HomeMy WebLinkAboutCouncil Reading File - Full Tri-County Feasibility StudyFINAL REPORT AUGUST 2017
TECHNICALFEASIBILITYSTUDY ON COMMUNITY CHOICE AGGREGATION
FOR THE CENTRAL COAST REGION
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EXECUTIVE SUMMARY
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Executive Summary
A. Community Choice Aggregation Overview
Community Choice Aggregation (CCA) is a program for local jurisdictions in California to procure
electricity supply for, and develop energy resources to serve, jurisdictional customers. According to the
Local Government Commission,1 the most common reasons for forming a CCA program are to:
▪ Increase use of renewable generation,
▪ Exert control over rate setting,
▪ Stimulate economic growth, and
▪ Lower rates.
When a CCA is formed, the local incumbent electric investor-owned utility (IOU) continues to deliver
power through its transmission and distribution facilities to customers within its service territory. The
IOU also provides monthly customer metering and billing services. The local CCA program procures the
electric commodity and sells it to its customers, with the intent that the electricity is less expensive, more
local, and/or uses more renewable generation than the current utility alternative. The two components,
delivery and generation, already appear separately on customer bills. The incumbent utility continues to
provide billing services, but the CCA’s generation rate replaces the IOU’s generation rate on customer
bills.
Jurisdictions in California have formed CCA programs in efforts to provide constituents the option to be
served with a greater mix of renewable and carbon-free energy generation than is provided by the
incumbent utility. Eight CCA programs are
currently operational in California, with ten more
launching in 2018. At least 17 additional
jurisdictions are exploring and/or are in the
planning stages for CCA.
B. Study Scope and Purpose
This technical feasibility Study for CCA for the
Central Coast Region (Study) was directed by the
Advisory Working Group (AWG), which was
formed by eleven governments in the Santa
Barbara, San Luis Obispo, and Ventura County
(Tri-County) Region. The Advisory Working
Group collectively has named the potential CCA
“Central Coast Power.” The Study’s purpose is to
advise and guide the Tri-County Region in
understanding the feasibility of forming a new CCA
program. This Study considers required startup
and operational processes and evaluates multiple
Ten local governments joined with the County of
Santa Barbara to fund this Study, and the
following jurisdictions formed an Advisory
Working Group in December 2015:
• Unincorporated San Luis Obispo County
• Unincorporated Santa Barbara County, plus:
o City of Carpinteria
o City of Santa Barbara
• Unincorporated Ventura County, plus:
o City of Camarillo
o City of Moorpark
o City of Ojai
o City of Simi Valley
o City of Thousand Oaks
o City of Ventura
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procurement scenarios to determine whether a CCA program in the Tri-County Region is: a) financially
feasible; and b) will meet its stated policy objectives. The Study results do not necessarily apply to one or
more of the Tri-County local governments joining an existing CCA program.
This Study evaluates the financial and economic viability of a CCA by:
• Forecasting the CCA electricity demand requirements (load) and potential customers by class;
• Estimating the costs of procuring the necessary electricity supply; and
• Projecting the costs of starting up and administering a CCA program.
The Study also enumerates the potential benefits and associated risks of a CCA program and discusses
implementation requirements.
C. Energy Procurement and Study Scenarios
Energy procurement is complex and the total cost
of procurement is subject to changes in both market
conditions (price) and consumption (volume). Load
Serving Entities (LSEs)—IOUs, CCAs, and Electricity
Service Providers (ESPs)—must manage both load
forecasting and energy procurement with a robust
risk management approach to account for the
dynamic and volatile nature of power markets and
load.
Given the uniqueness of multiple municipalities
partnering to commission this feasibility Study, the Advisory Working Group established eight geographic
participation scenarios. These eight scenarios were selected to explore the feasibility of different sizes and
configurations for the CCA program and the potential effect of customer demographics. Although the
entire Tri-County Region may not ultimately pursue CCA, certain jurisdictions may decide to move
forward with CCA. The eight participation scenarios defined for this Study are:
1. All Tri-County Region
2. AWG Jurisdictions
3. All San Luis Obispo County
4. Unincorporated San Luis Obispo County
5. All Santa Barbara County
6. Unincorporated Santa Barbara County
7. All Ventura County
8. City of Santa Barbara
In addition to the eight participation scenarios, three renewable energy content scenarios were
considered. All scenarios include a customer option to opt-up to a 100% renewable energy product. For
the purposes of this Study, 2% of customers were assumed to opt-up to the 100% renewable option. The
three renewable energy content scenarios are as follows:
Throughout the report, the term LSE is used to
provide illustrative trends that are affecting the
Tri-County Region as a whole, regardless of
whether the electricity is provided by an IOU,
ESP or CCA program. For our purposes, a CCA
program is a subset of the more broad LSE
term.
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• RPS Equivalent: This scenario assumes that Central Coast Power would offer its base electricity
product to all customers starting at 33% renewable content in 2020 and ramping up to 50%
renewable content by 2030 in alignment with the California minimum Renewable Portfolio
Standard (RPS).2
• Middle of the Road: This scenario assumes that Central Coast Power would offer its base
electricity product to all customers using 50% renewable content for the entire Study period.
• Aggressive: This scenario assumes that Central Coast Power would offer its base electricity
product to all customers using 75% renewable content for the entire Study period.
This Study evaluates an eleven-year period from 2020 to 2030, although a potential CCA program could
begin earlier than 2020. Figure ES-1 illustrates how the renewable energy content in the RPS Equivalent
scenario grows over time, and in the other two scenarios remains constant across the Study period. These
three scenarios were chosen to illustrate the relative differences in cost given different levels of renewable
supply content. Actual CCA implementation may choose to follow a progression of increasing renewable
generation over that period based on cost competitiveness. For example, Central Coast Power CCA may
launch in 2020 with 50% renewable content and progress to 75% renewable content by 2030, assuming it
can do so at a cost advantage to the IOUs.
To enhance report readability, the main body of this report presents results for the AWG Jurisdictions
participation scenario, for the RPS Equivalent, Middle of the Road, and Aggressive renewable energy
content scenarios. Detailed results for the other seven participation scenarios are provided in Appendices
C, and E through J.
Figure ES-1 Renewable Energy Content Modeled in this Study
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The fundamental operational role of a CCA is to forecast
customer electricity needs and procure energy and
associated energy related services. Power procurement
consists of forecasting and risk management tasks. Power
procurement planning and day-to-day decision making rely
heavily on short-term and long-term forecasts of consumer
demand for power. The procurement function must also
evaluate and assess the inherent risks associated with demand forecasting and develop appropriate risk
mitigation strategies. Though no one can predict future energy demand with 100% certainty, logical, data-
driven, industry-standard methodologies to forecasting are available to provide a realistic outlook of
energy demand under a variety of future scenarios. Brief discussions covering the forecasts for customer
power demand and power procurement costs are provided in the following segments.
D. Customer Demand
As shown in Figure ES-2, Ventura County is the largest
electricity consumer of the three counties considered in
this Study, followed by Santa Barbara and San Luis Obispo
Counties. Collectively, customers in the incorporated
cities in San Luis Obispo and Ventura Counties consume
more electricity than customers in the unincorporated
county. The reverse is true in Santa Barbara County.
The fundamental operational role of a
CCA is to forecast customer electricity
needs and procure energy and
associated energy related services.
Energy is measured in several units
throughout this study: kilowatt-hours
(kWh), which is the unit used on customer
bills; megawatt-hours (MWh), where 1
MWh equals 1,000 kWh; and gigawatt-
hours (GWh), where 1 GWh equals
1,000 MWh or 1,000,000 kWh.
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Figure ES-2 Annual Demand in Gigawatt-hours (GWh) by County
Figure ES-3 shows the annual electricity consumption for each of the Study’s eight geographic participation
scenarios. The consumption and number of accounts generally mirror each other, with the exception of
unincorporated San Luis Obispo and Santa Barbara Counties.
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Figure ES-3 Annual Demand in GWh for Each Geographic Participation Scenario
Electricity consumption is forecasted to grow moderately over the Study period, however continued
customer adoption of distributed generation (DG) solar photovoltaic (PV) is expected to offset this
growth. DG PV reduces the amount of energy that needs to be provided by the potential CCA. Figure ES
4 illustrates the growth of customer-owned DG PV since the year 2000 and illustrates a forecast for
additional DG PV capacity if this trend continues. Table ES 1 lists the forecasted annual energy
consumption, annual DG PV generation, and the annual net load (consumption-generation) served by the
potential CCA for the AWG Jurisdictions participation scenario. In summary, a Central Coast Power CCA
would likely sell less electricity each year given customer DG PV adoption.
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Figure ES-4 California Solar Initiative Incentivized Customer-Owned Solar Photovoltaic in the Region with
2030 Forecast
Table ES-1 Load, Distributed Generation, and Net Load Forecast, AWG Jurisdictions Participation
Scenarios
Year Annual Energy
Consumption
(MWh)
Annual DG Generation
(MWh)
Annual Net Load
Served by LSE (MWh)
2020 6,698,164 164,987 6,533,177
2021 6,735,965 202,979 6,532,985
2022 6,777,276 244,414 6,532,862
2023 6,811,982 287,988 6,523,995
2024 6,868,761 335,074 6,533,686
2025 6,888,329 381,954 6,506,375
2026 6,930,669 431,948 6,498,721
2027 6,971,608 483,660 6,487,948
2028 7,026,296 538,288 6,488,008
2029 7,047,280 592,489 6,454,791
2030 7,085,173 650,280 6,434,893
Forecast
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As explained in Section II Technical and Financial Analysis, the increasing amount of DG PV also creates
more volatile customer load due to the variable nature of its energy output. Solar generation depends on
solar irradiance, which can fluctuate significantly over very short periods of time (within seconds) due to
weather patterns and resulting cloud cover.
E. Power Procurement Cost Forecasts
CCAs, like all LSEs, satisfy customer demand for electricity by managing a power supply portfolio, a
collection of supply-side resources. For the purposes of this Study, a power supply portfolio is designed
to acquire two distinct commodities: energy, typically measured in MWh, and resource adequacy capacity,
typically measured in megawatts (MW). Energy resources include natural gas generation, RPS compliant
renewable energy generation, energy storage, and California Independent System Operator (CAISO) day-
ahead and real-time market purchases. Resource adequacy is used to make sure there is sufficient capacity
to produce electricity during peak demand periods.
This Study projects decreasing costs for all energy resources considered, except for energy procured in
the CAISO markets, where average pricing remains constant and large fluctuations are due to variability
in renewable generation for both utility scale resources and customer-owned DG PV. Actual CAISO real-
time market prices from January 2014 through October 2016 for the Tri-County Region average around
$36 per megawatt-hour (MWh). However, the range of prices around that mean varied greatly, reaching
a high of $4,377 per MWh during shortages of supply relative to demand, and a low of -$1,277 per MWh—
meaning that CAISO will pay participants to take power—when supply exceeds demand. The high level
of DG PV penetration in California, combined with solar and wind energy’s variable nature, accounts for
much of this market volatility. This Study has modeled renewable resource variability and the CCA’s
associated exposure to CAISO market prices.
Table ES-2 presents the Study forecast for the average annual power procurement cost for the AWG
Jurisdictions participation scenario for the three renewable supply scenarios. As can be seen in these data,
the average cost of power procurement for the CCA rises as more renewable energy content is added
because renewable generation is forecast to be more expensive than alternative non-renewable resources,
despite a slight downward trend in renewable energy prices.
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Table ES-2 Average Annual Power Procurement Costs ($ per MWh), AWG Jurisdictions Scenarios
Year RPS Equivalent Middle of the Road
(50% Renewable)
Aggressive (75%
Renewable)
2020 $67 $74 $87
2021 $66 $74 $85
2022 $66 $74 $85
2023 $66 $72 $85
2024 $66 $72 $84
2025 $66 $71 $84
2026 $67 $70 $84
2027 $68 $70 $84
2028 $68 $69 $83
2029 $68 $69 $82
2030 $68 $69 $81
The total energy requirements served by various power supply options, including PPAs, the CAISO day-
ahead and real-time markets, among others, change depending on scenario, however, the price of each
option does not. This is what would be expected in actuality, as the amount of energy procured by the
CCA would have little to no bearing on the prevailing PPA and market prices on a long-term basis.
In support of the power procurement cost forecast, data from the U.S. Department of Energy’s Energy
Information Administration’s Annual Energy Outlook 2017,3 which provides estimates of renewable
generation costs on a regional basis, were examined. This data is used by utilities, energy consultancies,
and others to help understand current and future energy-related pricing trends and is based on real-world
project construction, financing, ownership, and ongoing operations and maintenance costs. Table ES-3
shows the various costing components for a new solar photovoltaic project and a new wind project,
assuming they are installed on sites where there is no need to work within the constraints imposed by
existing buildings or infrastructure (greenfield projects). This cost data supports all-in pricing at around
$67 per MWh for wind resources and $101 per MWh for solar PV resources.
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Table ES-3 Energy Information Administration Cost Estimates for New Wind and Solar Energy Resources
in California
Description
Wind Farm –
Onshore
Utility-Scale
Photovoltaic
Configuration
100 MW; 56 turbines
at 1.79 MW each
20 MW, Alternating
Current, Fixed Tilt
Installation Type Greenfield Installation
Greenfield
Installation
Total Capacity (MW) 100 20
Capacity Factor (National Average,
Jan. 2016-Apr. 2017) 36.59% 26.76%
Total Project Cost, California-Mexico Region ($ per kW-installed) $2,010 $2,578
Total Project Cost, California-Mexico Region ($) $201,000,000 $51,560,000
Variable O&M ($ per MWh) $ - $ -
Fixed O&M ($ per kW-year) $46.71 $21.66
Weighted Average Cost of Capital (%) 5.50% 5.50%
Debt Finance Term (years) 20 20
Financing Costs per Year ($) $16,819,545 $4,314,506
Fixed O&M Costs per Year ($) $4,671,000 $433,200
Total Project Costs per Year ($) $21,490,545 $4,747,706
Energy Production per Year (MWh) 320,528 46,884
Per Unit Cost ($ per MWh) $67.05 $101.27
Like all energy price forecasts, the one used within the Study—just as those used within other CCA
feasibility studies—may or may not accurately reflect actual future conditions, which are unknown and
not predictable. Various market drivers may change resulting in different outcomes from those assumed
here. The forecast used herein is a reasonable estimate for the purposes of analyzing the feasibility of
CCA within the Tri-County Region, but no warranties as to the accuracy of forecast prices for power
purchase agreements or CAISO market commodities are implied or should be inferred. For example,
large hydroelectric generation resources owned and managed by the IOUs were not significantly utilized
during the recent drought years through 2016. Rainfall in the winter of 2016-2017 filled the hydroelectric
reservoirs, enabling a low cost, carbon-neutral generation resource for the IOUs. Generally speaking, all
other things being equal, increased hydro production will lower IOU generation revenue requirements
and could have a dampening effect on IOU rates, potentially lowering the rates required for the CCA to
be competitive.
F. Greenhouse Gas Emissions Impact
This Study also evaluated the greenhouse gas (GHG) emissions impact of the renewable energy content
of the CCA’s portfolio—including the 100% renewable energy product assumed to be chosen by 2% of
customers—relative to that of the incumbent IOUs, Southern California Edison (SCE) and Pacific Gas and
Electric (PG&E). For the purposes of this comparison, the IOU Base Case assumes the IOUs will progress
from currently published 2020 RPS levels of renewable generation linearly to the 50% RPS goal in 2030.
Although each IOU may elect to exceed RPS requirements as they have in recent history and relative to
2020 requirements, for example PG&E submitted a joint proposal to decommission the El Diablo nuclear
power station and voluntarily reach 55% RPS by 2031,4 neither IOU has publicly stated firm plans to
exceed RPS targets. California is currently considering Senate Bill 100, which would increase the renewable
energy mandate to: 50% by December 31, 2026 and 60% by December 31, 2030.5 Figure ES-5 summarizes
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the GHG impact analysis results for the IOU renewable scenario and three CCA renewable scenarios.
Figure ES-5 GHG Emissions Impact Analysis, AWG Jurisdictions Participation Scenarios
Large hydroelectric generation resources owned and managed by the IOUs do not count towards RPS
goals and were also not significantly utilized during the recent drought years through 2016. Rainfall in the
winter of 2016-2017 filled the hydroelectric reservoirs enabling a low cost, carbon-neutral generation
component for the IOUs. In addition, the pumped hydro energy storage that can balance the variability of
other sources of renewable generation also relies on rain to fill reservoirs. Future rainfall and drought
conditions are unknown, and therefore the future utilization of large hydroelectric generation by the IOUs
cannot be predicted. Additional use of hydro resources or increases to the IOU RPS content would result
in lower GHG emissions for the IOUs, potentially decreasing the additional GHG reduction benefit of the
CCA program.
G. Cost of Service and Financial Pro Forma Analysis
The cost of service analysis relied on traditional utility ratemaking principles and followed an industry
standard methodology for creation of a financial pro forma to forecast the future economic and financial
performance of the CCA program. The Study assessed financial feasibility in terms of the ability of the
CCA program to realistically deliver competitive costs for customers while paying its substantial start-up
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and agency formation costs and ongoing operating expenses.
The first step in the cost of service analysis was developing
the projected CCA program revenue requirement: the
amount of revenue required to cover the costs of the CCA
program, including all operating and non-operating expenses,
debt-service payments, a contingency allotment, a working
capital reserve, and a rate stabilization fund. The revenue
requirement was based on a comprehensive accounting of all
pertinent costs and projections of customer participation;
assumptions and input development are described later in this
report. Cost assumptions relied on historical publicly-available
information, power cost forecasts conducted for this Study,
data provided by PG&E and SCE, and subject matter expertise
gained working with a host of public utilities and similar
organizations. Table ES-4 summarizes the CCA program Test
Year revenue requirements for the AWG Jurisdictions
participation scenarios
Table ES-4 Test Year CCA Revenue Requirements, AWG Jurisdictions Participation Scenarios
CCA program customer participation was assumed to be constant for each participation scenario across
the three renewable energy content scenarios examined. For all scenarios, an opt-out rate of 15% was
used for all rate classes for all years, meaning that 15% of bundled customers by load in each rate class
were assumed to opt out of the CCA program.6 This 15% opt-out rate is in addition to an estimated
23.5% of AWG Jurisdictions scenario load that represents typically large commercial customers who are
RPS Equivalent
Middle of the
Road Aggressive
REVENUE REQUIREMENT
Baseload
Total Operating Expenses Excluding Power Costs 10,146,683$ 10,256,373$ 10,482,215$
Total Non-Operating Expenses 16,959,517 18,158,147 20,239,969
Power Costs 461,419,035 489,933,855 549,930,521
Contingency/Rate Stabilization Fund 54,171,111$ 57,535,423$ 64,613,615$
BASELOAD REVENUE REQUIREMENT 542,696,345$ 575,883,798$ 645,266,320$
Opt-up to 100% RPS
Total Operating Expenses Excluding Power Costs 207,075$ 209,314$ 213,923$
Total Non-Operating Expenses 346,113 370,574 413,061
Power Costs 12,617,576 12,617,576 12,617,576
Contingency/Rate Stabilization Fund 1,105,533$ 1,174,192$ 1,318,645$
OPT-UP TO 100% RPS REVENUE REQUIREMENT 14,276,297$ 14,371,657$ 14,563,205$
TOTAL REVENUE REQUIREMENT 556,972,642$ 590,255,454$ 659,829,525$
AWG Jurisdictions Participation Scenarios
Description
The Test Year is the future
annualized period for which operating
costs are analyzed and rate proxies
established. The Study Test Year is
based on forecasts of CCA operating
conditions for years 2022, 2023, and
2024 and represents a twelve-month
period of normalized operations
selected to evaluate the cost of
service for each customer class and
the adequacy of rate proxies to
provide sufficient revenue.
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likely to remain with their existing Direct Access (DA) ESP. Other CCA feasibility studies have supported
the assertion that opt-out rates, within a reasonable range, have little bearing on CCA feasibility. Figure
ES-6 and Figure ES-7 summarize Test Year customer accounts by rate class and Test Year customer usage
by rate class for the AWG Jurisdictions participation scenarios, respectively. Average CCA Test Year
customer profiles for the three AWG Jurisdictions participation scenarios are provided in Table ES-5.
Figure ES-6 Test Year CCA Customer Accounts, AWG Jurisdictions Participation Scenarios
0
50
100
150
200
250
300
350
400
PG&E Customers, AWG
Jurisdictions Scenarios
SCE Customers, AWG Jurisdictions
Scenarios
Total CCA Customers, AWG
Jurisdictions Scenarios
Thousands Test Year Customer Accounts by Rate Class Traffic Control
Residential CARE
Residential
Lighting
Small Comm
<200kW
Med Comm
200<500kW
Large Comm
500<1,000kW
Very Large Comm
>1,000kW
Agriculture
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Figure ES-7 Test Year CCA Customer Usage, AWG Jurisdictions Participation Scenarios
0
1,000
2,000
3,000
4,000
5,000
6,000
PG&E Customers, AWG
Jurisdictions Scenarios
SCE Customers, AWG
Jurisdictions Scenarios
Total CCA Customers, AWG
Jurisdictions Scenarios
GWH Test Year Customer Energy Usage by Rate Class
Traffic Control
Residential CARE
Residential
Lighting
Small Comm
<200kW
Med Comm
200<500kW
Large Comm
500<1,000kW
Very Large Comm
>1,000kW
Agriculture
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Table ES-5 Test Year CCA Customer Accounts and Usage, AWG Jurisdictions Participation Scenarios
While rate design was not part of the Study scope, based on the detailed pro forma analysis, CCA rate
proxies by customer class by IOU jurisdiction were developed. Rate proxies represent the amount of
revenue by customer class required to make the CCA financially solvent, based on the Test Year. Based
on this analysis, CCA baseline customers would have all-in rate proxies that are higher than both PG&E
and SCE for most rate classes for all participation and renewable energy content scenarios examined.
Table ES-6 through Table ES-8 present the generation rate differences between the CCA and PG&E and
SCE for the AWG Jurisdictions participation scenarios for the RPS Equivalent, Middle of the Road, and
Aggressive renewable energy content scenarios. The generation portion of customers’ bills is the only
cost component for which the CCA competes with the incumbent utilities. Customer billing and delivery
charges (transmission and distribution) are the same for both CCA and IOU bundled customers.
Generation rate comparisons are provided for the first five years of the Study period by rate class.7 The
Accounts Annual Load
Average Monthly
Load
Line Description (MWh)(kWh/Account)
1 BASELOAD
2 Agriculture 6,454 490,772 6,337
3 Very Large Comm >1,000kW 13 718,495 4,673,350
4 Large Comm 500<1,000kW 405 441,022 90,742
5 Med Comm 200<500kW 576 297,829 43,094
6 Small Comm <200kW 40,034 1,124,051 2,340
7 Lighting 1,757 26,357 1,250
8 Residential 256,812 1,709,325 555
9 Residential CARE 22,929 124,036 451
10 Traffic Control 841 2,811 278
11 TOTAL BASELOAD 329,821 4,934,699 1,247
12 OPT-UP TO 100% RPS (MWH)
13 Agriculture - - -
14 Very Large Comm >1,000kW - - -
15 Large Comm 500<1,000kW 9 10,071 90,742
16 Med Comm 200<500kW 29 15,106 43,094
17 Small Comm <200kW 538 15,106 2,340
18 Lighting - - -
19 Residential 9,078 60,425 555
20 Residential CARE - - -
21 Traffic Control - - -
22 TOTAL OPT-UP TO 100% RPS 9,655 100,708 869
23 TOTAL CCA 339,476 5,035,407 1,236
CUSTOMERS OPTING UP TO 100% RENEWABLES Portion of Opt Up Portion of Total CCA
24 Agriculture 0%0.00%
25 Very Large Comm >1,000kW 0%0.00%
26 Large Comm 500<1,000kW 10%0.20%
27 Med Comm 200<500kW 15%0.30%
28 Small Comm <200kW 15%0.30%
29 Lighting 0%0.00%
30 Residential 60%1.20%
31 Residential CARE 0%0.00%
32 Traffic Control 0%0.00%
33 TOTAL 100%2.00%
Test Year
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total anticipated bill impact to residential customers in 2020 is included in Table ES 9.
Table ES-6 Generation Rate Comparisons for PG&E, SCE, and CCA, AWG Jurisdictions RPS Equivalent
Renewable Energy Content Scenario
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
Agriculture 0.1175 0.0742 0.1175 0.0753 0.1175 0.0749 0.1175 0.0747 0.1175 0.0754
Commercial/Industrial Small <200kW 0.1183 0.1049 0.1183 0.1065 0.1183 0.1059 0.1183 0.1055 0.1183 0.1065
Commercial/Industrial Medium 200<500 kW 0.1190 0.1097 0.1190 0.1113 0.1190 0.1107 0.1190 0.1103 0.1190 0.1114
Commercial/Industrial Large 500<1000 kW 0.1145 0.1107 0.1145 0.1124 0.1145 0.1118 0.1145 0.1114 0.1145 0.1124
Residential 0.1220 0.1003 0.1220 0.1018 0.1220 0.1013 0.1220 0.1009 0.1220 0.1018
Residential CARE 0.1152 0.0936 0.1152 0.0950 0.1152 0.0945 0.1152 0.0941 0.1152 0.0950
Residential Solar Choice 0.1920 0.1265 0.1920 0.1284 0.1920 0.1277 0.1920 0.1272 0.1920 0.1284
Weighted Average 0.1193 0.0961 0.1193 0.0975 0.1193 0.0970 0.1193 0.0967 0.1193 0.0976
CCA Rate Premium/ (CCA Savings)24.10%22.27%22.92%23.37%22.22%
Rate Class
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
Agriculture 0.1050 0.0543 0.1050 0.0551 0.1050 0.0548 0.1050 0.0547 0.1050 0.0552
Commercial/Industrial Small <200kW 0.1072 0.0922 0.1072 0.0936 0.1072 0.0931 0.1072 0.0927 0.1072 0.0936
Commercial/Industrial Medium 200<500 kW 0.1064 0.0837 0.1064 0.0850 0.1064 0.0845 0.1064 0.0842 0.1064 0.0850
Commercial/Industrial Large 500<1000 kW 0.1057 0.0777 0.1057 0.0789 0.1057 0.0785 0.1057 0.0782 0.1057 0.0789
Residential 0.0999 0.0712 0.0999 0.0723 0.0999 0.0719 0.0999 0.0716 0.0999 0.0723
Residential CARE 0.0924 0.0635 0.0924 0.0645 0.0924 0.0641 0.0924 0.0639 0.0924 0.0645
Residential Green Tariff 0.1199 0.1127 0.1199 0.1144 0.1199 0.1138 0.1199 0.1134 0.1199 0.1144
Weighted Average 0.1034 0.0776 0.1034 0.0788 0.1034 0.0784 0.1034 0.0781 0.1034 0.0788
CCA Rate Premium/ (CCA Savings)33.23%31.26%31.97%32.44%31.21%
2026
Rate Class
2022 2023 2024 2025
Executive Summary
Technical Feasibility Study Central Coast Region
on Community Choice Aggregation August 2017
ES-17
Table ES-7 Generation Rate Comparisons for PG&E, SCE, and CCA, AWG Jurisdictions Middle of the Road
Renewable Energy Content Scenario
Table ES-8 Generation Rate Comparisons for PG&E, SCE, and CCA, AWG Jurisdictions Aggressive
Renewable Energy Content Scenario
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
Agriculture 0.1242 0.0742 0.1242 0.0753 0.1242 0.0749 0.1242 0.0747 0.1242 0.0754
Commercial/Industrial Small <200kW 0.1250 0.1049 0.1250 0.1065 0.1250 0.1059 0.1250 0.1055 0.1250 0.1065
Commercial/Industrial Medium 200<500 kW 0.1257 0.1097 0.1257 0.1113 0.1257 0.1107 0.1257 0.1103 0.1257 0.1114
Commercial/Industrial Large 500<1000 kW 0.1212 0.1107 0.1212 0.1124 0.1212 0.1118 0.1212 0.1114 0.1212 0.1124
Residential 0.1287 0.1003 0.1287 0.1018 0.1287 0.1013 0.1287 0.1009 0.1287 0.1018
Residential CARE 0.1219 0.0936 0.1219 0.0950 0.1219 0.0945 0.1219 0.0941 0.1219 0.0950
Residential Solar Choice 0.1987 0.1265 0.1987 0.1284 0.1987 0.1277 0.1987 0.1272 0.1987 0.1284
Weighted Average 0.1260 0.0961 0.1260 0.0975 0.1260 0.0970 0.1260 0.0967 0.1260 0.0976
CCA Rate Premium/ (CCA Savings)31.06%29.13%29.82%30.29%29.08%
Rate Class
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
Agriculture 0.1117 0.0543 0.1117 0.0551 0.1117 0.0548 0.1117 0.0547 0.1117 0.0552
Commercial/Industrial Small <200kW 0.1139 0.0922 0.1139 0.0936 0.1139 0.0931 0.1139 0.0927 0.1139 0.0936
Commercial/Industrial Medium 200<500 kW 0.1132 0.0837 0.1132 0.0850 0.1132 0.0845 0.1132 0.0842 0.1132 0.0850
Commercial/Industrial Large 500<1000 kW 0.1124 0.0777 0.1124 0.0789 0.1124 0.0785 0.1124 0.0782 0.1124 0.0789
Residential 0.1066 0.0712 0.1066 0.0723 0.1066 0.0719 0.1066 0.0716 0.1066 0.0723
Residential CARE 0.0991 0.0635 0.0991 0.0645 0.0991 0.0641 0.0991 0.0639 0.0991 0.0645
Residential Green Tariff 0.1266 0.1127 0.1266 0.1144 0.1266 0.1138 0.1266 0.1134 0.1266 0.1144
Weighted Average 0.1102 0.0776 0.1102 0.0788 0.1102 0.0784 0.1102 0.0781 0.1102 0.0788
CCA Rate Premium/ (CCA Savings)41.87%39.78%40.53%41.04%39.72%
2026
Rate Class
2022 2023 2024 2025
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
Agriculture 0.1382 0.0742 0.1382 0.0753 0.1382 0.0749 0.1382 0.0747 0.1382 0.0754
Commercial/Industrial Small <200kW 0.1390 0.1049 0.1390 0.1065 0.1390 0.1059 0.1390 0.1055 0.1390 0.1065
Commercial/Industrial Medium 200<500 kW 0.1397 0.1097 0.1397 0.1113 0.1397 0.1107 0.1397 0.1103 0.1397 0.1114
Commercial/Industrial Large 500<1000 kW 0.1352 0.1107 0.1352 0.1124 0.1352 0.1118 0.1352 0.1114 0.1352 0.1124
Residential 0.1426 0.1003 0.1426 0.1018 0.1426 0.1013 0.1426 0.1009 0.1426 0.1018
Residential CARE 0.1359 0.0936 0.1359 0.0950 0.1359 0.0945 0.1359 0.0941 0.1359 0.0950
Residential Solar Choice 0.2026 0.1265 0.2026 0.1284 0.2026 0.1277 0.2026 0.1272 0.2026 0.1284
Weighted Average 0.1399 0.0961 0.1399 0.0975 0.1399 0.0970 0.1399 0.0967 0.1399 0.0976
CCA Rate Premium/ (CCA Savings)45.56%43.41%44.18%44.70%43.35%
Rate Class
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
Agriculture 0.1258 0.0543 0.1258 0.0551 0.1258 0.0548 0.1258 0.0547 0.1258 0.0552
Commercial/Industrial Small <200kW 0.1280 0.0922 0.1280 0.0936 0.1280 0.0931 0.1280 0.0927 0.1280 0.0936
Commercial/Industrial Medium 200<500 kW 0.1272 0.0837 0.1272 0.0850 0.1272 0.0845 0.1272 0.0842 0.1272 0.0850
Commercial/Industrial Large 500<1000 kW 0.1265 0.0777 0.1265 0.0789 0.1265 0.0785 0.1265 0.0782 0.1265 0.0789
Residential 0.1208 0.0712 0.1208 0.0723 0.1208 0.0719 0.1208 0.0716 0.1208 0.0723
Residential CARE 0.1132 0.0635 0.1132 0.0645 0.1132 0.0641 0.1132 0.0639 0.1132 0.0645
Residential Green Tariff 0.1308 0.1127 0.1308 0.1144 0.1308 0.1138 0.1308 0.1134 0.1308 0.1144
Weighted Average 0.1242 0.0776 0.1242 0.0788 0.1242 0.0784 0.1242 0.0781 0.1242 0.0788
CCA Rate Premium/ (CCA Savings)59.94%57.58%58.43%59.00%57.52%
2026
Rate Class
2022 2023 2024 2025
Executive Summary
Technical Feasibility Study Central Coast Region
on Community Choice Aggregation August 2017
ES-18
Figure ES-8 and Figure ES-9 graphically depict the difference in generation rates between the CCA and
PG&E and the CCA and SCE, respectively, for the AWG Jurisdictions scenario for the three renewable
content scenarios.
Figure ES-8 CCA and PG&E Generation Rate Comparison Summary for AWG Jurisdictions Participation
Scenarios
0.0000 0.0500 0.1000 0.1500 0.2000
CCA Rates
PG&E Rates
CCA Rates
PG&E Rates
CCA Rates
PG&E Rates
AWG RPS EquivalentAWG Middle of The RoadAWG Aggressive2022-2026 Average Base Generation Rates ($/kWh)
Executive Summary
Technical Feasibility Study Central Coast Region
on Community Choice Aggregation August 2017
ES-19
Figure ES-9 CCA and SCE Generation Rate Comparison Summary for AWG Jurisdictions Participation
Scenarios
Table ES-9 shows the percentage change in average generation rates and the monetary change in monthly
Residential bills for CCA customers versus PG&E and SCE, and the percent change in GHG emissions for
all rate classes. This data is presented for year 2020. The previous Tables ES-6 through ES-8 present
weighted average rate impacts across all seven customer classes examined for years 2022-2026.
0.0000 0.0500 0.1000 0.1500 0.2000
CCA Rates
SCE Rates
CCA Rates
SCE Rates
CCA Rates
SCE Rates
AWG RPS EquivalentAWG Middle of The RoadAWG Aggressive2022-2026 Average Base Generation Rates ($/kWh)
Executive Summary
Technical Feasibility Study Central Coast Region
on Community Choice Aggregation August 2017
ES-20
Table ES-9 Summary of Forecasted Residential Class Outcomes by Renewable Energy Content Scenario,
AWG Jurisdictions Participation Scenarios, Year 2020
Participation
Scenario
Included
Jurisdictions
Renewable
Energy
Content
Pacific Gas & Electric Southern California Edison
Proportional
GHG
Comparison
Generation
Rate
Comparison
(% Increase/
Decrease for
CCA
Customers)
Monthly Bill
Comparison
($ Increase/
Decrease for
CCA
Customers)
Generation
Rate
Comparison
(% Increase/
Decrease for
CCA
Customers)
Monthly Bill
Comparison
($ Increase/
Decrease for
CCA
Customers)
All Tri-County
Region
All San Luis
Obispo County
All Santa Barbara
County
All Ventura
County
RPS
Equivalent
22% $11.25 41% $14.55 6%
50% 29% $14.62 51% $17.93 -9%
75% 43% $21.72 71% $25.05 -55%
Advisory Working
Group
Jurisdictions
San Luis Obispo
County
Santa Barbara
County
Carpinteria
Santa Barbara
Ventura County
Camarillo
Moorpark
Ojai
Simi Valley
Thousand Oaks
Ventura
RPS
Equivalent
22% $12.21 41% $16.08 6%
50% 29% $15.92 50% $19.79 -9%
75% 43% $23.68 70% $27.64 -55%
All San Luis
Obispo County
Arroyo Grande
Atascadero
Grover Beach
Morro Bay
Paso Robles
Pismo Beach
San Luis Obispo
Unincorporated
SLO County
RPS
Equivalent
29% $12.07 7%
50% 36% $14.89 -9%
75% 51% $20.77 -54%
Unincorporated
San Luis Obispo
County
Unincorporated
SLO County
RPS
Equivalent
35% $15.70 7%
50% 42% $18.77 -9%
75% 56% $25.21 -54%
All Santa Barbara
County
Buellton
Carpinteria
Goleta
Guadalupe
Santa Barbara
Santa Maria
Solvang
Unincorporated
Santa Barbara
County
RPS
Equivalent
24% $11.15 45% $14.53 7%
50% 31% $14.27 55% $17.69 -9%
75% 45% $20.78 75% $24.22 -55%
Executive Summary
Technical Feasibility Study Central Coast Region
on Community Choice Aggregation August 2017
ES-21
Participation
Scenario
Included
Jurisdictions
Renewable
Energy
Content
Pacific Gas & Electric Southern California Edison
Proportional
GHG
Comparison
Generation
Rate
Comparison
(% Increase/
Decrease for
CCA
Customers)
Monthly Bill
Comparison
($ Increase/
Decrease for
CCA
Customers)
Generation
Rate
Comparison
(% Increase/
Decrease for
CCA
Customers)
Monthly Bill
Comparison
($ Increase/
Decrease for
CCA
Customers)
Unincorporated
Santa Barbara
County
Unincorporated
Santa Barbara
County
RPS
Equivalent
26% $15.08 47% $19.29 7%
50% 33% $18.97 56% $23.23 -9%
75% 47% $27.11 76% $31.44 -54%
All Ventura
County
Camarillo
Fillmore
Moorpark
Ojai
Oxnard
Port Hueneme
Santa Paula
Simi Valley
Thousand Oaks
Ventura
Unincorporated
Ventura County
RPS
Equivalent
41% $15.87 6%
50% 50% $19.54 -10%
75% 70% $27.35 -55%
City of Santa
Barbara Santa Barbara
RPS
Equivalent
69% $17.91 6%
50% 78% $20.42 -10%
75% 100% $25.98 -55%
Table ES-10 shows annual operating results for the AWG Jurisdictions participation scenario for the RPS
Equivalent renewable energy content scenario. Net operating margins are negative for all years of the
Study period; meaning revenues are not sufficient to cover total operating and non-operating expenses
plus the contingency and rate stabilization fund. In the initial years of the study period, this is due to the
phasing in of customers and a lag in revenues versus expenditures. In later years, this revenue insufficiency
is caused by rates remaining unchanged even though the CCA experiences an increase in operating costs.
Rates were not increased because the CCA rate proxies were not competitive with IOU rates from the
onset of the Study through 2026. Raising rates would make them less competitive. Although working
capital initially is adequate, given the current debt assumptions that include a long-term bond financing in
year 2020 of $288 million, starting in year 2024, working capital declines below targeted amounts and
continues to decrease. The combination of increasingly negative net margins and a shortage of working
capital would indicate the need for a rate increase around year 2026, again which would further harm the
CCA program’s rate competitiveness relative to the IOUs. Table ES-11 presents this data for the AWG
Jurisdictions Middle of the Road renewable energy content scenario and Table ES-12 presents this data
for the AWG Jurisdictions Aggressive renewable energy content scenario. Generally speaking, results for
these alternate renewable energy content scenarios are similar to the RPS Equivalent scenario, although
Executive Summary
Technical Feasibility Study Central Coast Region
on Community Choice Aggregation August 2017
ES-22
net margins and working capital deficiencies are better due to the higher rate proxies, which are set at
the beginning and remain constant throughout the study period. Rate increases would still be required,
but around the 2028 timeframe.
Table ES-10 CCA Annual Operating Results, AWG Jurisdictions RPS Equivalent Scenario
Table ES-11 CCA Annual Operating Results, AWG Jurisdictions Middle of the Road Scenario
Year
Operating
Revenues
($000s)
Total Operating
Expenses Plus
Contingency/ Rate
Stabilization Fund
($000s)
Non-Operating
Revenues/
(Expenses)
($000s)
Debt Service
($000s)
Net Margin1
($000s)
Working Capital
Fund ($000s)
Working Capital
Target ($000s)
Working Capital
Surplus/
(Deficiency)
($000s)
Working
Capital
Surplus/
(Deficiency)
(%)
a b c d a - b + c - d e f e - f (e/f)-1
2020 110,694 139,109 1,145 11,515 (38,785) 211,653 47,077 164,575 350%
2021 445,293 469,267 2,227 11,515 (33,262) 189,905 159,570 30,335 19%
2022 545,838 533,627 2,046 17,276 (3,018) 186,887 181,993 4,894 3%
2023 556,361 541,735 2,028 17,276 (621) 186,266 184,808 1,458 1%
2024 556,922 543,639 1,925 17,276 (2,067) 184,199 185,916 (1,716) -1%
2025 555,121 543,720 1,985 17,276 (3,889) 180,310 186,453 (6,143) -3%
2026 554,190 551,493 1,903 17,276 (12,676) 167,634 189,470 (21,836) -12%
2027 553,316 556,757 1,721 17,276 (18,995) 148,639 191,885 (43,246) -23%
2028 553,165 566,687 1,396 17,276 (29,401) 119,238 195,934 (76,697) -39%
2029 550,808 569,985 1,183 17,276 (35,270) 83,967 198,148 (114,181) -58%
2030 548,923 581,521 386 17,276 (49,488) 34,479 203,224 (168,745) -83%
NPV of Net Margin:(176,175)
1 Net Margin includes Net Operating Income less Debt Service. The net present value (NPV) of the
Net Margin is determined using a 4% discount rate and is as of Year 2020. The discount rate
is equal to the interest rate on the long-term debt.
Year
Operating
Revenues
($000s)
Total Operating
Expenses Plus
Contingency/ Rate
Stabilization Fund
($000s)
Non-Operating
Revenues/
(Expenses)
($000s)
Debt Service
($000s)
Net Margin1
($000s)
Working Capital
Fund ($000s)
Working Capital
Target ($000s)
Working Capital
Surplus/
(Deficiency)
($000s)
Working
Capital
Surplus/
(Deficiency)
(%)
a b c d a - b + c - d e f e - f (e/f)-1
2020 117,525 150,875 1,235 12,330 (44,445) 223,724 50,583 173,141 342%
2021 472,491 504,655 2,323 12,330 (42,170) 193,883 170,117 23,766 14%
2022 579,072 568,848 2,082 18,499 (6,192) 187,691 192,494 (4,803) -2%
2023 590,222 575,366 2,044 18,499 (1,600) 186,092 194,836 (8,745) -4%
2024 590,817 570,966 1,962 18,499 3,314 189,406 194,067 (4,662) -2%
2025 588,906 566,609 2,098 18,499 5,896 195,302 193,284 2,019 1%
2026 587,918 570,586 2,132 18,499 966 196,268 195,171 1,096 1%
2027 586,991 571,282 2,109 18,499 (681) 195,587 196,227 (640) 0%
2028 586,831 576,506 1,991 18,499 (6,182) 189,405 198,875 (9,470) -5%
2029 584,330 574,978 2,033 18,499 (7,113) 182,292 199,652 (17,361) -9%
2030 582,330 581,643 1,541 18,499 (16,270) 166,022 203,279 (37,257) -18%
NPV of Net Margin:(100,693)
1 Net Margin includes Net Operating Income less Debt Service. The net present value (NPV) of the
Net Margin is determined using a 4% discount rate and is as of Year 2020. The discount rate
is equal to the interest rate on the long-term debt.
Executive Summary
Technical Feasibility Study Central Coast Region
on Community Choice Aggregation August 2017
ES-23
Table ES-12 CCA Annual Operating Results, AWG Jurisdictions Aggressive Scenario
H. Feasibility Outcome Summary
The two primary factors driving forecasted feasibility results
for the CCA include: 1) the competitiveness of CCA rates
against PG&E and SCE rates; and 2) the long-term financial
viability of the enterprise. Under all participation scenarios,
because the rate comparisons show most rate classes paying
more for power supplied by the CCA than from the
incumbent utilities and because the CCA does not maintain
sufficient revenues and working capital throughout the Study
period, the CCA is deemed infeasible Regarding rate
competitiveness, forecasted CCA revenue requirements are
primarily driven by power procurement costs and the Cost
Responsibility Surcharge (CRS), which consists of the
Competitive Transition Charge (CTC), the Department of
Water Resources Bond Charge (DWR-BC), and the Power
Cost Indifference Adjustment (PCIA). Together, these two
components represent 78% of the total of the overall
projected CCA revenue requirement and are thus primary drivers of rate competitiveness against the
two incumbent utilities.
Recent historical movements in the CRS and the allocation of incumbent utility revenue requirements
between generation and delivery (i.e., transmission and distribution) appear to disadvantage the CCA
program. The delivery portion of customers’ bills is paid equally by CCA and bundled IOU customers.
Generally speaking, in recent years the incumbent utilities appear to have been shifting costs from
generation to delivery, as discussed in more detail in Section II.E.1 Feasibility Drivers. The CCA only
competes against the incumbent utilities on generation. Given the assumptions of this Study, SCE and
PG&E forecasted generation rates are not high enough to support CCA feasibility at the forecasted level
of CCA power procurement and operational costs. Regarding long-term financial viability, the CCA would
Year
Operating
Revenues
($000s)
Total Operating
Expenses Plus
Contingency/ Rate
Stabilization Fund
($000s)
Non-Operating
Revenues/
(Expenses)
($000s)
Debt Service
($000s)
Net Margin1
($000s)
Working Capital
Fund ($000s)
Working Capital
Target ($000s)
Working Capital
Surplus/
(Deficiency)
($000s)
Working
Capital
Surplus/
(Deficiency)
(%)
a b c d a - b + c - d e f e - f (e/f)-1
2020 131,724 168,193 1,428 13,746 (48,788) 250,176 55,745 194,431 349%
2021 528,600 562,520 2,607 13,746 (45,059) 218,863 187,370 31,493 17%
2022 647,505 633,619 2,361 20,623 (4,375) 214,487 211,809 2,679 1%
2023 659,933 646,015 2,318 20,623 (4,388) 210,100 215,901 (5,801) -3%
2024 660,598 637,896 2,227 20,623 4,307 214,407 214,025 381 0%
2025 658,462 633,821 2,370 20,623 6,388 220,795 213,325 7,469 4%
2026 657,357 640,581 2,395 20,623 (1,452) 219,343 216,041 3,302 2%
2027 656,320 642,137 2,343 20,623 (4,096) 215,247 217,353 (2,106) -1%
2028 656,142 648,050 2,187 20,623 (10,344) 204,903 220,206 (15,303) -7%
2029 653,345 646,843 2,185 20,623 (11,936) 192,967 221,079 (28,111) -13%
2030 651,109 652,739 1,647 20,623 (20,605) 172,362 224,476 (52,114) -23%
NPV of Net Margin:(120,434)
1 Net Margin includes Net Operating Income less Debt Service. The net present value (NPV) of the
Net Margin is determined using a 4% discount rate and is as of Year 2020. The discount rate
is equal to the interest rate on the long-term debt.
In no participation or renewable
energy content scenario were the
CCA program’s rates competitive with
PG&E or SCE. Given the
underperformance of the CCA in
terms of being rate competitive,
consistently having negative net
margins, and failing to meet the
target for working capital, the CCA
under the assumptions used in the
Study is neither reliably solvent nor
financially feasible.
Executive Summary
Technical Feasibility Study Central Coast Region
on Community Choice Aggregation August 2017
ES-24
need additional rate increases around the year 2026 timeframe to maintain adequate working capital and
increase net margins, further decreasing rate competitiveness.
I. Sensitivity Analysis Results
Upon completion of the Study outcomes for each participation and renewable energy content scenario,
additional sensitivity cases were examined against the AWG Jurisdictions participation scenario to
determine how changes in key inputs affect feasibility outcomes. These sensitivities included: (1) Decreases
in power procurement costs; (2) Increases in IOU rate escalation; and (3) Decreases in staffing costs. Each
sensitivity was examined individually to determine the point at which the CCA could be feasible. As
discussed in more detail in Section II.E.2, Pro Forma Sensitivity Analysis, in order for the CCA to be
feasible:
• Power procurement costs would have to decrease 40% over the Study forecast, or
• PG&E and SCE rates would have to escalate at an additional 4.0% per year above the Study
forecast.
A staffing cost reduction alone is not expected to affect program feasibility. Although not examined as
part of this Study, some combination of changes to the Study assumptions could result in a more feasible
outcome. Like all feasibility studies, assumptions used herein are based on a forecast of future conditions
which may or may not occur. Various market and regulatory drivers may change resulting in different
outcomes from those assumed herein. The assumptions used in the Study are reasonable for the purposes
of analyzing the feasibility of CCA within the Tri-County Region, but no warranties as to the accuracy of
outcomes are implied or should be inferred.
FOR THE CENTRAL COAST REGION
APPENDIX L:
PEER REVIEW
AND RESPONSE
AUGUST 2017
TECHNICALFEASIBILITYSTUDY ON COMMUNITY CHOICE AGGREGATION
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APPENDIX L
PEER REVIEW AND RESPONSE
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Table of Contents
APPENDIX L: PEER REVIEW AND RESPONSE
1. MRW and Associates Peer Review .................................................................................................... L-1
2. MRW Extended Peer Review ............................................................................................................. L-23
3. Response to Peer Review ................................................................................................................. L-39
Exhibit A: Power Procurement Cost Comparison Results ........................................................................ L-64
Exhibit B: Decrease In Staffing Costs Comparison Results ........................................................................ L-69
Exhibit C: Annual Escalation of PG&E and SCE Rates Comparison Results .......................................... L-72
Exhibit D: Power Procurement Monte Carlo Simulation Model Questions ......................................... L-74
4. Response to Extended Peer Review ................................................................................................ L-83
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Appendix L: Peer Review and Response
This Appendix provides the initial and extended peer reviews conducted by MRW and Associates, LLC of
the Technical Feasibility Study on CCA for the Central Coast Region and the response of Willdan Financial
Services and EnerNex to the initial peer review.
1. MRW and Associates Peer Review
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MEMORANDUM
To: Jennifer Cregar, Project Supervisor, Energy and Sustainability Initiatives, County of
Santa Barbara
From: Mark Fulmer, David Howarth, Jeremy Waen, and Anna Casas Llopart
Subject: Peer Review of “Technical Feasibility Study on Community Choice Aggregation for
Central Coast Region” Draft Report dated May, 2017
Date: May 31, 2017
In late 2015, the County of Santa Barbara Board of Supervisors authorized funds to perform a Draft
Study and directed staff to explore regional interest in Community Choice Aggregation (CCA). Ten local
governments joined with the County of Santa Barbara to fund the Draft Study, and the following
jurisdictions formed an Advisory Working Group (AWG) in December 2015. The CCA Feasibility Study
was requested to provide an in-depth technical, economic, and financial analyses of the potential costs,
benefits, and risks of CCA for the Tri-county region (Santa Barbara, Ventura, and San Luis Obispo
counties) under a variety of future outcomes, or scenarios. The Draft Study is intended to provide policy
makers, stakeholders, and electricity consumers information for assessing the feasibility of a CCA
program for the Tri-County region.
On May 14, 2017, the County provided MRW & Associates, LLC (MRW) a draft report entitled “Technical
Feasibility Study on Community Choice Aggregation for Central Coast Region” Draft Report dated May
10, 2017 (the Draft Study), and requested MRW to provide a professional peer review of the Draft
Study. This memorandum provides MRW’s review. Beyond the Summary of Conclusions, it is organized
around the 10 questions concerning the Draft Study to which the County asked MRW to respond.
Summary of Conclusions
The Draft Study considered eight CCA composition scenarios, each with differing community
memberships, ranging from the “All Tri-County Region” to the City of Santa Barbara alone (See Table ES-
XIII). Like the Draft Study, MRW’s review effort concentrated on the AWG Jurisdictions scenario.
Overall, the Draft Study is detailed and comprehensive. Its assessment of loads and load forecast are
thorough and reasonable, and it provides an in-depth look into potential CCA operations.
Appendix L: Peer Review and Response
Technon Community Choice
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Unlike prior recent CCA technical studies, the Draft Study concluded that CCA was not economically
feasible even when only the state-required minimum renewable energy content was assumed. MRW’s
focused its review to identify areas where the Draft Study was potentially overly conservative or made
questionable assumptions that might explain why its conclusion was negative while others have been
affirmative.
In this regard, MRW identified several areas where Willdan, the Draft Study’s author, should consider
revising its assumptions:
1. CCA Renewable power contracts. The Draft Study’s use of utility-average renewable contract
prices does not reflect the most recently-reported contract prices and does not reflect the
general downward trend in renewable prices seen over the past few years.
2. “Uncollectible expenses.” The Study assumed from 5% to 8% of the revenues due to the CCA
from its customers could not be collected. This is an order-of-magnitude higher than that
experienced by either MCE Clean Energy (MCE),1 the longest-running CCA in the state, or Sonoma
Clean Power (SCP), the second longest-running CCA in the state. CCAs do not observe the same
level of uncollectible accounts as the IOUs due because CCAs are allowed to return non-paying
accounts to the corresponding IOU’s bundled service.
3. Administrative labor costs. The number of employees assumed in the pro forma analyses, as well
as their compensation, appear high relative to operating California CCAs.
4. CCA service fees. The incumbent utilities—Southern California Edison (SCE) and Pacific Gas and
Electric (PG&E)—charge CCAs in their respective territories certain fees for billing conducted on
behalf of the CCA as well as meter and data management. While the Draft Study reflects current
tariffed rate for these services, it does not account for the proposed dramatic uncontested
reductions being presented by both utilities. Similarly, it is unclear whether the ESP service fees
section of the Draft Study properly accounts for critical operational services such as data
management and scheduling coordination.
5. Assumed reserves funding. Beyond working capital, CCAs typically develop a “rate stabilization
reserve fund” which can be drawn upon in years’ where the CCA might not otherwise be able to
meet its rate targets. The Draft Study pro forma analysis appears to assume that approximately
$78 million (14% of total expenses) is contributed each year, rather than setting a target (e.g.,
15% of annual expenses), taking 3 to 5 years to achieve the fund, and then eliminate further
contributions until replenishment is needed.
6. PG&E and SCE Rate Forecasts. A fundamental concern is that the forecast of SCE and PG&E
rates is disconnected from the forecast of CCA rates. The utility rates against which the CCA
rates are compared are simply the current rates escalated at 0-0.5%. It does not account for: (i)
SCE’s or PG&E’s actual supply portfolio, (ii) the two utilities’ status with respect to State’s
renewable power content mandates, (iii) fuel price trends, or (iv) any other underlying
fundamentals. In particular, there is no explicit connection between the utilities’ generation
1 MCE began serving customers in May 2010 to select areas within Marin County. Presently serves approximately
255,000 accounts located within all of Marin and Napa Counties, as well as select cities within Contra Costa County
(Richmond, San Pablo, El Cerrito, Lafayette, and Walnut Creek ) and the City of Benicia in Solano County. MCE
serves a diverse customer base in terms of geographic, ethnic and socio-economic backgrounds.
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rates and the CCA generation cost, even though they would be purchasing from the same
wholesale market and vying for the same incremental renewable generation sources.
We are also concerned that the Draft Study assumes that the franchise fees (i.e., utility taxes) that would
flow to the respective cities’ and counties’ general funds if SCE or PG&E were providing service is assumed
to instead flow to the CCA. This treatment should be verified by the AWG or corrected.
Lastly, we recommend that sensitivity cases used to explore the impact of lower SCE and PG&E rates
and higher exit fees consider a wider range of potential values.
Responses to Questions
1. Does the Study consider all pertinent factors to determine current and future
electric energy requirements of the CCA?
The Draft Study notes, “…historical utility level consumption data for 2001-2016 was pulled from EIA
Form 861 for both PG&E and SCE. This data was analyzed and a logarithmic line of best fit was created
and extended through 2030. This data was then compared with the California Energy Commission’s
long-term procurement plan (LTPP)(sic) load forecasts, which are available through 2025 for the
respective planning areas. Because the two sources showed very different results by 2030, the average
between the LTPP sales projection and the EIA consumption data forecast was utilized for the load
forecast for Central Coast Power.”
The curve fit showed a much lower load growth rate than that from the CEC. Draft Study forecast shows
modest load growth. That is, natural load growth from increased economic activity is generally offset by
efficiency and behind-the-meter customer generation (e.g., rooftop solar).
Particularly given the relatively short time frame in which it conducts the economic analysis, this load
forecast is reasonable.
Direct Access (DA): Since DA customers are not likely to join a CCA due to an existing contract with an
Electric Service Provider (ESP), for purposes of this Draft Study DA customers have been excluded from
the load forecast.
Opt-out 15% base assumption. The Draft Study assumes that 15% of the eligible customers will opt-out
of the CCA and remain on bundled utility service. This value is conservative relative to the actual opt-
out rates experienced with the most recent CCAs.
2. Does the Study incorporate current power market conditions and reasonable
projections of expected future conditions?
The Draft Study provides a comprehensive review of current power market conditions, including a
qualitative summary of power procurement considerations (e.g., renewable portfolio standard (RPS),
resource adequacy and storage) as well as a quantitative analysis of recent historical pricing for
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renewable energy, natural gas generation and California Independent System Operator (CAISO) day-
ahead and real-time wholesale electricity markets. The Draft Study presents data on current
expectations regarding the relative levelized cost of energy for different generation technologies and
recent declines in solar photovoltaic (PV) costs. The Draft Study also presents data showing trends in
utility RPS compliance costs, as reported annually to the California legislature (i.e., the Padilla report)
and in the Biennial RPS reports.
Renewable Energy Procurement. To forecast CCA renewable energy procurement costs, the Draft Study
consultants developed a best-fit logarithmic curve using average utility RPS compliance costs depicted in
Figure ES-40 of the Draft Study. The resulting RPS price forecast is likely a conservative estimate of CCA
renewable energy procurement costs. This is because the data used to forecast RPS price trends do not
necessarily reflect the market in which the CCA will operate since the data reflect utility procurement
costs for energy delivered during a particular year. The renewable energy portfolios of utilities include
contracts struck over a period of time during which technology costs have been rapidly decreasing. As a
result, the decline in average costs incurred by the utilities for renewable energy deliveries has lagged
behind the decline in costs for new (incremental) resources. This point is referred to in footnote 97 of
the Draft Study, which quotes an explanation by California Public Utilities Commission (CPUC) staff.
The 2016 Padilla report,2 issued May 1, 2017, presents time-of-delivery-adjusted renewable energy
prices for bundled RPS contracts approved in 2016. The prices are aggregated to avoid revealing
confidential data, and for SCE include wind, geothermal and biomass contracts in addition to solar. The
weighted average prices for contracts approved in 2016 are $0.059/kWh for PG&E and $0.061/kWh for
SCE, well below the average 2016 expenditures of $0.11/kWh and $0.094/kWh, respectively. The prices
of contracts approved in 2016 are approximately 30% below the average RPS PPA cost of $88/MWh
assumed in the Report for 2020.
Since the CCA would be making RPS contract purchases at current and future market prices that are
lower than the average utility RPS compliance cost as reflected in Figure ES-40, the Draft Study has likely
overestimated RPS PPA costs in the pro forma analysis.
The Monte Carlo model used for the Draft Study is useful for reflecting uncertainty in forecasts of
procurement costs, by providing a statistically characterized range around this base forecast. The report
does not provide information concerning the way in which RPS price uncertainty was characterized in
the Monte Carlo model, so it is not possible to review the reasonableness of these assumptions.
Natural Gas Generation. In the case of natural gas generation prices, the Draft Study fit a curve to 2002-
2016 CAISO market implied prices to forecast prices for the period through 2035. Based on this analysis,
natural gas generation costs are forecast to decrease by 25% from $41/MWh in 2020 to $31/MWh in
2030. This trend analysis may be underestimating natural gas generation costs over the long term by
not differentiating between trends in market heat rates (the implied rate of conversion of natural gas
energy to electricity, in Btu/kWh) and natural gas prices, which may be driven by different market
dynamics not captured by the trend analysis. Natural gas prices are relatively low at present. In its 2017
2
http://www.cpuc.ca.gov/uploadedFiles/CPUCWebsite/Content/About_Us/Organization/Divisions/Office_of_Gover
nmental_Affairs/Legislation/2017/Final%20-%20Padilla%20Report%20-%20RPS%20Costs%202017.pdf
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Annual Energy Outlook, the Energy Information Administration forecasts natural gas prices for electricity
generation in the Pacific region to increase by an average of 3.5% per year between 2020 and 2030.
Based on this forecast of natural gas prices, the forecast of natural gas generation costs used in the Draft
Study suggests market heat rates will decrease by more than half between 2020 to 2030, or a compound
average rate of -6.1%. While there may be downward pressure on market heat rates as additional
renewable energy sources are brought on line, a 6% per year reduction in market heat rate is likely not
sustainable since it would be difficult for natural gas generators to recover costs. The Draft Study would
likely benefit from a review of this assumption and the associated discussion of the forecast. As with the
RPS cost forecast, additional information on how natural gas price uncertainty was reflected in the
Monte Carlo model would be needed to assess reasonableness.
Other Cost Components. Following the cost of RPS procurement and natural gas generation, resource
adequacy (RA) represents the remaining significant component of CCA procurement costs. The Draft
Study provides a reasonable forecast of RA costs. The remaining components, including CAISO day-
ahead and real-time markets and storage procurement represent a small fraction of total costs, just 2%
in the 50% RPS case. The forecasts used in the Draft Study for these cost components appear
reasonable.
3. Are the estimates of the GHG emissions intensity of the CCA scenarios relative to
the incumbent investor-owned utilities (IOUs), namely Pacific Gas and Electric
Company (PG&E) and Southern California Edison (SCE), reasonable and adequate?
The Draft Study’s projections of CCA greenhouse gas emissions are generally reasonable. Figure 1 below
replicates “Table ES-XL (sic) Jurisdictions scenario CO2 output comparison with IOU base case and trend”
from the Draft Study. Note that the IOU Base Case line (orange) converges with the CCA 50% RPS line
(green) by 2030. This reflects the fact that in 2030 the IOUs would be meeting the 50% RPS requirement
in 2030, the same renewable content as the CCA. However, implicit in this figure is that the CCA also can
procure non-RPS compliant carbon-free power (i.e., large hydroelectric) in an equal share to that which
SCE and PG&E have. This is particularly important with respect to PG&E, which has significant nuclear
and large hydroelectric resources3. Note also that this figure assumes that PG&E meets its goal of
replacing the output of the retiring Diablo Canyon Nuclear Power Plant (2022-2023) with carbon-free
resources.
The “IOU Trend” line in the figure (yellow) is interesting and provides a conservative benchmark against
which the CCA’s GHG emissions can be compared. However, it should not be used to provide the basis
for a GHG analysis.
3 Note that the power content labels included in the Draft Study for the two IOUs are for 2015, which due to the
drought conditions understates the typical hydroelectric output and thus overstates th e IOUs’ GHG emissions.
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Figure 1: Greenhouse Gas Emissions
Consistent with other CCA analyses conducted or peer reviewed by MRW, the Draft Study illustrates that
if a CCA wishes to reduce GHG emissions relative to remaining with the incumbent utility while
maintaining competitive rates, it would need to explicitly contract for non-RPS complying, GHG-free
power: that generated by large hydroelectric or nuclear facilities.
4. Does the Draft Study consider all pertinent factors in projecting future PG&E and SCE
rates for comparison to CCA costs/payment/rate projections?
MRW finds there are areas where the Draft Study can be improved and refined with respect to the
forecast of PG&E and SCE rates.
Error in Current IOU Rates. Table 1 compares current PG&E rates as presented in both the Draft Study
and PG&E’s 5011-E-A advice letter. While some rates are reasonably similar, others, particularly the
medium and large commercial and industrial rates, are not. The difference between these rates is
attributable to the study’s use of differing “billing determinants.”4 It appears the Draft Study assumes a
4 “Billing Determinants” are the usage values one multiplies times the rates to arrive at the total bill. For residential
customers, it is just the number of kilowatt -hours consumed. For large accounts, this include the seasonal on -
peak and off peak use (in kilowatt-hours) as well as the maximum demand (kilowatts) that occur during various
periods throughout the day and year.
-
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
2,000,000
2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030MT CO2CCA RPS Equivalent
IOU Base Case
CCA 50% Renewable
IOU Trend
CCA 75% Renewable
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17% load factor for Commercial/Industrial Large rate class; instead the average load factor for this rate
class should be in the range of 45%-65%.5 This should be corrected.
Table 1: Comparison of Draft Study’s estimated PG&E rates to PG&E’s actual rates
PG&E (¢/kWh)
Rate Class Schedule Draft Study Advice letter
5011-E-A
Agriculture AG-5B 14.0 16.6
Very Large Commercial >1,000kW E-20-T 10.9 11.7
Commercial/Industrial Large 500<1000 kW E-19SV 33.5 17.8
Commercial/Industrial Medium 200<500 kW A-10S 24.3 20.4
Commercial/Industrial Small <200kW A-1 22.2 23.0
Residential E-1 23.1 23.1
Residential CARE EL-1 13.6 13.7
Table 2 below provides a similar comparison for SCE rates presented in the Draft Study relative to
MRW’s estimated average rates. As was the case with Table 1, the rate differences occurring in Table 2
are due to differences in how the billing determinants are calculated. For example, for
Commercial/Industrial Small, the Draft Study assumes a 11% load factor; instead the average load factor
for this rate class should be in the range of 35-55%.
Table 2: Comparison of Draft Study’s estimated SCE rates to MRW’s estimates of SCE rates
SCE (¢/kWh)
Rate Class Schedule Draft Study MRW estimates
Agriculture TOU-PA-3 12.5 12.7*
Very Large Commercial >1,000kW TOU-8 -T Option B 8.5 9.1
Commercial/Industrial Large 500<1000 kW TOU-8 -P Option B 28.2 12.8
Commercial/Industrial Medium 200<500 kW GS3-RTIME 17.5 14.5**
Commercial/Industrial Small <200kW GS2-RTIME 31.3 16.9***
Residential D 19.6 19.4
Residential CARE D-CARE 12.1 12.1
* Average rate for agriculture rate class
** Rate for GS3-TOU-Option B
*** Rate for GS2 –Option B
5 “Load Factor” reflects how much the customer uses relative to its peak demand. A customer who uses power at
its peak demand level all time would have a “load factor” of 100%. Because large customer rates have per kW
demand charges, the higher the load factor, the more kilowatt -hours the demand charges are averaged over and
thus the lower the rate. Thus, there is a large difference between the average rate of a customer with a low load
factor, like 17%, and a higher one, such as 65% or higher.
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a. IOU Rates Forecasts
Figure 2 compares the PG&E generation rate forecast done by MRW for the CCA Technical Study for
Contra Costa County6 (Contra Costa Study) and the Draft Study. In both cases the current generation
rate, 2017, is based on the weighted average of Central Coast CCA Scenario 2 PG&E rate using the class
averages generation rates from AL 5011-E-A. The Draft Study forecasted 0% annual increase between
2017 and 2020, and -0.25% between 2020 and 2030. This is based on the Draft Study’s annual increase
of the power costs calculated using the Monte Carlo simulation. Instead, the Contra Costa Study forecast
was developed on a fundamentals basis, considering PG&E’s generation portfolio, contracts, power
markets, etc., and resulted in an annual average increase of 3% from 2017 to 2030. More precisely, the
Contra Costa Study forecasts a 1.5% annual increase between 2017 and 2022, followed by a 1.5% annual
decrease between 2023 and 2025 (due to the Diablo Canyon retirement), and finally a 5% annual
increase between 2026 and 2030.
Figure 2: Comparison of Draft Study’s and MRW’s forecasts of PG&E generation rates
Furthermore, the Draft Study extends its calculated escalator for generation rates to non-generation
rates. This is concerning because there is no direct relation between the cost drivers for generation and
non-generation utility services.
6 http://www.cccounty.us/DocumentCenter/View/43588
0
2
4
6
8
10
12
14
16
2017 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030¢/kWhContra Costa Study
Draft Study
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5. Does the Draft Study consider all pertinent factors in presenting a reasonably
accurate investor-owned utility (IOU) vs. CCA cost/payment comparison?
Our concerns regarding escalation of the PG&E and SCE delivery rate raised in response to Question 4
would not be material if the same delivery rate is used for both the utility and CCA rates. However, it is
not clear from the Draft Study report that a common delivery rate was used in the comparison of SCE and
PG&E rates and CCA costs. As noted above, the utility rate forecasts were based on the escalation of both
the generation and delivery rates. What would be helpful would be a comparison table that showed,
either on a class basis or on a system average basis the following (in $/kWh):
YEAR PG&E/SCE CCA
a b c = a+b d = a e f g h =
d+e+f+g i=(h-a)/a
Delivery
Rate
Genera-
tion rate
Total
Rate
Delivery
Rate
Ave. Power
Cost
Other
Costs PCIA Total
Rate
Pct.
difference
2022
2023
2024
2025
2026
…
6. Do the pro forma analyses consider all pertinent factors in projecting CCA’s
operating results?
Yes. However, the Draft Study may be treating the franchise fee revenues incorrectly. Franchise fees
are a percentage of utility customers’ bills that are paid to cities or counties for the nonexclusive right to
install and maintain equipment on streets and public rights of way (e.g., power poles, underground
power or gas lines). The Draft Study assumes that the franchise fees collected by PG&E and SCE from
CCA customers will be diverted from the general fund into the CCA. MRW is not aware of other CCAs
diverting the franchise fee revenue stream from the participant’s general fund to the CCA. The AWG
should verify that this is an acceptable treatment before it is included as a CCA revenue source. If it is
not, or is at all questionable, franchise fee revenue should be removed from the pro forma analysis.
Second, it is not clear that the franchise fees are correct. The rate modeling shows particularly high SCE
franchise fees as part of the CCA rates: around 9% of CCA revenue. Later, and in the pro forma, the
franchise fees are subtracted out.
Power Costs: As discussed above, there is a great deal of uncertainty in forecasts of power costs. The base
forecast of RPS procurement costs is likely conservative, while the forecasted costs of natural gas
generation may be lower than expected over the forecast period. To the extent that the pro forma
analyses include Monte Carlo simulation model results, the pro forma results may reasonably reflect the
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expected range of power costs. It is difficult to assess the reasonableness of the Monte Carlo simulation
model analyses with information presented in the Report.
Other Operating Costs. Operating costs consist of all costs directly associated with provision of the
business services and activities of the CCA—namely procuring and providing power to customers. The
Draft Study thoroughly presented the operating costs of a hypothetical CCA.
Salaries & Wages: Both the 45 FTE staff proposal and the average fully loaded salary costs seem
excessive for this proposed CCA. MCE has the largest staff of any CCA present and this is largely
due to two factors 1) they were the first CCA to form so resource sharing with other CCAs was
not an option until very recently, and 2) they are engaged in administering Energy Efficiency
programs utilizing ratepayer funds. The former is important because subsequent CCAs are
finding they can operate with much leaner staffing than MCE. The latter is important to consider
because the EE programs utilize a separate revenue stream from electricity sales. Additionally,
EE (and customer facing programs in general) commands a higher staffing requirement than
other core operations within a CCA. Additionally, based on this Draft Study the average loaded
proposed salary for the Central Coast Power CCA would be $156,743. Whereas based on MCE’s
projected FY 2016/17 financials their average fully loaded salary is $116,983. As a result, both
factors cause the “Salaries and Wages” expense category to be significantly larger than would be
prudent for a new CCA organization.
As such, we suggest that Willdan consider the following revisions:
1) Adjust the anticipated FTE downward (perhaps 20-30 FTE), especially at the upper end
of the staffing spectrum.
2) Adjusting the proposed salary costs downward.
IOU Service Charges: Based on analysis it appears the Draft Study uses a $0.83/MWh/month
multiplier to determine both PG&E’s and SCE’s service charges. Furthermore, this multiplier has
a 2% annual escalator applied. These assumptions seem problematic. First, PG&E and SCE have
notably different Meter Data Management Agent (MDMA) and Bill-Ready fees. (Note that
MDMA charges are on a per meter per month basis. Bill-Ready charges are on a per customer
per month basis.) PG&E’s present MDMA fee is dramatically higher than SCEs, though PG&E is
proposing in its present General Rate Case (GRC) Phase 2 to dramatically reduce this fee from
$7.67 to $0.14. PG&E has differing Bill-Ready fees based upon whether the CCA’s charges
appear on a separate page of the bill or not. In contrast SCE has differing Bill-Ready fees
depending upon whether the bill is delivered via printed or electronic means. Furthermore, both
PG&E and SCE have proposals before the CPUC to reduce these charges because they observe
increasing numbers of departing load customers over which these sorts of costs can be spread.
There is no reason to believe this trend won’t continue as more CCAs form. As a result, IOU
Service Charges seem a bit overestimated.
The PG&E and SCE CCA Start Up and Opt-Out charges that also roll-up into this total IOU Service
Charges category seem reasonable and do not require revising. As such, we recommend that
Willdan consider the following revisions:
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1) Use PG&E’s and SCE’s proposed revised MDMA and Bill-Ready fees that are likely to be
effective well before 2020 to more accurately approximate the resulting PG&E and SCE
Service Fees.
2) Either keep these fees level or approximate some small de-escalation factor to account
for the likelihood that these fees will further reduce as more load departs and as these
metering and billing departments of the utilities adapt to automate these processes.
ESP Charges: It was difficult to understand and extrapolate the various types of ESP services and
related charges that could be used to justify the $1.50/account/month multiplier used to
determine these overall charges. MCE presently has contracted a $1.15/account/month fee for
Data Management services with Calpine. Scheduling Coordination is a separate service that also
fits under this “ESP Charges” category and would add to the costs as well. It appears that this
$1.50/account/moth factor is in the correct ballpark to approximate these types of costs;
however, it is difficult to say if this figure is too high or too low.
As such, we recommend that Willdan consider looking to existing CCAs’ public contract
information to better approximate Data Management and Scheduling Coordination costs under
this category.
Jurisdictional Administration: It is atypical for a CCA to reimburse the local jurisdictions for staff-
time spent interfacing with the CCA. The one area where this might be practiced is with Single
Jurisdiction (rather than Joint Powers Authority) CCAs where staff is shared between local
government and CCA operations. Even in those cases this “Jurisdictional Administration”
category seems to overlap with the Salary & Wages category. As a result, these costs should not
be considered part of the CCA’s operating expenses. We therefore recommend that Willdan
consider excluding these costs from the Operation Expenses analysis.
Uncollectable Accounts: Per the draft report it appears that a 5% uncollectable accounts rate is
assumed for PG&E accounts and an 8% uncollectable accounts rate is assumed for SCE accounts.
Neither rate seems reasonable. First and foremost, CCA uncollectable account rates are not
directly comparable to IOU uncollectable account rates. If a CCA customer account is repeatedly
uncollected or under-collected it permitted practice to return that customer’s account to
bundled utility service.7 As such, CCAs observe a significantly lower uncollectable accounts rate
than IOUs. For example, MCE presently observes a 0.5% uncollectable accounts rate for its
255,000 customer accounts across its four-county service area.8 SCP also observes and plans for
7 PG&E and SCE Electric Rule 23 section Q.2 both state: “[PG&E/SCE] shall not disconnect electric service to the
customer for the non-payment of CCA charges. In the event of non-payment of CCA charges by the customer,
the CCA may submit a CCASR requesting transfer of the service account to [PG&E/SCE] Bundled Service according
to Section M.
8 See MCE fiscal year 2015/16 audited financial statements: https://www.mcecleanenergy.org/wp-
content/uploads/2016/09/MCE-Audited-Financial-Statements-2015-2016.pdf
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a 0.5% uncollectable accounts rate.9 As a result, the “Uncollectable Accounts” operating
expense category is significantly overestimated.
As such, we recommend that Willdan consider adjusting the uncollectable accounts rate
downward from 5% for PG&E accounts and 8% for SCE accounts to 0.5% for both PG&E and SCE
accounts.
PCIA: Included in operating costs is the Power Cost Indifference Amount (PCIA). The PCIA is the
state-mandated fee that SCE and PG&E imposes on all departed load (including CCA customers)
to ensure that the rates of utility customers who do not—or cannot—choose CCA service do not
increase because of CCA. The Draft Study relies upon a forecast of the PCIA rate from the
utilities’ green tariff forecasts. Because the PCIA is difficult to accurately forecast, this
assumption is not unreasonable, but as noted later, must be thoroughly explored in sensitivity
analyses.
Non-Operating Costs. Non-operating costs include initial capital outlays for longer-living assets required
to get the CCA up and running as well as the associated debt issuance and annual debt service required
to fund the CCA. Non-Operating Costs also include a contingency/rate stabilization fund. The Draft
Study thoroughly presented the non-operating costs of a hypothetical CCA.
The Study also assumes an initial long-term bond issuance for working capital equal to 5 months cash
flow plus the rate stabilization fund. MRW is concerned that the debt amount appears to be
unnecessarily high. Prior CCAs have started with an initial cash infusion of something closer to 3-4
months of cash flow only, and used rate revenue to build up the rate stabilization fund. Second, the
Draft Study does not note who might issue the long-term bonds. The CCA, as a brand-new entity, would
not have the financial history to issue long term bonds. Existing California CCAs have relied upon
shorter-term loans (3-5 years) for the initial (smaller) working capital infusion and relied upon rate
revenue to (slowly) fund the rate stabilization accout.
Figure 3 depicts the contingency/rate stabilization fund proposed in the Draft Study for the Central
Coast CCA. This fund is calculated every year as a sum of 10% of the total operating expenses (excluding
power procurement costs) and 17% of the total power procurement costs. Based on this calculation, the
contingency/rate stabilization fund increases every year and ultimately accumulates to $778 million
dollars in 2030. The blue bars within Figure 3 illustrate this annual accumulation of the contingency/rate
stabilization fund (even without the amount that seemed to be assumed in the initial bond).
9 See SCP fiscal year 2014/15 audited financial statements: https://sonomacleanpower.org/wp-
content/uploads/2015/01/08b-2015-and-2014-Final-Audited-Financials.pdf
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Figure 3: Draft Study’s proposed Central Coast contingency/rate stabilization fund
The Contra Costa Study also accounted for a contingency/rate stabilization fund. A crucial difference is
that the Contra Costa Study applied an accumulation cap of 15% of the annual operating cost to the
contingency/rate stabilization fund. In this case once this cap is reached, no further revenues would be
diverted to the contingency/rate stabilization fund unless the reserve funds were withdrawn. Creating a
contingency/rate stabilization fund is critical for smooth CCA operations, but revenue allocations to this
fund must be balanced against the ongoing need for the CCA’s rates to remain competitive with the
local utility’s rates.
In the case of the Contra Costa Study, MRW proposed using the contingency/rate stabilization fund to
adjust the CCA’s generation rates so that it could remain competitive with PG&E rates. During periods
when the total CCA customer rate (i.e. the CCA costs plus the PG&E exit fee) was below the projected
PG&E generation rate, the Contra Costa Study proposed increasing the CCA rates upwards to layaway
revenue into the contingency/rate stabilization fund up to the 15% cap, while still maintaining a
discount. During periods when the total CCA customer rate would otherwise exceed the projected PG&E
generation rate, the Contra Costa Study proposed drawing upon the revenue surplus within the
contingency/rate stabilization fund to offset some of the costs that would otherwise have to be
recovered from CCA customers through the CCA generation rate.
Based on this methodology, the Contra Costa CCA would meet the 15% cap for its contingency/rate
stabilization fund during the first three years of operation. After those first three years, there would be
minimal additions to the fund due to load growth.
Figure 4 illustrates MRW’s proposed accumulation of revenues for the Contra Costa CCA’s
contingency/rate stabilization fund.
$0
$100,000,000
$200,000,000
$300,000,000
$400,000,000
$500,000,000
$600,000,000
$700,000,000
$800,000,000
$900,000,000
2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Accumulated
Contingency/Rate
Stabilization Fund
Annual Costs
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Figure 4: Contingency/rate stabilization fund accumulations for the Contra Costa CCA Study
Pro-forma results and rate comparisons
Figure 5 presents a graphical summary of Draft Study’s pro-forma results for its Central Coast Scenario
2.10 The vertical bars represent the CCA total cost per kilowatt-hour, the green line represents the fixed
CCA rate (inclusive of the PCIA but not delivery charges or franchise fees), and the red line represents
the IOU average generation rate for the total CCA load. Power costs (in orange) represent on average for
2020-2030 approximately 60% of the total costs. The PCIA (in yellow) represents 13% of the total costs
during this same period, and other costs11 (in blue) represent 28%.
Based on Figure 5, the formation of the Central Coast CCA seems infeasible for two reasons: 1) the IOU
average rate is lower than the CCA average rate and 2) the negative difference between the CCA rate
and the CCA total cost.
Note, the IOU average rate is lower in the Draft Study than rates presented in other CCA feasibility
studies based exclusively within PG&E’s service area, because 67% of the total potential load for the
Central Coast CCA is within SCE’s service area. Presently, SCE generation rates are lower than PG&E’s
generation rates (e.g. on average SCE generation rates are 6.8¢/kWh and PG&E’s are 9.2¢/kWh).
10 We have kept the franchise fee, CTC, DWB, and all the delivery services charges out of the analysis.
11 Other costs include: salaries and wages, IOU service charges, ESP charges, other start-up costs, professional
services, jurisdictional administration, other operating expenses, uncollectable amounts, contingency/ rate
stabilization fund, non-operating expenses, interest earnings, unrestricted funds, and debt service.
$0
$100,000,000
$200,000,000
$300,000,000
$400,000,000
$500,000,000
$600,000,000
$700,000,000
$800,000,000
$900,000,000
2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Accumulated
Contingency/Rate
Stabilization Fund
Annual Costs
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Figure 5: Central Coast Scenario 2 Pro-forma results
In contrast with Figure 5, Figure 6 shows MRW’s pro-forma results from its Contra Costa Study,
specifically the RPS equivalent scenario. In this case, the power costs represent 82% of the total costs,
PCIA charges represent 13 % and other costs represent 6%. MRW’s Contra Costa Study concluded that
the CCA program could be feasible because the CCA rates are lower than the IOU average generation
rate.
Note, the IOU average rate is higher in the Contra Costa Study than in the Draft Study because Contra
Costa is located exclusively within PG&E’s service territory. Also note, another key difference between
these analyses is that for the Contra Costa Study, the CCA rate was kept equal to the CCA total cost per
kilowatt.
CCA rate
IOU rate
0
2
4
6
8
10
12
14
16
2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030¢/kWhOther costs
PCIA
Power Costs
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Figure 6: Contra Costa RPS equivalent Pro-forma results
As one last point of comparison, MCE appears to have other costs equivalent to 9% of its total power
procurement costs for 2016 (versus 7% forecasted for the Contra Costa CCA and 47% forecasted for the
Central Coast CCA).12
7. Do you have any other suggestions for reducing CCA costs in light of the evolving
California CCA market place?
Please see MRW’s suggested revisions in response to questions 4, 5 and 6.
8. Does the Draft Study present an adequate analysis of potential economic benefits
and challenges of various supply scenarios?
And
9. Should any additional benefits or challenges be considered?
The Draft Study considered the employment impacts of two separate mechanisms: those jobs created
by the increased disposable income from lower electric bills and the jobs associated with local
12 Based on MCE’s FY2015/16 audited financials: https://www.mcecleanenergy.org/wp-
content/uploads/2016/09/MCE-Audited-Financial-Statements-2015-2016.pdf
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investment in renewable resources. Given that the Draft Study found no bill savings, it did not perform
any analysis of employment impact associated with bill savings. If Willdan chooses to implement some
of the suggestions made in this memo and finds the CCA to be able to offer lower rates than the
incumbent utilities, then the bill savings-related jobs analysis should be conducted.
The Draft Study assessed the potential economic development benefits associated with CCA building 1,
5 or 10 megawatts of solar projects or 100 MW of wind projects using the Jobs & Economic Impact
Development (JEDI) model developed by the National Renewable Energy Laboratory. These projects are
not explicitly included in the pro forma analyses, and must be seen as illustrative only.
The JEDI model is the most commonly used tool to estimate these kinds of impacts of renewable power
project development, and is appropriate. The Draft Study also acknowledged that the opportunity for
larger-scale (i.e., not simple behind-the-meter rooftop) solar is limited within the study area.
The estimated impacts depend on the number of jobs created and the salaries for each position. In
addition, if the jobs are not sourced locally, but rely on workers from other areas of the country, state or
region, the local direct impacts would diminish. The JEDI model uses “economic multipliers” to
approximate impacts within the supply chain (e.g., manufacturing job creation). These multipliers are
only estimates of potential effects and, perhaps more importantly, may not fully take into consideration
that these effects may occur outside the local area. It is possible, for example, that the manufacturing
jobs created because of power projects would be out of the local area or the U.S. entirely.
The JEDI model estimates the direct, indirect and induced effects associated with new power projects,
but does not take into consideration that there could be a negative “ripple” effect associated with
higher rates necessary to pay for these projects over time. In other words, if residents and businesses
pay higher rates for local projects, they could spend less money in the local economy, which could have
negative indirect and induced multiplier effects. While we would not expect that these negative indirect
and induced effects would cancel out benefits of local projects, they were not acknowledged or included
in the analysis.
10. Does the Draft Study provide a thorough evaluation of the prospective CCA’s ability
to achieve rate competitiveness with PG&E and SCE? What other factors, if any,
should be considered?
Because the Draft Study was not finding CCA to be cost-effective, it did not explore any explicit
sensitivity cases. If Willdan chooses to implement some or all the recommendations and finds that the
CCA rates can be competitive, sensitivity cases should be run to evaluate how robust the results are to
reasonable variations in key inputs. These should include:
• Lower SCE and PG&E rates
• Higher PCIA
• Higher Renewable costs
• Higher gas prices
The Monte Carlo simulation modeling approach used in the Draft Study also provides an opportunity to
reflect uncertainty in CCA costs. It does not appear, however, that the rate comparisons in the Draft
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Study report utilize the Monte Carlo simulation model results. It would be helpful to incorporate these
results into the rate comparison.
11. Does the Draft Study consider all pertinent factors to assess the overall cost-benefit
potential of CCA?
Subject to the concerns and recommendations expressed in prior responses, all pertinent factors were
included.
12. Does the Draft Study consider all pertinent risk factors involved with establishment
and operation of the CCA program, and are such factors properly weighted and
analyzed?
Appendix B, sections 3 (technical risks) and 4 (external risks) of the Draft Study enumerate the major
risks and presents reasonable mitigations to those risks. With respect to technical risks, the
Power Procurement Risk: Power procurement risk includes wholesale power price spikes, uncertain
load, intermittent renewable generation. The Draft Study suggests that the CCA can mitigate risk by
“having a robust power supply plan, diversifying supply portfolios by production type, generation size
and location, contract length, timing of contract purchases, and the use of hedging instruments ….”
These are overall reasonable suggestions and should be refined and acted upon if the CCA moves
forward.
Regulatory Risk: The Draft Study accurately notes that the landscape for CCA is changing, and that
these changes must be monitored.
Exit Fee and Non-bypassable Charges: The Draft Study notes “The implication for the Central Coast
Power CCA [of exit fees] is that even if the CCA’s primary power supply portfolio were cost-competitive
with the existing supply costs, added PCIA and CRS charges may increase the overall costs such that the
CCA’s offering would ultimately not be competitive with the IOU. This is especially true when
considering the amount of load currently under consideration for CCA.” It further specifically identifies
the ongoing application by SCE and PG&E (along with SDG&E) to revise the exit fee structure, which
would likely increase further the IOU fees on CCA customers.
The Draft Study further suggests,
Given the relative size of the potential PCIA and CRS fees due to departing customers, Central
Coast Power could attempt to procure excess IOU RPS contracts, which would both reduce the
IOUs’ stranded costs and begin developing Central Coast Power’s renewable generation
portfolio.
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While MRW finds the prospect of restructuring the IOU renewable contracts to be remote, we fully
concur that it must be more fully evaluated if Central Coast Power moves forward towards CCA
implementation.
Opt-out risk: As shown in other CCA studies, the risk of higher- or lower-than expected initial opt-out is
relatively modest. The Draft Study correctly states that opt-out risk once the CCA has begun service can
be minimized by competitive rates (“economic advantage”), providing good customer services
(“customer experience”), and offer products and services desired by the CCA customers (e.g., easy to
implement solar rooftop agreements).
Renewable Generation risk: The Draft Study extensively discusses solar “over-generation” (i.e., solar
generating more power during some hours than is needed by the CCA) and what is needed to integrate
the solar into its overall power procurement profile. The observations in this section are accurate, and
should be addressed if the CCA pursues a portfolio with particularly high solar content.
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MEMORANDUM
To: Jennifer Cregar, Project Supervisor, Energy and Sustainability Initiatives, County of
Santa Barbara
From: Mark Fulmer, Anna Casas Llopart, and Jeremy Waen
Subject: Willdan Pro-Forma with Alternative Assumptions
Date: August 16, 2017 (Updated)
The County of Santa Barbara (“County”) provided to MRW a community choice aggregation (CCA) pro-
forma model that was originally created by Willdan Financial Services (“Willdan”) to inform Willdan’s
preparation of a technical feasibility study (“Draft Study”) for the County and participating jurisdictions
throughout San Luis Obispo, Santa Barbara, and Ventura Counties. At the request of the County, MRW
edited the Draft Study pro-forma model according to MRW’s recommendations detailed in a peer
review memorandum dated May 31, 2017. MRW made modifications to the pro-forma model for the
following scenarios:
Advisory Working Group (AWG) Middle of the Road (50% renewable) Scenario, where the AWG
includes 11 jurisdictions across San Luis Obispo, Santa Barbara, and Ventura Counties
Unincorporated Santa Barbara County Middle of the Road (50% renewable) Scenario
Unincorporated San Luis Obispo County Middle of the Road (50% renewable) Scenario
MRW made changes to the underlying community choice aggregator (CCA) cost assumptions and
updated Pacific Gas and Electric (PG&E) and Southern California Edison (SCE) rate forecasts based on its
professional opinion.
While the Willdan pro-forma model provides output comparisons for specific rate schedules, because of
the fundamentally different approach that MRW takes with respect to the rate comparisons, the
model’s specific rate output pages are not impacted by the changes MRW made to the CCA cost
assumptions or PG&E/SCE rates. That is, some of the original model functionality is lost. Notably,
changes made to the model do not allow an assessment of the annual net operating position. Instead,
MRW established average rates to recover 100% of revenues. Each year, the CCA’s net operating
position is, by definition, balanced by rate increases/decreases. To fully update the original pro forma
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model according to MRW’s rate-setting approach would require significant modification to the
spreadsheets, which was beyond the scope of our task.1
Summary of Conclusions
Using the MRW alternative assumptions, the average CCA operational costs (i.e., the average rate the
CCA could offer while covering all costs) for the AWG Middle of the Road Case is approximately 23%
lower, on average, than that with the base assumptions (see Figure 1). Nearly half of the decrease is
associated with the lower renewable power cost assumption; the bulk of the remaining cost reduction
comes from reduced uncollectible expenses, elimination of the franchise fees as an expense (as well as a
revenue) and revisions to the reserve fund. Some changes, including the cost of natural gas generation
and updates to the power cost indifference adjustment (PCIA), modestly increased the CCA costs. See
Table 1 for a summary of CCA cost impacts from the changes made by MRW.
This decrease in operating costs (and therefore CCA rates), coupled with the alternative PG&E/SCE rate
forecasts, shows, for the AWG Middle of the Road Case, the CCA initially would need to set its rates
higher than the investor-owned utilities (IOUs) in order to cover its costs in 2020 to 2022. The CCA may
be able to offer nominally similar rates as the IOUs for 2023 to 2027 and modestly lower rates
thereafter. See Table 2 and Figures 2, 3 and 4 for rate comparisons for the AWG Middle of the Road
Case.
An important factor in the analysis is that PG&E’s generation rates are significantly higher than SCE’s
generation rates. This has two implications for the analysis. First, it is more difficult for a CCA to offer
competitive rates in communities located in SCE’s service territory than those in PG&E’s. Second, the CCA
being considered here may choose to set different rates for customers located in PG&E’s service territory
versus those in SCE’s service area. The net result of this differential between the two utilities’ generation
rates is that a CCA is more likely to be rate-competitive—or even offer a rate savings—for customers
located in PG&E territory (i.e., San Luis Obispo County and northern Santa Barbara County); whereas, the
CCA is not likely to be able to offer rates that are competitive with SCE for customers located in SCE
territory (i.e., southern Santa Barbara County and Ventura County).
Because San Luis Obispo County and parts of Santa Barbara County are in PG&E territory, where a CCA
may be more competitive, MRW also used the Willdan pro-forma model to compare the potential CCA’s
rates for the Unincorporated San Luis Obispo County Middle of the Road and Unincorporated Santa
Barbara County Middle of the Road Cases. In both cases, after the first year phase-in, the CCA’s rates are
projected to be generally comparable to the weighted average of the SCE and PG&E rates (Santa Barbara
County) or PG&E rates (San Luis Obispo County).
Please note that MRW conducted this analysis using a tool which it did not design and an analytical
approach which MRW does not typically take. While the results for the unincorporated counties may
1 Sheets in red became nonfunctional after MRW edits. Also, in “CCA Operating Results,” “PG&E Escalation,” and
“SCE Escalation” sheets, cells inside a red square are nonfunctional.
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suggest that the CCA could offer competitive rates, MRW would need to perform additional, independent
analyses before offering a conclusion.
Model Changes
All the adjustments are highlighted using orange color2 in the MRW edited version of the pro-forma
model. Table 1 summarizes the quantitative impacts of the adjustments. The adjustments applied are
the following3:
1. CCA renewable contracts. The Draft Study’s use of utility-average renewable contract prices
does not reflect the most recently-reported contract prices and does not reflect the general
downward trend in renewable prices seen over the past few years. According to the 2016
Padilla report4, the weighted average prices for renewable contracts approved in 2016 are
$59/megawatt-hour (MWh) for PG&E and $61/MWh for SCE. Based on this and the flat
tendency showed in Table ES - I from the Draft Study, MRW considered $60/MWh as a price for
the renewable contracts for 2016-2030 (30% lower than Draft Study price estimates). MRW
edited column N from “Tri County RPS Equiv” sheet.
2. CCA natural gas generation. Based on the Draft Study’s analysis, natural gas generation costs
are forecast to decrease by 25% from $41/MWh in 2020 to $31/MWh in 2030. This trend
analysis may be underestimating natural gas generation costs over the long term. Natural gas
prices are relatively low at present, but according to the U.S. Energy Information
Administration’s (EIA’s) 2017 Annual Energy Outlook, natural gas prices for electricity
generation in the Pacific region are expected to increase by an average of 3.5% per year
between 2020 and 2030. Since natural gas generation is typically on the margin in the California
wholesale power market, power production costs for market power are driven by the price for
natural gas. MRW forecasted natural gas prices based on current New York Mercantile
Exchange (NYMEX) market futures prices for natural gas and PG&E’s tariffed natural gas
transportation rates. MRW used a standard methodology of multiplying the natural gas price
by projected heat rate for a gas-fired generator in the EIA’s 2017 Annual Energy Outlook5 and
adding in variable operations and maintenance costs to calculate total power production costs.
In addition, MRW added the cost of the greenhouse gas allowances calculated based on the
auction floor price stipulated by the California Air Resources Board’s cap-and-trade regulation.
Following this methodology, MRW estimated natural gas generation costs equal to $33/MWh
for 2020, increasing on average 3% annually. MRW edited cells T19:V29 and column N from
“Tri County RPS Equiv” sheet.
2 Cells with edited formulas are highlighted in light orange.
3 MRW edited row 24 from “CCA Expenses” expenses.
4http://www.cpuc.ca.gov/uploadedFiles/CPUCWebsite/Content/About_Us/Organization/Divisions/Office_of_
Governmental_Affairs/Legislation/2017/Final%20-%20Padilla%20Report%20-%20RPS%20Costs%202017.pdf
5 EIA 2017 AEO, Supplemental Table 55.20 (California)
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3. Jurisdictional administration. It is atypical for a CCA to reimburse the local jurisdictions for
staff-time spent interfacing with the CCA. The one area where this might be practiced is with
Single Jurisdiction (rather than Joint Powers Authority) CCAs where staff is shared between
local government and CCA operations. Even in those cases, this “Jurisdictional Administration”
category seems to overlap with the Salary & Wages category. As a result, these costs should not
be considered part of the CCA’s operating expenses. MRW excluded these costs from the
Operation Expenses analysis, editing cell D7 from “General Assumptions” sheet.
4. Administrative labor costs. The number of employees (45 full-time equivalents [FTEs]) assumed
in the Draft Study pro-forma analysis, as well as their compensation, appear high relative to
operating California CCAs. MRW lowered the staff to 35 FTE, editing column E from “Labor input
worksheet” sheet. MRW did not adjust the compensation.
5. CCA service fees. MRW updated the service fees based on more recent fee data from the Meter
Data Management Agent (MDMA), PG&E’s testimony6 and SCE’s settlement agreement.7 MRW
edited cells K15, K18, and K19 from “PG&E Annual Service Costs” sheet and K14, K18, and K20
from “SCE Annual Service Costs” sheet.
6. Franchise Charges. The Draft Study pro-forma analysis appears to assume the franchise fees as
an operating expense but not as a revenue for the CCA. Franchise fees are collected from CCA
customers by IOUs, not the CCA, using the Franchise Fee Surcharge. This means that the same
franchise fees are collected from CCA customers that would be collected from them had they
been bundled customers. As such, it has no impact on the bundled versus CCA rate
comparison. Therefore, MRW excluded from the analysis the franchise fees expense, editing
row 30 from “CCA Operating Results” sheet.
7. PG&E and SCE PCIA escalation. The Draft Study relies upon a forecast of the PCIA rate from the
utilities’ green tariff forecasts. This is not an unreasonable assumption, but doesn’t account for
CCA departure in 2020-2022. In general, in the 2020’s, MRW sees the PCIA rates tending to
decrease year to year. For conservatism, MRW kept PG&E and SCE’s PCIA constant starting in
2021. In addition, MRW updated the 2018 PCIAs according to the IOUs’ 2018 Energy Resource
Recovery Account (ERRA) applications. While these rates are not adopted, the ERRA
applications provide a good estimate as to what the upcoming year’s rates will be. MRW edited
I6:R14 and F17:F25 from “PG&E Escalation” and “SCE Escalation.”
6 PG&E 2017 General Rate Case, Phase 2 (CPUC Application 16-06-013), Testimony Exhibit PG&E-2, Appendix C.
June 30, 2016. https://pgera.azurewebsites.net/Regulation/ValidateDocAccess?docID=378139
7 SCE 2017 General Rate Case, Phase 1 (CPUC Application 16-09-001), Joint Motion of Southern California Edison
Company (U 388-E) and the City of Lancaster for Adoption of Settlement Agreement. January 19, 2017.
http://www3.sce.com/sscc/law/dis/dbattach5e.nsf/0/26C44E0FA545EC37882580AD0081F6BD/$FILE/A1609001-
Joint%20Motion%20for%20Adoption%20of%20Settlement%20Agreement%20City%20of%20Lancaster%20and%20
COS.pdf
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8. Reserve Fund. The Draft Study pro-forma analysis appears to assume that approximately $54
million (11% of total annual expenses) is contributed each year to the reserve fund, resulting in
a total accumulation of more than $597 million in 2030 (113% of total 2030 expenses). This
approach is incorrect. MRW rather set a target amount (e.g., a percent of annual expenses),
assumed 3 to 5 years to achieve the fund, and then eliminated further contributions until
replenishment is needed. MRW estimated the reserve fund to be set at 10% of the non-power
procurement expenses, plus 12% of the power procurement costs. Once this amount is
achieved, it is adjusted nominally to account for CCA cost escalation. MRW edited row 34 from
“CCA Expenses” sheet.
9. Interest earnings. The Draft Study pro-forma analysis accounts for the interest resulting from
the net annual balance. According to MRW’s methodology to evaluate the feasibility of the CCA
(explained under “Feasibility” on page 7), MRW simplified and didn’t account for any interest.
MRW edited row 45 from “CCA Operating Results” sheet.
Startup and Initial Financing Costs
MRW’s initial review of the Draft Study called out that the assumed 30-year bond financing was unusual
and the amount financed was relatively high. Because we did not offer specific alternatives, we did not
include any in our analysis. Nonetheless, as proposed, the start-up cost and financing is particularly
high.
In general, CCAs begin operations—finding executive staff, office space, etc.—using County funds. Once
they have a solid plan in place to deliver power (e.g., an implementation plan, power contractor in place,
indicative bids for power), the CCA would arrange for a short-term (5-year) loan to cover the costs
already paid for by the County, plus an amount for working capital to cover operating expenses until the
first electricity bill revenues are received. A fully-funded rate stabilization fund would not typically be
included in an initial financing; instead, the fund would be built with revenues over time. The initial
start-up costs would fall in the order of a few million dollars, with the working capital equal to about 90
days of cash flow, or $107 million for the AWG Middle of the Road Case.8 This need for cash flow
contributes to CCAs’ desire to phase in implementation.
Results of Changes
Table 1 and Figure 1 show the impacts on CCA total costs for each one of the MRW adjustments detailed
above. As Table 1 shows, using the MRW alternative assumptions, the average CCA operational costs
(i.e., the average rate it could offer while covering all costs) is approximately 24% lower, on average,
than that with the base assumptions. Nearly half of the decrease is associated with the lower
renewable power cost assumption; the bulk of the remaining reduction comes from reduced elimination
of the franchise fees as an expense (as well as a revenue) and revisions to the reserve fund. Some
changes, including the cost of natural gas generation and updates to the PCIA, modestly increased the
CCA costs.
8 This figure is 90 days working capital for the fully-implemented AWG case (i.e., after all customers had been
phased in).
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Table 1 Impact of MRW adjustments on CCA costs, AWG Middle of the Road Case
Adjustments
Average 2020-
2030 CCA costs
[$/MWh]
Change [%]
Willdan CCA costs - starting point 118.1
1. CCA renewable contracts 102.0 -13.6%
2. CCA natural gas generation 103.9 1.6%
3. Jurisdictional administration 103.8 0.0%
4. Administrative labor costs 103.5 -0.3%
5. CCA service fees 102.9 -0.5%
6. Franchise fees 95.7 -6.1%
7. PCIA escalation and 2018 update 99.1 3.0%
8. Reserve fund 90.0 -7.7%
9. Interest earnings 90.6 0.5%
MRW CCA costs (=CCA rate) 90.6 -23.2%
Based on the changes described above, the average CCA per-MWh cost obtained from the Draft Study
pro-forma has been reduced by 23% on average. Figure 1 shows the differences between both results.
The upper green line shows the CCA cost9 from the Draft Study pro-forma; the lower blue line shows the
average CCA cost with MRW modifications to the pro-forma. The average per-MWh CCA cost is higher in
2020 because the debt service is relatively constant year to year; whereas, only 30% of the CCA’s load
(MWh) is in place in 2020 due to Willdan’s assumptions about phasing in larger commercial and industrial
customers first. With fixed costs ($) spread over lower sales (MWh), the average per-MWh cost is higher
than later years when the full customer base is phased in.
9 The figures use “average CCA cost” interchangeably with “average CCA rate,” as we assume that rates will cover
costs, no matter their relation to SCE and PG&E rates.
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Figure 1 Comparison of CCA Average Cost (Rate) from Draft Study Pro-forma and MRW Edited Pro-
forma, AWG Middle of the Road Case
AWG Middle of the Road Case Rate Comparison Results
MRW used a different methodology than Willdan to assess the CCA feasibility. MRW considers a CCA
“feasible” if the CCA average per-MWh cost (i.e., average CCA rate) is lower, on average, than the
weighted average IOU generation rate.10 The MRW changes to evaluate the rate-competitiveness of the
CCA are detailed below:
10. Comparative IOU generation rates and CCA expenses. The Draft Study sets the CCA rates based
on the CCA expenses for 2022-2024 period. MRW assumes that CCA rates will be set to cover the
CCA expenses in each year. To account for our different rate-setting approach, MRW created six
new sheets “CCA IOU rates”, “PG&E RATES”, “SCE RATES”, “CCA IOU CTC+DWR”, “PG&E
CTC+DWR”, “SCE CTC+DWR” and added rows 13-17 to “CCA Operating Results.”
11. PG&E and SCE rate escalation. The Draft Study uses for the rate comparison the total IOU rates
(generation plus delivery). To forecast the generation plus delivery IOU rates, the Draft Study
uses the annual change in CCA power procurement costs. Instead, MRW only analyzes the
generation portion of the IOU rates.11 The MRW IOU generation rate forecast starts with 2018
rates from the IOUs’ 2018 ERRA applications and extends them using internally calculated
escalators.12 MRW entered the IOUs’ 2018 ERRA generation rates in cells P12:P20 from “PG&E
RATES” and “SCE RATES” sheets and the rate escalators in cells H65:S67 from “CCA IOU rates”.
10 To be consistent with the Willdan analysis, the comparison includes CTC and DWR in the IOU rate and in the CCA
expenses. Excluding both is equally valid.
11 See footnote 4.
12 The internal escalators are aligned with the CCA natural gas generation and the CCA renewable contract prices
assumed in this report.
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Table 2 compares the CCA’s average cost (i.e., generation rate) with each IOU’s generation rate
separately and as a combined weighted average for the AWG Middle of the Road Case.13 For
jurisdictions that are located in PG&E’s service area, the Average CCA Cost column can be compared to
the “Average PG&E Rate” column. Alternatively, for AWG regions located in SCE’s service area, the
Average CCA Cost column should be compared to the Average SCE Rate Column. The IOUs’ rates are
lower in 2020 because of the Draft Study assumption that larger commercial and industrial accounts are
transferred first to CCA service. Because these customers tend to have the lowest generation rates, the
CCA is having to compete with the IOUs’ lowest rate classes while facing high start-up costs. This makes
it particularly hard to compete in the first year of operations.
Table 2. Rate Comparisons ($/MWh), AWG Middle of the Road Case
Average SCE
Rate ($/MWh)
Average PG&E
Rate ($/MWh)
Weighted
Average Utility
Rate ($/MWh)
Average CCA
Cost ($/MWh)
2020 63.1 90.9 73.3 90.3
2021 71.8 103.2 79.7 86.0
2022 71.2 106.8 79.6 82.2
2023 73.9 105.3 81.5 81.9
2024 74.9 104.5 82.2 82.8
2025 75.9 97.6 81.6 83.3
2026 78.8 98.3 84.1 84.0
2027 79.6 103.8 85.9 84.8
2028 80.7 110.2 88.1 85.7
2029 81.9 117.6 90.5 86.4
2030 84.6 127.0 94.6 87.3
13 For Table 2, 3, 4, Figure 3, 4, 5, and 6, MRW didn’t include the CTC and DWR in the IOU generation rates or in the
CCA rates.
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MRW’s comparison between the IOU weighted average generation rate and the average CCA total costs
(rate) is shown in Figure 2. Through 2026, the expected IOU weighted generation rate14 (red line) is below
average CCA costs (blue line). After 2027, the expected IOU weighted generation rate is higher than the
average CCA costs, meaning the CCA may be able to offer competitive, or lower, rates after this 2027
transition point.
Figure 2 Comparison of Average CCA Cost (Rate) and Weighted Average IOU Rate, AWG Middle of the
Road Case
Figures 3 and 4 show the expected PG&E and SCE average generation rates compared to the CCA average
costs (generation rate), respectively. Because PG&E generation rates are higher than SCE generation rates,
the CCA may choose to set different rates for customers located in PG&E versus SCE service area. The CCA
is more likely to be rate-competitive—or even offer a rate savings—for CCA customers located in PG&E
territory (i.e., San Luis Obispo County and northern Santa Barbara County); whereas, the CCA is not likely
to be able to offer rates that are competitive with SCE for CCA customers located in SCE territory (i.e.,
southern Santa Barbara County and Ventura County).
14 The IOU rate depicted corresponds to generation rate plus CTC plus DWR. MRW included CTC and DWR because
both charges are included as CCA expenses.
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Figure 3. Comparison of Average CCA Cost (Rate) and PG&E Average Rate, AWG Middle of the Road
Scenario
Figure 4. Comparison of Average CCA Cost (Rate) and SCE Average Rate, AWG Middle of the Road
Scenario
As discussed on page 6, the particularly low SCE and PG&E average rates in 2020 are attributable to the
way that the original Willdan Study phased in the CCA’s customers starting with the largest commercial
customers, who also have the lowest IOU generation rates.
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Unincorporated Santa Barbara and San Luis Obispo Counties Middle of the Road Rate Comparison
Results
MRW was also asked to use the modified Willdan pro forma model to derive CCA-utility rate comparisons
assuming stand-alone CCAs covering either unincorporated Santa Barbara County or unincorporated San
Luis Obispo County. These analyses used the model changes noted above, plus reflected the load and
customer profiles of the unincorporated parts of the respective counties. The analyses did not change any
of the underlying CCA costs, which while predominantly fixed, could potentially scale downward with the
smaller CCAs.
Table 3 and Figure 5 show the results of the analysis for unincorporated Santa Barbara County. After the
first year phase-in, the Unincorporated Santa Barbara County CCA’s rates are projected to be generally
comparable to the weighted average of the SCE and PG&E rates. This is because of the large number of
PG&E accounts in the unincorporated area, where PG&E has higher generation rates relative to SCE.
Table 4 and Figure 6 show the results of the analysis for unincorporated San Luis Obispo County. After
the first-year phase-in, the Unincorporated San Luis Obispo County CCA’s rates are projected to be
generally comparable to the PG&E rates, although with a three-year period from 2025 through 2027
where the CCA rates are projected to be slightly higher than PG&E rates. This anomaly is due to the
retirement of the two Diablo Canyon Nuclear Power Plant generators, the output of which is expected to
be replaced with power that has a lower average cost than the power currently being generated by Diablo
Canyon.
Figures 5 and 6 also break down the CCA costs into the major components. This highlights the impact of
both the fixed costs and the PCIA. Because unincorporated San Luis Obispo County has smaller loads than
the AWG or unincorporated Santa Barbara County, the average fixed costs (upper teal segments of the
bar charts) are larger. Because SCE’s PCIA is lower than PG&E’s, Figure 5 shows that the green PCIA
segment of the bar charts are slightly smaller for unincorporated Santa Barbara County (which is partially
in SCE territory) than unincorporated San Luis Obispo County.
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Table 3. Rate Comparisons ($/MWh), Unincorporated Santa Barbara County Middle of the Road Case
Average SCE
Rate ($/MWh)
Average PG&E
Rate ($/MWh)
Weighted
Average Utility
Rate ($/MWh)
Average CCA
Cost ($/MWh)
2020 61.3 90.3 80.3 95.1
2021 67.8 101.3 88.1 89.7
2022 67.6 104.6 89.4 87.8
2023 70.2 103.1 89.8 87.9
2024 71.2 102.3 89.8 88.9
2025 72.1 95.6 86.5 89.5
2026 74.9 96.3 88.1 90.2
2027 75.7 101.6 91.5 91.1
2028 76.7 108.0 95.5 92.1
2029 77.8 115.2 100.0 92.8
2030 80.4 124.4 106.3 94.0
Figure 5. Rate Comparisons ($/MWh), Unincorporated Santa Barbara County Middle of the Road Case
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Table 4. Rate Comparisons ($/MWh), Unincorporated San Luis Obispo County Middle of the Road Case
Average SCE
Rate ($/MWh)
Average PG&E
Rate ($/MWh)
Weighted
Average Utility
Rate ($/MWh)
Average CCA
Cost ($/MWh)
2020 N/A 92.9 92.9 114.7
2021 N/A 106.1 106.1 105.0
2022 N/A 109.7 109.7 102.8
2023 N/A 108.2 108.2 102.5
2024 N/A 107.3 107.3 103.8
2025 N/A 100.3 100.3 104.5
2026 N/A 101.0 101.0 105.5
2027 N/A 106.6 106.6 106.7
2028 N/A 113.2 113.2 108.0
2029 N/A 120.8 120.8 109.0
2030 N/A 130.5 130.5 110.9
Figure 6. Rate Comparisons ($/MWh), Unincorporated San Luis Obispo County Middle of the Road Case
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MEMORANDUM
407.872.2467 | 200 South Orange Avenue, Suite 1550, Orlando, FL 32801 | www.willdan.com
OVERVIEW
The County of Santa Barbara (The County) forwarded to Willdan and EnerNex the above referenced peer
review prepared by MRW & Associates (MRW) dated May 31, 2017 (MRW Report). The MRW Report
identifies six recommended changes to Willdan’s pro forma analysis. Additionally, the MRW Report cites
a concern over the treatment of franchise fees and offers a recommendation concerning the need for
additional sensitivity analyses. This memorandum responds to these six suggested revisions and two
additional comments. The MRW Report also answers twelve questions posed by the AWG; this
memorandum responds to MRW’s responses to these AWG questions in the final section.
BACKGROUND
The peer-reviewed draft Study was prepared by Willdan Financial Services (Willdan), who conducted the
pro forma analysis, and EnerNex, who forecasted load and power procurement pricing. Initial Study
results found that the Central Coast Power (CCP) Community Choice Aggregation (CCA) program was
not feasible as it resulted in forecasted rate proxies1 that in most cases were higher than those of the
incumbent investor owned utilities (IOUs)—Pacific Gas and Electric (PG&E) and Southern California
Edison (SCE)—by rate class.
As noted on page 2 of the MRW review:
Unlike prior recent CCA technical studies, the Draft Study concluded that CCA was not
economically feasible even when only the state-required minimum renewable energy content
was assumed. MRW’s [sic] focused its review to identify areas where the Draft Study was
potentially overly conservative or made questionable assumptions that might explain why its
conclusion was negative while others have been affirmative.
Each of MRW’s six proposed changes, as discussed below, results in outcomes that favor CCP CCA
feasibility. Not one of MRW’s six recommended pro forma analysis changes negatively impacts CCP CCA
feasibility. Importantly, the two largest drivers of feasibility results are power pricing and IOU rate
forecasts. The former because power prices comprise nearly 70% of CCA annual operating costs; the
latter because IOU rate forecasts create the yardstick against which CCA rate proxies are measured. With
respect to the former, a large portion of Study effort was devoted to in depth load analysis using actual
data obtained from each IOU and power price forecasting as described more fully in the report and
1 The technical Study did not include rate design, rather rate proxies, the unitized revenue requirement by rate
class needed to meet the CCA programs financial obligations, were calculated based on cost of service principles.
TO: Jen Cregar
FROM: Willdan and EnerNex
DATE: August 1, 2017
RE: Response to MRW Peer Review of “Technical Feasibility Study on Community
Choice Aggregation for Central Coast Region” Draft Report Dated May 10, 2017
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appendices thereto. MRW has conducted no similar analysis. With respect to the latter, the primary scope
of the Study was modeling CCA operating costs. Although providing reference rate comparisons was part
of the scope, forecasting IOU rates was not part of the scope of work and would require significant
additional resources and cost. Even with a considerable budget devoted specifically to forecasting IOU
rates, results would at best be tenuous. IOU rates are driven by internal decision making, investor
concerns, the Public Utilities Commission, and a host of other factors in addition to wholesale power
market prices, all of which can fluctuate considerably. Lack of IOU rate forecasts is a challenge lacking
resolution that impacts all CCA feasibility studies. Willdan, therefore used publicly available information
and applied reasonable assumptions.
Willdan and EnerNex conducted an unbiased, third party review of CCP CCA feasibility. Given, as stated
on page 2 of MRW’s peer review—and included on page 1 of this memo—MRW specifically “focused its
review to identify where the draft Study was potentially overly conservative or made questionable
assumptions that might explain why its conclusion was negative,” we are concerned that the peer review
appears biased in favor of CCP CCA feasibility and caution that results based on these recommendations
may also be biased accordingly.
RESPONSE TO PEER REV IEW
1. CCA RENEWABLE POWER CONTRACTS
MRW SUGGESTION
The Draft Study’s use of utility-average renewable contract prices does not reflect the most recently-
reported contract prices and does not reflect the general downward trend in renewable prices seen over
the past few years.
WILLDAN RESPONSE
Power markets are volatile and dynamic, in particular for the regions addressed in this Study. For
example, the recent rain in California has filled the large hydroelectric reservoirs owned and
managed by both PG&E and SCE. In 2015, only 2% of SCE’s power content and 6% of PG&E’s
power content was produced by large hydroelectric resources.2 In contrast, these resources
provided 18% of electricity for PG&E and 7% of electricity for SCE in 2011.3 As a result, recent
rainfall is likely to decrease the overall portfolio cost for IOU generation. This weather-dependent
cost variable for hydroelectric generation is just one example of IOU power portfolio and retail
2 Power Content Label required by AB 162 (Statute of 2009) and Senate Bill 1305 (Statutes of 1997):
http://www.energy.ca.gov/pcl/labels/
3 Utility Annual Power Content Labels 2011:
http://www.energy.ca.gov/pcl/labels/2011_index.html
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cost volatility. Similar weather dependence applies to both sunshine and wind for renewable
generation portfolios.
Renewable Generation
The Study was initiated in the summer of 2016 using the 2016 Padilla Report,4 among other
resources; the preliminary results were released in May of 2017. The 2017 Padilla Report5 was
released in May 2017, more than four months after the Study forecast was finalized. As in any
Study of this nature, data must be analyzed as of a point in time. The forecast used in the Study
does capture the downward trend as of the forecast date and the team stands by the forecasts
presented as of the time of the Study. As discussed below, the forecast is not inconsistent with
the updated findings of the 2017 Padilla Report.
MRW cites the 2017 Padilla Report versus the Study as follows:
The weighted average prices for contracts approved in 2016 are $0.059/kWh for PG&E
and $0.061/kWh for SCE, well below the average 2016 expenditures of $0.11/kWh and
$0.094/kWh, respectively. The prices of contracts approved in 2016 are approximately
30% below the average RPS [Renewable Portfolio Standard] PPA [Purchase Power
Agreement] cost of $88/MWh [$0.088/kWh] assumed in the Report for 2020.
However, this information must be considered in light of the full set of data presented in the
report and against all trends reported. The 2017 Padilla Report notes that certain actual 2016
procurement costs increased over 2015: bundled renewable supply to $0.104/kWh from
$0.101/kWh in 2015. PG&E paid a premium for bundled RPS in 2016, an average of $0.1119/kWh.
SCE paid $0.0942/kWh that same year. SCE’s actual average cost for 2015 was revised upward to
$0.0905 from the $0.087 originally reported in the 2016 Padilla Report. The corresponding chart
in the CCP CCA study has been updated accordingly, is included below as Figure 1, and illustrates
that the RPS costs for all three IOUs are actually higher than the CCA forecast price for 2016.
4 http://www.cpuc.ca.gov/uploadedFiles/CPUC_Website/Content/Utilities_and_Industries/Energy/
Reports_and_White_Papers/Padilla%20Report%202016%20-Final%20-%20Print.pdf
5 http://www.cpuc.ca.gov/uploadedFiles/CPUCWebsite/Content/About_Us/Organization/Divisions/
Office_of_Governmental_Affairs/Legislation/2017/Final%20-%20Padilla%20Report%20-%20RPS%20Costs%202
017.pdf
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Figure 1 IOU RPS compliance cost.6
With significant solar generation growth in California, from both utility scale and distributed
customer owned photovoltaic resources, solar generation output sometimes exceeds electricity
demand during periods of peak solar output. California is entering an over-capacity condition for
solar generation during certain daylight periods which means that additional solar generation
capacity is not needed and that solar is no longer displacing fossil fuel generation. This over-
capacity condition results in negative pricing in the CAISO day-ahead and real-time markets
during periods when excess solar production exceeds demand. Battery energy storage is one
6 The basis of the renewable RPS cost analysis included data from the May 2016: Report on 2015 Renewable
Procurement Costs in Compliance with Senate Bill 836 (Padilla, 2011) Table A -2 Weighted Average TOD-Adjusted
RPS Procurement Expenditures (Bundled Energy Only) for 2015
http://www.cpuc.ca.gov/uploadedFiles/CPUC_Website/Content/Utilities_and_Industries/Energy/Reports_and_W
hite_Papers/Padilla%20Report%202016%20-Final%20-%20Print.pdf; Subsequent to the analysis an updated
report was produced and the data was consistent with the forecast analysis previously performed: May 2017:
Report on 2015 Renewable Procurement Costs in Compliance with Senate Bill 836 (Padilla, 2011) Table B -2
Weighted Average RPS Procurement Expenditures (Bundled Energy Only) for 2016
http://www.cpuc.ca.gov/uploadedFiles/CPUCWebsite/Content/About_Us/Organization/Divisions/Office_of_Gove
rnmental_Affairs/Legislation/2017/Final%20-%20Padilla%20Report%20-%20RPS%20Costs%202017.pdf.
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technology being pursued to help mitigate this overcapacity challenge. For reference, the LA
Times article: “California invested heavily in solar power. Now there's so much that other states are
sometimes paid to take it,”7 provides a clear discussion of this situation.
Natural Gas Generation
The supply cost for natural gas generation used in the Study incorporated two factors: 1) a
decreasing cost for the natural gas commodity as a result of increasing supplies from shale gas
and fracking; and 2) an improved heat rate efficiency for natural gas electric generation.
However, the cost of natural gas is also volatile as illustrated in the corresponding figures
“California natural gas generation cost based on natural gas price and heat rate conversion” and
“Natural gas generation supply cost” in the Study. The curve fitting regression analysis in the
“Natural gas generation supply cost” is an averaging and flattening of the recent natural gas
generation cost trend with actual historical prices being both above and below the cost forecast.
The Monte Carlo simulation model estimates the corresponding volatility of natural gas prices
($/MWh) based on the 2002-2016 data source.
CCA Renewable Power Contracts
The 2016 approved contracts referenced in the 2017 Padilla Report are primarily for supplies that
will be provided in the future, and likely after 2020, for deals entered today. Given the dynamic
nature of this market, prices may move in either direction. The forecast used in the Study stands
as reasonable.
Summary Comments
Finally, MRW indicates that the Study is over-estimating the cost of future renewables and
under-estimating the cost of natural gas generation. Although MRW suggests that we revise
downward the renewables forecast, it does not similarly suggest that we also revise upward the
natural gas generation price forecast. This one-sided recommendation further evidences a bias
towards a feasible outcome, which must be rejected.
Exhibit A hereto presents the results of sensitivity analyses conducted against Participation
Scenario 2: Advisory Working Group (AWG) Jurisdictions – Middle of the Road scenario that
illustrate the impact of changes in power costs to feasibility results. Demonstrating that, all other
assumptions held constant, a 40% reduction in power costs is required to achieve rate proxies
lower than both IOUs.
7 L.A. Times “California invested heavily in solar power. Now there's so much that other states are sometimes paid to
take it” by Ivan Penn, June 22, 2017: http://www.latimes.com/projects/la-fi-electricity-solar/
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2. UNCOLLECTABLE EXPENS ES
MRW SUGGESTION
a) The Study assumed from 5% to 8% of the revenues due to the CCA from its customers could not
be collected. This is an order-of-magnitude higher than that experienced by either MCE Clean
Energy (MCE), the longest-running CCA in the state, or Sonoma Clean Power (SCP), the second
longest-running CCA in the state.
b) CCAs do not observe the same level of uncollectible accounts as the IOUs due because CCAs are
allowed to return non-paying accounts to the corresponding IOU’s bundled service.
WILLDAN RESPONSE
a) The Study assumption was based on the actual filings by PG&E and SCE using the ratio of
Uncollectable Account allowance to total Receivables. In response to MRW’s suggestion,
additional research was conducted that revises this assumption.
In the 2014 General Rate Case Decision 14-08-0321, the California Public Utilities Commission
(Commission or CPUC) adopted a revised methodology to determine PG&E’s uncollectibles
factor, which is based on a 10-year rolling average using recorded uncollectible data. The 2015
uncollectibles factor using historical data from 2004 through 2013 is 0.003325.SCE’s authorized
uncollectibles factor for 2010 and 2011 was 0.00240 and for 2012 to 2013 was 0.00204. However,
SCE’s actual uncollectible expense exceeded the authorized amount in each of these years and
exhibits an increasing trend.
Based on these analyses, Willdan agrees that it makes sense to revise the pro forma assumption
to reflect the actual expense set by the CPUC for PG&E of 0.3325%; this factor has been applied
to both IOUs. Revision of this assumption in isolation does not materially impact forecasted
feasibility outcomes.
b) Willdan does not concur with MRW’s assertion in practice nor in principle. Although a CCA is
technically allowed to return clients to the IOU for non-payment, such treatment appears to
conflict with the CCA’s role in the public power paradigm. CPUC Code Section 366.2(c)(3) lists
requirements for CCAs that indicate if a public agency seeks to serve as a CCA, it shall offer the
opportunity to purchase electricity to all residential customers within its jurisdiction.
Furthermore, for purposes of a feasibility study, such an assumption defies industry standards
and practice and is, therefore, indefensible.
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3. ADMINISTRATIVE LABOR COSTS
MRW SUGGESTION
The number of employees assumed in the pro forma analyses, as well as their compensation, appear high
relative to operating California CCAs.
WILLDAN RESPONSE
Willdan based its labor analysis on the regional labor markets and a functional analysis of
required positions. Figure 2 below demonstrates the level of staffing is reasonable when
compared to other CCAs.8 Labor costs include benefits.
Figure 2: CCA Staffing Comparison
Figure 3 below illustrates the size of the CCP CCA relative to other currently operating CCAs by
Participation Scenario, illustrating the extreme range between scenarios assessed. Staffing
8 Based on Participation Scenario 2: AWG Jurisdictions.
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assumptions are adjusted by scenario and range from a low of 24 for Participation Scenario 8:
City of Santa Barbara and a high of 57 for Participation Scenario 1: All Tri-County Region.
Figure 3: Summary of CCA Size (GWh and Customer Accounts)
Willdan conducted sensitivity analyses concerning staffing levels. Exhibit B hereto presents the
results of this sensitivity analysis. Decreasing staffing by over 70% in isolation did not materially
alter feasibility outcomes.
4. CCA SERVICE FEES
MRW SUGGESTION
a) The incumbent utilities—Southern California Edison (SCE) and Pacific Gas and Electric (PG&E)—
charge CCAs in their respective territories certain fees for billing conducted on behalf of the CCA
as well as meter and data management. While the Draft Study reflects current tariffed rate for
these services, it does not account for the proposed dramatic uncontested reductions being
presented by both utilities.
b) Similarly, it is unclear whether the ESP service fees section of the Draft Study properly accounts
for critical operational services such as data management and scheduling coordination.
WILLDAN RESPONSE
a) As noted by MRW, the Study relies upon current tariffed rates for CCA Service Fee at the time
of the Study. No other assumption concerning pending proposals would be defensible.
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b) The Study adequately accounts for all required CCA functions as more fully described in the
report.
5. ASSUMED RESERVE FUND ING
MRW SUGGESTION
Beyond working capital, CCAs typically develop a “rate stabilization reserve fund” which can be drawn
upon in years’ where the CCA might not otherwise be able to meet its rate targets. The Draft Study pro
forma analysis appears to assume that approximately $78 million (14% of total expenses) is contributed
each year, rather than setting a target (e.g., 15% of annual expenses), taking 3 to 5 years to achieve the
fund, and then eliminate further contributions until replenishment is needed.
WILLDAN RESPONSE
A contingency fund is budgeted for unanticipated occurrences over the course of a year. The pro
forma assumes that each year a certain amount is set aside to cover unanticipated increases in
operating costs. In the most recent version of the pro forma, the annual amount set aside for the
rate stabilization fund was lowered to 12% of power costs (previously 17%). The contingency fund
remains at 10% of non-power O&M. Usage of the contingency fund was not modeled—there are
no withdrawals—so MRW’s assumption that the fund continues to grow is incorrect. The purpose
of the contingency fund is to provide adequate funding given a reasonable increase in operating
costs; given that the opt-out rate was set conservatively high and power procurement costs can
fluctuate significantly, it should be assumed that the contingency fund will be used.
Altering the level of contingency and reserve funding (while maintaining reasonable levels) in
isolation would not materially alter feasibility outcomes.
6. PG&E AND SCE RATE FO RECASTS
MRW SUGGESTION
A fundamental concern is that the forecast of SCE and PG&E rates is disconnected from the forecast of
CCA rates. The utility rates against which the CCA rates are compared are simply the current rates
escalated at 0-0.5%. It does not account for: (i) SCE’s or PG&E’s actual supply portfolio, (ii) the two
utilities’ status with respect to State’s renewable power content mandates, (iii) fuel price trends, or (iv)
any other underlying fundamentals. In particular, there is no explicit connection between the utilities’
generation rates and the CCA generation cost, even though they would be purchasing from the same
wholesale market and vying for the same incremental renewable generation sources.
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WILLDAN RESPONSE
In the prior section MRW contends that the renewable rates for the IOUs for 2016 are high and
not representative of the market from which the CCA would be purchasing. However, here MRW
contends that there should be an explicit connection between the IOU generation rates and the
CCA generation cost. Renewables are currently the most expensive resource in the IOUs’ supply
portfolio. On the one hand MRW contends that CCA prices for renewables should be much lower
than the IOUs are currently paying, but at the same time that IOU rates and CCA rates should be
connected. This appears to be contradictory, and depending on interpretation, could bias results
in favor of feasibility.
As noted in the Background section of this memorandum, forecasting IOU rates was not part of
the scope of work of this Study. Additionally, lack of insight into IOU rate forecasts is a challenge
faced by all CCAs. Furthermore, CCAs compete only on the energy-related component of rates.
CCA and IOU bundled service customers alike pay the delivery portion of the IOU bill which
covers transmission and distribution. Additionally, CCA customers pay an exit fee to reimburse
the IOU for generation related costs “stranded” when the CCA load leaves the IOU—i.e., the Cost
Recovery Surcharge (CRS), in particular the Power Charge Indifference Adjustment(PCIA). When
discussing rate forecasts and escalations, the non-energy component of IOU rates could escalate
by 15%, and not impact Study outcomes (independent of other potential adjustments to Study
assumptions) because both CCA and non-CCA customers would pay that increase.
As discussed in more detail with the following tables and figures, Willdan has demonstrated that
both PG&E and SCE have, over the last few years, been moving more of the revenue requirement
from generation to transmission and distribution costs—in other words shifting costs to the fixed
delivery charge paid by both CCA and non-CCA customers. Table 1 shows historical energy and
delivery charges for SCE for the Residential rate class since 2014, for the baseline consumption.
Overall for this period, the delivery charge has increased 89% while the energy component has
decreased 13%.
Table 1: SCE Rate Changes Since 2014, Residential Baseline
2014 2015 2016 2017
% Change
2014-2017
RESIDENTIAL, Baseline Usage
Basic Service Fee $/Meter/Month 0.94292 0.94292 0.94292 0.94292
Energy
Summer $/kWh 0.08555 0.0899 0.06887 0.07477
Winter $/kWh 0.08555 0.0899 0.06887 0.07477
Increase/Decrease 5%-23%9%-13%
Delivery
Summer $/kWh 0.04678 0.0586 0.08221 0.0884
Winter $/kWh 0.04678 0.0586 0.08221 0.0884
Increase/Decrease 25% 40%8%89%
California Climate Credit $0.00 ($4.83) ($6.33) ($5.17)
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Table 2 and Table 3 on the following pages show the historical rate changes occurring for the
Medium and Large Commercial classes, respectively. Overall for this period, the delivery charges
increased and the generation charges decreased for both classes.
Table 2: SCE Rate Changes Since 2014, Medium Commercial
2014 2015 2016 2017
% Change
2014-2017
GENERAL SERVICE, TOU-GS-3
Basic Service Fee $/Meter/Month 444.790 441.930 493.360 446.130
Increase/Decrease -1%12%-10%0%
Energy
Summer
On-Peak $/kWh 0.30087 0.33132 0.23913 0.28916
Increase/Decrease 10%-28%21%-4%
Mid-Peak $/kWh 0.10158 0.1119 0.08078 0.08281
Increase/Decrease 10%-28%3%-18%
Off-Peak $/kWh 0.03227 0.03555 0.02568 0.03226
Increase/Decrease 10%-28%26%0%
Winter
Mid-Peak $/kWh 0.05581 0.06148 0.04537 0.04662
Increase/Decrease 10%-26%3%-16%
Off-Peak $/kWh 0.03681 0.04055 0.02927 0.03712
Increase/Decrease 10%-28%27%1%
Voltage Discount, Energy
50kV<220kV $/kW (0.00404) (0.00440) (0.00320) (0.00461)
Increase/Decrease 9%-27%44%14%
Delivery
Summer
On-Peak $/kWh 0.02332 0.02691 0.02557 0.02718
Increase/Decrease 15%-5%6%17%
Mid-Peak $/kWh 0.02332 0.02691 0.02557 0.02718
Increase/Decrease 15%-5%6%17%
Off-Peak $/kWh 0.02332 0.02691 0.02557 0.02718
Increase/Decrease 15%-5%6%17%
Winter
Mid-Peak $/kWh 0.02332 0.02691 0.02557 0.02718
Increase/Decrease 15%-5%6%17%
Off-Peak $/kWh 0.02332 0.02691 0.02557 0.02718
Increase/Decrease 15%-5%6%17%
Demand Charges
Facilities Related $/kW $16.14 $16.07 $18.45 $17.81
Increase/Decrease 0% 15%-3%10%
Voltage Discount, Demand
Facilities Related
50kV<220kV $/kW (6.76000) (6.71000) (7.46000) (6.79000)
Increase/Decrease -1%12%-21% -12%
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Table 3: SCE Rate Changes Since 2014, Large Commercial
Unfortunately, this type of historical delivery data was not available for PG&E; PG&E does not
post historical tariffs on its website and provides only bundled data for previous years’ rates.
However, the California Public Utilities Commission April 2016 report entitled “Electric and Gas
Utility Cost Report” provides illustrative data comparisons between the rates and Revenue
Requirements of the three state IOUs: PG&E, SCE, and San Diego Gas and Electric (SDG&E).
Information from that report has been inserted into this memo for discussion purposes.
2014 2015 2016 2017
% Change
2014-2017
GENERAL SERVICE-LARGE, TOU-8-Option B
Basic Service Fee $/Meter/Month 321.60 319.47 356.41 303.25
Increase/Decrease -1%12%-15%-6%
Energy
Summer
On-Peak $/kWh 0.10485 0.11445 0.08309 0.07072
Increase/Decrease 9%-27% -15% -33%
Mid-Peak $/kWh 0.05449 0.05948 0.04318 0.04730
Increase/Decrease 9%-27%10%-13%
Off-Peak $/kWh 0.03241 0.03537 0.02568 0.03165
Increase/Decrease 9%-27%23%-2%
Winter
Mid-Peak $/kWh 0.05616 0.06130 0.04451 0.04579
Increase/Decrease 9%-27%3%-18%
Off-Peak $/kWh 0.03738 0.04081 0.02963 0.03645
Increase/Decrease 9%-27%23%-2%
Demand Charges
Time Related
Summer
On-Peak $/kW 28.23 30.81 22.38 22.55
Increase/Decrease 9%-27%1%-20%
Mid-Peak $/kW 0.00 0.00 0.00 3.63
Increase/Decrease 0%0%N/A
Delivery
Summer
On-Peak $/kWh 0.02162 0.02463 0.02331 0.02426
Increase/Decrease 14%-5%4%12%
Mid-Peak $/kWh 0.02162 0.02463 0.02331 0.02426
Increase/Decrease 14%-5%4%12%
Off-Peak $/kWh 0.02162 0.02463 0.02331 0.02426
Increase/Decrease 14%-5%4%12%
Winter
Mid-Peak $/kWh 0.02162 0.02463 0.02331 0.02426
Increase/Decrease 14%-5%4%12%
Off-Peak $/kWh 0.02162 0.02463 0.02331 0.02426
Increase/Decrease 14%-5%4%12%
Demand Charges
Facilities Related $/kW 11.64 14.88 16.89 18.34
Increase/Decrease 28% 14%9%58%
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Figure 4 shows the overall rate levels for the three California IOUs for 2015 and the component
parts. SCE and SDG&E appear to have about half of their rates attributable to the generation
component, with PG&E having more than half, estimated around 60%.
Figure 4: From CPUC, 2015 Rate Components for the Three California IOUs
Table 4 shows that in 2015 for PG&E, Distribution and Transmission account for approximately
44% of its total Revenue Requirement, in line with SCE at 43% and SDG&E at 44%. Generation
accounts for 48% of its Revenue Requirement, in line with SCE at 48% and higher than SDG&E
at 40%.
Table 4: From CPUC, 2015 Electric IOU Revenue Requirements ($000)
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Figure 5 and Figure 6 show transmission and distribution Revenue Requirements over time,
which have been more or less consistently growing for each of the three IOUs since 2005.
Figure 5: From CPUC, Trends in Transmission Revenue Requirements for the Three California IOUs
Figure 6: From CPUC, Trends in Distribution Revenue Requirements for the Three California IOUs
Figure 7 shows the generation Revenue Requirements over time; year 2015 generation Revenue
Requirements are lower than 2014 and currently near the 2011 levels.
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Figure 7: From CPUC, Trends in Generation Revenue Requirements for the Three California IOUs
Assuming PG&E follows the combined trends for the three utilities, this data would indicate that
transmission and distribution is making up a larger portion of the total Revenue Requirement for
the utility. This would, theoretically, justify a higher fixed component of rates—shifting revenues
from generation-related charges to delivery-related charges.
On April 14, 2017 Lancaster Choice Energy (LCE) filed a protest against SCE claiming
inappropriate shifting of generation related costs into the distribution component, and thus to
CCA customers.9 LCE’s filing supports the analysis presented above and the trend of cost shifting
to the distribution portion of the electric bill, reducing the margin against which the CCA
competes.
In addition, Exhibit C provides the results of sensitivity analyses of CCA results against rate
escalation relative to the IOUs.
7. FRANCHISE FEE TREATM ENT
MRW SUGGESTION
We are also concerned that the Draft Study assumes that the franchise fees (i.e., utility taxes) that would
flow to the respective cities’ and counties’ general funds if SCE or PG&E were providing service is
assumed to instead flow to the CCA. This treatment should be verified by the AWG or corrected.
9 Protest of Lancaster Choice Energy in the Application of Southern California Edison Company (U 338 -E) for
Approval of its Proposal to Implement Residential Default Time-Of-Use Rates, Application No. 17-04-015.
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WILLDAN RESPONSE
Franchise fees are generally not collected by public power entities. General fund transfers,
payments in lieu of taxes, or payments in lieu of franchise fees are typically made by a public
power entity. Ultimately, treatment of franchise fees would be a policy decision determined by
the participating jurisdictions.
Willdan has removed flowback of the franchise fees to the CCA. This change in isolation did not
alter feasibility results materially.
8. ADDITIONAL ANALYSES
MRW SUGGESTION
Lastly, we recommend that sensitivity cases used to explore the impact of lower SCE and PG&E rates
and higher exit fees consider a wider range of potential values.
WILLDAN RESPONSE
The sensitivities and supporting analyses conducted adequately bound the realm of outcomes
and exceed the contracted scope of services.
MRW RESPONSE TO QUESTIONS
The MRW Report answered twelve questions posed by the AWG. Willdan’s responses to this material
follow.
QUESTION 1: DOES THE STUDY CONSIDER ALL PERTINENT FACTORS TO
DETERMI NE CURRENT AND FUTUR E ELECTRIC ENERGY RE QUIREMENTS OF THE
CCA?
MRW RESPONSE TO QUES TION 1
MRW finds the analyses reasonable.
WILLDAN RESPONSE
No response required.
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QUESTION 2: DOES THE STUDY INCORPORATE C URRENT POWER MARKET
CONDITIONS AND REASO NABLE PROJECTIONS OF EXPEC TED FUTURE
CONDITIONS?
MRW RESPONSE TO QUES TION 2
Renewable Energy Procurement
MRW finds the analysis overestimates the cost of renewable energy and is unable to determine the
reasonableness of the Monte Carlo Simulation results.
Natural Gas Generation
MRW finds the analysis underestimates the cost of natural gas generation and is unable to determine
the reasonableness of the Monte Carlo Simulation results.
Other Cost Components
MRW finds study results reasonable.
WILLDAN RESPONSE
These items are addressed in other sections of this memorandum. No additional response
required.
QUESTION 3: ARE THE ESTIMATES OF THE GHG EMISSIONS I NTENSITY OF THE CCA
SCENARIOS RELATIVE T O THE INCUMBENT INVE STOR -OWNED UTILITIES (IOUS),
NAMELY PACIFIC GAS A ND ELECTRIC COMPANY (PG &E) AND SOUTHERN
CALIFORNIA EDISON (S CE), REASONABLE AND ADEQUATE?
MRW RESPONSE TO QUES TION 3
MRW finds the analyses reasonable.
WILLDAN RESPONSE
No response required.
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QUESTION 4: DOES THE DRAFT STUDY CONSIDE R ALL PERTINENT FACT ORS IN
PROJECTING FUTURE PG &E AND SCE RATES FOR COMPARISON TO CCA
COSTS/PAYMENT/RATE P ROJECTIONS ?
MRW RESPONSE TO QUES TION 4
Error in Current IOU Rates
MRW identifies an anomaly in load data, based on demand factors, for medium and large commercial
and industrial customers for PG&E and SCE.
a) IOU Rates Forecasts
i. MRW finds that the IOU rate forecast used in the Study is not consistent with a forecast of PG&E
rates prepared by MRW in March 2017 for the Contra Costa CCA Feasibility Study that predicts
PG&E annual changes as follow: an increase of 1.5% per year for 2017 to 2022; a decrease of
1.5% per year from 2023 to 2025; and annual increases of 5% thereafter.
ii. The Draft Study extends its calculated escalator for generation rates to non-generation rates.
This is concerning because there is no direct relation between the cost drivers for generation
and non-generation utility services.
WILLDAN RESPONSE
Error in Current IOU Rates
The demand level data anomalies resulted from the raw data set used in the load analysis. These
anomalies were being researched parallel to MRW’s review. The analysis presented in the final
report uses demand proxies to rectify this issue. This issue does not impact load forecasts used
in the Study, rather it results from attempting to retro-fit load forecasts into current IOU rate
structures.
a) IOU Rates Forecasts
i. Willdan, lacking access to the underlying data and analysis, cannot verify MRW’s
forecast. MRW claims the forecast is based on PG&E’s actual generation resources,
however it is not clear what portion of the rate escalation is associated with generation
assets that would ultimately be included in the PCIA charge and thus recovered from CCA
customers. Some, or all, of the PG&E escalation could appear not in the energy portion
of PG&E rates but instead be allocated to the PCIA component, that applies only to CCA
customers. The forecast is not consistent with the rate of change in PG&E’s Green Tariff
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Shared Renewables 20-year Rate Forecast—Feb. 2017, page 8 of pdf10–which is the only
long-term forecast publicly available.
The approach used in the Study is reasonable and consistent.
ii. The rate escalation applied to the non-generation portion of rates applies equally to CCA
and non-CCA customers and therefore the impact cancels out, having no impact on
Study outcomes.
QUESTION 5: DOES THE DRAFT STUDY CONSIDE R ALL PERTINENT FACT ORS IN
PRESENTING A REASONA BLY ACCURATE INVESTO R -OWNED UTILITY (IOU ) VS. CCA
COST/PAYMENT COMPARI SON?
MRW RESPONSE TO QUES TION 5
MRW’s concern is that it is not clear that the same delivery rate (and escalation) was used for both IOU
and CCA rates.
WILLDAN RESPONSE
The same delivery rate and escalation was used for both CCA and IOU customers, thus
canceling out.
QUESTION 6: DO THE P RO FORMA ANALYSES CO NSIDER ALL PERTINENT FACTOR S
IN PROJECTING CCA’S OPERATING RESULTS?
MRW RESPONSE TO QUES TION 6
Franchise Fees
i. MRW believes the Study may be treating franchise fees incorrectly by flowing them back to the
CCA.
ii. MRW believes the level of SCE franchise fees is incorrect.
Power Costs
MRW finds it difficult to assess the reasonableness of the Monte Carlo simulation model analyses based
on information presented in the report.
10 PG&E Green Tariff Shared Renewables 20 Year Rate Forecast:
https://www.pge.com/pge_global/common/pdfs/solar-and-vehicles/options/solar/Forecast.pdf
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Other Operating Costs
Salaries and Wages
MRW suggests the Study decrease both the number of FTEs and the salary costs.
IOU Service Charges
MRW suggests the Study decrease the charges below current IOU tariff rates based on the
expectation that these charges will decrease or be reduced in the future.
ESP Charges
MRW concedes that the fee used in the Study is reasonable assuming it includes Scheduling
Coordination.
Jurisdictional Administration Charges
MRW recommends that these costs be removed from CCA operating expenses.
Uncollectable Account Charges
MRW recommends that these costs be reduced to 0.5% based on rates experienced by operating
CCAs.
PCIA
MRW recommends sensitivity analyses around the level of the PCIA be conducted.
Non-Operating Costs
MRW takes issue with the Study’s assumptions around contingency funding and financing assumptions.
Pro Forma Results and Rate Comparisons
MRW concurs that the CCP CCA is infeasible for two reasons: 1) the IOU average rate is lower that the
CCA average rate; and 2) the CCA average rate does not cover costs starting in year 2026. MRW cites
Contra Costa CCA study results that indicate power costs are 82% of total costs, PCIA charges are 13%
and other costs are 6%. MRW claims that other non-power costs comprise 47% of Study costs.
WILLDAN RESPONSE
Franchise Fees
i. The treatment of franchise fees has been revised as discussed in this memorandum under
the response to Item No. 7.
ii. Based on the tariff applicable to CCAs, SCE’s franchise fees are correct.
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Power Costs
Exhibit D provides a memorandum concerning the Monte Carlo simulation prepared for the
AWG.
Other Operating Costs
Salaries and Wages
Refer to the response to Item No. 3.
IOU Service Charges
Refer to the response to Item No. 4.
ESP Charges
Willdan confirms that the ESP charges include Scheduling Coordination.
Jurisdictional Administration Charges
These charges are for external CCA coordinators located at member sites or to reimburse
members for use of FTEs performing coordination efforts needed to facilitate CCA
operations. These charges represent an additional labor requirement for members resulting
from creation of the CCA and are not captured elsewhere. Willdan does not concur with
removing such costs from CCA operating expenses but also notes that such costs in isolation
are immaterial to feasibility Study results.
Uncollectable Account Charges
Refer to the response to Item No. 2.
PCIA
Refer to the response to Item No. 8.
Non-Operating Costs
With respect to a contingency/rate stabilization fund, MRW incorrectly asserts that the Study
would accumulate $778M in contingency funds by 2030 (refer to Figure 3). Contingency funds are
intended to cover unanticipated events. Therefore, the Study prudently includes a contingency
amount in yearly budgeted amounts and assumes such funding is used to routinely cover power
cost fluctuations and other expenditures in excess of budgeted amounts. It is an erroneous belief
that such amounts would accrue in an account over time.
Pro Forma Results and Rate Comparisons
Willdan finds it difficult to respond to MRW’s cited percentages absent understanding what items
are included in cited amounts and the basis of comparison.
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For this Study, power costs represent approximately 70% of operating expenses—97% when
adding IOU service charges, the CRS component, and franchise fees—leaving other non-power
and non-IOU costs totaling approximately 3% of operating expenses.
Given that CCA rates were higher than the IOU rates in the first five years, no further adjustment
was made to CCA rates in outer years as the enterprise was deemed infeasible.
QUESTION 7: DO YOU HAVE ANY OTHE R SUGGESTIONS FOR RE DUCING CCA COS TS
IN LIGHT OF THE EVOL VING CALIFORNIA CCA MARKET PLACE?
MRW RESPONSE TO QUES TION 7
MRW’s suggestions appear in its responses to Questions 4, 5, and 6.
WILLDAN RESPONSE
Refer to Willdan’s responses to Questions 4, 5, and 6.
QUESTION 8: DOES THE DRAFT STUDY PRESENT AN ADEQUATE ANALYSIS OF
POTENTIAL ECONOMIC B ENEFITS AND CHALLENG ES OF VARIOUS SUPPLY
SCENARIOS? AND
QUESTION 9: SHOULD A NY ADDITIONAL BENEFI TS OR CHALLENGES BE
CONSIDERED?
MRW RESPONSE TO QUES TIONS 8 AND 9
MRW believes that the Study failed to model the negative indirect and induced effects canceling out
the benefits of local projects.
WILLDAN RESPONSE
Willdan believes that the entities involved are rational economic actors that would not proceed
with an infeasible enterprise and therefore no negative economic impacts would be realized.
QUESTION 10: DOES TH E DRAFT STUDY PROVID E A THOROUGH EVALUAT ION OF
THE PROSPECTIVE CCA’S ABILITY TO ACHIEVE RATE COMPETITIVENES S WITH PG&E
AND SCE? WHAT OTHER FACTORS, IF ANY, SHO ULD BE CONSIDERED?
MRW RESPONSE TO QU ESTION 10
MRW suggests additional sensitivities should have been run.
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WILLDAN RESPONSE
Refer to Willdan’s response to Item No. 8.
QUESTION 11: DOES TH E DRAFT STUDY CONSID ER ALL PERTINENT FAC TORS TO
ASSESS THE OVERALL C OST -BENEFIT POTENTIA L OF CCA?
MRW RESPONSE TO QUESTION 11
MRW has no additional factors to include.
WILLDAN RESPONSE
No additional response is needed.
QUESTION 12: DOES TH E DRAFT STUDY CONSID ER ALL PERTINENT RIS K FACTORS
INVOLVED WITH ESTABL ISHMENT AND OPERATIO N OF THE CCA PROGRAM , AND
ARE SUCH FACTORS PROPER LY WEIGHTED AND ANAL YZED?
MRW RESPONSE TO QUES TION 12
MRW finds the Study addressed all pertinent risk factors.
WILLDAN RESPONSE
No additional response is needed.
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EXHIBIT A
Original analysis conducted in May 2017; revised in August 2017 to reflect changes incorporated into the
final report.
POWER PROCUREMENT CO ST COMPARISON RESULT S
At the request of the AWG, all sensitivity analyses considered the AWG Jurisdictions Middle of the Road
scenario against changes in key input assumptions, including power procurement costs, staffing costs,
and IOU rate escalation. This Exhibit A presents the results of the power procurement cost sensitivity
analyses. Table A-1 depicts the difference in average power procurement costs between the AWG Middle
of the Road scenario and the 30% decrease in power procurement costs and 40% decrease in power
procurement costs sensitivity cases.
Table A-1: Average Power Procurement Costs, AWG Jurisdictions -
Middle of the Road Scenario, with 30% Decrease in Power Procurement Costs,
and with 40% Decrease in Power Procurement Costs
AWG Jurisdictions Middle of the
Road Scenario
Year
Original
Power
Procure
ment
Cost
($ per
MWh)
With
Power
Procure
ment
Cost
30%
Lower
($ per
MWh)
With
Power
Procurem
ent Cost
40%
Lower
($ per
MWh)
2020 74.54 52.18 44.72
2021 74.81 52.37 44.89
2022 73.55 51.48 44.13
2023 74.33 52.03 44.60
2024 72.80 50.96 43.68
2025 71.73 50.21 43.04
2026 71.69 50.18 43.01
2027 70.93 49.65 42.56
2028 70.56 49.39 42.34
2029 69.18 48.43 41.51
2030 68.64 48.05 41.18
Table A-2 presents the AWG Middle of the Road scenario average rate comparisons between the CCA
and PG&E and SCE over the rate comparison period of 2022 through 2026. Tables A-3 and A-4 present
this information for the 30% decrease in power procurement cost and 40% decrease in power
procurement cost cases, respectively.
As shown in Table A-3, the 30% decrease in power procurement costs results in CCA rate proxies that are
still not below both PG&E and SCE. The average rates for the CCA are between 2.93% and 4.51% higher
than PG&E and between 7.26% and 8.91% higher, depending on the year. While the premium across the
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EXHIBIT A
POWER PROCUREMENT COST COMPARISON RESULTS
classes between the CCA and the SCE has gone down over the AWG Middle of the Road scenario, shown
in Table A-2, the CCA power procurement costs still need to be even lower to be competitive with either
IOU.
Table A-4 shows that CCA rate proxies become competitive against both PG&E and SCE once power
procurement costs are decreased for the CCA by 40%. Compared to PG&E rates, a CCA rate proxy savings
(CCA customer pay less) of between 4.34% and 5.79%, results depending on the year. Compared to SCE
rates, a CCA rate proxy savings of between 2.01% and 3.50% results.
Table A-2: Rate Comparisons, Participation Scenario 2: AWG Jurisdictions - Middle of the Road Scenario
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EXHIBIT A
POWER PROCUREMENT COST COMPARISON RESULTS
Table A-3: Rate Comparisons Participation Scenario 2: AWG Jurisdictions - Middle of the Road, with Power Price Forecast
Sensitivity set at -30%
Table A-4: Rate Comparisons, Participation Scenario 2: AWG Jurisdictions - Middle of the Road, with Power Price Forecast
Sensitivity set at -40%
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EXHIBIT A
POWER PROCUREMENT COST COMPARISON RESULTS
Tables A-5 and A-6 show the operating results for the AWG Middle of the Road scenario and the 40%
decrease in power procurement costs sensitivity, respectively.
Table A-5: Operating Results, AWG Jurisdictions Middle of the Road Scenario
Table A-6: Operating Results, Participation Scenario 2: AWG Jurisdictions - Middle of the Road, with Power Price Forecast
Sensitivity set at -40%
Overall, financial performance is similar between the cases, with a sustained period of negative net
margins Iasting through 2023, followed by a few years of positive net margins (from 2024 to 2026 in the
AWG Middle of the Road scenario and 2024 to 2025 in the sensitivity), and then negative net margins for
all remaining years of the study period. The net present value of net margins is $108 million in the 40%
Year
Operating
Revenues
($000s)
Total Operating
Expenses Plus
Contingency/ Rate
Stabilization Fund
($000s)
Non-Operating
Revenues/
(Expenses)
($000s)
Debt Service
($000s)
Net Margin1
($000s)
Working Capital
Fund ($000s)
Working Capital
Target ($000s)
Working Capital
Surplus/
(Deficiency)
($000s)
Working
Capital
Surplus/
(Deficiency)
(%)
a b c d a - b + c - d e f e - f (e/f)-1
2020 117,525 150,875 1,235 12,330 (44,445) 223,724 50,583 173,141 342%
2021 472,491 504,655 2,323 12,330 (42,170) 193,883 170,117 23,766 14%
2022 579,072 568,848 2,082 18,499 (6,192) 187,691 192,494 (4,803) -2%
2023 590,222 575,366 2,044 18,499 (1,600) 186,092 194,836 (8,745) -4%
2024 590,817 570,966 1,962 18,499 3,314 189,406 194,067 (4,662) -2%
2025 588,906 566,609 2,098 18,499 5,896 195,302 193,284 2,019 1%
2026 587,918 570,586 2,132 18,499 966 196,268 195,171 1,096 1%
2027 586,991 571,282 2,109 18,499 (681) 195,587 196,227 (640) 0%
2028 586,831 576,506 1,991 18,499 (6,182) 189,405 198,875 (9,470) -5%
2029 584,330 574,978 2,033 18,499 (7,113) 182,292 199,652 (17,361) -9%
2030 582,330 581,643 1,541 18,499 (16,270) 166,022 203,279 (37,257) -18%
NPV of Net Margin:(100,693)
1 Net Margin includes Net Operating Income less Debt Service. The net present value (NPV) of the
Net Margin is determined using a 4% discount rate and is as of Year 2020. The discount rate
is equal to the interest rate on the long-term debt.
Year
Operating
Revenues
($000s)
Total Operating
Expenses Plus
Contingency/ Rate
Stabilization Fund
($000s)
Non-Operating
Revenues/
(Expenses)
($000s)
Debt Service
($000s)
Net Margin1
($000s)
Working Capital
Fund ($000s)
Working Capital
Target ($000s)
Working Capital
Surplus/
(Deficiency)
($000s)
Working
Capital
Surplus/
(Deficiency)
(%)
a b c d a - b + c - d e f e - f (e/f)-1
2020 82,848 105,426 760 8,677 (30,495) 158,236 37,030 121,205 327%
2021 334,087 355,046 1,651 8,677 (27,985) 138,928 125,496 13,432 11%
2022 409,860 402,108 1,493 13,019 (3,774) 135,154 142,754 (7,600) -5%
2023 417,805 407,071 1,470 13,019 (814) 134,340 144,631 (10,291) -7%
2024 418,226 405,861 1,383 13,019 730 135,069 144,811 (9,741) -7%
2025 416,874 404,605 1,477 13,019 727 135,796 144,949 (9,153) -6%
2026 416,175 408,857 1,460 13,019 (4,242) 131,555 146,919 (15,364) -10%
2027 415,518 411,523 1,376 13,019 (7,647) 123,907 148,560 (24,653) -17%
2028 415,405 417,554 1,189 13,019 (13,980) 109,927 151,447 (41,520) -27%
2029 413,635 419,905 1,133 13,019 (18,156) 91,771 153,379 (61,607) -40%
2030 412,220 428,262 527 13,019 (28,534) 63,237 157,508 (94,271) -60%
NPV of Net Margin:(107,507)
1 Net Margin includes Net Operating Income less Debt Service. The net present value (NPV) of the
Net Margin is determined using a 4% discount rate and is as of Year 2020. The discount rate
is equal to the interest rate on the long-term debt.
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EXHIBIT A
POWER PROCUREMENT COST COMPARISON RESULTS
decrease in power procurement cost sensitivity versus negative $101 million in the AWG Middle of the
Road scenario. In terms of surplus funds available for investment, both cases show the CCA has issues
maintaining adequate working capital for all but a few years of the study period.
This larger working capital shortage is attributable to several factors including a lowering of debt issuance
amount and the decrease in average rate revenue resulting from lower rates which is sustained
throughout the study period (debt issuance and rates are both driven lower due to the power
procurement costs being lower). Thus, the lowering of available cash and rates at the onset result in
negative financial impacts which worsen through time.
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EXHIBIT B
Original analysis conducted in May 2017; revised in August 2017 to reflect changes incorporated into the
final report.
DECREASE IN STAFFING COSTS COMPARISON RE SULTS
This Exhibit B presents the results of the staffing cost sensitivity analyses. Again, at the request of the
AWG, this analysis and all sensitivity analyses considered the AWG Jurisdictions Middle of the Road
scenario against changes in key input assumptions. Table B-1 shows the total staffing costs between
the AWG Middle of the Road scenario and the 70% decrease in staffing costs case.
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EXHIBIT B
DECREASE IN STAFFING COSTS COMPARISON RESULTS
Table B-1: Test Year Staffing Costs, AWG Jurisdictions -
Middle of the Road Scenario and with a 70% Decrease in Salary and Benefits Costs
Description
Number of
Positions
Salary and Benefits
Base Case ($)
Salary and Benefits
70% Decrease in
Staffing Costs Case
($)
Executive Management Positions:
General Manager 1 350,868 105,260
Assistant General Manager 1 241,563 72,469
Chief Financial Officer 1 301,680 90,504
Customer Service Manager 1 241,563 72,469
Human Resources Manager 1 241,563 72,469
Attorney 1 334,472 100,342
Total Executive Management Positions:6 1,711,709 513,513
Other/Departmental Management Positions
Accounting and Budget Manager 1 163,957 49,187
Rates and Regulatory Affairs Manager 1 226,260 67,878
Customer Information and Billing Manager 1 226,260 67,878
Key Accounts Manager 1 226,260 67,878
DSM Program Manager 1 174,887 52,466
Communications and Public Relations Manager 1 174,887 52,466
Power Supply and Planning Manager 1 213,144 63,943
Information Technology Manager 1 226,260 67,878
Procurement and Contracts Manager 1 163,957 49,187
Total Other/Departmental Management Positions 9 1,795,873 538,762
Analyst, Technical, Engineering Positions
Contracts Analyst 1 128,979 38,694
Accounting and Budget Analyst 3 386,938 116,081
Rates and Regulatory Affairs Analyst 0 - -
Power Supply Analyst 2 277,633 83,290
DSM Analyst 2 277,633 83,290
Total Analyst, Technical, Engineering Positions 8 1,071,184 321,355
Administrative, Customer Service, and Other Positions
Executive Administrative Assistant 3 341,030 102,309
Administrative Assistant 4 314,797 94,439
Customer Service Representative 4 314,797 94,439
Key Account Representative 7 994,671 298,401
Communications Specialist 1 122,421 36,726
IT Specialist 2 244,842 73,453
Human Resources Specialist 1 142,096 42,629
Total Administrative, Customer Service, and Other Positions 22 2,474,654 742,396
Total, All Positions 45 7,053,421 2,116,026
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EXHIBIT B
DECREASE IN STAFFING COSTS COMPARISON RESULTS
Table B-2 depicts the rate comparisons under the 70% decrease in staffing costs case. Even with this
large reduction in staffing costs, the CCA rate proxies under the AWG Middle of the Road scenario are
not competitive with PG&E and SCE.
Table B-2: Rate Comparisons Participation Scenario 2: AWG Jurisdictions - Middle of the Road, with Staffing Costs Sensitivity set
at -70%
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
Agriculture 0.1230 0.0742 0.1230 0.0753 0.1230 0.0749 0.1230 0.0747 0.1230 0.0754
Commercial/Industrial Small <200kW 0.1238 0.1049 0.1238 0.1065 0.1238 0.1059 0.1238 0.1055 0.1238 0.1065
Commercial/Industrial Medium 200<500 kW 0.1245 0.1097 0.1245 0.1113 0.1245 0.1107 0.1245 0.1103 0.1245 0.1114
Commercial/Industrial Large 500<1000 kW 0.1200 0.1107 0.1200 0.1124 0.1200 0.1118 0.1200 0.1114 0.1200 0.1124
Residential 0.1275 0.1003 0.1275 0.1018 0.1275 0.1013 0.1275 0.1009 0.1275 0.1018
Residential CARE 0.1208 0.0936 0.1208 0.0950 0.1208 0.0945 0.1208 0.0941 0.1208 0.0950
Residential Solar Choice 0.1975 0.1265 0.1975 0.1284 0.1975 0.1277 0.1975 0.1272 0.1975 0.1284
Weighted Average 0.1248 0.0961 0.1248 0.0975 0.1248 0.0970 0.1248 0.0967 0.1248 0.0976
CCA Rate Premium/ (CCA Savings)29.84%27.93%28.62%29.08%27.88%
Rate Class
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
Agriculture 0.1106 0.0543 0.1106 0.0551 0.1106 0.0548 0.1106 0.0547 0.1106 0.0552
Commercial/Industrial Small <200kW 0.1127 0.0922 0.1127 0.0936 0.1127 0.0931 0.1127 0.0927 0.1127 0.0936
Commercial/Industrial Medium 200<500 kW 0.1120 0.0837 0.1120 0.0850 0.1120 0.0845 0.1120 0.0842 0.1120 0.0850
Commercial/Industrial Large 500<1000 kW 0.1112 0.0777 0.1112 0.0789 0.1112 0.0785 0.1112 0.0782 0.1112 0.0789
Residential 0.1056 0.0712 0.1056 0.0723 0.1056 0.0719 0.1056 0.0716 0.1056 0.0723
Residential CARE 0.0979 0.0635 0.0979 0.0645 0.0979 0.0641 0.0979 0.0639 0.0979 0.0645
Residential Green Tariff 0.1256 0.1127 0.1256 0.1144 0.1256 0.1138 0.1256 0.1134 0.1256 0.1144
Weighted Average 0.1091 0.0776 0.1091 0.0788 0.1091 0.0784 0.1091 0.0781 0.1091 0.0788
CCA Rate Premium/ (CCA Savings)40.46%38.39%39.13%39.63%38.33%
2026
Rate Class
2022 2023 2024 2025
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EXHIBIT C
Original analysis conducted in May 2017; revised in August 2017 to reflect changes incorporated into the
final report.
ANNUAL ESCALATION OF PG&E AND SCE RATES COMPARISON
RESULTS
This Exhibit C presents the results of the PG&E and SCE rates escalation sensitivity analyses. Again, at
the request of the AWG, this analysis and all sensitivity analyses considered the AWG Jurisdictions Middle
of the Road scenario against changes in key input assumptions. Table C-1 depicts the difference in PG&E
and SCE generation rate escalation (the same escalation rates are applied to all classes for both IOUs)
between the AWG Middle of the Road scenario and the 4.0% increase in annual escalation of PG&E and
SCE rates case.
Table C-1: IOU Rates Escalation, AWG Jurisdictions -
Middle of the Road Scenario and with a 4.0% Increase
Year
Study’s Assumed Rate
Escalation
With IOU Rates
Escalated at
Additional 4.0%
2020 0.00% 4.00%
2021 0.85% 4.85%
2022 -0.49% 3.51%
2023 1.50% 5.50%
2024 -0.53% 3.47%
2025 -0.36% 3.64%
2026 0.94% 4.94%
Table C-2 depicts the rate comparison results of the 4.0% increase in annual escalation of PG&E and SCE
generation rates case. The increase of 4.0% in IOU generation rate escalation results in CCA rate proxies
being more competitive compared to the AWG Middle of the Road scenario (shown in Table A-2).
Compared to PG&E, CCA average generation rate proxies are less than PG&E beginning in year 2024;
savings continue to increase in years 2025 and 2026. CCA average generation rate proxies still are higher
than SCE rates through year 2025, and then become lower than SCE in 2026.
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EXHIBIT C
ANNUAL ESCALATION OF PG&E AND SCE RATES COMPARISON RESULTS
Table C-2: Rate Comparisons Participation Scenario 2: AWG Jurisdictions - Middle of the Road, with IOU Rates Escalation
Sensitivity set at +4.0%
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
CCA
Rates
PG&E
Rates
Agriculture 0.1242 0.0903 0.1242 0.0952 0.1242 0.0985 0.1242 0.1021 0.1242 0.1072
Commercial/Industrial Small <200kW 0.1250 0.1276 0.1250 0.1346 0.1250 0.1393 0.1250 0.1443 0.1250 0.1515
Commercial/Industrial Medium 200<500 kW 0.1257 0.1334 0.1257 0.1408 0.1257 0.1456 0.1257 0.1509 0.1257 0.1584
Commercial/Industrial Large 500<1000 kW 0.1212 0.1347 0.1212 0.1421 0.1212 0.1470 0.1212 0.1524 0.1212 0.1599
Residential 0.1287 0.1220 0.1287 0.1287 0.1287 0.1332 0.1287 0.1380 0.1287 0.1448
Residential CARE 0.1219 0.1138 0.1219 0.1201 0.1219 0.1243 0.1219 0.1288 0.1219 0.1351
Residential Solar Choice 0.1987 0.1539 0.1987 0.1623 0.1987 0.1680 0.1987 0.1741 0.1987 0.1827
Weighted Average 0.1260 0.1169 0.1260 0.1233 0.1260 0.1276 0.1260 0.1323 0.1260 0.1388
CCA Rate Premium/ (CCA Savings)7.74%2.13%-1.30%-4.76%-9.25%
Rate Class
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
CCA
Rates SCE Rates
Agriculture 0.1117 0.0661 0.1117 0.0697 0.1117 0.0721 0.1117 0.0748 0.1117 0.0785
Commercial/Industrial Small <200kW 0.1139 0.1122 0.1139 0.1183 0.1139 0.1224 0.1139 0.1269 0.1139 0.1331
Commercial/Industrial Medium 200<500 kW 0.1132 0.1018 0.1132 0.1074 0.1132 0.1112 0.1132 0.1152 0.1132 0.1209
Commercial/Industrial Large 500<1000 kW 0.1124 0.0946 0.1124 0.0998 0.1124 0.1032 0.1124 0.1070 0.1124 0.1123
Residential 0.1066 0.0866 0.1066 0.0914 0.1066 0.0945 0.1066 0.0980 0.1066 0.1028
Residential CARE 0.0991 0.0773 0.0991 0.0815 0.0991 0.0844 0.0991 0.0874 0.0991 0.0918
Residential Green Tariff 0.1266 0.1371 0.1266 0.1446 0.1266 0.1496 0.1266 0.1551 0.1266 0.1627
Weighted Average 0.1102 0.0944 0.1102 0.0996 0.1102 0.1031 0.1102 0.1068 0.1102 0.1121
CCA Rate Premium/ (CCA Savings)16.63%10.55%6.84%3.09%-1.76%
2026
Rate Class
2022 2023 2024 2025
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EXHIBIT D
POWER PROCUREMENT MO NTE CARLO SIMULATION MODEL
QUESTIONS
OVERVIEW
On Friday, May 19, 2017, EnerNex was sent a detailed inquiry from the Central Coast Power (CCP)
Advisory Working Group (AWG) related to the methodology utilized to establish the power procurement
cost component of the CCA Feasibility Study (Study) for the Tri-County region of Santa Barbara County,
San Luis Obispo County, and Ventura County. The full text of that inquiry is included below along with
EnerNex responses and clarifications. EnerNex welcomes any additional questions that may be needed
to further clarify the statistical analysis and Monte Carlo simulation model (MCSM) utilized to estimate
electricity usage, demand, and power procurement cost for the CCP feasibility study.
INQUIRY/RESPONSE
AWG PREAMBLE
This comment has to do with “Table XXXV. Weekday electricity usage Monte Carlo confidence interval”
and the narrative around it (and it is relevant to several other sections). We do not understand how the
Monte Carlo simulations are being applied here, and we are confused about the use of confidence interval
vs confidence level.
Figure ES - XXXV. Weekday electricity usage Monte Carlo confidence intervals.
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EXHIBIT D
POWER PROCUREMENT MONTE CARLO SIMULATION MODEL QUESTIONS
Figure ES - XXXVI. Weekend/holiday electricity usage Monte Carlo confidence intervals.
ENERNEX PREAMBLE
The statistical analysis that provides the basis for the load analysis and cost of power analysis utilize both
confidence intervals and statistical based simulations to estimate and forecast future electricity demand
and usage as well as power supply cost.
The figures/tables in question are an output rather than an input to the analysis. In this case, the figures
are depicting a range of possible electricity demand (kW) for each weekday and weekend hour in the year
2020 based on the historical CCA-Info data provided by the CCAs, the load forecast, and a projection of
customer owned distributed generation.
The figures are intended to illustrate the range of historic variability, as forecasted to future years. The
majority of load stays fairly close to the average, but outliers exist on the high and low ends. Managing
the cost exposure when serving the high and low extremities of the demand range can be the difference
between a successful and unsuccessful power procurement strategy. The depiction of the 95% simulation
upper end illustrates that the majority of electricity demand is close to the average and ut ilizing that
number for power procurement planning is a conservative approach.
However, the power procurement approach embedded in the Monte Carlo model does not utilize power
purchase agreements (PPAs) to procure power at this upper end 95% confidence level. Instead, the
model procures energy through PPAs to a lower bound 90% confidence level. Confidence levels as low
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EXHIBIT D
POWER PROCUREMENT MONTE CARLO SIMULATION MODEL QUESTIONS
as the 80% lower bound were explored as a PPA procurement strategy with the intent to minimize
exposure to California Independent System Operator (CAISO) market volatility – especially when there
is a correlation between high renewable supply content and the extreme high and low prices in the CAISO
markets.
Specific responses to the inquiries regarding the confidence intervals, confidence levels and Monte Carlo
simulation follow.
AWG INQUIRY 1
• We are working off the following definitions:
o Confidence interval: refers to the range of values around the sample value within which
we expect the population value to lie.
ENERNEX RESPONSE 1
• Correct. The CCA-Info data set was analyzed for every hour of every day of every month (with
differentiation between weekdays and weekends) to calculate the average demand and standard
deviation and confidence intervals. This specified range (low end to high end) i s the confidence
interval which is expressed in percentages. Put another way, with a 95% confidence interval,
there is a 95% statistical probability that the average price within a given hour is between the low
end of the range and the high end of the range based on historical sample data.
AWG INQUIRY 2
• Confidence level: refers to the degree of certainty (or probability, allows us to claim significance
at certain levels). It indicates how confident we are about the projected value lying within our
confidence interval.
ENERNEX RESPONSE 2
• Correct. Confidence level refers to the capability of the analysis to produce accurate confidence
intervals. Intervals and levels go hand in hand. As utilized in the figure, the confidence level
provides an estimate with a 95% probability that the actual result will be at or below the 95%
confidence level based on the historical data.
• Again, the figures in question are intended to be illustrative with the majority of load staying
fairly close to the average, while outliers exist on the high and low ends.
AWG INQUIRY 3
• And the following general understanding of MC simulation:
o 1. You want to model the output (in our case, cost) based on input (in our case, customer
load profile)
ENERNEX RESPONSE 3
• The load forecast and power purchase cost forecasts are developed independently.
• The forecasts are used as inputs to the MCSM. The output of the MCSM is a total cost of power
estimate for related products to serve the estimated future load.
o Load Forecast Inputs:
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EXHIBIT D
POWER PROCUREMENT MONTE CARLO SIMULATION MODEL QUESTIONS
▪ CCA-Info data for historical load
• Load forecast includes a statistical Monte Carlo simulation for every hour
in the Study to model load variability.
▪ Load forecast based on historical load, EIA data and IOU long term procurement
plan
▪ Forecast for continuing consumer adoption of rooftop solar which reduces the
load forecast
• The intermittency of customer owned rooftop solar is estimated with a
statistical Monte Carlo simulation for every hour in the Study to model
distributed generation variability.
o Power Purchase Cost Inputs ($/MW)
▪ Estimated cost forecast for natural gas generation
▪ Estimated cost forecast for RPS compliant renewable generation
▪ Estimated cost forecast for monthly resource adequacy
▪ Estimated cost forecast for meeting mandated energy storage procurement
▪ Estimated cost of day ahead and real-time CAISO market participation
• Power procurement cost forecast for CAISO includes a statistical Monte
Carlo simulation using a beta distribution for every hour in the Study to
model market volatility.
AWG INQUIRY 4
• 2. Given an input (average load) you can model the output (average cost to meet that load)
ENERNEX RESPONSE 4
• The Power Purchase Cost is estimated by multiplying the projected load (MW) for each hour by
the forecasted supply costs for that hour ($/MWh for energy and $/MW for capacity/resource
adequacy).
• The Monte Carlo model attempts to simulate the power purchase progression with increasing
certainty over shorter timeframes and the intent to minimize exposure to the CAISO wholesale
market.
o PPAs are utilized to purchase energy to serve the load forecast at the 90% lower bound
confidence level – with 90% certainty that at least that amount of energy will be needed.
▪ The 90% confidence level was utilized after a few iterations to minimize CAISO
market exposure.
o CAISO markets are then utilized to true-up the load forecast and energy supply to meet
the day-ahead forecast and the real-time demand.
AWG INQUIRY 5
• 3. But you don't know the impact that changes in input have on (model) output. (In our case: how
changes in customer load affect cost)
ENERNEX RESPONSE 5
• This is exactly the variable that the MCSM is intended to estimate – the electricity demand
variability relative to the forecast.
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EXHIBIT D
POWER PROCUREMENT MONTE CARLO SIMULATION MODEL QUESTIONS
o The output is the exposure to volatile CAISO wholesale electricity markets:
▪ Purchasing additional energy in CAISO markets when PPAs are short relative to
actual (simulated) electricity demand.
▪ Selling excess energy in CAISO markets when PPAs are long relative to actual
(simulated) electricity demand.
▪ The Monte Carlo calculates the energy transacted in CAISO as the differential
between the amount of energy procured through PPAs and the simulated
electricity usage for every hour during the 10 year study timeframe.
▪ The CAISO cost estimate utilizes a beta distribution aligning with the skewed
distribution of historical CAISO prices for each hour of each month with
differentiation between weekdays and weekends/holidays.
AWG INQUIRY 6
• 4. So, what you do is to first compute average load and use this to compute average cost
ENERNEX RESPONSE 6
• The procurement costs on a per unit basis is determined by the cost and forecast for:
o Natural Gas Generation
o RPS Compliant Generation (renewables)
o Resource Adequacy
o Storage
• Rather than using the average load, the Study estimates electricity demand using statistical
calculation of confidence intervals and application of confidence levels.
o PPAs are then used to procure energy/capacity to meet the lower bound 90 % confidence
level of the load forecast.
o The “per unit” cost is translated to an overall cost.
• The Monte Carlo then estimates the exposure to CAISO prices based on the differential between
the load forecast and the simulated actual load for each hour of the 10 year study period.
AWG INQUIRY 7
• 5. Then you try to model the variance in the load and use this to compute the variance in the cost
- but since you don't have an analytical formula to do this, you use MC simulations
ENERNEX RESPONSE 7
• Yes. This is the CAISO market exposure component (See the answer to item 3 above).
AWG INQUIRY 8
• 6. You generate hundreds, if not thousands, of model load profiles by assuming a normal
distribution around the load mean with the variance that's determined by statistical analysis of
the customer load profiles provided.
ENERNEX RESPONSE 8
• Yes.
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o For SCE, standardized tariff specific SCE load profiles11 are applied to the CCA-Info
electricity usage data within each customer tariff as provided in the CCA-Info data.
o For PG&E, the CCA-Info data contained actual hourly or 15 minute interval meter data
for each potential CCA customer. The interval data were combined into tariff totals.
o The CCA-Info data was then analyzed for any given hour of any given month with
differentiation between weekdays and weekends.
AWG INQUIRY 9
• 7. For each of these randomly generated load profiles, you generate your model output (in this
case, the cost of providing the energy)
ENERNEX RESPONSE 9
• The PPA cost and forecast ($/MW) is determined independently for each resource type and
applied to the load forecast for every hour of every day for the 10 year study period.
• The estimated exposure to CAISO markets is based on the MCSM simulated differential between
the energy procured through PPAs (with the lower bound 90% confidence interval hourly load
forecast) and the simulated actual demand for a specific hour.
AWG INQUIRY 10
• 8. After several thousand random input profiles you will have several thousand different model
output costs, but the average of these should be the same as the average given in (2) above.
However, what you have learned from all these simulations is the variation in the output (cost)
around this average.
ENERNEX RESPONSE 10
• A simple average can skew the estimate because of the variance in demand.
o For example, in the “All 27 Jurisdiction tri-county region” scenario, the average weekday
noontime electricity usage forecasted for August 2020 is 1,434,446 kWh. However, the
range of usage includes a minimum of 1,236,638 kWh, a maximum of 1,616,966 kWh and
a standard deviation of 67,053.
• The confidence level approach was utilized to procure PPAs to serve the lower bound 90%
confidence level load of 1,420,550 kWh with an embedded strategy to manage CAISO market
exposure in the Monte Carlo simulation.
o The average simulated usage for August 2020 on a weekday at noontime is 1,417,853
kWh – a little less than the forecast average as well as the 90% confidence interval lower
bound.
o The resulting CAISO market exposure for a simulated month of August 2020 at noontime
on weekdays is:
▪ $15,364 day-ahead market income from CAISO in the day ahead market to sell
excess energy while meeting the day-ahead forecast.
11 SCE Load Profiles http://bit.ly/LoadProfiles
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▪ $18,162 real-time market expenditure to purchase energy to meet simulated
estimates of actual customer electricity needs.
AWG INQUIRY 11
• 9. So now you have a distribution of output values (costs). The 95th percentile of these tells us
that we're 95% certain that our costs will lie at or below this value.
ENERNEX RESPONSE 11
• Correct. Utilization of the upper bound 95% confidence interval output for both the load forecast
and the power procurement forecast adds in some contingency margin for the many factors that
are unknown and not modeled for a forward looking estimate.
AWG INQUIRY 12
The way that the MC analysis is presented in this report doesn't make sense based on the above
understanding.
• Firstly, placing a 95% percentile on something where there are only 10 values makes no statistical
sense.
ENERNEX RESPONSE 12
• For each Monte Carlo run, each of the variables are calculated multiple times:
o The weekday noon estimate for August 2020 was calculated 22 times (22 weekdays in
that month) for each simulation or 220 times within the 10 run simulation.
o The scenarios also provide additional simulation runs to compare the procurement cost
estimates for each renewable scenario on a per unit basis:
▪ 8 participation scenarios x 22 occurrences of 5:00 in August x 10 simulation runs
per scenario = 1,760 simulations of that hour of each year
o Of course additional data would be nice, but the Study utilized the lowest resolution data
available including the effort to aggregate all customers’ interval meter data provided by
PG&E.
AWG INQUIRY 13
• Second, finding the 95th percentile of load curves is not an MC simulation. You actually have the
load data. The actual statistical curve can be calculated (including its 95th percentile).
ENERNEX RESPONSE 13
• Correct. The confidence interval is calculated using average and standard deviation.
o However, the MCSM is the model where the load forecast and cost forecasts are
combined even if the specific data is not simulated. EnerNex will update the label of the
figures accordingly.
• The Monte Carlo simulation sensitivity analysis is performed when simulating the hourly demand
and cost based on those same statistics (average and standard deviation):
o Load variability;
o Distributed generation intermittency; and
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o CAISO price volatility
AWG INQUIRY 14
• Thirdly, the MC simulation should take us from load profile to procurement cost, not from load
profile to model load profile.
ENERNEX RESPONSE 14
• The MCSM combines data elements into the load forecast by taking the historical load profile,
applying a load forecast and then deducting the customer adoption of distributed generation to
derive the amount of demand and energy needed in future years.
• The Monte Carlo analysis is designed specifically to address one of the fundamental challenges
for a load-serving entity: to develop a load forecast and procure energy to serve that load.
o The load forecast is straightforward using statistics as you suggest.
o Determining the accuracy of that load forecast and related exposure to CAISO market
prices requires a Monte Carlo simulation of both the customer demand forecast and the
CAISO market prices.
AWG INQUIRY 15
• When we look at the green line in the graph, there is only one, which makes it seem like we are
talking about a value, not an interval.
ENERNEX RESPONSE 15
• Correct. This should be relabeled “95% Confidence Level Upper End” as the graph is illustrating
the upper bound of the confidence interval for a 95% probability that the demand will be less
than or equal to the 95% confidence level based on the historical data.
AWG INQUIRY 16
So, it seems like the graph and supporting narrative is saying that the green line is in the 95th percentile
of model runs (again only based on 10 runs, so how is that statistically significant?). If that is true, why
would we select such an outlier as our projection? Shouldn’t we use something like the average, or maybe
something 1 standard deviation from average to be conservative?
In short, I’m very confused by their methodology. I don’t understand how the 95th percentile value was
used. Was it used to estimate procurement costs and if so, was the average profile used at all? And – we
should be looking at the 95th percentile in procurement costs, not the 95th percentile in load profile,
because these are 2 very different things.
ENERNEX RESPONSE 16
• The figures are intended to illustrate the range of historic variability projected forward to future
years. The majority of load stays fairly close to the average, and that outliers exist on the high
and low ends. The figures are an output rather than an input for the procurement strategy within
the MCSM.
• The actual procurement strategy embedded within the MCSM is intended to model how power
procurement progresses with long term, near term, day-ahead and real-time timeframes.
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o As stated, PPAs are utilized to meet the lower bound 90% confidence level. The model is
not over-procuring energy relative to the load forecast as implied in the question.
AWG INQUIRY 17
And finally, referencing a “95% confidence interval” seems to communicate a false sense of certainty. It
sounds like the study implies you are 95% sure that outcomes will fall within a certain range, which
suggests the findings are statistically significant. However, aren't you really saying that 95% of model
outcomes fall below that line, which is very different and may have important impacts on our outcomes?
ENERNEX RESPONSE 17
• Utilization of statistical confidence intervals and confidence levels are intended to account for
the variability within both load and cost. Proceeding with a simple average is likely to under
estimate the actual cost given the variability in demand illustrated in the figures. For example,
with the normal distribution analysis of the load data, there is an equal probability of the demand
being higher or lower than the average. Utilizing the upper bound 95% confidence level
incorporates some contingency to factor in unknown variables that can impact cost. However,
we will provide the full range of MCSM results for the load forecast and power procurement cost
including the maximum, minimum and average in the Study in order for the AWG and
stakeholders to make an informed decision.
• Just like a forecast and statistical analysis for stock prices, past behavior is not necessarily an
indicator of future performance. As stated in the Study, risk management is the primary focus of
developing a power procurement portfolio. The Study attempts to describe the wide variety of
risks associated with power procurement. The only thing that is certain is that the load and cost
forecast will not match reality.
o However, the Study results are as statistically accurate as possible given supporting data
and statistical/Monte Carlo model certainties to estimate a load forecast and simulate
the cost of power by including CAISO market exposure.
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Appendix L Peer Review and Response
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on Community Choice Aggregation August 2017
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4. Response to Extended Peer Review
Willdan received the MRW Extended Peer Review appearing in Section 2 of this Appendix L on August
24, 2017. Concerning the Extended Peer Review: Willdan has neither reviewed nor vetted assumptions;
was afforded no opportunity to review or question MRW’s methodology; and makes no representations
concerning the validity of its results, as related to this Study and outcomes. Further, Willdan has not been
provided the MRW-revised version of the Pro Forma Model. Willdan cannot therefore opine as to the
reasonableness of MRW’s alternative assumptions nor can Willdan determine the extent to which changes
to the Pro Forma Model implemented by MRW impaired functionality or the validity of outputs therefrom.
Any reliance upon the results of MRW’s alternative pro forma analysis presented in Section 2 of this
Appendix L is neither supported nor endorsed by Willdan.