customer distribution circuit, the effect of one of the TOU-8 customer’s load on the
5
circuit is calculated. Finally, the second and third steps are repeated a sufficient number of times to
6
produce statistically valid results. A similar approach is used to determine EDFs for subtransmission
7
(e.g., 66 kV) circuits. Due to the greater geographic area typically served by these higher voltage
8
circuits, a single typical customer profile is used for all customer types.
EDFs vary from around 20 to 30% for residential and small agricultural customers
9
10
to 60 to 80% for medium and large commercial and industrial customers. In general, higher load factor
11
customers have higher EDFs, since their peak demands are more coincident with circuit peaks. Also,
12
larger customers tend to have greater EDFs, since their load has proportionately greater influence on
13
circuit peaks than smaller customers. The load research study performed to compute EDFs (by customer
14
group and voltage level) is described in greater detail in Appendix B of this exhibit. Since EDFs relate
15
individual customer’s peak demand to their contribution to delivery system demand, the marginal cost
16
revenues associated with a rate group’s design demand are defined as the product of that rate group’s
17
annual noncoincident peak demand, the EDF for that rate group, and the marginal cost per unit of design
18
demand.
Currently, SCE has approximately 300 standby customers who self-generate to
19
20
meet a portion of their electrical requirements, and rely on SCE for generation service to supplement
21
their generation or when their generation facility is unavailable due to maintenance or a forced outage.
22
The EDFs that SCE has calculated are applied to both regular and standby customers, since SCE needs
23
to reserve sufficient delivery system capacity to serve standby loads without adversely affecting other
24
customers. For the proposed new standby rate groups, SCE determined specific EDFs based on the total
25
load reflected in each respective rate group.
26
27
28
c)
Customer Cost Driver
Finally, the number of customers is a cost driver, since each customer requires an
interconnection with the delivery system (called a service drop for smaller customers) and a meter to
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1
measure consumption.9 In addition, SCE incurs marginal costs in managing its relationship with
2
customers, including handling customer communications, measuring usage, maintaining records, and
3
billing.
The cost of interconnecting a customer to the delivery system varies by type of
4
5
customer, reflecting differences in size, service voltage, metering requirements, and other factors. The
6
change in cost associated with serving a small increment or decrement in the number of customers is
7
identified through typical customer cost studies. These studies are performed for customers in each rate
8
group, such as between single-family and multi-family residential dwellings, more than one typical
9
customer cost study is performed. The typical customer cost studies identify facilities directly associated
10
with the customer interconnection, such as the meter, service drop, protection equipment, and final line
11
transformer. Final line transformers are associated with the customer cost driver because the cost per kW
12
varies for customers in different rate groups. With respect to transformers, the study indicates whether a
13
transformer is shared (a typical urban residential transformer configuration is shared by approximately
14
21 customers) or if multiple transformers are required to serve accounts such as Schedule PA-1, 3-phase
15
accounts. SCE’s 18 studies are included in the workpapers for this Exhibit SCE-2.
The customer service component of marginal customer costs includes activities
16
17
such as handling customer communications, measuring usage, maintaining records, and billing. We
18
identify the specific activities and assets directly attributable to providing the particular services and
19
then calculate the associated marginal costs. These marginal costs are calculated by customer type and
20
size.
4.
21
Time-Of-Use (TOU) Issues
a)
22
Generation Marginal Costs
Generation marginal costs vary hourly, primarily because different generation
23
24
units are “on the margin” each hour based on the level of customer demand and the costs associated with
25
maintaining sufficient capacity to meet reliability targets. Generation marginal costs are averaged by
26
TOU period, corresponding to the pricing periods in SCE’s current TOU rate schedules. These TOU
9
Technically, this description refers to a service account. Some customers, such as a firm owning a chain of retail stores
or a large facility with several points of service at a single site, have more than one service account. For the vast
majority of our customers, the terms customer and service account are synonymous, so we use the term customer in this
testimony. In rare instances where customer usage is highly predictable, SCE provides unmetered service.
-10-
1
periods vary seasonally (summer and winter) and daily (on-peak, mid-peak, and off-peak) and are
2
intended to group together hours with similar marginal cost characteristics. SCE periodically reviews
3
the appropriateness of its TOU periods in conjunction with its marginal cost studies and recommends
4
changes when appropriate.
An analysis of SCE’s current TOU periods, and various alternative periods
5
6
including a narrower or later in the day summer on-peak period is provided in Appendix E. This
7
analysis shows that changing the existing summer on-peak period would not significantly improve the
8
accuracy of SCE’s current TOU periods. Thus, SCE does not recommend any changes to its existing
9
TOU periods. In addition, SCE notes that the factors which appear to trigger stresses in the generation
10
markets which lead to periods of low reliability or higher prices are not necessarily related to periods of
11
highest loads.10
b)
12
Delivery-Related Marginal Costs
Ideally, delivery-related marginal costs would be time differentiated to address
13
14
differences in the pattern of electricity consumption by individual customers within each group. For
15
instance, if peak demands on subtransmission and distribution facilities were consistently experienced
16
only on summer days throughout SCE’s service area, it would improve pricing accuracy to recover
17
delivery-related marginal costs based on customers’ summer daytime peak demands. This would allow
18
a customer that uses electricity predominantly in the winter or at night to pay proportionately less,
19
because if that customer’s peak load were to increase or decrease, there would be no impact on delivery-
20
system capacity requirements.
21
However, customer loads at the distribution circuit level show variation in the
22
hours when individual circuits peak.11 While a majority of circuits experience loads during summer
23
daytime hours that are at or near their annual peak loadings, a sizable minority (about one-third) of the
24
circuits experience peak loads during winter and nighttime periods that are equal to or near their annual
10
A 2002 conference paper presented by a PG&E employee observed that 29% of the California ISO’s summer season
Stage 1 emergency hours in 1998-2000 occurred outside of the summer peak period, for instance. Robert Levin, “Does
California Avoided Cost Methodology Undervalue Energy Efficiency,” presented at the 2002 Rutgers Economics
Conference.
11
A distribution circuit study was presented in SCE’s 2006 GRC Phase 2, Appendix D. SCE does not anticipate any
significant changes in circuit peak behavior since the study was performed, and the conclusions from the prior study
remain valid.
-11-
1
peaks. Not surprisingly, these latter circuits tend to be in coastal and mountain climate zones where
2
there is less air conditioning load. This suggests that for a substantial number of customers, an increase
3
in winter or nighttime usage would contribute to delivery system peak demand. Time differentiating the
4
distribution portion of rates to recover design demand-related marginal costs based on summer peak
5
period usage would reduce pricing accuracy for these customers. For this reason, it is not appropriate to
6
time differentiate delivery-related marginal costs.
5.
7
When computing marginal costs, SCE converts capital investments into annual costs
8
9
Annual Cost Of Capital Investments RECC Methodology
using a real economic carrying charge (RECC). This approach is sometimes called the economic
10
deferral or rental value method. Under this approach, which is illustrated in Figure I-1, the present
11
worth of the annual revenue requirements12 for an asset and its subsequent replacements are computed,
12
and then compared to the present worth of an equivalent asset and its replacements installed one year
13
later. The only difference between these two scenarios is that SCE loses the opportunity to use the asset
14
in the first year of the second scenario. Thus, the difference in present worth between the two scenarios
15
measures the economic (opportunity) cost of using the asset during the first year. The resulting annual
16
charge, when escalated at the rate of inflation over time and then discounted, yields the original cost (in
17
terms of revenue requirement) of the investment. As shown in Figure I-2, the net present value (NPV)
18
of the two payment streams are the same, but the RECC results in the same real payment over time.
19
This conclusion is important because in real terms the charge for an asset is the same over time and,
20
assuming electricity customers value the service they receive, the charge should be the same regardless
21
of the age of the equipment. Therefore, the proper charge can be calculated for both existing and new
22
customers by applying the RECC to the current cost of the equipment. This RECC approach is
23
documented in work prepared by the National Economic Research Associates for an Electric Utility
24
Rate Design Study, which was funded by various parties including the National Association of
25
Regulatory Utility Commissioners.13
12
The revenue requirement includes depreciation, return on investment, income taxes, property taxes, A&G, insurance, and
salvage costs.
13
NERA #15 Topic 1.3, “A Framework for Marginal Cost-Based Time Differentiated Pricing in the United States,
February 1977, pages 90-94, and Appendix C. See also NERA #23 Topic 4, How to Quantify Marginal Costs.
-12-
Figure I-1
Illustration of the RECC Methodology
Annual
Revenue
Requirement
Asset in
Asset in Year 1
Year 0
0 1
Year
-13-
Figure I-2
Annual Payment for $100 Capital Investment
10% Discount Rate and 3% Annual Inflation
$30
$25
RECC Payment
$20
$15
$10
Annual Revenue Requirement
$5
$0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Year
1
SCE continues to advocate use of the RECC method (rental value) as a more appropriate
2
measure of marginal costs. The NCO method includes only the cost of new customer interconnection,
3
spreading these costs across both existing and new customers. By ignoring the economic value of
4
existing interconnection facilities, the NCO method systematically understates marginal costs. Simply
5
because an asset is already installed and thus “sunk,” does not mean the asset loses its economic value.
6
As long as the interconnection has value to the customer, there is a price at which the customer is willing
7
to “buy” and the utility is willing to “sell” interconnection service. Since it ignores this economic value,
8
NCO is not a valid marginal cost methodology.
9
Considering the results of the RECC and NCO methods in the context of competitive
10
markets makes the economic issues clearer. As an example, in a fully competitive market for meter
11
ownership, SCE would be a price taker in competition with other entities who also offer meters. SCE’s
-14-
1
hypothetical competitors would not offer meters without a reasonable expectation of fully recovering the
2
associated investment cost from the customers to whom meters are provided. Thus, the market clearing
3
price for meters would reflect the full cost of a new (and newly installed) meter. In this environment,
4
SCE would not need to discount its charges for existing meters below what a competitor would charge
5
for a new meter. SCE would be able to fully recover the market cost of meters from existing customers,
6
regardless of the fact that SCE’s meters are a sunk cost.14 The NCO method, which ignores existing
7
meters in computing marginal costs, ignores this market reality.
8
Under certain conditions, the NCO method can create unreasonable results. This
9
sensitivity is unrelated to any possible measure of “market” conditions, and thus demonstrates a
10
weakness in the theoretical foundation of this method. For example, assume that a customer class is
11
expected to grow by 10 newly added customers and decline by 15 departing customers for a net
12
reduction of 5 customers. The NCO method would yield either a zero or negative marginal cost,
13
depending on how the method is applied. Yet the utility still incurs the new costs to install equipment
14
for the 10 customers that were added. Changes in the growth forecast can yield declining costs one year
15
and increasing costs the next, yet the underlying cost structure remains unchanged.
16
C.
Marginal Cost Methodology
This section describes the calculation of electricity usage marginal costs, design demand
17
18
marginal costs, and customer marginal costs. In addition, the marginal cost of streetlight facilities
19
(streetlight poles, luminaries, and lamps) is calculated.
1.
20
Electricity Usage Marginal Costs
The Commission has a long-standing policy of developing marginal generation costs
21
22
using the deferral value15 of a combustion turbine (“CT”) proxy for estimating the avoided cost of
23
capacity, and a system marginal energy cost for estimating the avoided cost of energy. Capacity and
24
energy costs were combined into a single market price when the market structure adopted in California
25
anticipated that generators would recover their fixed investment costs from market prices during periods
14
The concept of “sunk” investment is sometimes confused with “stranded” investment. If SCE spends $1000 on a meter,
then “sunk” investment (ignoring potential salvage value) is $1000. If new meters cost $1000, then SCE’s entire
investment is economic, and there is no stranded investment. If the price of new meters drops to $800, then only $800 of
SCE’s $1,000 sunk cost is economic and the remaining $200 is potentially stranded.
15
Also referred to as the real economic carrying charge (RECC) methodology.
-15-
1
of scarcity when the market prices rose above the variable operating costs of generators participating in
2
the market. However, the Commission’s implementation of resource adequacy requirements has
3
returned the utilities and the market to a structure that requires the clear separation of capacity and
4
energy costs as the basis for generation marginal costs. The separation of energy and capacity costs
5
makes it possible to evaluate the relative reliability contribution of different resources, and is more
6
flexible across a range of capacity factors and TOU periods.
Energy costs represent the variable operating costs such as fuel, O&M, and emissions,
7
8
which includes carbon valuation using the 2008 Synapse mid-case forecast. The Synapse mid-case
9
starts with a carbon value of $15 per ton (2007$) in 2013 increasing to $53.40 per ton (2007$) in 2030.16
The marginal cost analysis presented here is intended to represent conditions expected to
10
11
occur during 2012 through 2014. The results of SCE’s analysis are summarized in Table I-1, above.
a)
12
Generation Capacity Marginal Cost
The generation capacity marginal cost is based on the deferral value of a CT
13
14
proxy, net of any energy rents obtained from the market. The CT proxy is the estimated cost (in $/kW)
15
for a new SCE-owned combustion turbine in the Southern California region, including all permitting,
16
financing, development costs and inflation during the construction period.17 The annualized cost ($/kW-
17
yr) is then calculated using the RECC methodology, plus costs for fixed O&M and property taxes. SCE
18
has estimated the CT proxy cost based on a 2012 commercial operating date as shown in Table I-4
19
below:
16
This is consistent with the 2009 Market Price Referent treatment of carbon prices.
http://docs.cpuc.ca.gov/PUBLISHED/FINAL_RESOLUTION/111386.htm
17
The CT construction cost is an estimate is based in large part on information provided by an outside vendor that is
knowledgeable about generation construction costs and specializes in making such estimates for its clients.
-16-
Table I-4
CT Proxy Cost (2012$)
1
Combustion Turbine Installed (w/AFUDC) Cost (’12 COD)
$/kW
2
Real Economic Carrying Charge Rate
%
3
Annualized CT Installed Cost (line 1 * 2)
$/kW-yr
$105.6
4
Fixed O&M
$/kW-yr
$10.8
5
Property Tax
$/kW-yr
$6.8
6
Full CT Proxy Cost (’12) (Line 3 + 4 + 5) BOY
Full CT Proxy Cost (’12) Mid-Year Payment
Line 6 * (1 + 10%)^(1/2)
$/kW-yr
$123.1
$/kW-yr
$129.2
7
$952
11.09%
1
Energy rents are the operating profits that a proxy CT is able to earn when market
2
prices are above the CT’s variable operating costs, which principally consist of fuel, emission costs, and
3
variable O&M. Because these energy rents reduce the amount of the CT’s fixed costs that need to be
4
recovered in capacity markets, energy rents are also known as energy-related capital costs (ERCC). Due
5
to the separation of capacity and energy prices, the CT proxy cost must be reduced for any energy rents
6
obtained in the market price to avoid double counting the energy value. For example, if the marginal
7
energy price forecast is $90 per MWh, but the variable operating cost of a CT proxy is $60 per MWh for
8
that same hour, then the CT would realize a $30 per MWh contribution to its fixed costs and the value of
9
energy rents (or ERCC) is subtracted from the full CT proxy. Figure I-3, below, illustrates this
10
calculation.
-17-
Figure I-3
Derivation of Capacity Value – CT Proxy With Energy Rent Adjustment
Following this approach, the annualized value of 30 years of energy rent revenues
1
2
is divided by the name plate capacity which results in an average of $10.70/kW-year in energy rents
3
which is then subtracted from the CT proxy. As shown in Table I-5, below, the generation capacity
4
marginal cost is $125.1/kW-year (2012$).18
18
Compared to SCE’s 2009 GRC, it is reduction in energy rents – due to lower energy prices – that has led to an increase
in the net capacity value.
-18-
1
Table I-5
Generation Capacity Marginal Cost (2012$)
1
Full CT Proxy cost (mid-year payment)
$/kW-yr
$129.2
2
Less Energy Rents
$/kW-yr
($10.7)
3
Incremental Capacity Cost (line 1 – 2)
$/kW-yr
$118.5
4
5.6% General Plant Loader (line 3 * 5.6%)
$/kW-yr
$6.6
5
Generation Capacity Value Marginal Cost (line 3 + 4)
$/kW-yr
$125.1
At the generator level
The marginal capacity cost calculated above is an annualized number and is not
2
3
differentiated by time-of-use periods. SCE allocates the marginal capacity cost using relative LOLE
4
values to indicate time-differentiated values based on peak period usage.19 This is a theoretically valid
5
approach to assigning reliability costs to time periods. LOLE is closely related to Expected Unserved
6
Energy20 (EUE), which identifies the potential amount of generation-related outages (in MWh of
7
unserved energy) which would occur in a time period considering uncertainty in customer loads,
8
resource availability, and other market conditions. If available generation increases by one MW, then
9
LOLE is equal to the change in EUE which occurs as a result.21 Thus, LOLE measures the improvement
10
in reliability which occurs in a time period as a result of an increase in available generation or a decrease
11
in customer load. The capacity value allocation results are shown in Table I-6, below:
19
This approach is a standard utility practice and has been used in prior SCE GRC proceedings.
20
Also called energy not served, or ENS.
21
For example, if the likelihood of rolling blackouts due to a generation resource shortage is 10% in a particular hour (the
LOLE) and the utility adds 100 MWH of additional generation resources, then the expected amount of unserved energy
(the EUE) would go down by 10 MW (10% times 100 MW times one hour). Mathematically, LOLE is the first
derivative of EUE with respect to a change in available resources.
-19-
Table I-6
Generation Capacity Marginal Costs, 2012-2014
Average (2012$)
Summer
Annual On-Peak
125.1
87.70
Winter
Mid-Peak
Off-Peak
Mid-Peak
Off-Peak
25.65
1.13
10.13
0.50
Based on the time periods in the TOU-8 Tariff. At the generator level.
Includes general plant.
b)
1
Energy Marginal Cost
The marginal energy cost forecast was based on a fundamentals-based production
2
3
cost simulation using Market Analytics production simulation model. For gas prices used in the
4
production cost model, SCE blended gas market forwards and gas fundamental views by assigning a
5
declining weight factor to the market forwards over time.
SCE bases its fundamentals-based forecast of marginal energy costs using the
6
7
Ventyx’s Market Analytics production simulation tool, which utilizes ProSym as the underlying
8
engine.22 Market Analytics divides the Western Electricity Coordinating Council (WECC) into several
9
transmission areas (“transareas”) based on regional differentiation in the transmission grid. Each
10
transarea contains the hourly loads of each load-serving entity; the available thermal, hydro and
11
renewable supply resources; and their operating characteristics, including fuel costs. These transareas
12
are joined by paths, which reflect aggregated transmission capability between regions. Multi-year
13
simulations, in which resources are dispatched according to least-cost economics to meet the load, are
14
performed on an hourly basis. Energy transfer occurs between transareas, where possible, to find the
15
optimal solution that fits all of the user-defined constraints (operating limitations, reserve requirements,
16
etc.). The outputs of the simulations are typically the operating costs of generating units, energy-not-
17
served, and a forecast of market clearing prices for each region.
22
In May 2010 ABB purchased Ventyx (who purchased Global Energy Decisions, formerly known as Henwood Energy
Services, Inc.), is the developer of the EnerPrise software package. Market Analytics, a module within EnerPrise, is the
updated version of MarketSym. Both Market Analytics and MarketSym use ProSym as the core simulation engine.
-20-
As described in more detail in Appendix C, SCE modifies the default inputs from
1
2
Market Analytics for updated or more accurate information. This includes:
3
•
SCE load, including demand response and energy efficiency
4
•
Gas prices
5
•
Distributed generation
6
•
Transmission limitations
7
•
Renewable targets
8
•
Price of carbon emissions using the 2008 Synapse mid-case
Costs included in the energy price are those for incremental fuel, variable O&M,
9
10
emissions costs,23 startup costs, congestion charges, and no-load fuel. Costs excluded from the energy
11
price are capital costs, fixed O&M, and property taxes – these are explicitly included in the CT proxy
12
capacity price. Additionally, certain ancillary services costs are captured in the CT proxy capacity value
13
estimate.
The marginal energy prices are computed by the Market Analytics model for
14
15
years 2012 - 2014 in hourly increments. The prices are then sorted and averaged by time-of-use periods
16
corresponding to the pricing periods in SCE’s TOU-8 rate schedule. This forecast is based on three-year
17
average24 gas price of $5.36 per mm BTU for firm delivery into the SoCalGas system.
The results of SCE’s energy marginal cost analysis are shown in Table I-7, below:
18
23
Traded (valued) emissions costs (i.e, NOx and SO2) are included as is carbon dioxide using the 2008 Synapse mid-case
forecast.
24
2012 – 2014; 2012 dollars.
-21-
1
Table I-7
Energy Marginal Costs, 2012-2014
Average (2012 ¢/kWh)
Summer
Winter
Annual
On-Peak
Mid-Peak
Off-Peak
Mid-Peak
Off-Peak
4.944
6.156
5.550
4.664
5.183
4.635
Based upon the time periods in the TOU-8 tariff. At the generator level. Assumes an
average gas price of $5.36/mmBTU.
The key drivers of the energy marginal price forecast are the gas and load
2
3
forecasts. The gas price assumption is a blend of market forwards25 and a fundamentals forecast from
4
three vendors. The WECC load forecasts are from the Transmission Expansion Planning Policy
5
Committee (TEPPC), except for California-based system loads, which are from the 2009 Integrated
6
Energy Policy Report (IEPR) and SCE’s internal estimate for annual peak and energy requirements.
7
Additional details on gas and load assumptions are provided in Appendix C.
c)
8
Impacts Of Renewable and Greenhouse Gas Policy on Energy Costs
Table I-8
Ratio Between On-Peak and Off-Peak Energy Prices
2012 GRC (nominal $)
2012 GRC
Cents/kWh
25
Summer on-peak
6.156
Summer off-peak
4.664
Winter mid-peak
5.183
Winter off-peak
4.635
Ratio
1.32
1.12
NYMEX natural gas futures (Henry Hub plus SoCal basis swaps) plus intrastate transport charges.
-22-
1
Since the 2009 forecast (developed in 2008), many changes have occurred which
2
impact energy prices, such as lower energy use due to the recession, lower gas prices, introduction of
3
regulations on carbon emissions, and the increase in the renewables portfolio standard from 20% to
4
33% of sales. This section reviews the impact of increasing the renewable performance standard (RPS)
5
from 20% to 33% of sales. This section reviews the impact of increasing the RPS to 33% by 2020 and
6
greenhouse gas (GHG) polices have impacted the energy prices, specifically in the ratio of on-peak to
7
off-peak prices, as shown in Table I-8.
8
First, the increase in renewable energy due to the RPS has altered the net load
9
shape served by dispatchable generation. The large amount of solar power projected to come online
10
over this GRC cycle will reduce the daytime net load by a larger proportion than net load during the
11
night-time hours. Figure I-4 shows the impact of the increased renewable generation assumption on the
12
load shape for an average day in August 2013. Because of the change to the net load shape, which will
13
impact prices, we can expect the on-peak price to decline more than the off-peak price as demand
14
moves down the marginal supply curve.
-23-
Figure I-4
Impact of Renewables Resources on the Load Shape
Typical August Day 2013
25,000
20,000
Megawatts
SCELoad
15,000
SCE Load, Net of Wind & Solar
10,000
5,000
Wind & Solar Generation
0
To quantify the impacts of RPS and GHG polices on energy prices26, SCE made
1
2
the following adjustments (scenarios 1-3) to the Market Analytics model used in the 2012 GRC
3
(scenario 4) in order to compare price impacts:27
4
1. 20% RPS without GHG costs [assumption used in the 2009 GRC]
5
2. 20% RPS with GHG costs
6
3. 33% RPS without GHG costs
7
4. 33% RPS with GHG cost [2012 GRC model]
8
As shown in Table I-9, the impact of increasing amount of renewables in the resource mix has
9
resulted in the on-peak price declining by a greater percentage than the off-peak price.
26
27
GHG policy impact was tested by removing the cost of carbon as a generation cost based upon the 2008 Synapse mid-case.
The scenarios reflect different resource build-outs towards either a 20% or 33% RPS in 2020.
-24-
Table I-9
Impact of Renewable Generation on Energy Prices
1
20% RPS w/ GHG
$/MWh
(scenario 2)
33% RPS w/ GHG
$/MWh
(scenario 4)
Percent
Change
Summer on-peak
65.94
61.56
(7%)
Summer off-peak
47.59
46.64
(2%)
Winter mid-peak
54.48
51.83
(5%)
Winter off-peak
47.33
46.35
(2%)
The second factor impacting prices is the GHG policy to price carbon emissions.
2
Because coal has significantly higher carbon emissions, compared to natural gas, the application of a
3
carbon cost causes the cost of coal to increase more than natural gas. Thus when coal is on the margin,
4
the cost increase will be higher than if it was natural gas. Furthermore, the cost of carbon may cause
5
fuel switching between natural gas and coal. When this occurs, this also results in higher increases to
6
the off-peak marginal cost. Table I-10 shows the impact of the carbon on the 33 percent RPS case in
7
which the costs have increased at a higher rate in the off-peak than in the on-peak.
Table I-10
Impact of Carbon Prices Associated With GHG Policy
8
9
33% RPS w/o GHG
$/MWh
(scenario 3)
33% RPS w/ GHG
$/MWh
(scenario 4)
Percent
Change
Summer on-peak
55.72
61.56
10%
Summer off-peak
48.84
46.64
21%
Winter mid-peak
44.76
51.83
16%
Winter off-peak
38.04
46.35
22%
The net impact is the prices have increased in the off-peak at a much higher
percentage than in the on-peak, resulting in a decrease in the on to off-peak ratio as shown in Table I-11.
-25-
Table I-11
Comparison of Energy Prices Between 20% RPS
Without GHG Cost and 33% RPS With GHG Cost
20% RPS w/o GHG
$/MWh
(scenario 1)
33% RPS w/ GHG
$/MWh
(scenario 4)
Percent
Change
Summer on-peak
57.83
61.56
6%
Summer off-peak
39.45
46.64
18%
Winter mid-peak
46.70
51.83
11%
Winter off-peak
38.92
46.35
19%
d)
1
Loss Of Load Expectation
There is always some likelihood, however small, that the electricity system will
2
3
be unable to serve demand. The risk of a generation shortage can be reduced by over-supplying
4
generation, but over-investment and high operating costs would significantly increase customer rates.
5
Determining the optimum supply and demand balance requires the study of expected system operations
6
using a probabilistic risk assessment approach. Analysis of a system’s LOLE is one appropriate risk
7
assessment approach – it is a measure of system reliability that indicates the ability (or inability) to
8
deliver energy to the load. An LOLE analysis can provide insight into the appropriate planning reserve
9
requirement for each load-serving entity (LSE) in a region.28
The LOLE metric provides a method for allocating annualized capacity value
10
11
across time-of-use periods in proportion to when the loss of load is likely to occur.29 For example, if the
12
LOLE is greatest in the summer period primarily due to load conditions, particularly during the on-peak
13
period, then most of the value SCE attributes to capacity will be assigned to that period. Similarly, if the
14
probability for loss-of-load is nearly zero during winter off-peak periods, SCE will assign very little
28
In D.04-10-035 the Commission directed load serving entities under its jurisdiction to plan based upon meeting a 15 to
17 percent resource adequacy requirement. This implicitly reflects a balancing of customer risks and costs.
29
The purpose of SCE’s LOLE analysis is not to forecast the precise timing of future low-reserve margin events, nor is it to
forecast the absolute magnitude of any single loss-of-load event. Rather, it is intended to be a relative distribution of risk
used to allocate capacity value across time of use periods.
-26-
1
capacity value to that period. LOLE makes it possible to evaluate the relative reliability contribution of
2
different resources across a range of time-of-use periods.
SCE used the Market Analytics model and the “Medium Load Plan Scenario”
3
4
from its 2004 Long-Term Procurement Plan (LTPP) as the basis to calculate a probabilistic estimate of
5
the fraction of time the SCE system is unable to meet demand.30 SCE’s analysis employed a Monte
6
Carlo approach by way of two-factor mean reversion sampling of loads and resources. The analysis
7
performed 250 simulations each unique with regard to hourly supply and demand. From the Monte
8
Carlo analysis, SCE was able to extract hourly resource availability and loads from each of the 250
9
simulations. An LOLE event occurs in hour h when the load (L) exceeds available resources (R).
Lh – Rh > 0
10
For each simulation, the load in a particular hour can be compared to each of the
11
12
250 Monte Carlo outcomes of resource availability in that same hour. In other words, the load in hour h
13
is assumed to have the same likelihood of occurring in any of the 250 resource outcomes in hour h. The
14
same is true from another viewpoint: the resource availability in hour h is assumed to have the same
15
likelihood of occurring in any of the 250 load outcomes in hour h. Effectively, this approach yields 250
16
× 250 or 62,500 possible combinations of load and resources in hour h. The above equation can be
17
modified to illustrate this method.
Lh, i – Rh, j > 0
18
19
Where i and j are from the respective simulations for load and resources.
20
The range of loads and resources is determined by stochastic parameters tied to
21
historical performance. Each load and resource combination is given equal probability of occurring
22
assuming short-term variations in loads (i.e., weather) and available resources (i.e., forced outages) are
23
random. Combinations in which available resources are unable to meet the load (hence, loss-of-load)
24
contribute to the LOLE for that hour. For example, if 125 out of the 62,500 combinations resulted in
25
loads exceeding available resources, then the LOLE for that hour is 0.2% (125 divided by 62,500), or a
26
probability of 1 in 500.
30
SCE relied on this same analysis in SCE’s 2006 and 2009 GRC Phase 2 proceedings.
-27-
The hourly LOLE, or stochastic LOLE, is normalized over all hours of the year
1
2
such that the sum of the normalized LOLE equals 1. This creates a relative relationship of the hourly
3
LOLE across time. The results of SCE’s LOLE analysis are shown in Table I-12, below.
Table I-12
Relative LOLE Factors (Sum = 1)
Summer
On-Peak
0.701
Mid-Peak
0.205
Winter
OffPeak
0.009
MidPeak
0.081
Off-Peak
0.004
The stochastic LOLE approach takes into account as much uncertainty as SCE
4
5
can reasonably capture within the limitations of the model. These are the same uncertainties facing
6
today's system operators (load forecast, supply availability, and hydro conditions). This approach
7
provides a reasonable estimation of the relative risk of not serving the load in any given period, realizing
8
that not all of the market's inefficiencies can be captured in any single model.
9
10
2.
Delivery-Related Marginal Costs
Delivery-related marginal costs are the marginal costs of delivering electricity to
11
customers through the transmission and distribution (T&D) system. The calculation of delivery-related
12
marginal costs involves identifying design demand; a component related to a customer’s maximum
13
demand (kW). In past GRC’s, SCE has applied the NERA regression methodology to ten years of
14
historical data and five years of forecasted data to determine delivery-related marginal costs. For this
15
GRC, SCE proposes to use fifteen years of historical data and five years of forecasted data. Generally
16
more data points improve regression accuracy, so it is with this intention that SCE has increased the
17
number of historical years to fifteen. Also, to fulfill part of the 2009 GRC Settlement Agreement, SCE
18
worked with the Agriculture interveners, AECA and The Farm Bureau, to improve the regression model
19
by correcting for “serial correlation,” SCE used the Maximum Likelihood Estimates (MLE) correction
20
factor to correct for serial correlation. This provided a model with an improved fit. The slope value of
21
the regression is what SCE uses as the input to the $/kW Design Demand calculation.
-28-
1
Consistent with the prior Commission’s practice, the cost of the final line transformer,
2
service drop and meter are removed from the distribution component and included in the customer
3
component since.
4
a)
5
NERA Regression Methodology
The regression model fits incremental costs to load growth to obtain costs. Fifteen
6
years of historical expenditures on Transmission and Distribution capital additions, as reported in the
7
FERC Form-1, are adjusted to remove the cost of replacement capital, so only expenditures related to
8
load and customer growth are captured. In addition, the value used to determine distribution demand
9
growth has been changed to reflect the systems’ “planned capacity” versus “actual” capacity. This will
10
mitigate “cost-to-growth” distortions that are caused by intermittent negative load growth caused by the
11
recent recession, the 2002 conservations efforts, or cool temperature years. By taking this step, SCE
12
was able to spread the distribution costs over a large usage value thus lowering the distribution cost for
13
this GRC by more than 15 percent.
14
Five years of forecasted load growth expenditures are taken from studies that
15
support Phase 1 of SCE’s 2012 GRC. The forecast data is on a closed to plant basis and includes
16
capitalized allowed funds used during construction (AFUDC) and administrative and general (A&G)
17
overhead, so that historical and forecast are on a similar basis. For load growth, the regression uses
18
historical planned capacity and forecast annual peak load for the A-Bank (transmission) and B-Bank
19
(distribution). The NERA regression uses the Ordinary Least Squares (OLS) regression methodology to
20
calculate a trend line through 20 years of cumulative expenditure and load data (MW) to capture the
21
trend relationship between expenditures and MW. In fulfilling part of the 2009 GRC Settlement
22
agreement, SCE worked with the agricultural interveners, AECA and The Farm Bureau , to improve the
23
regression analysis and to remove “serial correlation”, or the non-randomness of the data point to the
24
trend line (residuals). To correct for serial correlation, SCE re-ran the regression applying a Maximum
25
Likelihood Estimate (MLE). The RECC was then applied to yield an annual capital-related incremental
26
cost. Loaders for general plant are applied to yield the delivery-related capital marginal cost.
27
28
The O&M cost is calculated using fifteen years of annual historical O&M divided
by the annual peak load. The historical expense data was obtained from the FERC Form-1 O&M
-29-
1
accounts, adjusted to include employee pension and benefits. For transmission costs, the annual costs of
2
the reliability must-run payments were removed.
The sum of the capital and O&M components yields the following delivery-
3
4
related marginal costs:
Table I-13
Delivery-Related
Marginal Costs (2012$)
$/kW
Subtransmission Cost (Non-ISO)
Distribution
*Includes both capital and O&M.
3.
5
35
91
Customer Marginal Costs
Customer marginal costs include: (1) the investment-related costs associated with
6
7
connecting a customer to the T&D system and related ongoing O&M; and (2) the expenses associated
8
with customer-related services such as meter reading, billing, and other customer service functions. For
9
calculating the annual capital-related marginal customer cost, SCE used the real economic carrying
10
charge (RECC) methodology.
For calculating investment-related costs, the methodology includes the cost of equipment,
11
12
which is directly associated with providing service access to a typical customer, i.e., the customer’s
13
service drop and meter. Service drops and meters are dedicated to individual customers. For larger
14
customers, final line transformers are generally dedicated to an individual customer. For residential and
15
groups of small business customers, the transformers are generally shared and, since this represents the
16
majority of our customers, these costs are left under the distribution umbrella of expenses.
The investment-related costs for each rate group are further broken down into subgroups
17
18
depending on the specific hook-up configuration. These subgroups are, for example, single-phase and
19
three-phase, interval or cumulative meter, service voltage, etc., and are based on the average load
20
characteristics for each rate group.
Customer hook-up facilities and associated costs are determined via a typical customer
21
22
cost study. This study identifies the current typical31 equipment necessary to connect a particular
31
Typical is the most frequent type of customer hook-up.
-30-
1
customer type and is used to estimate the costs of final line transformers, service drops, and meters. The
2
annual per customer investment cost is calculated by multiplying the investment-related cost by a real
3
economic carrying charge, which yields the annual customer investment cost. Then, O&M expense
4
related to the final line transformer (FLT) is added to the annual capital cost.32 Finally, an amount for
5
general plant is added by applying a general plant loader. The marginal customer investment cost for
6
each rate group is the weighted average of these investment costs for configuration of service phase,
7
voltage, or time of use measurement.
The next step is the calculation of customer marginal costs for metering (meter services
8
9
and meter reading), billing, and customer service. Marginal cost estimates for these activities are based
10
on labor requirements and frequency of each activity. Each component is summed to calculate a
11
customer service marginal cost.
The customer marginal cost study was grouped into the following categories: Residential;
12
13
small and medium agricultural and pumping (less than or equal to 200kW) and large agriculture and
14
pumping (greater than 200 kW); and three commercial groupings of small commercial (less than 20
15
kW), medium commercial (between 20 and -200 kW), and large commercial and industrial (greater than
16
200 kW). Each of these categories includes the following components:
17
•
Meter Services – repair, change-out
18
•
Meter Reading
19
•
Customer Services and Billing
20
o Bill Presentation
21
o Interval Data Management
22
o Field Services
23
o Billing exceptions
24
o Customer inquiry
25
o Monthly payment processing
26
o Uncollectibles
27
o Collections
32
FLT O&M is based on the GRC forecast and is allocated to rate groups based on each group’s percentage of total system
final line transformer cost.
-31-
1
o Credit checks
2
o Major Account Executives
Service establishment, reconnection, and the like, are not included as customer marginal
3
4
costs. Revenue for these services are recovered through separate customer charges.
In general, a customer who receives single-phase service has lower hook-up costs than a
customer who receives service from a three-phase line. Customer marginal costs are disaggregated for
single-phase and three-phase, so that these cost differences can be taken into account in rate designs.
The annual investment-related and customer service costs are added together to calculate
5
6
the total customer marginal cost by rate group. As an example, the cost components for GS-1 customers
7
are shown in Table I-14 below.
Table I-14
Customer Marginal Cost Components For GS-1 Customers
(In $/Customer-Year, 2012$)
GS-1 Component
Final Line Transformer*
Service Drop*
Meter*
Transformer O&M
Customer Services (meter
reading, billing, etc.)
Subtotal
Single Phase
$/Customer-year
64.37
27.68
36.59
0.91
Three Phase
$/Customer-year
191.16
65.10
53.82
2.70
33.88
163.43
33.88
346.68
163.43
346.68
Total Customer Marginal Cost
*includes general plant loader
The marginal customer costs for all rate groups are shown in Error! Reference source
8
9
not found..
10
-32-
1
Table I-15
Customer Marginal Costs (in $/Customer-Year, 2012)
Customer Costs (2012$)
Domestic
Single Family
Multiple
TOUs
Dom-Master Meter
$ /Customer Year
Capital
O&M
132.28
34.96
95.97
34.96
132.28
102.33
994.74
133.08
$ /Customer Year
Total
167.24
130.93
234.61
1,127.83
GS-1
Single Phase
Three Phase
Primary
128.64
310.09
34.80
36.59
163.43
346.68
209.05
34.41
243.46
Single Phase
Three Phase
Primary
994.74
1,909.22
1,367.79
132.82
142.20
123.72
1,127.57
2,051.42
1,491.51
Single Phase
Three Phase
Primary
984.07
1,909.22
1,367.79
132.82
142.20
123.72
1,116.89
2,051.42
1,491.51
Secondary
Primary
3,045.55
1,367.79
1,075.20
1,046.77
4,120.75
2,414.56
TOU-8-Sec
TOU-8-Pri
TOU-8-Sub
4,599.63
1,826.54
18,618.54
1,091.99
1,046.77
1,046.77
5,691.63
2,873.31
19,665.32
Single Phase
Three Phase
Secondary
Primary
486.36
1,227.31
1,341.47
1,367.79
96.94
105.45
105.45
105.45
583.30
1,332.76
1,446.91
1,473.24
Three Phase
Primary
2,967.63
1,367.79
755.75
755.75
3,723.38
2,123.54
Per Customer
128.64
34.38
163.02
TC-1
GS-2
GS-2T
GS-3
TOU-8
AG <= 200
AG > 200
Street Lights
Metered Only (AL & LS-3)
Unmetered*
Per Lamp
LS-1
LS-2
OL
DWL
7.40
8.00
7.64
3.42
7.40
8.00
7.64
3.42
* Unmetered includes both a customer and per lamp customer marginal cost
-33-
1
2
3
4.
Street Lighting and Outdoor Lighting Marginal Cost
Street lighting customers can take service with various lamp sizes and types, and may
choose to own and/or maintain portions of their street light facilities.
4
The three major categories of street lighting services are:
5
1. SCE-Owned and Unmetered (LS-1/OL-1/DWL-A): SCE owns and maintains the
6
street lighting or walkway equipment and associated facilities and provides
7
unmetered service at secondary distribution voltage.
8
9
2. Customer-Owned and Unmetered (LS-2/DWL-B): The customer owns the street
lighting or walkway equipment under two classes of service and the customer is
10
responsible for maintaining the facilities. SCE provides unmetered service from the
11
secondary distribution voltage.
12
a. LS-2 Rate A: The customer owns all the street lighting facilities after the
13
delivery point including, but not limited to, the pole, mast arm, luminary and
14
lamp, and all connecting cable and conduit in the street light system. The
15
customer takes unmetered service from a dedicated distribution system circuit
16
with a single photocell controller located at the SCE side of the point of
17
delivery which services multiple customer-owned lights
18
b. LS-2 Rate B: The customer owns the pole, mast arm, luminary, photocell, and
19
lamp. SCE owns and maintains the conductor to the point of delivery which
20
is located at the base of a customer-owned fixture. The customer takes service
21
from individual SCE feed points instead of a customer-owned street light
22
circuit serving multiple lights.
23
3. Customer Owned and Metered (LS-3): The customer owns the street lighting
24
facilities and takes metered service from the distribution system either at secondary or
25
primary voltage.
26
27
SCE has developed marginal street lighting costs separately due to the unique
characteristics of street light service. Marginal street lighting facilities costs are incurred in addition to
-34-
1
marginal T&D and customer costs. Street lighting marginal cost is the sum of O&M costs and street
2
light facilities costs.
O&M costs are expenses associated with lamp replacement, repair, routine inventory and
3
4
mapping, field inspection, and night patrolling. Facilities costs are specific to the type of street lighting
5
facilities provided, which may include bracket, bolt, pole, luminaire, lamp, photo controller, handholds,
6
conduit, and overhead or underground cable. Series service includes the cost of regulated output
7
transformer which is a special transformer necessary for providing service at primary voltage level
8
(known as series service) to customer-owned street lighting systems.
9
Street lighting investment costs have been annualized by taking the current replacement
10
costs for each component of street lighting facilities, and multiplying them by a real economic carrying
11
charge (RECC). The total replacement cost for each component includes supply expense, administration
12
expense, and general plant. The sum of annual investment costs plus O&M expense is the total facility
13
cost for street lights, or the marginal street lighting cost.
In general, SCE-Owned (LS-1) facility charges are based on the costs of a "standard
14
15
installation", i.e., a standard luminaire mounted on a wood pole served by overhead lines. When
16
customers request other than the standard installation, they must pay the difference between the actual
17
installation cost and the cost of the standard installation. This amount is known as the differential
18
facilities cost. The cost of a standard installation includes a wood pole, six-foot arm, lamp drop, photo-
19
controller, cable, insulation, grip and luminaire.
In prior rate cases, SCE capped the rates for street light customers by using the ‘capped
20
21
wood pole’ cost in the marginal cost study.33 In the 2009 GRC, SCE used the current wood pole cost in
22
the marginal cost study and for the 2012 GRC proceeding, SCE continues this practice. Using the
23
current costs would result in a substantial increase in marginal cost for LS-1 customers. To mitigate this
24
increase, the rates for streetlight facilities will be capped as discussed in the Exhibits SCE-3 and SCE-4.
33
In SCE’s 1992 GRC, the pole cost was ‘capped’ at the 1988 cost level because of rapidly rising cost of wood poles, and
then escalated to current year dollars using the Handy-Whitman index. The capped wood pole cost practice continued in
the 1995 GRC and 2003 GRC.
-35-
1
SCE calculates the cost of street light services based upon a typical standard installation
2
to calculate the length of circuits, lamps per circuit, span length, lamp size, and transformer connections.
3
SCE has reviewed the assumptions for the typical installation and has revised some of the input, the
4
most significant being an increase in the conductor length from 110 to 180 feet. Tables I-16 through
5
I-19 show the marginal street light costs.
-36-
Table I-15
Monthly Street Light Facility Marginal Costs (2012$)
LS-1: SCE Owned Streetlights
Watts
Lumens
Facilities
per Lamp-mo
INCANDESCENT LAMPS
103
1,000
202
2,500
327
4,000
448
6,000
MERCURY VAPOR LAMPS
100
4,000
175
7,900
250
12,000
400
21,000
700
43,000
1,000
60,000
HIGH PRESSURE SODIUM
50
4,000
70
5,800
100
9,500
150
16,000
200
22,000
250
27,500
400
50,000
LOW PRESSURE SODIUM
35
4,800
55
8,000
90
13,500
135
22,500
180
33,000
METAL HALIDE
70
5,600
100
8,500
175
12,000
250
19,500
400
36,000
1,000
110,000
1,500
155,000
Tap Devices
2.70
2.59
2.58
3.04
24.45
24.34
24.33
24.65
21.75
21.61
22.84
24.08
23.87
23.87
1.21
1.20
1.21
1.21
1.22
1.22
22.96
22.81
24.05
25.29
25.09
25.09
21.75
21.61
21.61
22.90
24.08
23.71
23.87
1.21
1.20
1.20
1.21
1.21
1.21
1.22
22.96
22.81
22.81
24.11
25.29
24.92
25.09
22.43
22.43
23.97
24.57
24.45
n/a
n/a
1
-37-
Total
per Lamp-mo
21.75
21.75
21.75
21.61
27.81
27.81
29.09
28.56
29.49
Tap Devices
Monthly Costs
O&M
per Lamp-mo
2.98
1.93
1.93
2.03
2.19
2.73
29.74
29.74
31.12
30.75
32.22
2.67
2.33
1.89
1.78
1.36
n/a
n/a
25.10
24.76
25.86
26.35
25.81
n/a
n/a
2.98
Table I-16
Monthly Street Light Facility Marginal Costs (2012$) Continued
LS-2: Customer Owned Streetlights
Facilities
per Lamp-mo
Monthly Costs
O&M
per Lamp-mo
Total
per Lamp-mo
LS-2 Rate A
Series Service
Multiple Service
31.29
1.61
0.67
0.49
31.97
2.10
Multiple Service
8.19
0.10
8.29
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0.67
0.66
0.66
0.67
0.67
0.67
n/a
0.68
0.67
0.66
0.66
0.67
0.67
0.67
n/a
0.68
LS-2 Rate B
Relamp Service (in addition to costs for LS-2A or LS-2B)
HIGH PRESSURE SODIUM
Watts
Lumens
50
4,000
70
5,800
100
9,500
150
16,000
200
22,000
250
27,500
310
37,000
400
50,000
LS-3 Metered Service
Facilities
per service
account-mo
Series Service
Multiple Service
751.11
n/a
-38-
Monthly Costs
O&M
per service
account-mo
13.20
n/a
Total
per service
account-mo
764.32
n/a
1
Table I-17
Monthly Street Light Facility Marginal Costs (2012$) Continued
OL-1: Outdoor Lighting
Watts
Lumens
MERCURY VAPOR LAMPS
175
7,900
400
21,000
HIGH PRESSURE SODIUM
50
4,000
70
5,800
100
9,500
150
16,000
200
22,000
250
27,500
400
50,000
LOW PRESSURE SODIUM
35
4,800
55
8,000
90
13,500
135
22,500
180
33,000
METAL HALIDE
75
5,600
100
8,500
175
12,000
250
19,500
400
36,000
1000
110,000
1500
155,000
-39-
Facilities
Monthly Costs
O&M
Total
per Lamp-mo
per Lamp-mo
per Lamp-mo
21.61
24.08
1.20
1.21
22.81
25.29
21.75
21.61
21.61
22.90
24.08
23.71
23.87
1.21
1.20
1.20
1.21
1.21
1.21
1.22
22.96
22.81
22.81
24.11
25.29
24.92
25.09
27.81
27.81
29.09
28.56
29.49
1.93
1.93
2.03
2.19
2.73
29.74
29.74
31.12
30.75
32.22
22.43
22.43
23.97
24.57
24.45
n/a
n/a
2.67
2.33
1.89
1.78
1.36
n/a
n/a
25.10
24.76
25.86
26.35
25.81
n/a
n/a
Table I-18
Monthly Street Light Facility Marginal Costs (2012$) Continued
DWL: Domestic Walkway Lighting
Monthly Costs
Facilities
O&M
Total
per Lamp-mo
per Lamp-mo
per Lamp-mo
Rate A: SCE Owned Facilities
HIGH PRESSURE SODIUM
50
70
100
150
METAL HALIDE
100
175
MERCURY VAPOR LAMPS
75
12.94
12.94
12.94
12.94
1.21
1.20
1.20
1.21
14.15
14.14
14.14
14.15
12.94
12.94
2.33
2.33
15.27
15.27
12.94
1.21
14.15
1.61
0.49
2.10
Rate B: Customer Owned Facilities
MULTIPLE SERVICE
Rate C: Optional Re-lamp Service (in addition to costs for Rate B)
HIGH PRESSURE SODIUM
50
70
100
150
METAL HALIDE
100
175
MERCURY VAPOR LAMPS
75
40
n/a
n/a
n/a
n/a
0.67
0.66
0.66
0.67
0.67
0.66
0.66
0.67
n/a
n/a
1.79
1.35
1.79
1.35
n/a
0.67
0.67
II.
1
SALES AND CUSTOMER FORECAST
2
The kilowatt-hour sales forecast for Test Year 2012 (“Sales Forecast”) forms the basis for the
3
4
billing determinant forecast discussed below and is used for rate design purposes in this phase of the
5
GRC.34 The Sales Forecast reflects the energy SCE expects to deliver to bundled service customers and
6
direct access customers in its service territory during the 2012-2014 period. It excludes sales to public
7
power customers, contractual sales, or interchange energy with other utilities.
8
Historical sales data are statistically related to the historical values of key economic drivers,
9
electricity prices and weather conditions. Thus, SCE uses econometric models to construct its sales
10
forecasts for the major revenue classes: Residential; Commercial; Industrial; Agriculture and Other
11
Public Authority (OPA). Revenue class data are used in the models because they have been defined in a
12
consistent manner throughout the historical period used in the econometric models. The sales forecast
13
for each revenue class is produced monthly and summed up to an annual value.35 The resulting
14
regression equations, in conjunction with forecasts of the economic drivers, electricity prices and normal
15
weather conditions, are used to predict sales by revenue class. Model-generated forecasts may be
16
modified based on current trends, judgment, and events that are not specifically modeled in the equation.
Table II-19 shows our forecast of grid kilowatt-hour sales and customers for years 2012 through
17
18
2014.
34
The Sales Forecast is discussed in detail in SCE’s Phase 1 A.10-11-015. See SCE-10, Results of Operation (R/O), Vol.
1, Chapter V, pages 43-51.
35
Model inputs include monthly data for electricity sales, electricity prices and employment. Quarterly or annual
economic or demographic data have been distributed to monthly values.
41
Table II-19
Forecast Grid Sales and Customers
For Years 2012 Through 2014
Line No.
1
2
3
4
5
6
7
2012
2013
2014
Grid Sales* (GWh)
Residential
Commercial
Industrial
OPA
Agriculture
Total Retail
28,666
41,934
8,224
5,729
1,368
85,920
29,120
42,962
8,355
5,746
1,410
87,593
29,543
43,789
8,462
5,770
1,441
89,006
8
9
10
11
12
13
14
Customer Additions
(1,000's)
Residential
Commercial
Industrial
OPA
Agriculture
Total Retail
29,696
6,478
-112
260
-2
35,800
35,795
8,285
-70
-202
2
43,809
38,938
9,515
-352
-137
7
47,971
15
16
17
18
19
20
21
Customers (1,000's)
Residential
Commercial
Industrial
OPA
Agriculture
Total Retail
4,338,689
554,556
11,400
46,265
22,295
4,973,206
4,374,484
562,841
11,330
46,063
22,297
5,017,015
4,413,422
572,356
10,978
45,925
22,304
5,064,986
*Grid sales include bundled service and Direct Access customers.
1
2
1.
Billing Determinants And Present Rate Revenue
A forecast of billing determinants for Test Year 2012 is required to evaluate the present
3
rate revenues, that is, the revenues that SCE’s current rates would be expected to provide in Test Year
4
2012, and to verify that SCE’s proposed rates will collect the test year revenue requirement. In addition,
5
billing determinants are used to determine marginal costs revenues in order to allocate revenues across
6
rate groups. Billing determinants refer to the number of customers by rate group, sales by time period
7
by rate group, demands by time period by rate group, and other miscellaneous measures of service by
8
which SCE assesses charges.
42
SCE develops billing determinants by rate group which correspond to the revenue class
1
2
sales and customer forecast described immediately above. The principles and processes by which this is
3
done is explained in SCE’s prepared testimony in Phase 1 of SCE’s 2012 GRC.36 Rate Group billing
4
determinants used in the revenue allocation and rate design process are included in the Workpapers to
5
this exhibit.
Present rate revenues are defined as the revenues SCE would expect to collect in a future
6
7
period (in this case the year 2012) given its current rates and forecast of test year billing determinants.
8
Present rate revenues are provided for each revenue component, e.g., distribution, transmission,
9
generation, etc., and for each rate group. As with billing determinants, SCE’s Phase 1 testimony
10
contains a description of the methodology SCE uses to develop present rate revenues.37
36
Exhibit SCE-10, Results of Operation (R/O), Vol. 1, Chapter VI, pages 50-55, in A.10-11-015.
37
Exhibit SCE-10, Results of Operation (R/O), Vol. 1, Chapter VI, pages 55-56, in A.10-11-015.
43
Appendix A
Glossary
Ancillary Services: Services necessary to support the reliable provision and transmission
of energy from resources to loads. Currently in California, these services are provided by the
ISO and include regulation (both up and down), spinning and non-spinning reserves, replacement
reserves, reactive voltage (var) support and black start capability.
Back-Up Load: Electric energy or capacity supplied by an electric utility to replace
energy or capacity ordinarily self-generated by a customer during an unscheduled outage of the
customer’s generation facility.
Bypass Cogeneration: Cogeneration used to bypass part or all of a utility’s generation
and transmission and distribution systems.
California Independent System Operator (ISO): A state chartered non-profit corporation
with centralized control of the statewide transmission grid, charged with ensuring the efficient
use and reliable operation of the transmission system.
Cogeneration: A process which uses either power plant waste heat to satisfy industrial
heat requirements or uses industrial waste heat for the steam generation of electricity.
Coincidence Factor: The ratio of the maximum group coincident demand to the sum of
individual customers’ non-coincident peak demands.
Coincident Demand: The aggregated demands of a group of customers at a particular
time, usually at the time of a customer group peak or the system peak.
Cost Drivers: Cost drivers are those fundamental aspects of customer demand for
services that directly cause SCE to incur costs.
Consumer Price Index (CPI): A measure of the annual rate of inflation for all urban
consumers.
Customer Marginal Costs: The change in total costs associated with providing customer
services.
Demand-Side Management (DSM): Utility sponsored end-user programs or activities
that enable the efficient use of energy and/or demand reduction by the end-use customer.
A-1
Design Demand: The amount of delivery capacity (in kW) that is necessary to reliably
expand service to an additional customer or group of customers.
Design Demand Marginal Cost: The change in cost associated with providing additional
capacity in the T&D system to delivery electricity to consumers.
Direct Access Customers: Customers who have arranged to have their electricity usage
supplied by an organization other than their local utility.
Dispersion Statistics: Statistical measurements, or indices of variation, that reveal how
widely the distribution varies around its mean (average).
Distributed Generation: Distributed Generation (DG) is a form of electric generation
smaller in size than a traditional central station power plant. A DG unit may be connected
directly to a customer's facilities (on-site) or on a utility's distribution system (on-grid).
Distribution System: The portion of the electrical system (50 kV and below) that
transmits electric energy from convenient points on the transmission system to consumers.
Diversity of Use: The difference between the sum of the maximum of two or more
individual connected loads, at a customer’s site, and the expected coincident maximum load.
Effective Demand: The contribution to peak demand that a customer places on
transmission and distribution circuits.
Effective Demand Factor (EDF): The ratio of a customer’s contribution to the peak load
on a transmission or distribution circuit to the customer’s annual non-coincident peak demand.
Electricity Usage Marginal Cost: The change in costs associated with providing an
additional amount of electricity to customers at a given moment.
Equal Percent of Marginal Cost (EPMC): A revenue allocation method that assigns
authorized revenue requirements to customers in each rate group in proportion to the rate group’s
share of marginal cost revenue.
A-2
Federal Energy Regulatory Commission (FERC): The federal agency responsible for
regulating wholesale electricity and natural gas markets pursuant to the Federal Power Act of
1935, as amended.
Handy-Whitman Index: A measure of the annual rate of inflation in capital investments.
Indexes are published for a wide range of industries and investment categories.
Kilowatt-Hour: A basic unit of electrical energy. A kilowatt-hour is equal to one
kilowatt of power supplied to or taken from an electric circuit steadily for one hour (kWh = kW x
hour).
Load: The amount of electric power delivered or required at any specified point on an
electrical system. Load primarily originates at the power-consuming equipment of the customer.
Load Diversity: The difference between the sum of the maximum of two or more
individual loads and the coincident or combined maximum load, usually measured in kilowatts.
Load Research: The use of statistical methods to measure and analyze the levels and
patterns of electric usage to provide a thorough and reliable understanding of electric usage and
load characteristics of various customer groups.
Load Serving Entity (LSE): Any entity that procures power to provide to end use
customers.
Loss of Load Expectation: The portion of time that available generation capacity will be
inadequate to supply customer demand at any given moment, such as one event per 10 years.
Marginal Cost: The change in total cost due to a small change in the quantity produced.
Marginal Cost Revenues: The revenues that would result if all aspects of electric service
were priced to reflect the marginal costs of providing such service.
Street Lighting Marginal Costs: The marginal cost of providing street lighting service,
which is in addition to the marginal cost of delivery, interconnection, and producing electrical
energy for such service.
A-3
Monte Carlo Simulation: A study in which repeated random selections from a sample are
used to create (simulate) a population (or a sub-set of population) which the sample represents.
Non-Coincident Peak Demand: The individual customer’s peak demand measured
irrespective of the time of system peak and irrespective of the peak demand of any other
customer or group of customers.
Primary Voltage: Facilities at which electric power is taken or delivered at voltages
between 2 and 50 kV, generally at either 12 kV or 33 kV.
Rate Group: Categories into which similar customers are grouped for revenue allocation
and rate design.
Real Economic Carrying Charge (RECC): A measure of the per dollar savings of
deferring an investment one year, taking account of the stream of replacement investments. See
"A. Framework for Marginal Cost-Based Time Differentiated Pricing in the United States,"
prepared by the National Economic Research Associates, Inc., # 15 NERA 1.3, Attachment G,
"An Economic Concept of Annual Costs of Long-Lived Assets," February 21, 1977.
Revenue Allocation: The process of assigning authorized revenue requirement to rate
groups.
Revenue Requirement: The costs of providing utility services that the Commission has
determined are appropriate to recover through customer rates.
Secondary Voltage: Facilities at which electric power is taken or delivered at below 2kV,
generally at either 120 or 480 volts.
Street Lighting Facilities: Equipment dedicated to the exclusive use of street lighting
customers. For SCE-owned street lighting systems, facilities include the pole and luminaire. For
customer-owned street lighting systems, facilities may include switching equipment and
specialized transformers.
A-4
Subtransmission System: A portion of the transmission system (typically 66 kV and
115 kV), which SCE identifies separately because it has operational characteristics related to
distribution facilities.
System Reliability: A measure of the electrical system's ability to meet some or all
customer demands, given uncertainty in the availability of generating resources. System
reliability is often measured using Loss Of Load Probabilities.
Transmission System: The transmission system transport electric energy in bulk (above
50 kV) from the source of supply (generating system) to other parts of the utility system or to
other utilities.
Western Electricity Coordinating Council (WECC): A nonprofit corporation formed by
members of the interconnected western grid with the objectives of maintaining a reliable electric
power system, assuring open and non-discriminatory transmission access, and providing a forum
for dispute resolution.
A-5
Appendix B
Circuit Analysis For Determination Of Effective Demand Factors
Circuit Analysis And Effective Demand Calculations
This section describes the methodology used in calculating the Effective Demand for each rate
group. The approach used is based on Monte Carlo simulations using load research samples. Monte
Carlo data simulation techniques construct a database to accurately estimate each customer’s
contribution to the circuit from which it is being served. This approach has been used in the past by
SCE for the same analysis in the 2003, 2006, and 2009 GRCs and also in projects such as residential
transformer loading or calculating the diversity of residential and small commercial customers’ loads at
the final line transformer in our 1995 GRC and 2003 GRC. The Effective Demand Factors were
calculated for SCE’s 14 rate groups38. The Domestic rate group (residential) was further broken down
into its three main subgroups: single-family, multi-family, and master-metered. The Effective Demands
were calculated at the circuit level (12 kV), at the 66 kV level (sub-transmission), and at the 220 kV
(transmission) levels. The following section explains the step-by-step methodology.
Circuit Level (12kV)
Step 1: Number Of Customers By Circuit
This step involves the identification of the typical population of customers on each class of
facilities (primary circuit, sub-transmission and transmission). This is a conditional distribution, since it
varies by rate group. That is, if a circuit serves a large TOU-8 customer, it is likely to serve a very
different mix of customers than a circuit in an area without any large TOU-8 customer. Also, the large
load of the TOU-8 customer will crowd out other customers. Thus, the typical population distribution is
not simply an average of all customers. We approached this step by building a table of customers by
circuit.
This table was created using a two-step process. First, a database was created that contained all
SCE active meters (customers) along with the customer identification number, the transformer pole
number, circuit, and the substations to which they were connected. This database was created by
38
The changes in the proposed rate groups from 2009 GRC resulting in the total number of rate groups to increase from 13 to
14. The changes are as follows: Agricultural and Pumping proposed rate groups are two as opposed to four groups and
TOU-8 rate groups have been divided into Standby and others.
B-1
extracting and merging three tables from SCE’s customer database. The data was verified using data
obtained from Outage Management System. This resulted in identifying 4,130 circuits serving 4.7
million customers. Then each customer (active meter) was mapped to its appropriate rate group.
Once this database was generated, we developed a profile of the number of customers by rate
group on a typical circuit. “Typical” circuit means a “weighted average” circuit, using number of
customers in all rate groups on all circuits. This is a weighted average calculated using the following
formula39 (illustrated for the Domestic and GS-1 customer groups):
Domestic Customer Distribution:
∑ N ( DOM , i ) * N ( DOM , i ) ∑ N ( DOM , i )
∑ N ( DOM , i ) * N (GS1, i ) ∑ N ( DOM , i )
∑ N ( DOM , i ) * N (GS 2, i ) ∑ N ( DOM , i )
i
i
i
i
i
i
etc.
GS-1 Customer Distribution:
∑ N (GS1, i ) * N ( DOM , i ) ∑ N (GS1, i )
∑ N (GS1, i ) * N (GS1, i ) ∑ N (GS1, i )
∑ N (GS1, i ) * N (GS 2, i ) ∑ N (GS1, i )
i
i
i
i
i
i
etc.
As a simple example of this methodology, consider a system of three circuits, with four, three,
and two customers respectively, as follows:
39
Notation is as follows: N(c,i) is the number of customers in rate group c on circuit i.
notation – the sum of N(c,i) for a particular rate group c across all circuits.
B-2
∑
i
N ( c , i ) is the summation
Circuit
# of Domestic
Customers
A
B
C
Total
# of GS-1
Customers
3
2
0
5
DOM * DOM
1
1
2
4
9
4
0
13
GS-1 * GS-1
DOM * GS-1
1
1
4
6
3
2
0
5
A typical Domestic (residential) circuit would be derived by weighting circuit A with a factor of
3 and circuit B with a factor of 2. A typical GS-1 circuit would be derived by weighting circuits A and
B with a factor of 1 and circuit C with a factor of 2. The results would be as follows:
Typical
Number of Customers
Circuit
Domestic
Domestic
2.6
1
GS-1
1.2
1.5
GS-1
The results are contained in Table B-1, in an 15 by 15 matrix. The 14 rate groups and the
subgroups of the Domestic rate group comprise the 17 rows and columns of this matrix.
Step 2: Monte Carlo Simulations
We performed a series of Monte Carlo simulations for each customer group. For example, the
typical circuit serving Domestic (residential) customers serves 1,767 Domestic, 166 GS-1, 5 TC-1, 36
GS-2, 2 TOU-GS, 4 PA-1, 1 PA-2, 1 AG-TOU , 1 TOU-8-Secondary, and 2 Street Lighting customers.
Therefore, we made repeated random drawings of 1,767 Domestic, 166 GS-1, …etc. from load research
sampled customers. For this study, only the sampled customers with full year of load data were used.
The interval load data from these randomly selected accounts were used to build each particular circuit’s
load profile in each of the simulations. We used a different number of Monte Carlo simulations for each
of the typical customer distributions.
Based on the dispersion statistics observed in the results, we concluded that 50 simulations
(samples) were statistically sufficient for the Domestic, GS-1, and GS-2 rate groups. Based on the same
results, we increased the number of simulations for all other rate groups to 100 and to 150 for TOU-8
B-3
rate groups. We did not see any significant improvement in the statistics after we reached these levels.
In other words, the effective demands converged to their limits. As we expected, there was a
relationship between the rate group load homogeneity and the number of samples we needed.
Step 3: Effective Demand Calculation
We compared a customer’s non-coincident peak demand with its contribution to the peak
demand on the circuit, in order to calculate Effective Demand. In the example above, there are 50
Monte Carlo simulations for the Domestic customer distribution circuit, each containing 1,767
customers. This provided 88,350 observations. (Obviously, many customers were duplicates since this
is larger than the total residential load research sample40). For each of these 88,350 Domestic
customers, we calculated the peak demand for the Monte Carlo selection that the customer was in, both
with and without the customer. The difference was the customer contribution to the circuit peak load.
For each customer in each sample (simulation), we computed the ratio of that customer’s contribution to
circuit peak load to its non-coincident peak demand. The average of these ratios, in each sample,
provided the Effective Demand Factor (EDF) for that particular simulation. Next, we averaged the
EDFs over all the 50 samples (simulations) to obtain the EDF at the distribution circuit level. The
statistical distribution of these EDFs, across all the simulations, also provided the dispersion statistics
used in determining the final number of simulations. TOU-8-subtransmission customers are excluded
from the EDF calculation at the distribution circuit level since they are hooked up to the system at higher
voltage levels.
These simulation runs are extremely time-consuming. Therefore, we also used another approach
to verify the results with a higher number of repeated samples. In this alternative approach, we used the
load profiles for the average customers in various rate groups to estimate the circuit’s load profile. One
random account then was added to the circuit to calculate the incremental load. Effective Demands
were then calculated based on 500 random samples (accounts). The results were very close to what was
observed in the simulations described above. Although, the results from this approach were not used at
40
The selection is done with replacement. That is, each sampled customer can appear more than once in a simulation and in a
series of simulations.
B-4
the circuit level, the same approach was used later, when estimating the Effective Demand at the 66 kV
and 220 kV levels (described below).
Sub-Transmission And Transmission Level
Unlike distribution circuits, sub-transmission and transmission facilities span a sufficiently large
geographic area that it appears reasonable to ignore conditional distributions. Instead we used “average”
circuit, assuming all the circuits attached to a substation to be the same. The number of customers in
each rate group on the average circuit is then the number of customers in the rate group divided by the
number of circuits.
66 kV:
We considered a substation configuration in which six circuits and no TOU-8 Sub-transmission
customer are attached to the 66 kV line. We then used the average load profiles for each rate group to
build the substation load. One account at a time was added to the circuit to calculate the additional load
of that customer (or that customer’s contribution to the circuit peak).
Effective Demand was calculated using four simulation runs, each with 500 random accounts.
220 kV:
We considered a configuration in which 65 circuits and four TOU-8-Subtransmission customers
are attached to the 220 kV transmission line. Then we used the same methodology as the 66 kV to
calculate the Effective Demands.
Table B-2 contains the EDFs for all the rate groups at the circuit level (12 kV), sub-transmission
substation (66 kV), and transmission substation (220 kV). The EDFs for the TC-1 group (traffic control
accounts) is set to 1.00 by definition since these customers have a flat load.
B-5
Table B-1
Effective Demand Calculation
Monte Carlo Simulations
Typical Circuit For Each Rate Group
Domestic
Typical Circuit
TOU-8
MasterSingle Multiple Metered
1767
Domestic (Total)
TC-1
GS-2 TOU-GS
PA-1
Street
AG-TOU TOU-PA-5 Secondary Primary Subtran Lighting
PA-2
Total
166
5
36
2
4
1
1
0
1
0
0
3
1986
145
5
31
2
4
1
2
0
0
0
0
3
1898
1338
365
Multiple
992
936
4
221
6
48
2
3
1
1
0
1
0
0
3
2218
Master-Metered
995
643
10
207
5
43
2
4
1
1
0
1
0
0
3
1915
GS-1
954
537
3
232
5
54
3
4
1
2
0
1
0
0
3
1799
TC-1
986
436
3
179
7
48
3
3
1
1
0
1
0
0
3
1671
GS-2
795
450
3
209
6
63
4
3
1
1
0
1
0
0
3
1539
TOU-GS
772
355
2
169
5
55
5
3
1
1
0
1
0
0
3
1372
PA-1
723
173
2
121
2
21
1
86
7
13
0
0
0
0
1
1150
PA-2
750
221
2
123
3
25
1
42
9
15
1
1
0
0
2
1195
AG-TOU
691
175
1
109
3
22
1
33
7
20
1
0
0
0
2
1065
TOU-PA-5
817
240
3
144
4
36
2
19
5
9
2
1
0
0
2
1284
TOU-8-Primary
610
253
2
123
4
39
3
4
1
2
0
1
1
0
2
1045
TOU-8-Secondary
575
271
2
142
5
51
4
3
1
1
0
3
0
0
3
1061
1046
487
3
197
6
51
3
3
1
2
0
1
0
0
15
1815
Single
Street Lighting
2
GS-1
Domestic
AG&PTypical
Circuit
AG&P
MasterSingle Multiple Metered
GS-1
TC-1
TOU-8
GS-2 TOU-GS <= 200 kW > 200 kW Secondary Primary
Subtran
Street
Lighting
Total
AG&P <= 200 kW
714
177
2
118
2
21
1
91
1
0
0
0
1
1128
AG&P > 200 kW
829
239
2
125
4
30
2
28
3
1
0
0
2
1265
TOU-8
Secondary
Primary
NonNonStreet
AG-TOU TOU-PA-5 Standby Standby Standby Standby Lighting
Domestic
Standby Typical
Circuit
MasterSingle Multiple Metered
GS-1
TC-1
GS-2 TOU-GS
PA-1
PA-2
TOU-8-S
796
356
1
154
5
53
4
4
1
3
0
1
1
0
0
2
TOU-8-P
577
207
1
125
4
33
2
2
0
1
0
0
1
1
0
3
Domestic
Non Standby
Typical Circuit
MasterSingle Multiple Metered
GS-1
TC-1
GS-2 TOU-GS
PA-1
Secondary
Primary
NonNonStreet
AG-TOU TOU-PA-5 Standby Standby Standby Standby Lighting
PA-2
TOU-8-S
571
270
2
142
5
51
4
3
1
1
0
0
3
0
0
3
TOU-8-P
613
257
2
123
4
39
3
4
1
2
0
0
1
0
1
2
B-6
Table B-2
Effective Demand Factors
Rate Group
Domestic
Domestic: Single
Domestic: Multiple
Domestic: Master Mtrd
GS-1
TC-1
GS-2
TOU-GS-3
AG&P < 200 KW
AG&P > 200 KW
Large Power Excluding Standby
Accounts:
TOU-8-Secondary
TOU-8-Primary
TOU-8-Subtran
Large Power Standby Accounts:
TOU-8-Secondary-Standby
TOU-8-Primary-Standby
12 kV
66 kV
220 kV
0.33
0.32
0.31
0.35
0.34
0.34
0.26
0.25
0.24
0.73
0.72
0.70
0.35
0.37
0.34
1.00
1.00
1.00
0.62
0.64
0.63
0.75
0.74
0.73
0.27
0.25
0.25
0.48
0.44
0.40
0.73
0.73
0.71
0.73
0.69
0.66
0.74
0.67
0.68
0.65
0.63
0.73
0.60
0.54
0.35
0.21
0.00
0.00
TOU-8-Subtran-Standby
Street Lighting
0.00
Standby Customers
Standby customers have generators with the ability to serve all or part of their load. SCE delivers
energy to these customers in the event their generators do not produce enough energy to meet the
customer’s load. The energy provided by SCE to these customers is defined as “maintenance”, “back-up”,
or “supplemental.” Supplemental energy is the power provided to the customer by SCE that is in addition to
what the customer generates on the site. Back-up energy is energy supplied by SCE during unscheduled
outages of the customer’s own generation.
B-7
Standby customers are defined as those commercial and industrial customers in TOU-8 rate group
with demands in excess of 500 kW that have standby demand in SCE’s billing files. By the end of 2009,
SCE had about 213 standby customers whose regular service load was billed on schedules in the TOU-8 rate
group. These customers have different mixes of generation technologies such as bio-mass, co-generation,
geothermal, small hydro, wind, and solar.
In the 2009 GRC filing, the EDFs for Standby customers were calculated using the back-up portion
of each standby customer’s load. In this filing, we are treating the Standby customers in the large power rate
groups as separate rate groups and we are using their total load, like other rate groups, to calculate the EDFs.
We used the same methodology as used for other rate groups, explained in Section 1 of this appendix, to
define the typical circuit and to calculate EDFs. Typical circuit for Standby customers is shown in the third
section of Table B-1. The results are shown in Table B-2.
B-8
Appendix C
Marginal Energy Cost Analysis
Major Input Assumptions Applied In The Simulated Marginal Energy Cost Analysis
Market Analytics Database Overview
SCE’s forecast of the fundamentals-based hourly marginal energy prices were based
predominately on the input parameters contained in the Ventyx database.41. Specifically, this database
contains all the necessary resource, load, fuel, transmission, and transaction assumptions throughout the
entire Western Electricity Coordinating Council (WECC) necessary to perform a price forecast. Where
appropriate, SCE updated certain elements of the database to include its own expectations of the market
environment or to reflect more recent forecast conditions. For example, natural gas fuel prices from
February 2011 were used to update the Ventyx database, so they reflect more recent forecast conditions.
Forecast Horizon
SCE’s WECC model has resource assumptions through 2020. This model serves as the basis for
the GRC marginal cost analysis. Since the GRC marginal cost analysis relies on the forecast years 2012
through 2014 only, the description blow focuses on this period only.
Inflation
The results from SCE’s marginal cost analysis originate in 2010 real dollars. An inflation factor
is applied to scale the results into nominal dollars when appropriate. As in prior GRC proceedings, SCE
used the gross domestic product implicit price deflator (GDP IPD) from Global Insight, Inc. as the basis for
the inflation rates, shown in the following table.
41
http://www.ventyx.com /analytics/market-analytics.asp
C-1
Annual GDP IPD
Year
Growth Rate
2009
1.17%
2010
0.96%
2011
1.49%
2012
1.48%
2013
1.64%
2014
1.98%
Natural Gas Price
The gas price assumption is a blend of market forwards and fundamentals forecasts from three
vendors as of February 2011. Market forwards are used for the front end.42 To account for the declining
liquidity of the market view, SCE incorporates a fundamental view in the back-end. The two forecasts are
then blended together.
Load Forecast
Load forecasts for entities outside of California were based upon the Transmission Expansion
Planning Policy Committee (TEPCC)43. For non-SCE California entities information was from the 2009
Integrated Energy Policy Report (IEPR). For SCE, its recent forecast, dated October 2010, of annual peak
and energy requirements was used.
Energy Efficiency
The table below represents SCE’s forecast of annual incremental energy efficiency savings from
both existing and future programs.44
42
NYMEX Henry Hub plus Southern California basis differential as of October 26, 2010 .
43
The TEPCC is part of the Western Electricity Coordinating Council (WECC).
44
From the 2009 IEPR
C-2
SCE Incremental Annual
Energy Efficiency Forecast
Year
GWh
2012
1523
2013
861
2014
784
Demand Response
The table below represents SCE’s forecast of annual Demand Response.45
SCE Annual Demand
Response Forecast
Year
MW
2012
1,859
2013
2,153
2014
2,368
Distributed Generation (DG)
Below is the expected SCE Annual DG from the California Solar Initiative.46
45
SCE Smart Connect Business Case and 2009 Load Impact Evaluation of CA DR programs
46
2011 IEPR
C-3
SCE Annual Distributed
Generation Forecast
Year
GWh
2012
624
2013
732
2014
844
Renewables
SCE’s build out assumes compliance with California’s Renewables Portfolio Standard, in which
SCE plans to meet the statutory requirement that 33 percent of its retail energy sales be served by renewable
generation by 2020 per Senate Bill x-2.
California Renewables Generation
Year
GWh
2012
51,881
2013
58,079
2014
69,466
Carbon Emission Costs
Starting in 2012, the 2008 Synapse mid-case forecast for carbon price was used in the model.47
Carbon Emission Cost
$/short-ton
47
Year
$ Nominal
2012
$10.18
2013
$17.40
2014
$20.56
The values used are consistent with the Commission-adopted Market Price Referent.
C-4
Transmission Assumptions
The database includes capacity ratings and variable cost assumptions for all major transmission
paths between major delivery points in the WECC. The transmission capacity ratings are consistent with
the WECC’s Path Rating Catalog (2009). The capacity value for each transmission path assumes a “nonsimultaneous” transmission rating (MW) which represents the maximum path transfer capability
independent of flows on other lines. In reality, the actual transfer capability may be different due to
simultaneous interactions between major transmission paths. To be more consistent with actual
transmission operating parameters, SCE modified the database ratings into Southern California to better
represent expected, real-life, “simultaneous” transmission flows, thereby limiting total transfer capability
into SCE in compliance with the limits established by the Southern California Import Transmission (SCIT)
nomogram import limits.
Comparison of SCIT
Transfer Limit (MW) in 2011
Season
Default
Database
WECC SCIT
Nomogram
Summer
19,392
16,451
Winter
19,392
16,370
Retirement Assumptions
SCE’s assumed generation resource retirements over the period 2012-2014 for SCE’s area are48:
Retirements in SCE’s Area
48
Year
MW
2012
0
2013
0
2014
335
http://www.cpuc.ca.gov/PUC/energy/Procurement/LTPP/LTPP2010/2010+LTPP+Tools+and+Spreadsheets.htm
C-5
Appendix D
SCE Costing Period Study
SCE Costing Period Study
I. Introduction and Summary of Existing Time-of Use Rate Structure
Time-of-use (TOU) rates improve the “price signals” which utility customers face as a result of their
consumption decisions and result in improved economic efficiency in comparison to flat rates which do not
vary by time of day or season.49 It would be impractical to have rates which vary hourly based on a
forecast, so a set of well-designed TOU costing periods provide a balance between practical retail pricing
considerations and economic efficiency objectives. The objective in choosing a set of TOU costing periods
is to group together hours with similar marginal costs and differentiate hours with marginal costs that are
not similar, while limiting the overall number of costing periods. SCE’s current TOU-8 rate schedule is
based on the following TOU periods:50
Table D-1
TOU-8 Periods
On-Peak
Mid-Peak
Off-Peak
Summer
(June-September)
Noon – 6:00 p.m.
Non-Holidays, Weekdays
8:00 a.m. – Noon
6:00 pm. – 11:00 p.m.
Non-Holiday Weekdays
All other hours
Winter
(October – May)
8:00 a.m. – 9:00 p.m.
Non-Holiday Weekdays
All other hours
SCE periodically performs a costing period study to determine whether a change in the TOU-8 rate
structure is warranted based on economic considerations. Based on the review described herein, SCE
concludes that the current TOU rate structure appropriately reflects the distribution of marginal generation
49
Well designed costing periods increase economic efficiency by discouraging customers from using electricity for low value
applications during times when the cost of producing the electricity is high, and conversely encouraging customer to use
electricity for low value applications when the cost of producing the electricity is low. This is an improvement over flat
rates, which may result in customers consuming electricity which costs more to produce than the value gained by the
customer or alternatively results in a customer foregoing consumption which would have been more valuable than the cost to
produce the electricity.
50
The summer and winter seasons begin on the first Sunday of the month to avoid mid-week changes. The TOU-8 costing
periods are used in many of SCE’s other TOU rate options. However, due to various practical considerations, other costing
period definitions are used in certain optional rates, such as TOU-SOP and TOU-EV. In these instances, adherence to the
TOU-8 structure would not reflect the particular characteristics of customer usage and thus might limit customer
participation.
D-1
costs on a seasonal and time-of-day basis. This conclusion takes into account the SCE and ISO load patterns
and marginal generation costs forecast in SCE’s service area.
II. Framework for Analysis
Marginal generation costs include an energy component and a capacity component. The energy
component reflects the incremental cost of operating generating units that are “on the margin,” since these
units set prices in a competitive market. Since lower-cost units are operated first, and higher cost units only
operate when load is sufficiently high, there is a tendency for marginal energy costs to be higher in periods
with higher load. The capacity component reflects the incremental cost of acquiring sufficient generating
resource capacity to meet customer demands during “stress” conditions, taking into consideration the
uncertainty associated with customer demand and generating units available. Marginal capacity costs are
concentrated in high stress conditions, where variations in customer loads or an outage of generation
resources has the greatest potential to result in customer outages.51
In general, SCE’s TOU periods include a summer season where “stress” conditions are more likely
to occur due to the higher weather-driven loads and a winter season where capacity reserves are generally
adequate and higher cost generating resources seldom operate.52 The TOU periods also include on-peak,
mid-peak and off-peak time-of-day periods. The on-peak-period (summer only) reflects times when
marginal generating costs are highest and stress conditions particularly likely. The off-peak period reflects
times when loads are low and marginal generation costs are at their lowest levels. The mid-peak period
represents intermediate times where the likelihood of stress conditions is low, but more expensive
generation resources are in operation and marginal generating costs are at moderate levels.
51
As discussed in Section I.C.1 of this exhibit, marginal energy costs are determined using a combination of market-based price
measures and production cost modeling, and marginal capacity costs are determined using a combustion turbine proxy
resource allocated to time periods using loss of load probabilities.
52
This is a simplified description of fairly complex behavior by generating facility owners. Typically, generating facilities are
shut down periodically during mild weather periods to perform routine maintenance, so less efficient facilities may be
operated to replace more efficient facilities during such maintenance. Also, hydropower facilities are able to store some
water for use during the more valuable summer period, but may fully utilize storage in years with abundant rainfall,
necessitating the sale of large quantities of power in the spring. Finally, natural gas prices tend to be higher in the winter due
to heating demand, so the relative incremental cost when natural gas-fired units on the margin may be higher than in the
summer. These and other factors are taken into consideration in the marginal energy costs used in this analysis.
D-2
Since customer loads are a major factor influencing marginal generation costs, SCE begins its
costing period study by comparing its existing TOU-8 costing period to typical SCE and ISO load shapes to
determine whether the costing period definitions match the load shapes. The results of this analysis are
presented in Section III below.
In addition to a visual inspection of how well the current TOU-8 costing periods match the SCE and
ISO load shapes, SCE performed a quantitative analysis of “goodness of fit” of various costing period
definitions on both load and generation cost. For each costing period definition, the average load or
marginal generation cost (sum of the marginal generation costs divided by the number of observations) is
calculated for each costing period. Next, each hourly marginal generation cost observation is subtracted
from the corresponding average value and the result squared. These hourly squared differences are then
summed (the sum of squared residuals or SSR) and used as a measure of goodness of fit. This is the same
measure of goodness of fit as used in standard least squares regression analysis to evaluate the goodness of
fit of a variable.53 By comparing various TOU rate proposals using this goodness-of-fit, the rate structure
that best fits the load and pricing patterns can be identified. This analysis is presented in Sections III and IV
below.
III. Load Analysis
a. Visual Analysis
The data used in the load analysis included ISO hourly load data for years 2003 through 2009 and
SCE hourly load data for the same period. Both the ISO and the SCE systems are summer daytime peaking
systems. The summer peak for the SCE system is more pronounced than that of the ISO system, reflecting
the greater concentration of load in hot summer regions. The daily patterns between the SCE and ISO
systems are also very similar. The ISO system tends to have a slightly later average daily peak during both
summer and winter than is observed in the SCE system.
53
There is also a reasonable economic interpretation to the use of SSRs in this fashion. An analysis of economic efficiency
associated with TOU pricing shows that the economic improvement associated with the introduction of TOU periods is given
by -½ΔPΔQ, where ΔP is the difference between the average rate and the TOU rate, and ΔQ is the change in consumption as
a result of the price change. If elasticity is constant, then ΔP and ΔQ are proportional, and economic efficiency varies
directly with ΔP squared. Thus, SSR (which also is based on ΔP squared) is proportional to the improvement in economic
efficiency.
D-3
Figure D-1 shows an overlay of the typical ISO and SCE summer weekday load pattern 54. The SCE
and ISO patterns are very similar. On average, the SCE system peaks between approximately 3:00 PM and
4:00PM in the summer, while the ISO peak is somewhat more pronounced at 3:00PM. Both SCE and the
ISO experience a secondary peak around 8:00PM, corresponding to evening lighting load.
Figure D-2 shows an overlay of the typical ISO and SCE winter weekday load pattern. Once again,
the SCE and ISO patterns are similar. Evening lighting load is the daily peak in both systems; however the
daily peak is more pronounced in the ISO loads.
54
The TOU periods are marked by the vertical lines. E.g., the first line shows the beginning of the mid-peak period at 7 AM
(PST) while the second line shows the beginning of the on-peak period at 11 AM (PST).
D-4
Figure D-1
Average Summer Weekday Load Patterns (2009)
D-5
Figure D-2
Average Winter Weekday Load Patterns (2009)
In general, SCE’s current TOU-8 costing periods fit the observed average load shapes well. The
12:00PM to 6:00PM summer on-peak period is generally centered around SCE’s typical peak. (The ISO
peak corresponds well with SCE’s current costing period definition). This visual inspection of the data does
not address the question of whether there is merit to narrowing the existing period to fewer than six hours.
Whether or not this makes sense depends on whether the marginal generation costs in the hours at the
“edge” of the on-peak period are more closely related to the other on-peak hours than to the mid-peak hours.
This question is addressed in Section b. below.
b. Regression Analysis on Load
A linear regression can be used to estimate the goodness of fit for a particular model to explain load
by calculating a best fit line through the data. The difference between the best fit line and the observed
value is known as a residual. Two different models can be evaluated by comparing the SSR, and the model
with the lower SRR has a better fit.
The regression model is the following:
D-6
MW t = a 0 +
+
∑a
∑a
∑a
i =1
+ Year i
* Year j * Summer
3k
* Year k * Summer * MidPeak
4l
* Year l * Summer * OnPeak
m
* Year m * Winter * MidPeak
16
∑a
1i
2j
16
i =1
+
i =1
16
i =1
+
∑a
16
i =1
+
16
Where: Year takes the value of one if the calendar year of the observations equals 1991+i and zero
otherwise. Summer, Winter, OnPeak, and MidPeak is equal to one for each respective season and time
periods and zero otherwise.
In this regression model, the estimated values for the alpha coefficients give the differences between
the mean values of the system load falling within different season and time period categories. The
coefficient for the equation intercept give the mean value of the system load falling within the winter offpeak for 1991, The a1i coefficients give the difference between that mean value and the mean value of the
system load falling within the winter off-peak period for the year 1991+i. The a2j coefficients give the
differences between the mean winter off-peak period in year 1991+j and the summer off-peak period in year
1991+j, and so forth. Thus the regression equation defines a step function that best explains the historical
system pattern over the historical period. Annual parameters allow for annual variation in each step
function parameters. This allows the best fit for each historical year to be determined (given season and
time period constraints) using a single measure of error.
As shown in Table D-2, various alternatives to the existing time periods in the TOU-8 rate scheduled
were compared on the basis of load. Five alternatives to the standard TOU-8 were analyzed and compared
to the existing TOU-8 time periods. Cases B-E change the current definition of the summer on-peak period.
Case B removes one hour from the start of the current on-peak period and Case C starts the six hour OnPeak one hour later. Case F shortens the summer season by a month from the first Saturday in July through
D-7
the first Saturday in October, and Case G is from July – October15. Case H compares the current TOUSuper Off Peak period which consists of Summer On-Peak of 1 – 5 pm and a Super-Off-Peak period of
midnight to 6 am. Case J extends the current summer mid peak to include weekends.
Table D-2
TOU – 8 Peak Periods vs. Five Alternative Cases
Case
TOU-8
Case B
Case C
Case D
Case E
Case F
Case G
Case H
Case I
Case J
Peak Periods
Summer Weekday On-Peak, 12:00 PM – 6:00 PM
Summer Weekday On-Peak, 1:00 PM – 6:00 PM
Summer Weekday On-Peak, 1:00 PM – 7:00 PM
Summer Weekday On-Peak, 2:00 PM – 6:00 PM
Summer Weekday On-Peak, 2:00 PM – 7:00 PM
Summer From July – September
Summer From July – October 15
Current TOU-SOP: 1-5 pm On-peak, midnight – 6am Super-off
Summer from June 15- October 15
Extend mid-peak to include weekends
Cases D and E are similar to Cases B and D, except that the On-Peak is shortened by two hours or
moved two hours later in the day.
The results of the regression analysis on SCE’s hourly historical load from 1991-2009 load is shown
in Table D-3.
D-8
Table D-3 Analysis on Load
TOU – 8 Peak Periods vs. Five Alternative Cases
Case
Existing TOU-8: Summer On-Peak Noon-6pm
Rank of SSR
Lower number is better
100%
Case B: Summer On-Peak 1-6 p.m.
101%
Case C: Summer On-Peak 1-7 p.m.
101%
Case F: Change Summer to July - September
101 %
Case D: Summer On-Peak 2-6 p.m.
102%
Case E: Summer On-Peak 2-7 p.m.
102%
Case G: Change Summer to July – October 15
Case H: Current TOU-SOP: 1-5 pm On-peak,
midnight – 6am Super-off
105 %
126%
Based upon historical load, the TOU periods cannot be improved by either narrowing or moving the
Summer On-Peak later in the day. Alternatives to the current Summer Season of June – September do not
show an improvement. Finally, the existing TOU-8 Super Off Peak performs 26% worse than the default
TOU-8 periods.
IV.
Price analysis
In Section III, a load analysis was presented, indicating that SCE’s current TOU-8 structure
generally fits the SCE and ISO load data well. In this section, minor variations in SCE’s on-peak summer
period are investigated, including narrowing the on-peak period and shifting it later in the day. As described
in Section II, this analysis is based on marginal generation costs, with SSRs used as the “goodness of fit”
measure. The specific scenarios investigated are summarized in Table D-2. The same regression model
was used, except that the explanatory variable is generation cost instead of hourly load. Generation cost is
the sum of the hourly market price plus the value of capacity which is assigned to each hour of the year
using the hourly LOLE. The hourly market energy price is based upon the Ventyx model results for South
of Path 15, which includes SCE’s service area. The Ventyx prices are more comparable to the day-ahead
prices as the model assumes load and available resources are known. The LOLE data used is based on 2007
D-9
information55. The LOLE data construction is described in section I.C.1.d of this exhibit. The 2007 LOLE
values are used to allocate the capacity value to the 2012-2014 hourly energy prices.
Various alternatives to the standard TOU-8 time periods were analyzed and compared to the existing
TOU-8 time periods (Case A). Cases B-E change the current definition of the summer on-peak period by
shortening the on-peak or moving it later in the day, or both. Case F shortens the summer season by a
month from the first Saturday in July through the first Saturday in October and Case G is July 1 – October
15. Case H compares the current TOU-Super Off Peak period which consists of Summer on-peak of 1 – 5
pm and a super-off-peak period of midnight to 6 am. Case J would include weekends in the Summer MidPeak.
In presenting results, the SSR performance of each of these scenarios was normalized so that the
current TOU-8 periods were set equal to 1.00. The performance of the other scenarios is then a rough
measure of the relative change in economic efficiency. An index value of 0.99 represents a one percent
improvement, while a value greater than one shows a poorer “goodness of fit” than the current TOU-8
periods. Results from the analysis are reported in Table D-4.
Cases B-E show a possible one to three percent improvement in price by moving the 6 hour onpeak later in the day or by shortening the on-peak, or both. However, this potential gain may not be actually
realized due to customer’s reaction to the time period change by shifting their usage to nearby periods which
will reduce the actual benefit. While this may be what was intended, it may be counter productive because
in period II (after the adjustment) it may be beneficial to reverse the narrowing of the on-peak period.
Case J of extending the mid-peak into the weekend has no improvement to the costing periods.
Case F of shortening the Summer Season and Case 5 of shortening the on-peak to 1-5pm decrease the price
efficiency by 5-7 percent.
55
2007 data is the latest LOLE data available.
D-10
Table D-4 Break Analysis on Generation Cost
TOU–8 Peak Periods vs. Five Alternative Cases
Pricing Efficiency Factor
(lower number is better)
97%
Case
Case E: Summer On-Peak 2-7 p.m.
V.
Case C: Summer On-Peak 1-7 p.m.
98%
Case D: Summer On-Peak 2-6 p.m.
98%
Case B: Summer On-Peak 1-6 p.m.
99%
Existing TOU-8: Summer On-Peak Noon-6pm
100%
Case J: Extend Summer Mid Peak to include weekends
100%
Case I: Change Summer to June 15- October 15
105%
Case F: Change Summer to July – September
106%
Case G: Change Summer to July 1 – October 15
106%
Case H : Existing TOU- Super Off Peak
107%
Recommendations
As a result of this analysis, no change is advisable to the current TOU–8 pricing periods. The load
analysis concludes that alternative time periods offer no improvement to the existing time periods, while the
analysis on generation cost produces only a slight benefit. Given the margin of error inherent in any
methodology and the second order effects that could occur when customers react to the new time periods by
changing their usage behavior thereby reducing the possible 3% benefits that could be obtained, a change in
the TOU-8 time periods is not warranted at this time. Furthermore, the current TOU periods have been in
place for over 25 years and TOU customers have grown accustomed to them. Customer impacts offset any
potential minor gain.
D-11
Appendix E
NCO Marginal Cost Methodology
Marginal Customer Costs Based On the New Customer Only (NCO) Methodology
In SCE’s 1995 GRC, the Commission adopted the New Customer Only (NCO) methodology
to calculate customer marginal costs. As mentioned in Section I.D., of this exhibit, SCE continues to
support the RECC methodology as the correct way to calculate customer marginal costs. Despite the
lack of foundation for the NCO method, this section presents a calculation of customer marginal
costs using a modified NCO methodology.
The NCO method only considers new customers in calculating the marginal capital costs.
Thus, existing assets currently providing services to customers are ignored in the NCO calculation.
There is also an assumption that a percentage of installations, based on the life of the meters, will
need replacement. Customer service costs (i.e., meter reading, billing, etc.), are calculated in the
same manner as the RECC methodology, based on all customers.
The NCO method is calculated as follows:
1.
Instead of applying a RECC to capital costs to calculate an annual payment, the single
life present worth of the capital investment is calculated. This is done for the service
drop and meter for each customer type.
2.
The result from step 1 is multiplied by the number of average forecasted new
customers per year and number of replacements to calculate the total present worth
capital for new investment. New customers are calculated from the average annual
2010 to 2012 customer growth. An annual replacement rate of 5 percent was
assumed based upon the 20 year depreciation life for electronic meters which is the
large portion of the customer cost. The replacement rate is multiplied by the 2009
customer base to calculate annual customer replacements. The units added in each
year is the sum of the customer growth and customer replacements for total new
capital investment.
E-1
3.
The total present worth capital for new investment is then divided by the actual total
number of customers in each customer group to calculate dollars per customer. This
becomes the $/customer for NCO capital costs.56
4.
The customer services (metering, billing, etc.) marginal cost is included. This is
identical to SCE’s approach.
5.
The NCO capital cost is added to the customer services marginal costs to yield the
total customer marginal cost.
Several customer groups are forecasted to decline in number over 2010-2012 due to actual
decline or rate group switching. The decline creates difficulty because there is negative customer
growth. The NCO methodology has problems when there is negative customer growth because it
can create negative marginal costs. SCE will cap the negative growth at zero and use the annual
replacement rate based on the 2012 customer forecast.
The table below shows the marginal customer costs based on the NCO methodology.
56
At this point the cost of the new customers and replacement is assigned to existing customers, not just new
customers.
E-2
Table E-1
NCO Customer Marginal Cost
Total NCO
Capital 2012$
Domestic
Total O&M
2012$
Annual NCO
Customer Cost
2012$
78.44
35.25
113.69
GS-1
132.24
35.49
167.74
TC-1
126.45
34.41
160.86
GS-2
999.31
140.51
1,139.81
1,784.98
1,074.54
2,859.52
Secondary
2,629.30
1,091.99
3,721.30
Primary
973.04
1,046.77
2,019.81
Sub-Trans
10,755.96
1,046.77
11,802.73
648.87
104.33
753.20
3,369.70
755.75
4,125.45
80.97
34.38
115.35
TOU-GS-3
TOU-8
AG <= 200
AG > 200
Metered Street Lights
E-3