Document name
2013 Interconnection-wide Plan
Data and Assumptions
Category
( ) Regional reliability standard
( ) Regional criteria
( ) Policy
( ) Guideline
( ) Report or other
( ) Charter
Document date
September 19, 2013
Adopted/approved by
The WECC Board of Directors
Date adopted/approved
September 19, 2013
Custodian (entity
responsible for
maintenance and
upkeep)
TEPPC
Stored/filed
Physical location:
Web URL:
Previous name/number
(if any)
Status
( ) in effect
( ) usable, minor formatting/editing required
( ) modification needed
( ) superseded by _____________________
( ) other _____________________________
( ) obsolete/archived)
(Page intentionally left blank)
Page 2 of 121
2013 Interconnection-Wide Plan
Data and Assumptions
By
WECC Staff
Western Electricity Coordinating Council
September 19, 2013
Page 3 of 121
2013 Interconnection-wide Plan
Data and Assumptions
Summary
The assumptions and data used to create the Plan span numerous sources and
categories such as loads, existing and incremental generation facilities, generation
characteristics, and existing and incremental transmission facilities. Aside from these
data and assumptions required to model the physical aspects of the Western
Interconnection, WECC staff and stakeholders also develop assumptions and gather
equally important data on policies, costs, reliability and environmental considerations.
Below is a summary of the types of data and assumptions, including improvements,
used to create the Plan.
Loads and Demand-side Management – The base load forecasts for the next 10 years
are obtained from WECC’s Loads and Resources Subcommittee (LRS) and then
adjusted based on state-provided energy efficiency (EE) and other demand-reducing
programs that are not already accounted for in the LRS forecasts. There are also a
number of demand response (DR) adjustments made to the loads in various study
cases.
Generation additions and retirements – TEPPC uses LRS data submittals for data on
generation additions and retirements, augmented by information in state and federal
databases.
Renewable Resource Profiles – TEPPC uses data from the National Renewable Energy
Laboratory to create output profiles for VG generators (i.e., wind and solar) for use in
the various studies. For the 10-year studies analyzed in the PCM, the profiles are
created based on the amount of generation desired from a particular area. For the 20year studies analyzed using the LTPT, the data is used to populate the available
generation and capacity factor information for the various generation development
zones in the LTPT.
Transmission Additions – For transmission addition assumptions TEPPC utilizes two
sources of information. For local and lower-voltage additions, TEPPC uses transmission
additions reported to WECC through the Planning Coordination Committee base case
development process. For regional high-voltage additions, TEPPC uses the Subregional
Coordination Group (SCG) 2022 Common Case Transmission Assumptions (CCTA) list.
Transmission Project Information Portal – TEPPC maintains the portal that contains
developer-provided information on major transmission projects within the Western
Page 4 of 121
Interconnection. This allows TEPPC and stakeholder to understand the status of project
development in the Western Interconnection. TEPPC uses these projects as proxies
when transmission expansion needs are identified.
Environmental and Cultural Data Consideration – This planning cycle, WECC has
incorporated environmental and cultural data into the planning analyses. Through its
Environmental Data Task Force (EDTF), WECC has developed an Environmental Risk
Land Classification System that estimates the environmental and cultural sensitivity of
the different area types using a four-category risk scale.1 The EDTF reviewed
environmental and cultural datasets and used the risk classifications to create a
seamless Environmental and Cultural Risk Classification Data Layer, which categorizes
the entire Western Interconnection using the risk scale. The LTPT uses this data in its
analysis. In the next planning cycle, this will provide critical input to the “line bending”
work.
Capital Costs –The capital cost calculation methodology and data, identified as a
limitation in the 2011 Plan, was redesigned from the ground-up. TEPPC included
improved generation capital costs estimates, as well as transmission capital cost
estimates that break the costs down into transmission line and substation components
and consider terrain and construction difficulty factors.
Fuel Prices – Fuel prices for natural gas, coal, and nuclear were obtained from
specialized price-forecasting services and approved by the Technical Advisory
Subcommittee (TAS). Although some of the transmission planning studies resulted in
significant increases in electric sector natural-gas usage, the impact to prices was not
investigated – the Plan analyses did not include an investigation of gas-price elasticity
or physical delivery issues.
Generation cycling costs – WECC procured generation cycling costs but all PCM tools
available at the time of study were not able to incorporate such costs into their model.
Given modeling improvements made during this study cycle, generation cycling costs
can be taken into consideration in the next.
Flexibility reserves – WECC worked with the National Renewable Energy Laboratory to
develop flexibility reserve inputs that are included in the 2013 studies.
EDTF, “Environmental Risk Classification System” (2012):
http://www.wecc.biz/committees/BOD/TEPPC/SPSG/EDTF/Shared%20Documents/Environmental_Reco
mmendations_for_Transmission_Planning/Final_Recommendations_Report/Tables%20D-1%20to%20D4_Revised_2011_0725.pdf
1
Page 5 of 121
Contents
Summary and Findings ................................................................................................... 4
Background ..................................................................................................................... 7
Loads and Demand-side Management (DSM) ................................................................ 7
Generation .................................................................................................................... 40
Transmission ................................................................................................................. 59
Fuel Prices .................................................................................................................... 69
Capital Costs ................................................................................................................. 77
Reliability ....................................................................................................................... 99
Environmental Data ..................................................................................................... 103
Appendix A: Hydro Data .............................................................................................. 110
Page 6 of 121
Background
The biennial TEPPC study cycle requires WECC staff and stakeholders to develop
model assumptions and datasets that describe two study horizons: 10-year and 20year. Studies in the 10-year planning horizon were performed in a production cost
model for the year 2022 while studies in the 20-year planning horizon were analyzed in
a capital expansion model—Long-term Planning Tool (LTPT)—for the 2032 study year.
The assumptions and data presented in this section are fed into WECC’s
aforementioned planning tools and models, which allow for the technical analysis upon
which the Plan is based. As with all simulation-based studies, TEPPC study results
reflect the input assumptions and underlying data used in the analysis.
This section is a detailed explanation of the sources of data and assumptions used in
the technical analysis that supports the Plan. The purpose is to describe the pedigree of
the data and assumptions used in the Plan analysis in order to create a foundation of
credibility, as well as providing those who wish to use this data the information
necessary to locate, utilize and defend the information. The section is organized by type
of data or assumption, and is further broken down by study horizon and other details
related to that data or assumption.
Assumptions are only provided for the 2022 Common Case and 2032 Reference Case
as all other TEPPC studies use these cases as their starting points. Any assumption
specific to a study other than the 2022 Common Case and 2032 Reference Case is
outlined in that particular study’s individual write-up report.
Loads and Demand-side Management (DSM)
Load projections are a vital input to any transmission planning model. Load levels
dictate how much and when generation is either dispatched or built, depending on the
model. Although the input parameters for load in each study horizon are quite divergent,
it is a key input assumption for both.
Required Data
The following are descriptions of the load data required for both the 10- and 20-year
models. In both the 2032 Reference Case and 2022 Common Case, loads are defined
by the topology presented in Figure 1, which represents the load areas used in the
models.
Page 7 of 121
Figure 1: Load Topology
10-Year
For 10-year studies performed in the hourly production cost model environment, load is
defined by specifying monthly peak (MW) and energy (MWh) for each of the TEPPC
load bubbles in PROMOD. From there, PROMOD uses a historical or generic hourly
load shape to create an 8,760-hour load profile for each load area. This 8,760-hour load
profile is a defined load level, in MW, for each load area for every hour of the study
year. This level of detail is necessary for the hourly production cost model to perform its
optimization. For the 2022 studies, TEPPC used 2005 historic hourly load shapes by
which to create the synthetic 2022 hourly profiles in PROMOD. Once the 2022 hourly
load profile is created by PROMOD for each load area, it disaggregates the load to the
busses within each Balancing Authority (BA), load bubble, based on the load distribution
factors provided by the initial power flow (2020 HS1A) used to import that transmission
topology into the dataset. This results in an hourly load for each load bus in the Western
Interconnection.
20-Year
As with much of the data used in the 20-year studies, the 2022 Common Case loads
serve as the starting point assumption. However, the LTPT requires less granular load
data since it is a capital expansion model, not an hourly production cost model. The
LTPT requires the user to input an annual energy requirement for each TEPPC load
Page 8 of 121
area. This value determines how many resources must be added to meet the system
energy demand.
It is also necessary to provide load demands for the TEPPC load areas for each of four
system conditions: heavy summer, light fall, heavy winter, and light spring. For example,
the user must specify that the load level in a particular Balancing Authority (BA) for the
heavy summer system condition. For more information on system conditions and how
these load inputs are used by the tool, see the LTPT section of the “Tools and Models”
section.
The demands and annual energy requirements used in the 2032 Reference Case are
based on extending the assumptions used in the 2022 Common Case for an additional
10 years from 2022 to 2032.
Energy Efficiency
As energy efficiency (EE) programs become more prevalent in states throughout the
West, it is necessary to ensure that this reduction in load, due to implementing more
efficient technologies, is reflected in the TEPPC studies.
10-Year
The 2022 Common Case load forecasts were developed by starting with the load
forecasts submitted by BAs in response to WECC’s 2011 Loads and Resources
Subcommittee (LRS) data request (henceforth referred to as the LRS load forecasts),
which cover the period 2011-2021. The State-Provincial Steering Committee (SPSC)
Demand-Side Management (DSM) Work Group determined – with extensive input from
BA load forecasting staff and review by the TEPPC Data Work Group – the extent to
which the LRS load forecasts fully capture the expected impact of existing EE policies
and program plans implemented over the forecast period. To the extent that the
forecasts were determined to not fully capture the expected EE policy impacts, they
were adjusted downward accordingly, yielding the 2022 Common Case load forecast for
the year 2021. This basic process is illustrated schematically in Figure 2. The 2021
forecasts were then extrapolated to 2022, the horizon year for TEPPC’s 10-year study
during its 2011 study cycle, using the average annual growth rate over the 2010-2021
period.
Page 9 of 121
Figure 2: Illustration of Energy Efficiency Adjustment for the TEPPC2022 Common Case Load
Forecasts
In developing projections of expected EE savings and the associated adjustments to the
LRS load forecasts, two classes of EE policies were of primary interest: 1) customerfunded energy efficiency programs,2 and 2) federal minimum efficiency standards for
appliances, lighting and other end-use equipment (henceforth referred to as “federal
standards”). The DSM Work Group focused on these two classes of policies on the
grounds that they were likely to be the most significant sources of policy-driven EE
savings over the forecast period, and thereby ignored other EE policies and programs,
such as state appliance efficiency standards, state building codes and American
Recovery and Reinvestment Act (ARRA)-funded efficiency programs. This narrow focus
is one among several “conservatisms” built into the analysis.
In addition, the analysis focused on specific timeframes. For customer-funded efficiency
programs, the analysis focused on the impact of programs implemented over the 20112021 period. It was assumed that the LRS load forecasts adequately captured residual
savings from historical programs implemented prior to 2011, and therefore no further
accounting was conducted for historical programs. For federal standards, the DSM
Work Group focused specifically on standards adopted and updates to existing
standards scheduled to occur through January 2013. This is another conservatism in
2
Customer-funded energy efficiency programs, also referred to as ratepayer-funded energy efficiency
programs, refer to the class of energy efficiency programs that are funded by utility ratepayers and
administered either by the utility or by an independent program administrator.
Page 10 of 121
the methodology, as the U.S. Department of Energy (DOE) is required to continue
updating standards regularly after that date.3
Customer-Funded Energy Efficiency Programs
Customer-funded EE programs have been implemented in the United States for more
than three decades and, in many states and regions, have accelerated rapidly in recent
years as a result of new or increased policy support. The 2022 Common Case load
forecasts are intended to fully reflect the expected savings from customer-funded
programs implemented over the LRS load forecast period (2011-2021), given current
policies and utility resource plans.
As part of the 2011 LRS data request, BAs were asked to provide projections of the EE
program savings incorporated into their LRS load forecast.4 Lawrence Berkley National
Laboratory (LBNL) reviewed these EE savings projections and, with input from the DSM
Work Group participants and other state and regional EE experts, assessed the
consistency of these savings projections with applicable statutory and regulatory
policies, recent utility integrated resource plans and utility DSM program plans.
LBNL then communicated with load forecasting staff at many of the individual BAs in
order to: 1) clarify any ambiguities in the savings projections submitted through the LRS
request; 2) discuss any apparent discrepancies between the submitted savings
projection and what would be expected under current policies and program plans; and
3) confirm whether the 2011 LRS load forecasts fully accounted for the expected EE
program savings. If the savings projection provided by a BA differed significantly from
the expected amount, or if the load forecast did not fully account for the BA’s savings
projection, then the LRS load forecast was adjusted downward accordingly. As shown in
Table 1, such adjustments were made for 11 BAs.
3
The decision to focus only on scheduled updates through January 2013 was justified on the grounds
that standards adopted at later dates would likely have minimal impacts on loads in 2022, given the lag
between the conclusion of rulemakings and the date that standards go into effect, and given the pace of
equipment stock turnover. If, however, adjustments were being made over a longer time horizon, such as
for TEPPC’s 20-Year study, some consideration of continued updates to federal standards over time may
be warranted in order to ensure that the forecasts reflect reasonable assumptions about the likely impacts
of federal standards.
4
This was a new element in the LRS data request for 2011. Responses to this question, however, were
not mandatory, and therefore not all BAs provided this data. Moreover, many BAs evidently did not
interpret the question as intended, and therefore the submitted savings projections were of widely varying
quality, requiring a significant amount of follow-up with BA staff.
Page 11 of 121
Table 1: Adjustments to 2011 LRS Load Forecasts for Customer-Funded Efficiency Programs
AVA
CISO
IPCO
NWMT
PACE
&
PACW
PSCO
PNM
SRP
Avista Corporation (AVA): The LRS load forecast only accounts for savings from
existing efficiency programs, but not for any of the planned new programs within
Avista’s most recent Integrated Resource Plan (IRP). As such, the forecast was
adjusted downward based on the IRP savings projection for new efficiency
programs.
California Independent System Operator (CISO): The LRS load forecast accounts
only for "committed" energy efficiency savings, and therefore excludes
"uncommitted" savings associated with investor-owned utility (IOU) programs
implemented after 2012, savings from publicly-owned utility (POU) programs
implemented after 2010, and savings from other future changes to codes/standards.
The forecast was therefore adjusted downward based on the sum of: (a) the
incremental uncommitted savings assumed by the California Public Utility
Commission (CPUC) within the IOUs' long-term procurement proceeding and (b) the
estimated savings from EE programs implemented by POUs within the CISO
footprint, based on those utilities' most recent long-term EE savings targets.
Idaho Power Company (IPCO): The LRS load forecast accounts for only existing
programs in the IPCO 2011 IRP, but not for the projected savings from planned new
programs identified within the IRP. The forecast was therefore adjusted downward
based on the IRP savings projection for new efficiency programs.
NorthWestern Energy (NWMT): The LRS load forecast accounts for a continuation
of energy efficiency programs at NWMT’s historical rate of savings. The load
forecast was therefore adjusted downward based on the difference between the
utility’s planned savings level, as projected within its 2009 Electric Default Supply
Procurement Plan, and the utility’s historical rate of savings.
PacifiCorp East (PACE) and West (PACW): The LRS load forecast assumes a level
of savings based on the target from PacifiCorp’s 2008 IRP Update rather than the
updated efficiency targets in the utility’s 2011 IRP. Therefore, the PACE and PACW
load forecasts were adjusted downward by an amount equal to the difference
between the savings targets in the 2011 IRP and the 2008 IRP Update.
Public Service Company of Colorado (PSCO): The LRS load forecast assumes a
level of savings based on an earlier set of long-term savings targets established
under Docket. 08-0560. The forecast was therefore adjusted downward slightly in
order to account for the higher level of savings required under the updated savings
goals adopted in March 2011 (Decision No. C11-0442).
Public Service Company of New Mexico (PNM): The LRS load forecast does not
include any impacts from future energy efficiency programs, and was therefore
adjusted downward based on the savings required of PNM to comply with New
Mexico’s energy efficiency resource standard.
Salt River Project (SRP): The LRS load forecast roughly accounts for the level of
savings required to meet SRP’s Sustainable Portfolio Plan savings targets through
2017, but does not include any savings from programs implemented in subsequent
Page 12 of 121
years. The load forecast was therefore adjusted downward to account for the
expected savings needed to meet the Sustainable Portfolio Plan savings targets in
2018-2021.
Tucson Electric Power Company (TEPC): The LRS load forecast partially accounts
for the effects of planned customer-funded energy efficiency programs over the
forecast period, but not at the level necessary to meet the Arizona Energy Efficiency
TEPC
Standard. As such, the load forecast was adjusted downward based on the
additional amount of savings required of Tucson Electric and Unisource to comply
with standard.
Western Area Power Administration, Colorado-Missouri Region (WACM): The load
forecast that Colorado Springs Utilities (CSU) provided to WAPA, which is then
rolled up into the LRS load forecast for the WACM balancing authority, does not
WACM
account for the impacts of any future energy efficiency programs. As such, the
WACM forecast was adjusted downward based on the energy efficiency savings
projection provided by CSU.
Federal Appliance, Lighting, and Equipment Standards
The U.S. federal government establishes minimum efficiency standards for a wide
variety of consumer appliances, lighting technologies and other end-use equipment. For
most of these products, the DOE is responsible for setting the standard and is required
to conduct periodic rulemakings to evaluate potential updates to the standard. For a
number of other products, the U.S. Congress has instead established the initial
minimum efficiency standard directly through legislation. A notable example is the
federal standard for general service lamps enacted through the 2007 Energy
Independence and Security Act (EISA), which mandates a phase-out of traditional
incandescent light bulbs.
Figure 3 shows the projected total U.S. savings over time from federal standards
adopted or updated through January 2013, based on analysis conducted by the
American Council for an Energy Efficient Economy (ACEEE) and the Appliance
Standards Awareness Project (ASAP).5 The projected savings are separated into two
components.
1. Savings from standards established prior to 2009 (some of which do not go into
effect until some number of years afterward, such as the EISA lighting standard
that began its phase-in in 2012).
5
Note that the ACEEE/ASAP study was conducted in 2009, and therefore the savings projections for
standards adopted between then and January 2013 were based on assumptions about the standard level
that DOE would adopt.
Page 13 of 121
2. Savings from standards adopted or updated over the 2009-2013 timeframe
(listed in Table 2) also includes a number of updates that had not yet occurred at
the time of this analysis but that DOE had committed to update by January 2013.
Figure 3: Projected Cumulative Savings from Federal Standards 6
As indicated in Figure 3, savings from the set of standards established prior to 2009,
which represent the bulk of the total projected savings, accumulate over the 2010-2020
period at the same rate as over the 2000-2010 period. This growth in savings occurs as
a result of stock turn-over as old, inefficient equipment is replaced with more-efficient
models that comply with the existing federal standard. Additional savings from updates
issued during 2009-2013 also accumulate over the 2010-2020 period and represent an
acceleration in the rate of savings from federal standards relative to the historical growth
rate. As described in Table 2, the fact that the 2009-2013 updates represent
acceleration in savings is central to the DSM Work Group’s process of adjusting the
LRS load forecasts to reflect the expected impact of federal standards.
Derived from estimates reported in ACEEE/ASAPt, “KaBOOM: The Power of Appliance Standards
Opportunities for New Federal Appliance and Equipment Standards” (July 2009).
6
Page 14 of 121
Table 2: Federal Standards Expected to be Adopted over 2009-2013
Commercial
Residential
Product
Battery chargers
Central AC and heat pumps
Clothes dryers
Clothes washers
External power suppliers
Furnace fans
Microwave ovens
Refrigerators
Room AC
Water heaters
Beverage vending machines
Commercial clothes washers
Fluorescent ballasts
Fluorescent lamps
Incandescent reflector lamps
BR/exempted reflector lamps
Liquid-immersed transformers
Low volt. dry-type transformers
Metal halide lamp fixtures
Reach-in refrigerators & freezers
Small electric motors
Walk-in coolers & freezers
Planned Final Rule
Date*
Jul-11
Jun-11
Jun-11
Dec-11
Jul-11
Jan-13
Mar-11
Dec-10
Jun-11
Mar-10
Aug-09
Jan-10
Jun-11
Jun-09
Jun-09
Jan-10
Jan-13
Jan-13
Jan-12
Jan-13
Feb-10
Jan-12
Compliance
Date
2014
2014
2014
2015
2014
2016
2014
2013
2014
2013
2012
2013
2014
2012
2012
2013
2016
2016
2015
2016
2013
2015
* Planned final rule date, as of the time that the analysis for the 2022 Common Case was being
conducted
LBNL adjusted the LRS load forecasts to account for the expected impact of all federal
standards adopted and all updates to existing standards scheduled to occur through
January 2013. To do so, LBNL sought information from the load forecasting staff of
individual BAs regarding the manner by which their forecasts model the impact of
federal standards. Based on the information received, one of two potential standardized
methods was used for most BAs to adjust the LRS load forecasts to account for the
expected impact of federal standards (see Table 3 for a summary, and see Appendix A:
Hydro Data for BA-specific details)
Method 1: Many BAs indicated that their load forecasts do not explicitly model the
impact of federal standards. The default assumption in these cases is that, by virtue of
the underlying econometric models, these load forecasts implicitly extrapolate into the
future the historical rate of savings from federal standards, and that they therefore fully
capture the savings from pre-2009 federal standards, but do not capture any of the
Page 15 of 121
expected savings from 2009-2013 standards. These load forecasts were therefore
adjusted downward based on the projected savings from the 2009-2013 updates (the
olive-colored wedge in Figure 3). For each BA, the expected savings from 2009-2013
standards was estimated from the state-level projections presented in Table 4, by
prorating the state-level savings based on the portion of the statewide load within the
BA; the LRS load forecast was then adjusted downward by that amount.
Method 2: In other instances, BAs reported that their load forecasts were based on enduse models or included statistical adjustments that were able to capture the impact of
specified federal standards. In practice, these load forecasts generally capture the
impact of all federal standards adopted as of the date that the forecast was prepared,
but do not model the impact of scheduled updates to federal standards. For these BAs,
the load forecasts were assumed to capture both the impact of pre-2009 standards (as
in Method 1) as well as the impact of standards adopted between 2009 and year-end
2010 (a portion of the olive-colored wedge in Figure 3). These load forecasts were
therefore adjusted downward based on the expected impact of only those standards
that had not yet been adopted at the time the forecasts were prepared but for which the
DOE had scheduled an update by January 2013. For each BA, the expected savings
from prospective standards scheduled for adoption by January 2013 was estimated
from the state-level projections for “Prospective” standards presented in Table 4 by
prorating the state-level savings based on the portion of the statewide load within the
BA; the load forecast was then adjusted downward by this amount.
For three other BAs (CISO, PACE, and PACW), neither Method 1 nor Method 2 was
applied. The adjustments to the LRS load forecasts for these BAs addressed multiple
EE policies simultaneously, including federal standards, and therefore no separate
adjustment for federal standards was required. Specifically, in the case of CISO, the
LRS load forecast was adjusted downward based on the California Energy
Commission’s estimate of the “incremental uncommitted” savings associated with
achievement of the state’s savings goals for the investor owned utilities (IOU). The
incremental uncommitted savings was assumed to largely capture the impact of the
2009-2013 updates to federal standards, and therefore no separate adjustment was
applied to the CISO forecast for federal standards. Similarly, for PACE and PACW, the
adjustment made to the LRS load forecast was based on the savings projection in
PacifiCorp’s IRP, and that savings projection (according to PacifiCorp staff) was
inclusive of savings from future federal standards.
For the remaining BAs, no adjustments to the LRS load forecasts were made for federal
standards, though the reasons for this treatment vary. For the Bonneville Power
Administration (BPA) and most of the NorthWestern public utility district BAs (PUD No.
1 of Chelan County (CHPD), PUD No. 1 of Douglas County (DOPD), PUD No. 2 of
Grant County (GCPD), and City of Tacoma, Department of Public Utilities (TPWR)), the
Page 16 of 121
LRS load forecasts were determined to be net of the NPCC’s conservation targets, and
those targets were assumed to largely capture the savings from recent and future
federal standards updates; therefore, no adjustment to the LRS load forecasts was
made in these cases. For the three non-U.S. BAs (Alberta Electric System Operator
(AESO), British Columbia Transmission Company (BCHA), and Comisión Federal de
Electricidad (CFE)), no adjustment was made because these regions are not directly
subject to U.S. federal efficiency standards (though provincial standards exist in BC7,
and spillover in efficiency impacts from U.S. standards could occur across borders into
all three regions). Finally, for the Western Area Power Administration (WAPA), Lower
Colorado Region (WALC) and the Upper Great Plains West (WAUW), no adjustments
were made simply due to a lack of information about how the LRS load forecasts were
prepared.
Table 3: Approaches Used to Adjust LRS Forecasts to Account for the Impact of Federal
Standards
Adjustment Approach
Balancing Authorities
Method 1
APS, EPE, IID, IPC, NWMT, PGE, PNM, PSE, SCL, SMUD, SRP,
TEP, TID
Method 2
AVA, LADWP, NEVP, PSCO, SPP
Federal standards impact
included in broader
adjustment
CISO, PACE, PACW
BPA, CHPD, DOPD, GCPD, TPWR: Impact of federal standards
captured in LRS forecast
No adjustment
AESO, BCTC, CFE: U.S. federal standards not applicable
WALC, WAUW: Insufficient information
7
Information was sought from BCTC to determine whether the load forecast submitted to WECC reflects
the expected impact of provincial efficiency standards, but a response was not received within the
required timeframe for this analysis.
Page 17 of 121
Table 4: Projected Savings in 2021 from Federal Standard Updates Issued from 2009-20138
Energy Savings (GWh)
State
Already
Adopted
AZ
CA
CO
ID
MT
NV
NM
OR
UT
WA
WY
TOTAL
(2009-2010)
967
4,377
688
206
141
365
286
515
305
878
79
8,806
Peak Demand Savings (MW)
Prospective
(2011-Jan.
2013)
Total
Already
Adopted
(2009-2010)
Prospective
(2011-Jan.
2013)
Total
1,395
6,183
1,051
314
221
592
421
764
466
1,315
129
12,851
2,362
10,560
1,739
520
362
956
707
1,279
771
2,193
208
21,657
304
966
126
40
26
104
62
94
63
158
14
1,956
475
1,472
191
60
39
159
95
143
96
242
24
2,998
778
2,438
317
100
65
263
158
237
159
400
38
4,954
Expected Energy Efficiency Savings
The 2022 Common Case load forecasts are intended to reflect the expected impact of
customer-funded EE programs and federal standards. The projected savings from these
two sets of policies are summarized in Figure 4 (annual energy) and
Figure 5 (annual peak demand), focusing specifically on the savings from customerfunded EE programs implemented over the 2011-2021 and the savings from new
federal standards or updates to existing standards issued from January 2009 to January
2013. Savings are expressed in terms of the percentage reduction in 2021 loads for
each BA and for the Western Interconnection as a whole. Note that for a number of
BAs, the underlying policies or data sources do not distinguish between the savings
from customer-funded programs and from federal standards, in which case the figures
present only the combined impact (the green bars).
As shown, the set of EE policies considered for the 2022 Common Case are expected
to reduce Interconnection-wide annual energy requirements by roughly 10 percent in
2021, and aggregate non-coincident peak (NCP) demand by 12 percent. Naturally, the
impacts vary considerably across individual BAs, reflecting differing degrees of
Sources: DOE Technical Support Documentation accompanying adopted standards (for “Already
Adopted” standards); ACEEE/ASAP "KaBOOM: The Power of Appliance Standards Opportunities for
New Federal Appliance and Equipment Standards" (for “Prospective” standards with final rules scheduled
by January 2013).
8
Page 18 of 121
underlying policy support as well as differences in customer mix, climate, end-uses and
other factors.
Figure 4: Expected Energy Efficiency Savings for 2022 Common Case: Annual Energy
Figure 5: Expected Energy Efficiency Savings for 2022 Common Case: Non-Coincident Peak
Page 19 of 121
20-Year
Given that the 2032 Reference Case loads were grown from the 2022 Common Case
loads, the EE assumptions in the 2022 Common Case are included in the 2032
Reference Case. However, based on this implementation, there were no assumptions
made on any additional reduction in loads due to EE impacts in the 2022-2032
timeframe.
Load Assumptions Data
10-Year
To the extent that the LRS load forecasts submitted by the BAs were determined to not
fully capture the expected impacts of current EE policies and program plans, they were
adjusted downward accordingly. These adjustments are summarized in Figure 6
(annual energy) and Figure 7 (peak demand), in terms of the percentage of reduction to
the annual energy and peak demand in 2021. As noted previously, adjustments were
defined for the year 2021 as that was the terminal year of the LRS load forecasts; once
the adjustments were made, the forecasts were then extrapolated out to 2022, the
horizon year of TEPPC’s 10-Year study.
As shown, no adjustments were made for 10 BAs (i.e., the 2022 Common Case load
forecasts for those BAs are equal to the original LRS load forecasts submitted to
WECC). The remaining 22 BA load forecasts were adjusted downward, at a minimum,
to account for the expected acceleration in savings from federal standards. The
magnitude of this individual adjustment ranges from roughly 1 to 3 percent of annual
energy and 1 to 4 percent of NCP demand, depending on the extent to which the LRS
load forecasts account for updates to federal standards scheduled to occur through
January 2013. In addition, the forecasts for 11 BAs were adjusted downward to account
for the expected impact of customer-funded efficiency programs. Those adjustments
were quite sizeable in the case of several BAs where existing policies mandate
substantial efficiency savings that are not fully captured within the LRS load forecasts.
In aggregate, Interconnection-wide load in 2021 was adjusted downward by 3.2 percent
for annual energy and 5.0 percent for peak demand. More than half of the overall
Interconnection-wide adjustment consists of the adjustment to the CISO forecast, which,
as noted previously in Table 1, did not account for a large portion of expected savings
from efficiency programs and policies over the forecast period.
Page 20 of 121
Figure 6: Adjustments to LRS Load Forecasts (Annual Energy)
Figure 7: Adjustments to LRS Load Forecasts (Non-Coincident Peak)
2022 Common Case Load Forecasts
The 2022 Common Case load forecasts are based on the LRS load forecasts submitted
by each BA, adjusted downward in many cases to reflect the expected impact of current
EE policies and program plans. Figure 8 and Figure 9 present the 2022 Common Case
load forecasts for each BA in terms of its compound annual growth rate (CAGR) over
the 2010-2022 period. The figures also show the impact of the EE adjustments in terms
Page 21 of 121
of the associated reduction in CAGR for each BA. The total height of the stacked bars in
Figure 8 and Figure 9 indicate the CAGR of the original LRS load forecasts.
Across the entire Western Interconnection footprint, load growth in the 2022 Common
Case occurs at a CAGR of 1.4 percent per year in terms of annual energy, and 1.3
percent per year in terms of aggregate NCP demand. The EE adjustments reduced the
forecasted Interconnection-wide growth rate by 0.3 percent per year for annual energy
and by 0.5 percent per year for aggregate NCP. As expected, growth rates vary
considerably across BAs ranging from 0.3 to 4.0 percent per year for annual energy and
from -0.2 to 4.0 percent per year for NCP demand. Variation in growth rates across BAs
largely reflects differences in the original LRS load forecasts submitted by the BAs.
However, the EE adjustments made by the DSM Work Group also differed across BAs,
and therefore variation in the 2022 Common Case growth rates across BAs also reflects
differences in the size of the EE adjustments. Of particular note, given its size, the
growth rate for CISO was reduced from 1.5 to 0.8 percent per year for annual energy,
and from 1.5 to 0.3 percent per year for peak demand.
Figure 8: 2022 Common Case Load Forecast Growth Rates (Annual Energy, 2010-2022)
Page 22 of 121
Figure 9: 2022 Common Case Load Forecast Growth Rates (Non-Coincident Peak, 2010-2022)
As previously mentioned, these resulting forecasts for both peak demand and energy
are used in conjunction with 2005 historical load shapes in deriving the 2022 load
shapes for all the load areas in the TEPPC topology.
Transmission losses are included in the load forecasts and TEPPC does not have
information to separate losses from the load data. The 2022 Common Case load
assumptions are presented in Table 5.
Table 5: 2022 Common Case Loads
Area Native Loads* - 2022 TEPPC Common Case
Area
Peak
(MW)
Energy
(GWh)
Area
Peak
(MW)
Energy
(GWh)
AESO
APS
AVA
BCTC
BPA
CFE
CHPD
DOPD
EPE
Far East
GCPD
IID
15,867
9,787
2,720
11,996
10,463
3,461
722
424
2,244
725
858
1,201
114,180
42,745
14,673
66,325
53,972
14,950
4,056
1,975
10,935
3,040
5,177
4,336
NEVP
NWMT
PACE_ID
PACE_UT
PACE_WY
PACW
PG&E_BAY
PG&E_VLY
PGN
PNM
PSC
PSE
6,734
1,833
862
8,487
1,858
4,266
8,940
12,126
4,220
2,976
7,954
5,322
27,030
11,390
4,460
38,195
13,889
22,914
46,667
62,891
23,463
17,169
47,266
26,485
LDWP
8,200
31,499
SCE
22,311
109,736
Magic Vly
1,382
5,327
SCL
1,909
10,751
Area
Peak
(MW)
Energy
(GWh)
SDGE
SMUD
SPP
SRP
TEP
TIDC
TPWR
TREAS VLY
WACM
WALC
WAUW
4,817
4,303
2,158
7,521
3,128
674
1,040
2,777
4,724
1,600
153
23,631
17,491
12,803
36,091
14,999
2,863
5,480
11,278
30,513
7,622
856
Interconnectionwide
172,082
999,120
* These loads are native loads only (pumping loads and pump storage pumping are excluded)
Page 23 of 121
20-Year
The 2022 Common Case loads were assumed as the starting point for the 2032
Reference Case loads. Area level growth rates from 2012-2022 were applied to 20222032 to get the final 2032 Reference Case loads. This resulted in a Western
Interconnection-wide energy CAGR of 1.54 percent, and a Western Interconnectionwide peak CAGR of 1.41 percent.9 The 2032 Reference Case loads (annual peak and
energy) are provided in Figure 10.
Figure 10: 2032 Reference Case Loads
9
CAGRs specific to each TEPPC area were used, resulting in the above CAGRs in the Western
Interconnection.
Page 24 of 121
Demand Response
Demand response is defined as being electric customer’s reduction in electricity usage,
such that the reduction differs from the customer’s normal consumption patterns and is
in response to price changes or incentive payments designed to lower electricity use at
times of system stress or high market prices. With demand response programs
becoming the norm in the Western Interconnection, TEPPC has taken major steps in
recent study cycles to include demand response assumptions and impacts in studies.
10-Year
The TEPPC 2022 Common Case non-firm load forecasts for 2022 were developed in a
two-part approach. First, the maximum demand response (DR) resource capacity
available for each WECC BA was developed by validating – and, if warranted, adjusting
– the BAs 2021 non-firm load forecasts submitted to WECC’s LRS. Second, an hourly
load modifying profile of non-interruptible DR resources was developed using a
simulated dispatch of those DR resources for each WECC load zone. This section
describes both parts of the approach.
Developing DR Resource Quantities
WECC requires that each BA submits, on an annual basis, a 10-year non-firm load
forecast consisting of monthly non-firm load. These load forecasts are submitted as part
of a broader data collection process administered by WECC’s LRS, and are herein
referred to as the “LRS non-firm load forecasts.” Prior to the 2010 TEPPC study cycle,
WECC directly used the LRS non-firm load forecasts as the TEPPC 2032 Reference
Case load forecast. For the 2010 TEPPC study and again for the 2011 study cycle,
however, the SPSC study request specifically recommended that the 2032 Reference
Case non-firm load forecast be developed in a manner consistent with current demand
response policies and utility resource plans.10 This required that the LRS non-firm load
forecasts be validated and, if necessary, adjusted in order to bring them in line with
current policies and resource plans.
The 2022 Common Case relied specifically on the non-firm load forecasts submitted in
response to WECC’s 2011 LRS data request, which cover the period 2011 to 2021 and
are segmented into four program types: interruptible load, direct load control (DLC),
critical peak pricing (CPP) with controls, and load as a capacity resource (i.e., demandside resources that can be committed for pre-specified load reductions under certain
10
For the 2010 TEPPC study, WECC staff modeled one scenario based directly on the LRS non-firm load
forecasts; that scenario was termed the “base case” scenario, and then a second scenario, termed the
“SPSC reference case,” which contained adjustments to the LRS data. In the 2011 study cycle, however,
there was only a single Common Case, which followed the basic methodology used in the prior year for
the SPSC reference case.
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system conditions).11 These program types are consistent with the North American
Electric Reliability Corporation’s (NERC’s) reporting requirements for dispatchable (i.e.,
controllable) DR resources. BAs could also voluntarily submit demand response
program-specific information, breaking the major program type forecasts into individual
demand response programs. This program-specific reporting was incorporated in the
LRS data collection process for the first time in 2011.
BAs forecasted ~6,303 MW of DR resources in 2021 across all four program types.
These forecasts represent the maximum available DR capacity and are on a NCP basis.
DLC programs accounted for the largest program type with ~2,633 MW (~42 percent of
total DR resources) forecasted in 2021. The smallest program across all BAs was CPP
with ~26 MW of maximum available resource. Table 6 summarizes the 2021 non-firm
load forecasts by program type.
Table 6: 2021 Non-Firm Load Forecast
DR Program Type
Interruptible
DLC
CPP
Load as a capacity resource
Total
2021 Forecast (MW; NCP)
2,335
2,633
26
1,309
6,303
We validated the non-firm load forecasts by comparing each BA’s forecast to utility
IRPs, Federal Energy Regulatory Commission (FERC) DR Survey results, and state
regulatory filings. We then contacted BA and utility staff responsible for non-firm load
forecasts to understand differences between the LRS non-firm load forecasts and what
was included in the public validation sources. Preliminary adjustments were presented
to the SPSC DSM Work Group and we received explicit approvals of recommended
adjustments and, in some cases, instructive feedback from utility and state agency staff.
Adjustments to the LRS non-firm load forecast were as follows:

Arizona Public Service (APS) – We increased the interruptible load from 0 MW to
105 MW in 2021. This assumed the entire amount of 105 MW planned in the
utility’s IRP and the ability for DR to contribute towards compliance with the
Arizona Energy Efficiency Standard (EES).
11
The 2011 TEPPC Study used 2022 as its horizon. The LRS non-firm load forecasts were based on a
2021 horizon, so we assumed them to be constant to 2022 because DR capacity does not necessarily
scale exactly with load. Also, drivers for DR capacity (e.g., program participation, incentive levels) were
not likely to change significantly from one year to the next.
Page 26 of 121









Imperial Irrigation District (IID) – We increased the interruptible load from 0 MW
to ~10 MW in 2021 that was voluntarily reported as program-specific information.
We confirmed with utility staff the interruptible DR program was not included in
the non-firm load forecast submission.
Northern CISO (NISO) – We increased the interruptible load from 46 MW to ~308
MW, increased the direct load control load from 175 MW to ~543 MW, increased
the pricing program load from 0 MW to ~350 MW, and decreased load as a
capacity resource load from 574 MW to ~305 MW in 2021. These adjustments
were based on the Pacific Gas and Electric (PG&E) 2020 Ex Ante forecast to the
California Public Utilities Commission (CPUC) Long Term Transmission Plan
(LTPP). Time-of-use and permanent load shift (PLS) programs were not included
as a pricing program, as they are considered non-event based DR.
PacifiCorp - East (PACE) – We increased the interruptible load from 252 MW to
281 MW in 2021. This reflected the amount of interruptible load in the utility’s
2011 IRP and was confirmed with utility staff.
PacifiCorp - West (PACW) - We increased the interruptible load from 45 MW to
63 MW in 2021. This amount was recommended by utility staff.
Portland General Electric Company (PGE) - We increased the direct load control
load from 0 MW to 60 MW and increased the pricing program load from 0 MW to
20 MW in 2021. These amounts were forecasted by the utility in the 2010 FERC
DR Survey.
Puget Sound Energy (PSEI) – We increased the direct load control load from 0
MW to 144 MW in 2021. This reflected the amount of direct load control
programs forecasted in the utility’s 2011 IRP.
Southern CISO (SISO) - We increased the interruptible load from 694 MW to
~723 MW, increased the direct load control load from 736 MW to ~1,082 MW,
increased the pricing program load from 27 MW to ~582 MW, and decreased the
load as a capacity resource load form 751 MW to ~157 in 2021. These
adjustments were based on the Southern California Edison (SCE) 2020 Ex Ante
forecast to the California Public Utilities Commission (CPUC) Long Term
Transmission Plan (LTPP). Real time pricing (RTP) programs were not included
as a pricing program.
Sacramento Municipal Utility District (SMUD) - We increased the pricing program
load from 0 MW to 143 MW in 2021. This was based on the utility’s forecast of
DR in the 2010 FERC DR Survey.
Salt River Project (SRP) - We increased the pricing program load from 0 MW to
78 MW in 2021. This was based on the utility’s forecast of DR in the 2010 FERC
DR Survey.
Page 27 of 121
These adjustments to the 2021 non-firm load forecasts resulted in a net 1,660 MW or
~26 percent increase in the DR resource size, relative to the LRS non-firm load
forecasts and reflects current demand response policies and program plans. The largest
adjustments (1,048 MW or ~63 percent of the total adjustment) were made to the
California IOU non-firm load forecasts, which account for ~51 percent of the adjusted
2021 non-firm load (~4,051 MW out of ~7,963 MW). Figure 11 summarizes the adjusted
non-firm load forecasts for those BAs that were adjusted.
Figure 11: 2021 BA adjusted non-firm load forecasts
The 2021 Common Case non-firm load forecasts, after all adjustments to account for
current DR policies and program plans, totaled ~7,963 MW of maximum available DR
capacity. DLC programs comprised the largest share of this amount, with ~3,615 MW of
maximum available resources. After accounting for more DR pricing programs, in
particular among the California IOUs, the 2021 Common Case non-firm load forecast
showed ~1,173 MW of maximum available pricing program capacity.
Page 28 of 121
Table 7 summarizes 2021 Common Case non-firm load forecasts before and after
adjustments.
Page 29 of 121
Table 7: 2021 Common Case Non-Firm Load Forecast
DR Program Type
Interruptible
DLC
CPP
Load as a capacity resource
Total
2021 BA Forecast
(MW; NCP)
2,335
2,633
26
1,309
6,303
2021 Adjusted Forecast
(MW; NCP)
2,714
3,615
1,173
462
7,963
Among the BAs, those in California accounted for the largest share of DR resources in
the 2022 Common Case with ~4,051 MW. The Southwest had ~ 2,387 MW of DR
capacities and the Northwest had ~ 678 MW of DR capacities. There were several BAs
that had no DR resources assumed in the 2022 Common Case (see Figure 12).12
See Considering environmental and cultural information in transmission planning will
create results (transmission alternatives) that more effectively limit environmental and
cultural risks and constraints that could affect transmission development. Transmission
alternatives that take these risks and constraints into account result in more realistic
potential transmission corridors, and also facilitate a more collaborative, stakeholderinclusion, and comprehensive transmission planning process. Further, considering
environmental and cultural risks at the planning level would be expected to expedite the
siting process when these factors are considered in greater detail. The use of
environmental and cultural information in transmission planning puts these real-world
considerations on par with demand, generation resources, energy policies, impacts on
transmission reliability and other factors that have traditionally been considered in past
planning cycles.
12
WECC, through its Environmental Data Task Force (EDTF), has identified, collected,
and processed geospatial information (data layers) on environmental and cultural
resource for use in transmission planning. These data layers were identified by EDTF
members and subject matter experts familiar with the current state of environmental
data. The environmental and cultural data layers identified by the EDTF provide a basis
for considering of these resources during transmission planning. WECC has aggregated
these data layers into a seamless “Risk Classification Data Layer” that depicts
environmental and cultural risks and constraints across the entire Western
Interconnection; the Risk Classification Data Layer has been incorporated into the longterm planning tools.
Page 30 of 121
Environmental Data Used
Data Types
Incorporating environmental and cultural data into the transmission planning process
requires a clear understanding of what is considered “environmental and cultural data.”
For the purposes of WECC data collection effort, “environmental and cultural data”
included the following resource categories:







Land (including visual resources)
Wildlife
Cultural
Historical
Archaeological
Water Resources
Transmission and rights-of-ways
WECC’s environmental and cultural data collection effort focused on free, publicly
available, data. However, to allow WECC to consider certain resource categories that
were not available from free public sources, exceptions were made to allow fee-based
and other non-public data, such as the inclusion of NatureServe’s Multi-Jurisdictional
Data Base of Species Occurrence, which describes rare and Threatened/Endangered
Species, and transmission line data.
Data Sources
Data layers considered for use in transmission planning were identified by EDTF
members and subject matter experts. Identified data layers were collected and
categorized into the following categories based on the original data source:





Federal (including Canada) Data
State/Provincial Data
Non-Governmental Organization Data
Vendors’ Data
Cultural and Tribal Data
These five categories were chosen as an easily identifiable, base-level categorization
system to track and organize the large amounts of data being collected.
Page 31 of 121
Federal
Federal data originates from a federal agency in the United States or Canada and
usually spatially represents multi-state or multi-provincial areas. Data from the following
U.S. federal agencies was collected and catalogued:







Department of the Interior
Fish and Wildlife Service
Federal Emergency Management Agency
Forest Service
Geological Survey
National Park Service
National Resources Conservation Service
State and Provincial
State and provincial data originates from a state and provincial level agency or from a
local government agency such as a county or city municipality. Only limited county and
city data were collected because the data was often too fine of a scale for use in
regional transmission planning. Data from the following state/provincial sources was
collected and catalogued:









Province of British Columbia
AltaLIS (Province of Alberta)
Cal-Atlas (California)
Wyoming Game and Fish Department
Colorado Division of Wildlife
Montana Fish, Wildlife and Parks
Oregon Department of Fish and Wildlife
Washington State Department of Natural Resources
Arizona Game and Fish Department
Non-Governmental Organization
For the purposes of data collection Non-Governmental Organizations (NGOs) were
defined as any organization that operates independently from the government and is not
a data vendor (seller). This definition can include both for-profit and non-profit
organizations, or data from other environmental documents. Data from the following
NGOs was collected and catalogued:



NatureServe
Conservation Biology Institute
West-Wide Energy Corridor Programmatic EIS
Page 32 of 121



ConserveOnline
National Audubon Society
Western Renewable Energy Zones Phase 1 Report (Canada Only)
Vendors
Vendor data was defined as any for-profit company that is not government-related and
is in the business of selling data or software even though the data received from the
vendor may not have a fee associated with it. Data from the following vendors was
collected and catalogued:


Environmental Systems Research Institute
Platts
Cultural Resources Data
Cultural resources data is comprised of features of archaeological, anthropological,
historical, and tribal interest. Cultural resources data are typically collected from the
State and provincial government agency (e.g., the state historic preservation offices).
Cultural resources data sets are unique from most types of environmental data in that
they:

Contain information of a sensitive or protected nature; release of such data to the
public could compromise the protection of resources;
 Are only released by the controlling government agency to authorized users;
 Are restricted for publication, distribution, and presentation;
 Are maintained in a variety of electronic formats that may or may not be
manageable in a GIS.
The list of preferred data available for transmission planning currently includes several
sources of cultural data, including data for National Historic Trails and National Historic
Monuments. In addition to these currently available data layers, WECC is in the process
of working with state agencies to acquire additional relevant cultural resource data,
including:


The locations of known cultural sites
The locations of cultural survey (inventory) areas
Data Quality
Following their identification and collection, WECC determined the quality of each
environmental and cultural data layers using a two-step process. First, all data layers
were examined using section 3.1 of the Data Quality Protocol. Data layers that passed
the Data Quality Protocol were added to the EDTF Data Inventory, which details all the
environmental and cultural data layers considered for use in transmission planning.
Second, data layers in the EDTF Data Inventory, section 3.2, were reviewed by the
Page 33 of 121
diverse stakeholder group that comprises the EDTF membership, and data found useful
for transmission planning were identified as “preferred data” for use in regional
transmission planning.
Data Quality Protocol
The purpose of the Data Quality Protocol is to provide data users and stakeholders a
standardized, structured/step-wise protocol for performing a fitness-for-use (data
quality) assessment of environmental and cultural data layers identified by WECC.
Ultimately, the Data Quality Protocol is intended to assist the EDTF in assessing
whether catalogued data layers should be considered “preferred data” (data that meets
quality standards and is potentially useful to consider in regional transmission planning).
Implementation of the Data Quality Protocol involves collaborations between GIS
analysts and stakeholders and other subject matter experts.
Preferred Data Sets
Preferred data are those environmental and cultural data sets that stakeholders and
subject matter experts determined are useful to inform transmission planning. In
general, preferred data can be quantified and occur at a scale conducive to regional
transmission planning. The current list of preferred data for transmission planning may
change over time as new information becomes available (e.g., through routine data
updates or the biennial open season process).
Risk Classification System
The EDTF stakeholders assigned Risk Classification Categories to each data layer
identified as preferred. The EDTF’s Risk Classification System was developed to allow
the categorization of risk to transmission development from various environmental and
cultural features on the landscape. Because of the large area covered by the Western
Interconnection, a basic four-point scale was used for categorization. Under this system,
Risk Classification Category 1 represents the lowest risk and Risk Classification
Category 4 represents the highest risk (i.e., areas were transmission development is
precluded by law or regulation). The four-point Risk Classification System was based on
the suitability criteria used in other studies, (i.e., Electric Power Research InstituteGeorgia Transmission Commission [EPRI-GTC] Overhead Electric Transmission Line
Siting Methodology, Renewable Energy Transmission Initiative, and Arizona Renewable
Resource and Transmission Identification Subcommittee), and professional judgment of
stakeholders and subject matter experts. The EDTF stakeholder group agreed to this
approach because it allowed an easily understandable, “planning level” method to
aggregate risk and constraints from a variety of environmental and cultural features.
The four Risk Classification Categories are described in detail below:
Page 34 of 121
1.) Least Risk of Environmental or Cultural Resource Sensitivities and Constraints:
Areas with minimal identified environmental or cultural resource constraints and with
existing land uses or designations that are compatible with or encourage transmission
development. These areas would present few or minimal environmental and cultural
mitigation requirements and are least likely to result in project delays.
2.) Low to Moderate Risk of Environmental or Cultural Resource Sensitivities and
Constraints: Areas where development may encounter one or more environmental or
cultural resource sensitivities or constraints that would require low to moderate permit
complexity or mitigation costs. This category also includes areas in the Protected Areas
Database of the United States (PAD-US) dataset that have an unknown land use
designation or degree of restriction to transmission development.
3.) High Risk of Environmental or Cultural Resource Sensitivities and Constraints:
Transmission development is likely to encounter one or more environmental or cultural
resource sensitivities or constraints that will substantially increase permitting complexity
and which could result in project delays and high mitigation costs. This category also
includes areas identified as avoidance areas (based on environmental and cultural
sensitivities) in Canada from the WREZ Phase 1 Report.
4.) Areas Currently Precluded by Law or Regulation: Areas where transmission
development is currently precluded by federal, state, or provincial law, policy, or
regulation, and areas identified as exclusion areas (based on environmental and cultural
sensitivities) in Canada from the WREZ process.
The Risk Classification System was applied to the preferred data based on reviews of
applicable laws, regulations, policies as well as input from relevant subject matter
experts and the stakeholders. The full justification for the Risk Classification
assignments is available in the Environmental Recommendations for Transmission
Planning Report (also Appendix D).
The application of the Risk Classification to the preferred data was used to create a
seamless, GIS-based Risk Classification Data Layer that depicts environmental and
cultural risks and constraints across the entire Western Interconnection for use in
WECC’s long-term planning tool.
Limitations on Environmental and Cultural Data
Data used by WECC to describe environmental and cultural features through the
preferred data sets will change as new and updated information becomes available. To
remain relevant, a regular process of data review and update for preferred data has
been developed and is being implemented. Data providers are continuing to improve
their existing data as well as adding new or previously unmapped data layers. Because
of the large amount of data providers who supplied data for the analysis and the varied
timelines of data updates by those providers, it is difficult to have the most current data
Page 35 of 121
for all necessary layers simultaneously for analysis. Issues of data currency make it
important to explicitly label products with “current as of” dates.
WECC recognizes there is not currently data available for some of the environmental
and cultural features that EDTF stakeholders and subject matter experts identified as
having the potential to affect transmission development. Below are the primary data
gaps identified for environmental and cultural information.
Cultural Resources Data
Currently, cultural geospatial data that is seamless across administrative boundaries is
limited due to a variety of factors, including the sensitivity of the data for public release.
WECC is working closely with several state historic preservation offices to determine an
appropriate method and scale for acquiring and using the cultural resource location and
inventory (survey) data they collect. The goal is to develop a process and product that
respects the sensitivity of cultural data and the need to protect the locations of these
irreplaceable resources from public release, while providing sufficient information to
allow the consideration of cultural resources during regional transmission planning.
Tribal and First Nations data can also be sensitive. Other than reservation lands and
other legal boundaries, WECC has not been successful in identifying data for important
Tribal and First Nations cultural resources and traditional use sites.
Wildlife Data
WECC has collected wildlife data from state, NGO and federal data sources. However,
inconsistencies exist in naming conventions and the overall lack of seamlessness of
wildlife data across jurisdictional boundaries. As data from the Western Governors’
Association state Crucial Habitat Assessment Tool program become available across
the U.S. portion of the Western Interconnection, many of these issues should be
addressed.
Canadian Data
Currently, the bulk of the dataset catalogue consists of U.S. data. While the existing
seamless Risk Classification Data Layer contains information for Canada, additional
and updated data are needed from the Canadian portions of WECC in British Columbia
and Alberta. WECC is working to update and improve Canadian data by conducting
concentrated data outreach with Canadian data stewards and stakeholders.
Appendix A: Hydro Data for a table of DR resource capacities by BA and program type.
Page 36 of 121
Figure 12: 2022 Common Case Non-Firm Load Forecast by BA
Page 37 of 121
Figure 13 shows the DR resources expressed as a percent of 2021 peak demand (on
an NCP basis). The 2022 Common Case peak demand was 191,678 MW in 2021 and
non-firm load represents ~4.2 percent of peak demand. DR capacity as a percent of
peak demand ranges from 0 percent for a number of BAs to ~8.2 percent (CISO).
Figure 13: 2022 Common Case DR Resources as Percent of 2021 Peak Demand
Hourly Shaping of DR Resource Availability
LRS non-firm load forecasts are expressed as the available monthly DR capacity in the
peak hour of each month. However, the availability of DR resources varies by month
and hour for each BA and for some programs. It is important to capture the monthly and
hourly availability in the modeling of DR resources in order to ensure DR programs
typically used during system peak months and hours are not utilized at full availability in
non-system-peak months and hours.
The DR resource capacity was shaped to the hourly load profiles for each BA. The DR
resources scaled with hourly load were assumed because the end-uses driving demand
were also the end-uses that could respond to DR program signals. The hourly shaping
factors represent the hourly load divided by the maximum annual load (i.e., annual
peak) so that only one hour of the year had 100 percent DR resource availability.
Figure 14 shows an example of this hourly shaping applied to the SCE load zone on its
peak day in 2022. The hour ending 1600 is the annual peak and thus has 100 percent
availability of the non-interruptible DR resources (~1,493 MW). The shaping factor and
corresponding available DR resource changes on an hourly basis as the hourly load
increases or decreases relative to the annual peak load.
Page 38 of 121
Figure 14: Example Hourly Shaping Factor for SCE on 2022 Peak Day
Simulated Dispatch of DR Resources
The 10-year horizon studies conducted by TEPPC utilize a production cost model
(PROMOD) to model the dispatch of generation resources in the Western
Interconnection. Our objective of realistically modeling DR resources within the
constraints of PROMOD led to the development of a modeling approach for economicbased DR programs in the 2010 and subsequent study cycles. This modeling approach
employed a two-part methodology: 1) develop assumptions about the DR dispatch
characteristics (e.g., expected hours of dispatch per year, resource availability); and 2)
simulate dispatch of DR resources in a manner consistent with the dispatch
characteristics.
Developing DR Resource Dispatch Characteristics
To develop DR resource characteristics, we reviewed regulatory filings and other
publicly available information related to the DR programs operated by load serving
entities in the Western Interconnection, focusing primarily on the California IOUs’ DR
programs, which collectively represent more than 50 percent of the Interconnectionwide non-firm load in the 2022 Common Case. Based on this information, we developed
DR dispatch characteristics with the following considerations for each of the LRS nonfirm load forecast program types (see Table 8):

Interruptible load programs: We assumed that these programs are utilized
primarily for reliability-based events and are therefore rarely dispatched under 1in-2 conditions (as the 2022 Common Case is intended to represent). The review
of California IOUs’ DR program information showed that all California IOUs offer
Page 39 of 121



a Base Interruptible Program (BIP) with similar program rules, and 2009 event
data showed that these interruptible programs were rarely, if ever, dispatched.
This is not surprising as the reliability-based events are infrequent. Ten hours of
dispatch per year were assumed for the 2022 Common Case (i.e., 2 to 3
interruptions per year, of 2 to 6 hours per event), given that some of these
resources may be dispatched occasionally in response to soft, non-reliabilitybased triggers (e.g., energy market conditions).
DLC programs: It was assumed that DLC programs could be dispatched for both
reliability and economic purposes. The California IOU 2009 event data for DLC
resources confirmed that these resources were called on more frequently than
“reliability class” DR resources. Utilities typically compensate participating
customers with a reservation payment (i.e., a capacity-based bill credit) for the
right to control their load, and therefore it was assumed that the utility would
dispatch these DR resources for close to the maximum amount allowed under
program rules, which was stipulated as 10 times per year, for four hours per
event, or 40 hours per year.
Pricing programs: It was assumed that pricing programs are dispatched for both
reliability and economic purposes. Pricing program participants receive
discounted rates during non-peak hours and, in order to maintain revenue
neutrality, it was assumed that utilities would come close to maximizing the
number of dispatch hours each year, even under 1-in-2 conditions. Therefore, 50
hours of dispatch (i.e., 10-12 peak events per year, at 4-6 hours per event) was
assumed under 1-in-2 conditions, based on a review of the typical tariff program
rules for pricing programs.
Load as a capacity resource programs: DR capacity in this program type was
reported only by CISO in its non-firm load forecasts. It was assumed that these
programs are dispatched for both reliability and economic purposes. It was
further assumed that participating customers receive a capacity-based
reservation payment and that therefore the utility will also dispatch these DR
resources relatively frequently, even during a 1-in-2 year, driven in part by the
fact that many of these resources are performance-based contracts with
Curtailment Service Providers (i.e., aggregators). There was an assumed 60
hours of dispatch (i.e., 15 dispatch events at 4 hours per event). These
assumptions are based on a review of the program rules and operating history of
the specific California aggregator managed programs. As a point of reference,
PG&E dispatched its Capacity Bidding Programs (CBPs) 14 times in 2010 and
Southern California Edison (SCE) dispatched its CBPs 25 times in 2010.13
13
Braithwait, S. and D. Hansen. Load Impact Evaluation: Aggregator Programs. Demand Response
Measurement and Evaluation Committee (DRMEC) Spring Workshop. Presentation, April 26, 2011.
Page 40 of 121
Therefore, it is believed that 60 hours of expected dispatch is a reasonable
assumption.
Table 8: 2022 Common Case Expected Dispatch Hours per Year
DR Resource Class
Interruptible Load
Direct Load Control
Pricing
Load as a Capacity Resource
Expected Dispatch Hours per Year
10
40
50
60
Consideration of Alternative Approaches for DR Dispatch
DR programs are used by utilities for planning, operational and reliability purposes in
different ways, and DR resources are dispatched in a manner distinct from supply-side
resources. For example, DR programs are often subject to program rules limiting their
operation to a maximum number of hours per year, and have restrictions on the
minimum or maximum number of continuous hours of operation and on the frequency
with which the customers can be curtailed.
Production cost models such as PROMOD have limited functionality in terms of their
ability to accurately model the dispatch and operation of DR programs. Several potential
modeling approaches within PROMOD were tested and evaluated to simulate DR
resource dispatch, each with advantages and disadvantages:


High-cost combustion turbine (CT) generating unit: DR resources were modeled
within PROMOD as a set of CT generation units. This is the method that WECC
used for all DR resources prior to the 2010 study cycle. Under this approach, DR
resources within each BA are represented as a proxy CT unit dispatched based
on its merit order. The resource parameters (i.e., heat rate, fuel cost, and
variable O&M costs) are tuned through an iterative process until the set of DR
resources is dispatched for approximately the targeted number of hours per year.
The disadvantages of this approach are, first, that the iterative tuning process
cannot realistically be done for each BA individually, but rather only in an
approximate manner across all DR resources within the Western Interconnection.
Second, the approach is unable to realistically simulate other important features
of DR program operation (e.g., limited number of hours per event or frequency of
events).
Peak-shaving hydroelectric (“hydro”) unit: DR resources were modeled as an
energy-limited hydro resource by establishing a maximum energy output for each
unit and setting the operating limits with respect to monthly dispatch
assumptions. For example, an operating limit of zero would allow the DR
resource to be dispatched over the entire month up to the set annual energy limit.
Page 41 of 121

In exploring this option further, it proved unrealistic for DR resources because
they were often utilized to the maximum energy potential early in the year and
not utilized in later months when the DR was more likely to be dispatched by the
utility (e.g., summer peaking months).
Dispatchable transactions: This was initially a promising approach whereby DR
could be modeled and dispatched by load and price. It was assumed that DR
resources could be dispatched in this manner by adjusting a price-level to reach
the desired dispatch assumptions. In effect, defining economic blocks of DR
resources could build a DR supply stack to mimic the incrementally higher-cost
DR resources. In testing this approach with varying size resources and pricelevels, however, it was determined that the DR would not dispatch realistically.
The DR was dispatched at small amounts (often <1MW) and for thousands of
hours per-year.
Ultimately, none of the three aforementioned modeling options within PROMOD proved
capable of realistically simulating the dispatch of DR resources. As a result, a technique
was developed, instead, that involved developing hourly load modifying profiles of DR
resources, thereby incorporating DR program operation into PROMOD via the hourly
load data, rather than as a proxy generation unit.14
Demand Response Dispatch Tool
To develop these hourly load modifying profiles, we created the LBNL DR Dispatch Tool
(DRDT). The DRDT dispatches DR resources during high-price hours according to
program constraints and resource availability. The tool requires three user-defined
inputs: 1) maximum monthly DR capacity for each (non-interruptible) DR program type
and BA; 2) hourly energy load for each BA; and 3) hourly PROMOD locational marginal
prices (LMPs) for each BA from PROMOD runs without DR. These inputs were specific
to the 2022 Common Case (i.e., 2022 Common Case hourly loads and LMPs). The
DRDT then identifies the highest-average LMP consecutive-hour blocks for each BA
and dispatches the DR resources in those hours. The amount of DR available to be
dispatched in any given hour is based on the hourly shaped DR resource availability, as
described previously. The DR load reductions in each hour are then deducted off of the
load forecast and PROMOD re-runs using the modified (post-DR) hourly load forecast
for each BA.
Figure 15 shows the results of the simulated dispatch of non-interruptible DR resources
and summarizes the results on a non-coincident annual peak demand reduction. The
left axis and corresponding bars in the graphic show the annual peak reduction as a
14
We continued to model interruptible programs as a high-cost CT unit.
Page 42 of 121
percent of annual peak demand, and the right axis and corresponding markers show the
annual peak reduction as a MW amount. The results are reported here for each WECC
load zone with some amount of DR capacity (the BAs without an annual peak reduction
in the figure only had interruptible DR programs). The WECC load zones show annual
peak reductions from non-interruptible DR programs at or below the non-interruptible
DR resource capacity. This is due to both the hourly shaping that takes into account DR
resource availability and the results of the simulated dispatch during consecutive highprice events. These results show a more realistic approach of modeling DR resources
because the approach factors in typical program tariff rules (e.g., maximum and
expected hours of dispatch per year), resource availability and system price as a
dispatch trigger.
Figure 15: Simulated Dispatch Results for 2022 Common Case Non-Interruptible DR Programs
Page 43 of 121
20-Year
WECC’s 20-year study relies on a capacity expansion model which seeks to build out
generation capacity to meet peak demand on a seasonal basis within each WECC load
zone. DR impacts are an input to the model and are specified in terms of a reduction in
peak demand.
Instead of the two-part approach used in the 2010 and 2011 TEPPC studies to develop
load modifying profiles of DR, when it was first determined the DR resource capacity
then simulated the dispatch of DR resources during high-price hours, a three-part
approach was alternatively developed: 1) simulated the hourly dispatch of the DR
resource levels assumed in the 2022 Common Case; 2) calculated the associated
reduction in seasonal peak demand for 2022; and 3) extrapolated the 2022 seasonal
peak demand reductions to 2032. This chapter describes the three-part approach.
Developing the System Peak Demand Reductions in the 2032 Reference Case
The 2032 Reference Case (20-year horizon) is an extrapolation of the 2022 Common
Case (10-year horizon), and as such, the DR resource capacities in the 2022 Common
Case were used as the starting place (see Chapter 2). The hourly loads for each WECC
load zone from the 2022 Common Case were also used to calculate both the pre-DR
seasonal peaks and to trigger dispatch of the DR resources.
Simulated Hourly Dispatch
LMPs could not be used as a trigger for the simulated dispatch of DR resources in the
2032 Reference Case because the 20-year study employed a capacity expansion
modeling tool that did not rely on production costs. High-load periods were used instead
of high-price periods as triggers for DR dispatch. This assumed that DR would be used
in the capacity expansion tool to minimize load and therefore maximize the benefit of
DR in deferring or avoiding generation expansion.
The LBNL DRDT was utilized when using the 2022 Common Case DR resource
capacities and hourly loads to produce an hourly profile of DR resources for each
WECC load zone. The following assumptions were made about expected hours of
dispatch for each of the DR program types:

Interruptible load programs: The 2010 and 2011 TEPPC studies modeled
interruptible load programs as a high-cost CT unit, adjusting unit operating
parameters (e.g., fuel cost, heat rate) to achieve a desired number of hours of
dispatch per year. For the 20-year study, interruptible load programs were
included in the LBNL DRDT and a load modifying profile of interruptible load
resources was created assuming 10 hours of dispatch per year (i.e., five events
at two hours per event.)
Page 44 of 121



Direct load control (DLC) programs: It was assumed that DLC programs were
dispatched 40 hours per year (i.e., 10 events at four hours per event).
Pricing programs: It was assumed that pricing programs were dispatch 50 hours
per year (i.e., 10 events at five hours per event).
Load as a capacity resource programs: It was assumed load as a capacity
resource programs were dispatched 60 hours per year (i.e., 10 events at six
hours per event).
These assumptions about the expected hours of program utilization per year were the
same as used in the 2022 Common Case, which were developed by reviewing actual
historical dispatch in utility regulatory filings and DR program rules (see Chapter 2).
The DR resource capacity was limited by its availability in any given hour to ensure DR
programs typically used during system peak months and hours were not utilized at full
availability in non-system-peak months and hours. This was the same hourly shaping
approach in the 2011 TEPPC studies in which it was assumed that the size of the DR
resource in each hour was proportional to the ratio of total load in each hour to the
annual peak load.
The operation of the DR resources was simulated using the 2022 Common Case hourly
loads for each load zone and triggering the dispatch of DR based on the top-load days
and contiguous hour periods within those days, representing the highest average hourly
periods. For example, pricing programs were dispatched for 50 hours per year, and it
was assumed that each dispatch event would be five hours in duration. Therefore,
pricing programs were dispatched on the “10” days with the highest load over a
contiguous five-hour period. This produced hourly load profiles of DR resources.
2022 System Condition Peak Demand Reductions
The next step in the approach was to calculate the 2022 system condition peak demand
reductions. To do this, the hourly load profiles of DR resources were subtracted from
the 2022 Common Case hourly load forecast (i.e., “pre-DR”) to produce a 2022 hourly
load forecast post-DR. The system condition peak demand was then calculated for each
WECC load zone pre- and post-DR. The last step was to calculate the 2022 system
condition peak demand impacts as the difference of the pre- and post-DR system
condition peak demand. The system condition DR peak demand reduction is, therefore,
equal to the difference between the system condition peaks with and without DR.
Extrapolating System Condition Peak Demand Reductions to 2032
The last step in the approach was to extrapolate the 2022 quarterly peak demand
impacts to 2032. Because the 2032 Reference Case was an extrapolation of the 2022
Common Case, the same compound annual growth rates (CAGRs) assumed for the
Page 45 of 121
load forecasts was used for each WECC load zone to linearly extrapolate the 2022
system condition peak demand impacts to 2032.
Results
Table 9 shows the 2032 quarterly peak demand reductions for the 2032 Reference
Case.15 The California load zones have the largest system condition peak demand
reductions (PGE_BAY, PGE_VLY, SCE, and SDGE), which is expected given the large
DR resource capacities for those BAs. On a quarterly basis, the results show third
quarter 2032 with the highest frequency of peak-demand reductions and is consistent
with those load zones where system peak demands fall in the third quarter. For those
load zones that typically have system peaks occurring in the winter, peak demand
reductions in the respective quarters (i.e., first and fourth quarters) were observed.
On an annual peak-reduction basis, WECC load zones with DR resources in the 2032
Reference Case had annual peak reductions, on a non-coincident peak basis, ranging
from 0 to ~7 percent (see Figure 16). Several load zones with sizeable DR resources
may show no DR peak reduction or lower than expected DR peak reduction (e.g.,
PACW). In general, this is due a shift in the peak day within the quarter. Thus, although
the percentage reduction in peak demand may be relatively large for an individual day
when DR is dispatched, the percentage reduction in the quarterly peak demand may be
much smaller if the peak demand is simply shifted to another day when DR was not
dispatched.
Table 9: 2032 Reference Case Quarterly Peak Demand Reductions
WECC Load Zone
AESO
APS
CHPD
EPE
FAR_EAST
IID
LDWP
MAGIC_VLY
NEVP
PACE_ID
PACE_UT
PACE_WY
PACW
PGN
2032 Reference Case Quarterly Peak Demand Reduction (MW)
Q1
Q2
Q3
Q4
97
0
0
91
0
0
138
0
29
0
0
16
0
60
71
0
0
54
64
0
0
0
11
0
0
0
193
0
0
67
94
0
0
0
254
0
0
33
24
0
0
0
598
0
0
0
1
0
0
0
65
0
91
0
0
67
15
See Technical Appendix, Table A-3 for 2022 and 2032 WECC Reference Case quarterly peak demand
reductions by WECC load zone.
Page 46 of 121
WECC Load Zone
PGE_BAY
PGE_VLY
PNM
PSCO
PSE
SCE
SDGE
SMUD
SPPC
SRP
TEPC
TREAS_VLY
WACM
2032 Reference Case Quarterly Peak Demand Reduction (MW)
Q1
Q2
Q3
Q4
0
396
334
0
0
0
90
0
0
0
50
0
0
0
133
0
150
0
0
157
0
0
1,763
0
0
305
309
0
0
0
279
0
0
0
56
0
0
0
214
0
0
0
56
0
0
0
151
0
0
1
1
0
Figure 16: 2032 Reference Case Annual Peak Reduction from DR (Non-Coincident Peak Basis)
Page 47 of 121
WACM
TREAS_VLY
TEPC
SRP
SPPC
SMUD
SCE
SDGE
PSE
PNM
PSCO
0
PGE_VLY
0.00%
PGN
300
PGE_BAY
1.00%
PACW
600
PACE_WY
2.00%
PACE_UT
900
NEVP
3.00%
PACE_ID
1,200
MAGIC_VLY
4.00%
IID
1,500
LDWP
5.00%
FAR_EAST
1,800
EPE
6.00%
CHPD
2,100
APS
7.00%
Reference Case Annual Peak Demand Reduction (MW)
Annual Peak Demand Reduction (MW)
2,400
AESO
Reference Case Annual Peak Reduction (% Annual Peak)
Annual Peak Reduction (% Annual Peak)
8.00%
Generation
Existing system generators, projects under construction, renewable resource additions
that are required to meet statutory Renewable Portfolio Standards (RPS), and additional
thermal generation needed to achieve a reasonable load/resource balance for
subregions of the Western Interconnection are all included in TEPPC’s studies.
Assumed operational characteristics (e.g., capacity factors, generation profiles, heat
rates, ramp rates, maintenance schedules) for various generation types (e.g., gas, coal,
wind, solar, geothermal) are provided by BAs, other organizations (such as National
Renewable Energy Laboratory (NREL) for wind and solar characteristics), and other
publicly available information sources.
Existing Thermal Generation
10-Year
The 2011 LRS data submittals served as the main source of data for existing thermal
generation. In accordance with the LRS definitions for the 2011 data collection period,
existing resources are those assumed to be online by December 31, 2010. In addition to
the LRS data, resources and their capacities were identified via the SSG-WI16 2005
dataset, WECC power flow case, Energy Information Administration (EIA) data, Utility
IRPs, CA PUC cases, and other databases provided by stakeholders. Stakeholder
comments are included in the dataset to the extent possible. Note that in all TEPPC
cases, thermal unit generating capacities are net of station service.
20-Year
The 20-year study timeframe definition of an existing resource is any resource in
operation on December 31, 2022 in the 2022 Common Case. Based on this, the
assumptions that form the existing thermal generation in the 2032 Reference Case are
the same assumptions and data that supplied generation information in the 2022
Common case.
New/Incremental Thermal Generation
10-Year
Incremental resources are resources expected to be in service between 2011 and 2022
(inclusively). This resource data was collected from the 2011 LRS data submittals,
WECC power flow case, EIA data, utility IRPS, CA CPUC cases, and other databases
16
Seams Steering Group of the Western Interconnection
Page 48 of 121
as needed. In some cases, supplemental data from utility IRPs was used to bridge the
gap in meeting 2022 policy goals or planning margins.
20-Year
The 2032 Reference Case started with the assumptions from the 2022 Common Case.
Based on this, any generation that was new or existing in the 2022 Common Case was
deemed existing for the purposes of the 20-year studies. From the 2022 starting point,
the Long-term Planning Tool (LTPT) was used to select the remaining resources
needed to serve loads, ensure reliability, and meet RPS requirements in the 2022-2032
timeframe. Based on this, no new or incremental generation was explicitly added to the
2032 Reference Case since one of the main purposes of the tool is to optimize this
incremental generator build-out.
Retired Thermal Generation
For some of the study cases, some fossil resources are scarcely used or not dispatched
at all. These resources are identified as retired or displaced in the results; however, the
savings and costs associated with actually retiring these facilities are not reflected. In
addition, some of the resources that are not retired in a scenario will require interim
capital investment to continue operating given federal environmental regulations. These
life-extension costs are not included in current assumptions. Improvements in the
consideration and quantification of these savings and costs would enhance the next
study cycle. For example, this information would allow a comparison of the lifeextension costs of an aging unit requiring retrofit with the potential stranded costs
associated with early retirement of the unit.
10-Year
Information derived from the LRS data submittals, utility IRP postings, and other
sources was used to develop generation retirement schedules for the 2022 Common
Case. Ongoing work to identify likely retirements will be important because some
generation that is assumed to be available in models will likely be retired for economic
or environmental reasons. Failure to capture these retirements may distort the system
dispatch.
California state policy, to comply with Section 316(b) of the Clean Water Act, has set
targets for the shutdown or repowering of several generation plants that utilize oncethrough-cooling (OTC). Stakeholders from California provided an implementation plan
that was used in the 2022 Common Case. Table 10 provides a summary of the plan. A
summary of the non-OTC retirements is provided in Table 11.
Page 49 of 121
Table 10: California Once-through-cooling Compliance Plan
Resource
Name
Type
Alamitos 1-6
STM
2,010
Contra Costa6
STM
337
Contra Costa 7 STM
El Segundo 3
El Segundo 4
El Segundo RP
2009
Group17 LSE18
Capacity
SCE
Capacity
Added
Year Added or
Retired
2020
OTC - PG&E
337
2014
337
OTC - PG&E
337
2014
STM
335
OTC -
SCE
335
2013
Replaced by NRG El Segundo
Repower Project (see below)
STM
335
OTC -
SCE
335
2017
Not part of unit 1-3 repower, may be
repowered separately later
OTC +
SCE
560
STM
945
OTC - SDGE
945
2017
Encina GT
CT
15
OTC - SDGE
15
2017
CC
OTC +
SCE
1,000
2020
CT
OTC +
SCE
1,000
2020
Haynes 5
STM
341
Haynes 6
STM
341
Haynes GT 1-6
Huntington
Beach 1
Huntington
Beach 2
Huntington
Beach 3
Huntington
Beach 4
CT
OTC - LDWP
341
OTC - LDWP
341
OTC + LDWP
Replaced by Marsh Landing Project
(see below)
2013
Encina 1-5
Generic CC
(SCE)
Generic CT
(SCE)
Comments
2,010
CC
OTC -
Capacity
Retired
2013
2013
600
2012
See addition of Generic CC/CT
(SCE) above.
STM
226
OTC -
SCE
226
2020
STM
226
OTC -
SCE
226
2020
STM
225
OTC -
SCE
225
2013
STM
227
OTC -
SCE
227
2013
Mandalay 1-2
STM
430
OTC -
SCE
430
2020
See addition of Generic CC/CT
(SCE).
Marsh Landing
CC
2014
Replacing Contra Costa 6 & 7
OTC + PG&E
719
Morro Bay 3
STM
325
OTC - PG&E
325
2015
Morro Bay 4
STM
325
OTC - PG&E
325
2015
Moss Landing
1-2
CC
PG&E
2017
Moss Landing 6 STM
754
OTC - PG&E
754
2017
Moss Landing 7 STM
756
OTC - PG&E
756
2017
Ormond Beach
STM
1-2
1,516
1,516
2020
OTC -
SCE
Pittsburg 5
STM
312
OTC - PG&E
312
2017
Pittsburg 6
STM
317
OTC - PG&E
312
2017
Redondo
Beach 5-8
STM
1,343
1,343
2020
OTC -
SCE
See Walnut Creek; retired early to
transfer air permits to WC.
considering retrofit of existing units
also considering retrofit; assume
retirement
See Generic CC/CT (SCE).
“OTC -“ represents retirements due to OTC implementation. “OTC +” represents generator additions or
retrofits intended to replace retired OTC generators.
17
18
Load serving entity
Page 50 of 121
Resource
Name
Scattergood 3
Type
STM
Scattergood CC CC
2009
Group17 LSE18
Capacity
445
OTC - LDWP
Capacity
Retired
Capacity
Added
445
OTC + LDWP
Year Added or
Retired
Comments
2016
509
2016
Table 11: Generation Retirements (>100 MW)
Province/State
Unit Name
Capacity (MW) Retirement Year
Battle River 3,4
296
2013
Alberta
HR Milner
143
2021
Sundance 3
353
2021
19
British Columbia Burrard Thermal 1-6
904
2014
Coolwater 1,2
145
2015
Kearny 1-3
136
2013
California
Mandalay 3
130
2020
Pittsburg 7
682
2017
see OTC list for other OTC related retirements
Arapaho 3,4
154
2013
Cherokee 1,2
217
2012
Colorado
Cherokee 3,4
505
2016
Valmont 5
178
2017
Zuni 1,2
115
2013
Fort Churchhill 1,2
234
2021
Reid Gardner 1-3
330
2020
Nevada
Sunrise 1,2
149
2020
Tracy 1,2
141
2015
Four Corners 1-3
560
2021
New Mexico
Rio Grande 6,7
93
2017
Oregon
Boardman 1
510
2020
Texas (EPE)
Newman 1-3
247
2019
Centralia 1
728
2020
Fredonia 1,2
208
2019
Washington
Fredrickson 1,2
149
2016
Whitehorn 1,2
149
2016
19
Burrard Thermal is subject to regulation that will restrict its use to generating under emergency
conditions only once certain system reinforcements are completed. The 2022 PC1 Common Case
assumes these reinforcements will occur by 2014.
Page 51 of 121
20-Year
Since the 2032 Reference Case generation assumptions were based on those
generators in service at the end of the 2022 Common Case study year, the OTCretirements and other generator retirements reflected in Table 10 are included in the 20year studies. One shortcoming on the 2032 Reference Case resource assumptions is
that additional OTC retirements occurring within the 2022-2032 period were not
considered. Any retirement assumptions between the 10- and the 20-year timeframes
would have to be explicitly designated in the study criteria of a 20-year case study.
Existing Renewable Generation
10-Year
The generation additions for the 2022 Common Case were selected from resources
proposed in utility IRPs, BA submittals to the LRS, and where Western Renewable
Energy Zone (WREZ) resource screening results indicated a need.
20-Year
Existing renewable generation for the 20-year study cases is taken directly from the
2022 Common Case. These assumptions serve as the starting point for the 20-year
studies.
New/Incremental Renewable Generation
10-Year
RPS Gap Generation: The Technical Advisory Subcommittee (TAS) Studies Work
Group (SWG) determined the required RPS generation for each state to meet the
respective state RPS requirements in 2022. Where there were insufficient renewable
resources specified from the existing and incremental generation, generic RPS
resources were added based on input obtained from utility IRPs, feedback collected
from utility resource planners, and by using the WREZ Peer Analysis Tool20.
WGA, “WREZ Peer Analysis Tool”:
http://www.westgov.org/index.php?option=com_content&view=article&catid=102%3Ainitiatives&id=220%
3Awrez-transmission-model-page&Itemid=81
20
Page 52 of 121
20-Year
All of the 20-year study cases completed to support this Plan include significant
additions of variable resources, primarily wind and solar. The model assumes that
natural gas resources would be the default flexibility resource to ensure reliability when
variable resources are not available. While there are a number of measures that may
reduce the cost of integrating variable generation, and perhaps provide sufficient
reliability without relying on natural gas resources, these measures were not included in
this study cycle and will be considered in future study cycles.
Renewable Resource Profiles
10-Year and 20-Year
Solar and wind generation are modeled as fixed-shape resources in TEPPC’s 10-year
production cost model. This means that solar and wind generation is forced into the
model as must-take generation because these units have no production cost. The
software user must explicitly specify this fixed hourly profile when modeling wind and
solar.
NREL, as part of the Western Wind Dataset effort, created hourly solar and wind mesoscale shapes for roughly 30,000 sites throughout the Western Interconnection. Each
NREL profile in the Western Wind Dataset represents a small generation site (2 km by 2
km) and the historical resource availability in that small region. The original data is
based on extensive meteorological modeling efforts that result in wind speed or
irradiance (in the case of solar) data for the specific region that can then be converted to
power output.
TEPPC profiles capture a much larger region and are used to represent a shape that
would be more characteristic of an average generation site in that area. Solar and wind
profiles used in the TEPPC datasets are created by aggregating NREL profiles. Instead
of representing a single 2 km by 2 km grid, the aggregated TEPPC shapes represent a
much larger area. This methodology was adopted for two key reasons:
1) Aggregating NREL profiles to represent regionally based profiles is the most
efficient way to accurately assign generators within that region a specific, yet
accurate, shape. TEPPC could attempt to develop a profile for each individual
generator in the dataset, but this would require a substantial amount of time and
effort. Because of this, TEPPC creates aggregated regional profiles that are
assigned to plants within that region.
2) Aggregating NREL profiles into a representative TEPPC profile captures the
appropriate amount of geographic diversity that is supplied by the resources.
Page 53 of 121
Simply taking a single NREL profile and assigning it to a large capacity of
resources would result in a shape with overstated variability.
A number of NREL profiles - identified through the site selection process - needed to
represent the approximate capacity of wind/solar in a given geographic vicinity are
aggregated to produce a single TEPPC profile. Enough NREL profiles need to be
selected to fulfill the required amount of resource capacity for the site being modeled.
For example, to model a 300-MW plant in the TEPPC dataset, 300 MW worth of NREL
solar or wind profiles would need to be gathered and aggregated. All plants within the
same geographic vicinity are then applied the same (per unit) aggregate shape that gets
scaled according to the individual plant’s capacity, as previously described. This method
more accurately depicts the output of an actual wind or solar site, compared to the
alternative option which uses a generic shape. The process for creating solar and wind
aggregate shapes is the same for both TEPPC solar and wind profiles.
TEPPC does not monitor low-voltage transmission and focuses on interregional flows.
Only the total output from the region is important. For this reason, the aggregated
TEPPC profile for a region is analytically equivalent to creating an individual shape for
each generator in that region.
Figure 17 shows an example of the TEPPC site selection process. This is the process
that normally selects the NREL profiles that are to be aggregated into a representative
TEPPC profile. In this process, groupings of NREL profiles are taken in each
geographic region that is being modeled. These profiles are then filtered and the highest
capacity resources are selected first. This process ensures that the variability of
generation in each geographic region is preserved and a profile is created to represent
that region.
Page 54 of 121
Figure 17: TEPPC site selection process of NREL wind and solar profiles
TEPPC wind and solar sites are created to reflect actual, planned or future sites in each
region. The profiles, where applicable, are created corresponding to the WREZ hubs
and are named according to the nearby WREZ hub21.
Wind: Hourly wind shapes used to model all wind resources were supplied by 3TIER
and the NREL as part of the Western Wind Dataset. Profiles used for the 2022 dataset
reflect the recent three-day seams issue fix applied to the Western Wind Dataset. The
wind shapes used in the TEPPC dataset were derived using historical weather from
2005. Each NREL profile is created by NREL to represent a 30 MW wind site. For
example a 1,500 MW plant would then require 50 NREL profiles to form an aggregate
shape. Wind is treated as a fixed input to the model.
Table 12 shows capacity factors for each TEPPC wind profile used in the 2022
Common Case and 2032 Reference Case. Capacity factors will vary with the availability
of wind resources in each region. The TEPPC aggregate profiles are meant to reflect
that variability.
21
Google Maps (2013): http://www.google.com
Page 55 of 121
Table 12: TEPPC capacity factors of wind profiles
Solar: Solar production profiles (from 2005) were generated by NREL as part of the
profiles created for the Western Wind and Solar Integration Phase II Study. This data
was derived by NREL using 2005 solar irradiance data using a .81 derate factor to
account for inverter losses and panel age. Unlike the NREL wind profiles, the solar
profiles do not have the standard capacity of 30 MW. The solar profiles are created
using irradiance data in a geographic region making it impossible to standardize the
size of each potential solar site. TEPPC solar profiles are created in the exact same
way as wind profiles.
The following solar technologies are reflected in the NREL solar data used for TEPPC
profiles:


Concentrated Solar Power (CSP):
o CSP with no storage with a solar multiplier = 1
o CSP with 6 hours storage
o Note – Solar output values greater than the profile nameplates are found
in the CSP profiles. According to NREL, CSP plants consume energy
overnight and have peak consumption right before they start producing
energy each morning. Values greater than nameplate capacity are
possible due to thermal inertia in the system.
Photovoltaic (PV):
o Rooftop PV modeled as a fixed tilt of 20˚ (non-optimal)
o Large scale single axis tracking
o Large scale fixed tilt with an optimum angle for the latitude of the plant
Page 56 of 121
For each year of solar data provided, 10-minute profiles are provided along with hourly
average profiles. To this point, TEPPC has only used the hourly average solar profiles.
Table 13 shows capacity factors for each TEPPC solar profile used in the 2022
Common Case and 2032 Reference Case. Capacity factors will vary with the availability
of wind resources in each region. The TEPPC aggregate profiles are meant to reflect
that variability.
Table 13: TEPPC capacity factors of solar profiles
Distributed Generation
Distributed generation (DG) is a growing resource in the Western Interconnection and
all indications point toward its future growth. Gaining a better understanding and ability
to project DG levels is important to accurately reflecting potential resources. For
example, in the case of CA, the current projections for PV DG may underestimate future
levels of DG in that state in light of its goal of deploying 12,000 MW of DG.
Current efforts to include DG in the 10-and 20-year work can be improved in the next
study cycle. For example, the levelized cost of DG does not take into consideration
consumer choice because the LCOE does not include certain key value and cost drivers
that are important for consumer selection. The creation of a tool that can capture DG
policy targets and reflect consumer choice will greatly improve how DG is projected
Page 57 of 121
Hydro Generation
Hydro generation is a significant resource in the Western Interconnection. In the 2022
Common Case, and all other PCM studies performed by TEPPC, hydro generation is
modeled using a variety of methods that attempt to capture the unique operating
characteristics of the resource. A mixture of fixed historical time series, proportional load
following (PLF) algorithms, and a hydrothermal co-optimization (HTC) technique were
used to model hydro generation in these 10-year planning studies. Hydro dispatchability
constraints due to environmental or other operational factors (e.g., irrigation water
deliveries, flood control, environmental release) were captured in the model using
minimum and maximum operating levels, monthly energy limits, monthly load
proportionality constants (K values), and monthly hydrothermal co-optimization fractions
(p factors), when applicable. In all regions, plants were categorized as large (> 10 MW
capacity) or small (< 10 MW capacity). The exception to this was in California, where a
category of 10 MW < Nameplate Capacity < 30 MW was designated for RPS. Plants
smaller than 10 MW capacity were rolled up and modeled as a PLF K=0 large plant.
The roll-up designation is outlined in the region sections that follow. Table 14
summarizes the number of plants in each category by region.
Table 14: Plant Modeling Method Summary by Region
# Plants >10 MW
(CA >30 MW)
# Plants
10<MW<30 (RPS,
CA Only)
HD
PLF
HTC
# Plants <10 MW
(Small Plant
Rollups, PLF K=0)
Region
States Included
HD
PLF
HTC
Northwest
(NW)
Oregon, Washington,
Idaho, Montana west of
the Continental Divide
30
78
18
NA
NA
NA
83
California
(CA)
California
36
2
30
26
19
2
74
East
Arizona, Colorado,
Nevada, New Mexico,
Montana east of the
Continental Divide, Utah,
Wyoming
21
26
4
NA
NA
NA
72
Alberta
5
0
4
NA
NA
NA
British
Columbia
0
2
6
NA
NA
NA
Page 58 of 121
Small plants are
provided as a
large plant rollup
from Alberta data
source
Small plants are
provided as a
large plant rollup
from BC data
source
In Figure 18, an Interconnection-wide monthly hydro energy summary is depicted, with
breakouts by model type. Figure 19 shows the percentage of the total 2022 hydro
energy for each model type.
Figure 18: Interconnection-wide Monthly Energy Summary by Model Type
Page 59 of 121
Figure 19: Interconnection-wide Hydro Modeling Energy Distribution (% Total Hydro Energy)
The next two figures summarize the regional hydro energy for the 2022 Common Case.
Figure 20 shows the monthly hydro energy for each region, while Figure 21 displays the
total hydro energy for 2022 for each region.
Figure 20: Monthly Energy by Region for 2022 Common Case
Page 60 of 121
Figure 21: Regional Total 2022 Hydro Energy for 2022 Common Case
Assumptions and data sources for the hydro generation modeling in the 2022 Common
Case are summarized by region.
Northwest Federal and Non-Federal, Mid C Non-federal, and PacifiCorp
The PLF/HTC modeling methods were used to model the majority of NW hydro
generation in the 2022 Common Case dataset. Plant modeling assumptions are shown
in Table 15.PLF constants were obtained by regressing historical data and loads for
federal projects, or were supplied by plant operators for non-federal projects. For Grand
Coulee, The Dalles, Chief Joseph, and John Day, average K values were calculated
using data years 1999, 2001 to 2003, and 2005 to 2010. Monthly average generation
values for both HTC and PLF plants came from the EIA 906/920 data for 2005. Smaller
plants were modeled using estimated PLF constants and EIA 906/920 generation
values.
Plants determined to not follow load historically were modeled using historical hourly
shapes. These included Bonneville, McNary, the lower Snake River, and federal storage
plants. Historical data came from the U.S. Army Corps of Engineers and Northwestern
Division website for the same years as the EIA data. Wanapum, Priest Rapids and Rock
Island data came from the BPA PI system with concurrence from Grant County and
Chelan County PUDs. For Swift 1, Yale, Boyle, Toketee, Lemolo 1 and Merwin
generators, 2005 historic data received from PacifiCorp was used. Clearwater 1 and 2
and Slide Creek K values were calculated for 2004 to 2006 and averaged. Plants with
nameplate capacities of less than 10 MW were rolled up into state “plants” with summed
monthly EIA averages; these state “plants” were modeled using PLF K=0 (flat monthly
generation).
Page 61 of 121
Table 15: NW Hydro-generation Modeling Method Summary
Plant Modeling
Method
HD
PLF
PLF K=0 (Flat)
HTC
Total
Capacity
(MW)
10,808
2,261
1,938
17,621
32,628
% of Total
Capacity
33.12%
6.93%
5.94%
54.01%
Net Hydro
Energy (GWh)
36,505
8,214
7,593
66,040
118,352
% of Total NW
Hydro Energy
30.84%
6.94%
6.42%
55.80%
California
The California hydro data is from the CISO PI dataset that was aggregated to the river
system. Plant modeling assumptions are shown in Table 16. “Historical” individual plant
data was then disaggregated proportionally to EIA 906/920 monthly generation values.
A combination of historical shapes, PLF and HTC were used to model California hydro
generation. For the few plants not in CISO’s PI system, PLF or HTC modeling using EIA
906/920 data for 2005 were used. California small hydro was disaggregated from the
conventional hydro to more accurately track its contribution to RPS requirements (this
includes plants between 10- and 30-MW capacity). Plants with nameplate capacities of
less than 10 MW were rolled up into operating area “plants” with summed monthly EIA
averages; these area “plants” were modeled using PLF K=0 (flat monthly generation),
and contributed toward RPS.
Table 16: California Hydro-generation Modeling Method Summary
Plant Modeling
Method
HD
PLF K=0 (Flat)
HTC
Total
Capacity
(MW)
5,985
705
3,584
10,274
% of Total
Capacity
58.26%
6.86%
34.89%
Net Hydro
Energy (GWh)
21,552
2,351
15,398
39,302
% of Total CA
Hydro Energy
54.84%
5.98%
39.18%
East
WAPA plants were modeled using 2005 historical hourly hydro data, with the exception
of Hoover, Blue Mesa, and Yellowtail, which used PLF/HTC. Non-federal plants were
modeled using PLF based on EIA 906/920 data for 2005. Plants with nameplate
capacities of less than 10 MW were rolled up into state “plants” with summed monthly
EIA averages; these state “plants” were modeled using PLF K=0 (flat monthly
generation). Plant modeling assumptions are shown in
Page 62 of 121
Table 17.
Page 63 of 121
Table 17: East Hydro-generation Modeling Method Summary
Plant Modeling
Method
HD
PLF
PLF K=0 (Flat)
HTC
Total
Capacity
(MW)
2,678
88
629
2,416
5,810
% of Total
Capacity
46.09%
1.51%
10.82%
41.59%
Net Hydro
Energy (GWh)
7,002
176
2,837
4,359
14,374
% of Total East
Hydro Energy
48.71%
1.23%
19.74%
30.32%
Canada
BC Hydro generation data are determined by BC Hydro’s Generalized Optimization
Model using a 2022 load forecast and average inflows (1968 water conditions). The
analysis included Revelstoke 5 and Mica Units 5 and 6. The Generalized Optimization
Model results were used to calculate PLF constants for use by the HTC modeling
method for the G.M. Shrum, Peace Canyon, Site C, Revelstoke, Mica, and small plants.
The Arrow Plant and IPP (Independent Power Projects) rollup are modeled with no
flexibility (PLF, K=0). AESO provided 2005 data that was used as historical data for
some plants and to calculate PLF/HTC constants for Bighorn, Bow River aggregate, and
Brazeau plants. The historical monthly averages were used as the model energy inputs
for the PLF/HTC plants. Plant modeling assumptions are shown in Table 18 and Table
19.
Table 18: BC Hydro-generation Modeling Method Summary
Plant Modeling
Method
HD
PLF K=0 (Flat)
HTC
Total
Capacity
(MW)
0
4,370
13,676
18,046
% of Total
Capacity
0.00%
24.22%
75.78%
Net Hydro
Energy (GWh)
0
21,181
56,214
77,395
% of Total BC
Hydro Energy
0.00%
27.37%
72.63%
Table 19: Alberta Hydro-generation Modeling Method Summary
Plant Modeling
Method
HD
PLF K=0 (Flat)
HTC
Total
Capacity
(MW)
85
100
811
996
% of Total
Capacity
8.53%
10.04%
81.43%
Net Hydro
Energy (GWh)
291
569
2,010
2,871
% of Total Alberta
Hydro Energy
10.13%
19.84%
70.03%
Additional hydro data is available in the Appendix – Hydro Data section at the end of
this report.
Page 64 of 121
Generation Characteristics
The more granular nature of the 10-year studies requires more detailed generation
characteristics than what is used for the 20-year studies performed in the LTPT. The
following sections have more thorough descriptions of the 10-year data.
Thermal Unit Operational Information
10-Year:

Thermal unit commitment is modeled in the 10-year studies by way of the hourly
production cost model optimization. Data requirements for unit commitment
include capacity information, planned and forced outage assumptions, heat rate
curves, ramp rates, minimum up/down times, start-up costs, and non-fuel
variable O&M costs.
 Thermal units are broken into categories on the basis of fuel type, technology
type, vintage, and capacities. A set of assumptions is developed for each
generator category, with more detailed data included for gas-fired units.
 There was an effort to improve and validate the thermal data during 2011. The
primary focus was on unit heat rates, capacities, startup costs, variable O&M
rates, and capacity factors. Data updates included data review by the Data Work
Group (DWG), and data changes from submittals and observations by TEPPC
stakeholders, including implementation of the cycling cost data provided by
Intertek/APTECH. The updated data is a mixture of unit and category level data
designed to avoid the use of confidential data.
20-Year:

Detailed thermal unit operation information is not required for the 20-year studies
as the dispatch that results from operational parameters of a particular resource
is a study assumption, not a study result. In other words, the 20-year studies
assume a typical dispatch for a given resource, while the 10-year studies use
operational data and optimization to determine what that dispatch might be.
Thermal Forced and Scheduled Outages
10-Year:

Sources such as the NERC Generating Availability Data Systems database were
used to develop forced and planned maintenance outage rates. The forced
outage rates are used by the production cost model to force units off-line using
Monte Carlo or other probabilistic techniques. The scheduled maintenance
requirements (annual hours) were used to derive scheduled maintenance
outages for each subregion within the Western Interconnection.
20-Year:
Page 65 of 121

There is no explicit planned maintenance or schedule maintenance input data for
the 20-year studies. However, to the extent that unit capacity factors assumed in
the model reflect those down-times, the outages would be reflected in the 20year studies.
Thermal Heat Rates
10-Year:

The unit heat rates are derived from public data sources unless the generator
owners/operators have provided actual heat rate data for use in the TEPPC
public database. Generic heat rates are applied to units where data is not
available.
 The non-combined cycle heat rates used in the TEPPC database were derived
from the heat rates in the SSG-WI dataset and the heat rates provided by
NewEnergy Associates (NewEnergy used Environmental Protection Agency
Continuous Emission Monitoring System (CEMS) data to construct the heat rate
curves). Both sets of heat rate curves were used because both heat rate
datasets were deficient in one way or another. The TEPPC incremental heat
rates for some units were abnormally low and the SSG-WI average heat rates at
the minimum capacity for some units were unusually small. Since the
deficiencies in the datasets did not overlap, by combining the two heat rate
datasets the deficiencies in both datasets were fixed, resulting in a composite
dataset that is much better than either original dataset alone.
 The plant level heat rates for the combined cycle (CC) plants were derived
previously using the operations data from the CEMS databases. In cases of
insufficient data, generic rates were used. CC plants are currently modeled at an
aggregated level (i.e., steam generator aggregated with associated combustion
turbine generator(s)). This is a common practice where specific unit level heat
rate data is not publicly available. The steam generators in CC plants use the
exhaust heat from one or more combustion turbines to produce steam that then
turns the turbine generator. Since the CEMS data is only provided for units that
produce emissions, the heat rate data for CC plants is more difficult to derive.
Also, the multiple operating configurations make CC plants more difficult to
model as the heat rate changes with each configuration.
20-Year:

Thermal heat rates are not used in the 20-year studies.
Thermal Start-up costs, Minimum Up/Down Time, Ramp Rates
10-Year:
Page 66 of 121

Start-up costs were updated with data provided by Intertek/APTECH, and include
fuel, O&M and other costs to reach a point of synchronization. The minimum up
and down times and ramp rates are based on average values reported by a few
SSG-WI participants with subsequent updates from New Energy and the
Modeling Work Group (MWG). A detailed explanation of the work can be found in
the Appendix.
20-Year:

The LTPT is not an hourly model thus it does not require generator
characteristics that relate to the hourly dispatch of generators such as start-up
costs, up/down times, and ramp rates.
Page 67 of 121
Transmission
Along with generation and load, the assumed transmission infrastructure in any
transmission planning study will drive the result. If too much transmission is included in
the study, congestion and production costs will be understated. The opposite holds true
if the future network happens to be less expanded than what is assumed in the study –
congestion and production costs will be overstated. Furthermore, transmission is often
one of the most time-intensive and technical assumptions that go into planning studies.
Topology Assumptions
10- and 20-Year
Topology: The area topology approximately matches the Balancing Authorities, with
exceptions to accommodate variations in load types and shapes as follows.

The CISO is split into PG&E Bay, PG&E Valley, SCE, and SDG&E bubbles.

The IPCO footprint is divided into three bubbles (Far East, Magic Valley, and
Treasure Valley).

The PACE load is divided by state with loads in Idaho, Utah, and Wyoming.
With these changes, the 2020 topology includes a total of 39 bubbles, as shown in
Figure 22.
Page 68 of 121
Figure 22: TEPPC Load Areas
Legend
AESO
Alberta Electric System Operator
APS
Arizona Public Service
AVA
Avista
BCH
British Columbia Hydro
BPA
Bonneville Power Administration
CFE
Comision Federal de Electricidad
CHPD
Chelan Co PUD
DCPD
Douglas Co PUD
EPE
El Paso Electric
Far East
Far East (Idaho Power)
GCPD
Grant Co PUD
IID
Imperial Irrigation District
LDWP
Los Angeles Dept. of Water & Power
Magic Vly
Magic Valley (Idaho Power)
NEVP
Nevada Power
NWMT
Northwestern Montana
PACW
PacifiCorp West
PACE ID
PacifiCorp East – Idaho
PACE UT
PacifiCorp East – Utah
PACE WY
PacifiCorp East -- Wyoming
PG&E Bay
Pacific Gas & Electric Bay Area
PG&E VLY
Pacific Gas & Electric Valley Area
PGN
Portland Gen Electric
PNM
Public Service New Mexico
PSC
Public Service Colorado (Xcel)
PSE
Puget Sound Energy
SCE
Southern California Edison
SCL
Seattle City Light
SDGE
San Diego Gas & Electric
SMUD
Sacramento Municipal District
SPP
Sierra Pacific Power
SRP
Salt River Project
TEP
Tucson Electric Power
TIDC
Turlock Irrigation District
TPWR
Tacoma Power
TreasVly
Treasure Valley (Idaho Power)
WACM
Western Area Power Admin Colorado/Missouri
WALC
Western Area Power Admin Lower Colorado
WAUW Western Area Power Admin Upper Missouri
Existing Transmission System Network
For the purposes of this discussion, the existing transmission system network is defined
as the topology and engineering assumptions that were originally imported into the
model.
10-Year
The existing transmission network, including the associated electrical characteristics
and operational limitations, came from WECC’s Technical Studies Subcommittee
approved 2020 HS1a power flow case. The Technical Studies Subcommittee manages
a central database of technical information about the Western Interconnection
transmission system and performs reliability studies. TEPPC uses these cases as a
starting point from which to build its own assumed transmission systems. The power
flow is supplemented by a series of transmission additions and removals specified by
the 2022 Common Case Transmission Assumptions (CCTA).
Page 69 of 121
In some instances, incremental generation added to the case had specific integration
transmission associated with it. In cases where this detail was provided (mainly in the
CISO), the transmission needed for integration was added to the case.
20-Year
Since the 2032 Reference case operates in a model (LTPT) that focuses on deciding
what transmission to build in the 2022-2032 timeframe, the Reference Case starts with
the full set of transmission assumptions in the 2022 Common Case. This detailed
transmission system is reduced through a “network reduction” process in order to
simplify the system, while maintaining its electrical properties. This reduced network is
more manageable for the transmission optimization performed in the LTPT.
Incremental Transmission Facilities
10-Year
The TEPPC 2022 Common Case transmission network is comprised of two main
components: the 2020 HS1A power flow and the Subregional Planning Group (SPG)
Coordination Group (SCG) 2022 CCTA. Existing, as well as future, transmission
additions are brought into the model via the TEPPC-approved 2020 HS1A power flow.
The power flow is supplemented by the SCG’s 2022 CCTA, a list of regionally
significant transmission projects that have a high probability of being in service by 2022.
Regionally significant transmission in the 2020 HS1A power flow that is not on the
CCTA list is removed from the Common Case. Projects on the 2022 CCTA that are not
in the 2020 HS1A power flow are added to the case.
Although the CCTA projects did not result from TEPPC analysis, they reflect major
projects that have advanced the farthest through the SPG’s respective planning
processes, and are those most likely to be built by 2020. These projects are included as
input assumptions in the Expected Future case.
Additional projects beyond the CCTA were studied in the 2022 studies via expansion
cases where specific transmission projects were added to the model in conjunction with
certain resource and/or scenario assumptions. These expansion case projects were not
assumed in the 2022 Common Case.
The purpose, process, and projects on the CCTA are explained in the SCG 2022 CCTA
Report.22 The projects included in the SCG 2022 CCTA are shown in the map in Figure
23.
22
SCG, “CCTA Report”: http://www.wecc.biz/committees/BOD/TEPPC/External/SCG_CCTA_Report.pdf
Page 70 of 121
Figure 23: 2022 Common Case Transmission Assumptions
Page 71 of 121
20-Year
Incremental transmission is determined by the LTPT model. The LTPT model has the
option to add any combination of ~1,000 candidate transmission lines made up of 230kV, 345-kV, and 500-kV single and double circuits. This “candidate transmission”
connects 2022 Common Case buses, TEPPC area hubs, WREZ hubs and natural gas
market hubs.
Capital Costs for transmission additions is provided by TEPPC’s transmission capital
cost tool, which included transmission and substation costs. Capital costs are described
in a later section in this document. Candidate transmission lines between “hubs” are
shown in Figure 24.
Figure 24: LTPT Candidate Transmission
Page 72 of 121
Transmission Losses
10-Year
Since the load forecasts already include transmission losses, the AC losses are
considered in the dispatch but not realized as additional load. In the current version of
PROMOD, the DC losses do produce additional load, which appears to penalize flows
on the DC lines. ABB Ventyx continues to work to improve the loss modeling within
PROMOD. In the meantime, nomograms are employed where applicable to force flows
onto the IPP DC line and other DC expansion projects.
20-Year
Since the load forecasts already include transmission losses, the losses are considered
in the dispatch but not realized as additional load. Loss models will continue to be
refined in the LTPT for future study cycles, consistent with that of the 10-year planning
processes and models.
Hurdle Rates
A hurdle rate represents a transfer “tariff” that are commonly used in production cost
models to represent market friction and to better represent the inherently conservative
nature of actual operating practices.
10-Year
The limitations of the production cost model, as utilized by TEPPC, include the inability
to capture the effects of transactions made under long-term contracts, the impacts of
scheduling rules on system dispatch, and the inherently conservative nature of actual
operating practices. The TEPPC studies compensate for the limitations to some extent
by applying hurdle rates that bring interchange solutions closer to reality. These transfer
“tariffs” are designed to act as “hurdle” rates for region-to-region transfers and model
the economic realities of costs for transporting power over long distances. The objective
is to improve the economic dispatch and resulting power flows across the eight pools
modeled in the PROMOD production simulations.
Regional transfer hurdle rates are included in the 2022 Common Case. The rates listed
in
Page 73 of 121
Figure 25 were carried over from the 2020 studies. The values were derived from the
assumed wheeling rates applied in the SSG-WI 2008 Base Case and are fully
incorporated into PROMOD’s economic dispatch algorithm. The Alberta rates are
designed to model the AESO market and the position within the AESO supply stack in
which imports fall.
Page 74 of 121
Figure 25: 2022 Common Case Hurdle Rates
Study Year 2022
Forward Reverse
Region Connections
$/MWh
$/MWh
Alberta
British Columbia
$0.00
$48.00
Alberta
NWPP
$0.00
$48.00
AZNMNV
RMPP
$5.40
$2.60
BASIN
AZNMNV
$5.40
$2.60
BASIN
CALIF_N
$5.40
$3.00
BASIN
CALIF_S
$2.30
$2.30
BASIN
NWPP
$5.40
$2.70
BASIN
RMPP
$5.40
$2.70
British Columbia NWPP
$4.70
$2.80
CALIF_N
CALIF_S
$0.00
$0.00
CALIF_N
NWPP
$5.90
$6.10
CALIF_S
AZNMNV
$2.60
$2.60
NWPP
CALIF_S
$5.90
$7.70
NWPP
RMPP
$7.30
$2.80
20-Year
Since the LTPT does not perform and economic dispatch, hurdle rates are not
applicable and are not represented in the modeling.
Path Ratings
10-Year
To develop assumption for WECC path ratings in the 2022 Common Case, TAS SWG
started with the 2011 WECC Path Rating Catalog and applied modifications to capture
operating limits for a number of key paths and to capture rating changes due to the
transmission additions as part of the CCTA.



Path 1: The Alberta - BC limits were decreased from 1,200 MW West to East to
720 MW, and from 1,000 MW East to West to 700 MW to reflect operational
limits.
Path 3: The Northwest to BC limit was increased from 2,000 MW South to North
to 3,000 MW to reflect the inclusion of the Path 3 Upgrade project in the 2022
dataset.
Path 6: The West of Hatwai limit was increased from 4,277 MW East to West to
4,800 MW due to the inclusion of the Path 8 upgrade in the 2022 dataset
(western portion only).
Page 75 of 121










Path 14: The Idaho to Northwest limits increased from 1,200 MW West to East to
2,250 MW, and from 2,400 MW East to West to 3,400 MW to reflect the inclusion
of the Hemingway-Boardman project in the 2022 dataset.
Path 17: The Borah West limit was increased from 2,557 MW East to West to
4,450 MW to account for Gateway West Phase 1.
Path 19: The Bridger West limit was increased from 2,200 MW East to West to
4,100 MW to account for Gateway West Phase 1.
Path 20: The Path C limits were increased from 1,600 MW North to South to
2,250 MW, and from 1,250 MW South to North to 2,250 MW to reflect the
inclusion of the Energy Gateway West project in the 2022 dataset.
Path 23: The Four Corners limits (both 345 to 500 kV and 500 to 345 kV) were
increased from 840 MW to 1,000 MW because the path owner informed TEPPC
that a higher rating transformer would be in place by 2022.
Path 35: The TOT 2C limits were increased from 300 MW North to South to 600
MW, and from 300 MW South to North to 580 MW to reflect the addition of the
second Sigurd – Red Butte 345-kV line in the 2022 dataset.
Path 37: The TOT 4A limit was increased from 937 MW Northeast to Southwest
to 2,175 MW to reflect the Post-Gateway rating/definition.
Path 42: The IID to SCE limit was increased from 600 MW East to West to 1,500
MW to account for the Path 42 upgrade.
Path 71: The South of Allston limit was increased to 4,100 MW due to the
inclusion of the I-5 Corridor Reinforcement project in the 2022 dataset.
Path 78: The TOT 2B1 limit was increased from 560 MW North to South to 600
MW in accordance with the phase 3 path rating resulting from the addition of the
Gateway projects in the 2022 dataset.
Page 76 of 121
The changes listed above, along with the established 2011 Path Ratings, resulted in the
final assumptions for path ratings that went into the 2022 Common Case. These
assumptions are summarized in Figure 26.
Figure 26: List of Major Paths in 2022 Common Case
Page 77 of 121
A number of the WECC paths included in the model are depicted in the map in Figure
27.
Figure 27: Map of WECC Paths
20-Year
While the rating of underlying branches that comprise paths are enforced, the WECC
path ratings themselves are not constrained at fixed levels, but rather are allowed to
increase as needed under different future scenario conditions. In other words, the LTPT
does not enforce WECC path ratings at fixed transmission transfer capabilities (TTC),
but rather determines where the TTC may need to increase by optimally adding
transmission expansions under different future scenario conditions.
Transmission Outages
10- and 20-Year - Transmission forced outages are not modeled in both study horizons.
This is because transmission maintenance outages typically occur during off-peak
usage, which has a low impact on production costs. Furthermore, forced transmission
outages occur infrequently and given that these are not power flow reliability studies;
there is not a need to consider them.
Page 78 of 121
Fuel Prices
10-Year Gas Prices
The base or median Henry Hub price was assumed to be $5.43/MMBtu (2012 dollars)
for the 2022 Common Case. Aside from the gas sensitivity studies (-25 percent for low
and +75 percent for high), this price was held constant throughout the TEPPC studies.
The NW Power and Conservation Council’s (NWPCC’s) methodology (Sixth Power
Plan) for using implicit basis differentials to derive the area burner tip prices was used to
develop the area prices provided in. These prices are further broken out by TEPPC load
area in The differentials are implicit in that the NWPCC forecasts burner tip prices
describe pricing for various areas of the Western Interconnection using econometric
equations. The differentials adopted by the DWG were also backed out by comparing
the burner tip prices with the major trading hub forecasts. The TEPPC burner tip prices
will differ from those used in the Sixth Power Plan because the DWG uses a different
origination price (Henry Hub) than the value used for the NWPCC Sixth Power Plan.
For intra California transportation rates, the TEPPC values are consistent with the
California Energy Commission (CEC) values using utility tariffs. Gas prices by subregion
are provided in Figure 28.
Figure 28: 2022 Common Case Gas Prices by Subregion
Henry Hub:
Delivery Area
PNW East
E. MT
N. CA
N. NV
AB
UT
WY
PNW West
S. ID
BC
CO
S. CA
AZ
NM
S. NV
5.4313
Basis
AECO
Rockies
AECO
AECO
AECO
Rockies
Rockies
Sumas
Rockies
AECO
Rockies
Permian
San Juan
Permian
Permian
Basis Spread from Henry Hub
-0.78
-1.22
-0.78
-0.78
-0.78
-1.22
-1.22
-0.62
-1.22
-0.78
-1.22
-0.45
-0.76
-0.45
-0.45
Page 79 of 121
Inferred Differential
$0.16
$2.79
$0.37
$1.75
-$0.25
$0.56
$0.49
$0.55
$1.82
$0.25
$0.53
$0.13
$0.93
$0.67
$0.22
Delivered Cost
$4.82
$7.00
$5.02
$6.40
$4.41
$4.77
$4.70
$5.36
$6.03
$4.90
$4.74
$5.12
$5.60
$5.65
$5.20
Table 20: 2022 Common Case Gas Prices by Area
Table 21: 2022 Common Case Gas Prices by Area
20-Year Gas Prices
Fuel prices modeled within the LTPT for the 2032 study year were obtained from the
Energy Information Administration (EIA) 2012 Annual Energy Outlook (AEO2012)
projections. Since the AEO2012 projections were presented in 2010 dollars, the fuel
prices were adjusted to 2012 dollars using the same annual inflation rates as those
modeled in the common case, resulting in an effective 2012 over 2010 multiplier of
Page 80 of 121
1.048099. All fuel price assumptions for the 2032 Reference Case can be found in
Table 22.
Delivery price differentials were modeled within the LTPT on a state-by-state basis and
extrapolated from the data found within “Table E1” of the EIA State Energy Data
Systems (SEDS).
Table 23 and Table 24 provide the state multipliers for each fuel.
For natural gas, the projected Henry Hub price was used as a basis and all
transportation differentials were relative to Henry hub. For all other fuels, the US
average price was used as a basis.
Since no projections for biomass or uranium were found in the AEO2012, a 2 percent
price escalation, as obtained from the Energy + Environmental Economics (E3)/TEPPC
capital cost tool, was used to project 2032 prices for biomass and uranium from the
2010 US price basis contained within the AEO2012.
Table 22: 2032 Reference Case Fuel Prices
Fuel Reference Prices
Fuel
Price ($/Mmbtu) Escalation (%)
Bio
5.37
2.00
Coal
2.84
0.90
Gas
6.90
1.40
Geothermal
0.00
Solar
0.00
Uranium
0.97
2.00
Waste
5.37
2.00
Water
0.00
Wind
0.00
Wood
5.37
2.00
Table 23: 2032 Reference Case Delivery Multipliers
Fuel State/Province Delivery Price Differential Multipliers
State/Province
Bio Coal Gas Geothermal Solar Uranium
Alberta
1.00 1.00 1.00
1.00
1.00
1.00
Arizona
4.57 0.75 0.94
1.00
1.00
0.26
British Columbia 1.00 1.00 1.00
1.00
1.00
1.00
California
1.27 1.21 0.95
1.00
1.00
0.23
Colorado
1.77 0.66 0.90
1.00
1.00
0.28
Idaho
0.71 0.97 1.01
1.00
1.00
0.26
Montana
3.17 0.59 1.13
1.00
1.00
0.26
Mexico
1.00 1.00 1.00
1.00
1.00
1.00
New Mexico
1.06 0.85 0.89
1.00
1.00
0.25
Page 81 of 121
Nevada
Oregon
Texas
Utah
Washington
1.40
1.24
1.45
1.45
1.34
1.01
0.71
0.76
0.71
0.96
0.98
0.97
0.71
0.85
1.22
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.26
1.00
0.26
1.00
0.26
Wyoming
1.58
0.54
0.82
1.00
1.00
1.00
Table 24: 2032 Reference Case Delivery Multipliers (cont.)
Fuel State/Province Delivery Price Differential Multipliers
State/Province
Waste
Water Wind Wood
Alberta
1.00
1.00
1.00
1.00
Arizona
4.57
1.00
1.00
4.57
British Columbia
1.00
1.00
1.00
1.00
California
1.27
1.00
1.00
1.27
Colorado
1.77
1.00
1.00
1.77
Idaho
0.71
1.00
1.00
0.71
Montana
3.17
1.00
1.00
3.17
Mexico
1.00
1.00
1.00
1.00
New Mexico
1.06
1.00
1.00
1.06
Nevada
1.40
1.00
1.00
1.40
Oregon
1.24
1.00
1.00
1.24
Texas
1.45
1.00
1.00
1.45
Utah
1.45
1.00
1.00
1.45
Washington
1.34
1.00
1.00
1.34
Wyoming
1.58
1.00
1.00
1.58
Page 82 of 121
10-Year Coal Prices
The coal price forecast was derived from the NWPCC’s locational coal price forecast.
The assume prices for coal are provided in Figure 29.
Figure 29: 2022 Common Case Coal Prices
2022 Coal Price Forecast (in 2012$/MMBtu)
Area
Price
Area
Price
Area
AESO
$1.48
NEVP
$1.48
SCL
APS
$1.52
NWMT
$1.10
SDG&E
AVA
$1.56
PACE_ID
$1.14
SMUD
BCTC
$1.79
PACE_UT
$0.98
SPP
BPA
$1.76
PACE_WY $0.89
SRP
CFE
$1.52
PACW
$1.76
TEP
DOPD
$1.76
PG&E_BAY $1.74
TIDC
EPE
$1.37
PG&E_VLY $1.74
TPWR
FAR EAST
$1.14
PGN
$1.76
TREAS VLY
GCPD
$1.76
PNM
$1.37
WACM
IID
$1.52
PSC
$1.18
WALC
LDWP
$0.98
PSE
$1.76
WAUW
MACIG VLY $1.14
SCE
$1.74
Price
$1.76
$1.74
$1.74
$1.48
$1.52
$1.52
$1.74
$1.76
$1.14
$1.18
$1.52
$1.10
20-Year Coal Prices
All 2032 fuel assumptions are presented in the natural gas section.
Other Fuels
10-Year
The prices for other fuels are based on assumptions used by the CEC and the NWPCC.
These assumed prices are provided in Figure 30.
Figure 30: 2022 Common Case “Other” Fuel Prices
Other Fuels (in 2012$/MMBtu)
Fuel
Price
Biomass – Black Liquor
$0.00
Biomass – Landfill Gas
$1.57
Biomass – Other
$2.43
Biomass – Solid Waste
-$1.00
Biomass – Wood Residue $1.68
Oil – H
$20.96
Fuel
Oil – L
Petroleum Coke
Refuse
Uranium
Waste Heat
Price
$22.93
$7.27
$4.84
$0.73
$1.22
20-Year
All 2032 fuel assumptions are presented in the natural gas section.
Page 83 of 121
RPS Assumptions
A number of states and provinces in the Western Interconnection have state RPS or
goals, as shown in Figure 31. TEPPC models all enacted state policy so it is a key goal
of the Common Case and Reference Case to represent RPS-compliant futures.
Figure 31: States/Provinces with RPS Requirement or Goal
Note: BC’s goal is not shown as it is not modeled in the TEPPC process, as the majority of the
requirement is made up of hydro energy
10-Year
The process for identifying state RPS requirements, then selecting resources for that
state is broken out into the following steps:
1. Split BA loads out by state
o Eight BAs cover two or more states
o Splits by state provided via FERC Form 1 or from BAs directly
2. Adjust state loads for transmission plus distribution losses
o Assumed to be 7 percent for California, 6 percent for the rest of the
Western Interconnection
3. Identify shares of state loads supplied by IOUs, publics, coops, federal, and
others using EIA data
4. Multiply load by RPS percent based on provider type
5. Adjust for special provisions (e.g., set asides and multipliers)
Page 84 of 121
This process establishes the RPS requirement for each state. From there, the
renewable generation data previously described is assigned to each state, thus
ensuring that each state meets their required RPS. If a state is deficient, hypothetical
“gap” resources are added via the WREZ tool to ensure compliance. RPS percentage
requirements are provided in Figure 32.
Figure 32: RPS Requirements
Page 85 of 121
A detailed breakdown of the RPS assumptions is provided in Figure 33.
Figure 33: RPS Assumptions
20-Year
The 2032 Reference Case also adheres to enacted RPS requirements. However, the
assumption is quite different in that the generators are not inputs to the model rather
than outputs. The RPS requirement is a modeling goal the LTPT selects resources
(based on LCOE) to meet this requirement in the most cost effective manner possible.
The 2032 RPS requirements are developed in a similar manner to that described for the
Common Case.
Page 86 of 121
Capital Costs
Capital costs are not a direct input into the TEPPC modeling process; however, capital
costs for added generation and transmission, which can be substantial, are included in
Plan. Using the updated capital cost tool,23 TEPPC has identified estimated capital costs
associated with incremental generation and transmission. The following discussion of
capital costs describe the methods, assumptions, and data use to create the generation
and transmission capital cost tools. A description of the capital cost tools and their
application to the 10- and 20-year analyses can be found in the Tools and Models
section.
Capital Costs for Transmission
Transmission capital cost estimates were developed for transmission line costs and
substation costs using a bottom-up approach, detailing the component and land costs
and then adjusting these to take into consideration potential cost variations such as
location, terrain, and additional components.
Transmission line capital costs
Transmission line capital cost estimates were developed based on the cost of
equipment and the cost of land. Capital cost estimates were developed for the following
seven voltage classes:







230-kV Single Circuit
230-kV Double Circuit
345-kV Single Circuit
345-kV Double Circuit
500-kV Single Circuit
500-kV Double Circuit
500-kV DC Bi-Pole
To develop the equipment costs, a baseline transmission cost per mile was established
for each voltage class. Information for the baseline cost came from the 2008 WREZ
project. From there assumptions were made about conductor type, tower structure, and
line length to develop the baseline transmission cost. Then, in order to account for
variations in costs, cost multipliers were developed for conductor type, tower structure,
WECC, “Transmission Capital Cost Tool”:
http://www.wecc.biz/committees/BOD/TEPPC/External/121101_TEPPC_TransCapCost_Calculator.xlsx
23
WECC, “Generation Capital Cost Tool”:
http://www.wecc.biz/committees/BOD/TEPPC/External/121101_TEPPC_GenCapCost_Calculator.xlsm
Page 87 of 121
line length, terrain, and re-conductoring. Cost assumptions for the cost multipliers came
from knowledge held by WECC and its contractors.
Equipment Capital Costs
Conductor Type: Conductor type is an important cost consideration because conductor
selection affects the cost of the entire project. Aluminum Conductor Steel Reinforced
(ACSR) conductor is the most common conductor type in Western Interconnection, and
is the type assumed in the baseline cost. Cost multipliers were quantified for ACSR as
well as for Aluminum Conductor Steel Supported, and High Tensile Low Sag
conductors.
Tower Structure: Tubular and lattice-style tower structures are used throughout the
Western Interconnection, depending on the characteristics of the transmission line,
location and terrain. Tubular structure was assumed for 230-kV line baseline costs,
while lattice-style structure was assumed for all other voltages. Capital cost multipliers
were quantified for costs associated with each type of structure.
Line Length: Line length affects the cost-per-mile of transmission line. Typically, the
cost-per-mile of transmission line decreases as the line gets longer because design and
engineering costs are non-linear. The baseline cost assumed a line length greater than
10 miles. Line length multipliers were quantified for lines greater than 10 miles, 3-to-10
miles, and less than 3 miles long.
Terrain: Terrain is a substantial factor in total transmission line costs. Cost multipliers
were developed for nine different terrain types: 1) desert; 2) scrub/flat; 3) farmland; 4)
forested; 5) rolling hill (2-8 percent slope); 6) mountain (>8 percent slope); 7) wetland; 8)
suburban; and 9) urban. Data on terrain cost multipliers was gathered from PG&E, SCE,
SDG&E and the WREZ project.
Re-Conductoring: In areas where there are existing transmission lines, it may be
necessary or more cost-effective to re-conductor existing transmission rather than to
build a new line. Therefore, it was important to develop cost considerations for reconductoring. For the purposes of developing capital cost estimates, re-conductoring
was defined as replacing an existing conductor to increase ampacity, with no upgrade of
poles or insulators.
Page 88 of 121
Table 25 summarizes the cost multipliers applied to the baseline cost estimate for
transmission line equipment.
Table 25: Transmission Line Equipment Cost Multipliers
Equipment
Base Cost
230-kV
Single
Circuit
$927,0
00
Conductor
ACSR
1.00
ACSS
1.08
HTLS
3.60
Structure
Lattice
0.90
Tubular Steel
1.00
Length
> 10 miles
1.00
3-10 miles
1.20
< 3 miles
1.50
New v. Re-Conductoring
New
1.00
Re-conductor
0.35
Terrain
Desert
1.05
Scrub / Flat
1.00
Farmland
1.00
Forested
2.25
Rolling Hill (2-8%
1.40
slope)
Mountain (>8%
1.75
slope)
Wetland
1.20
Suburban
1.27
Urban
1.59
230-kV
Double
Circuit
$1,484,0
00
345-kV
345-kV
Single
Double
Circuit
Circuit
$1,298,0 $2,077,0
00
00
Cost Multipliers
500-kV
Single
Circuit
$1,854,0
00
500-kV
Double
Circuit
$2,967,0
00
500-kV
HVDC
Bi-Pole
$1,484,0
00
1.00
1.08
3.60
1.00
1.08
3.60
1.00
1.08
3.60
1.00
1.08
3.60
1.00
1.08
3.60
1.00
1.08
3.60
0.90
1.00
1.00
1.30
1.00
1.30
1.00
1.50
1.00
1.50
1.00
1.50
1.00
1.20
1.50
1.00
1.20
1.50
1.00
1.20
1.50
1.00
1.20
1.50
1.00
1.20
1.50
1.00
1.20
1.50
1.00
0.45
1.00
0.45
1.00
0.55
1.00
0.55
1.00
0.65
1.00
0.55
1.05
1.00
1.00
2.25
1.05
1.00
1.00
2.25
1.05
1.00
1.00
2.25
1.05
1.00
1.00
2.25
1.05
1.00
1.00
2.25
1.05
1.00
1.00
2.25
1.40
1.40
1.40
1.40
1.40
1.40
1.75
1.75
1.75
1.75
1.75
1.75
1.20
1.27
1.59
1.20
1.27
1.59
1.20
1.27
1.59
1.20
1.27
1.59
1.20
1.27
1.59
1.20
1.27
1.59
Land / Right-of-Way Costs
Land capital costs for transmission were developed as a product of acre-per-mile right
of way width estimates (Table 26) and per-acre right of way costs (Table 27) for each
voltage class. Right-of-way width estimate information came from several sources:
FERC and NERC documents, individual utility estimates, and actual project right-of-way
widths from existing and proposed projects throughout the Western Interconnection.
Page 89 of 121
Right-of-way costs were developed using information from the Bureau of Land
Management’s (BLM) Linear Right of Way Schedule for Year 2015.24
Table 26: Right of Way Width Estimates
230-kV
Single
Circuit
Acres/Mile 15.14
230-kV
Double
Circuit
18.17
345-kV
Single
Circuit
21.20
345-kV
Double
Circuit
24.23
500-kV
Single
Circuit
24.23
500-kV
Double
Circuit
30.29
500-kV
HVDC Bipole
24.23
Table 27: BLM Land Rental and Land Capital Costs by Zone
BLM Zone Number Land Rental Cost ($/Acre) Land Capital Cost ($/Acre)
1
$9
$ 85
2
$ 17
$ 171
3
$ 34
$ 341
4
$ 52
$ 512
5
$ 69
$ 683
6
$ 103
$ 1,024
7
$ 172
$ 1,707
8
$ 345
$ 3,414
9
$ 690
$ 6,828
10
$ 1,035
$ 10,242
11
$ 1,724
$ 17,071
12
$ 3,449
$ 34,141
24
The values in the BLM Schedule are rental values, which were converted to capital costs. For more
information [link to B&V report]. The BLM schedule identifies twelve cost zones, based on counties.
Page 90 of 121
Calculating Capital costs for Transmission Lines
Transmission line capital costs can be calculated by inputting the information shown in
the tables above into the following formula:
(Base Transmission Cost) x (Conductor Multiplier) x (Structure Multiplier) x
(Re-conductor Multiplier) x (Terrain Multiplier)
+
(ROW Acres/Mile) x (Land Cost/Acre)
Subtotal x (# of Miles) = Total Transmission Line Cost
Substation Capital Costs
Substations represent a substantial capital cost element for incremental transmission,
and, depending on how many substation facilities are needed to connect the
transmission to the existing grid, the costs can vary greatly. Substation capital costs
were estimated by establishing a base substation cost (e.g., land, fencing, control
building) and then adding major equipment costs for line/transformer positions,
transformers, reactive components, and high voltage direct current (HVDC) converter
stations. No multipliers were developed for terrain type or location. Base costs were
developed for 230-kV, 345-kV, and 500-kV facilities.
Line Position: A line position is defined as a transmission line entering or exiting and
terminating at the substation, (i.e., one transmission line looping into a substation
requires two line positions). All of these require circuit breakers and switches for
isolation of equipment. The two most common configurations of isolation equipment are
the ring bus and breaker-and-a-half (BAAH). Cost multiplier estimates were developed
for both configurations. The estimated costs are shown in Table 28.
Transformers: Transformers are necessary when a voltage change is required, such as
when voltage must be decreased to deliver electricity to load. Transformers vary by
voltage, as well as by current carrying capability, and transformer costs vary
substantially based on variables such as copper commodity prices and freight. Cost
multipliers for transformers include foundation and oil containment costs. Table 28
summarizes transformer cost multipliers.
Reactive components: Reactive components are necessary on transmission systems
that require reactive support for voltage maintenance. In general, transmission networks
can absorb changes in voltage; however, reactive power support is sometimes needed
to maintain grid reliability. The amount of reactive support, and the speed with which the
support needs to be transferred to the grid, will determine what type of reactive
component is required at the substation. To develop costs for reactive components
Page 91 of 121
three key reactive components were identified: 1) Shunt Reactor, 2) Series Capacitor,
and 3) Static VAR Compensator (SVC). Shunt Reactors reduce voltages due to highline charging on lightly loaded transmission networks. Series Capacitors do the exact
opposite, i.e., increase voltage. Static VAR Compensators can both reduce and
increase voltage.25 Capital costs for reactive components are summarized in Table 28.
Table 28: Substation Capital Cost Summary
Equipment
230-kV Substation 345-kV Substation 500-kV Substation
Base Cost (New Substation)
$1,648,000
$2,060,000
$2,472,000
Cost Per Line/XFMR Position
$1,442,000
$2,163,000
$2,884,000
Ring Bus Multiplier
1
1
1
Breaker and a Half Multiplier
1.5
1.5
1.5
115-/230-kV XFMR
$7,000
-
-
115-/345-kV XFMR
-
$10,000
-
115-/500-kV XFMR
-
-
$10,000
138-/230-kV XFMR
$7,000
-
-
138-/345-kV XFMR
-
$10,000
-
138-/500-kV XFMR
-
-
$10,000
$10,000
-
Transformer Cost ($/MVA)
230-/345-kV XFMR
230-/500-kV XFMR
$11,000
-
$11,000
345-/500-kV XFMR
-
$13,000
$13,000
Shunt Reactor ($/MVAR)
$20,000
$20,000
$20,000
Series Capacitor ($/MVAR)
$30,000
$10,000
$10,000
SVC Cost ($/MVAR)
$85,000
$85,000
$85,000
Reactive Components
HVDC converter stations are not a typical component of every substation; however, the
capital costs associated with HVDC converter stations is substantial and should be
considered in cases where HVDC transmission is being considered.
25
Information on industry costs for SVCs came from HydroOne, Arizona Public Serve Company, and the
Peer Review Group.
Page 92 of 121
Table 29 shows the typical capital costs for a 500-kV converter station. The cost of a
HVDC converter station is in addition to the substation capital costs identified above.
Page 93 of 121
Table 29: HVDC Converter Station Costs
HVDC 500-kV Converter Station
MW Rating
Cost Components
Converter Terminal (including DC switching station equipment)
Reactive Support (synchronous condensers, SVCs, etc.)
AC Switchyard
Total Cost
3,000 MW
$275,000,000
$150,000,000
$20,000,000
$445,000,000
Calculating Substation Capital Costs
The following formula is used to calculate substation capital costs:
+
+
+
+
+
+
Substation Base Cost
(Line/XFMR Position Base Cost) x (# of Line/XFMR Positions) x (RB or BAAH
Multiplier)
(XFMR Cost/MVA) x (XFMR MVA Rating) x (# of XFMRs)
(SVC Cost/MVAR) x (# MVARs)
(Series Cap. Cost/MVAR) x (# MVARs)
(Shunt Reactor Cost/MVAR) x (# MVARs)
(HVDC Converter Station Cost)
Total Individual Substation Cost
Allowance for Funds Used During Construction (AFUDC) and Overhead Costs
Transmission and substation costs are given as “overnight” costs (i.e., the cost if the
project could be engineered, procured and constructed overnight without financing or
overhead costs). To address these additional costs, AFUDC and overhead costs were
estimated and can be added to the transmission and substation costs to produce
realistic total project cost estimates. In general, AFUDC is defined as the cost of debt
and equity funds used to finance construction projects; overhead is defined as the
miscellaneous costs required to maintain an organization but are not directly tied to a
specific project (e.g., administrative costs, legal costs, internal management costs).
AFUDC and overhead costs are usually estimated as a percentage of transmission and
substation costs. TEPPC used an AFUDC value of 7.5 percent and overhead costs of
10 percent, for a total of 17.5 percent. The total project cost can be calculated using the
following formula:
Total Project Cost =
(Transmission Capital Cost + Substation Capital Cost) x (AFUDC + Overhead)
Page 94 of 121
Capital Costs for Generation
In order to determine future incremental generation costs for the 10- and 20-year
analyses, TEPPC uses a generation capital cost tool (generation tool). The generation
tool provides estimated levelized costs for each generation technology considered in the
2013 study program (see Table 30). Assumptions for future capital and fixed O&M costs
were developed and input into the annualized resource cost calculators in the tool. The
following discussion lays out the methods, assumptions, and data used to develop the
generation tool. A detailed description of the generation tool can be found in the found
in the Tools and Models section.
Generation technology types and associated costs (see Table 28) were approved by
TEPPC. This list has been vetted through extensive stakeholder review. As
technologies change, so too will the contents of this list. The quality and completeness
of this list, as with all WECC planning models, are reliant upon stakeholder participation
and review.
Table 30: Generation Technologies Included in Capital Cost Analysis
Technologies
Biogas
Biomass
Coal
CHP
Gas CCGT
Gas CT
Geothermal
Hydro
Subtypes
Landfill
Other
PC
IGCC w/ CCS
Small (<5 MW)
Large (>5MW)
Basic, Wet Cooled
Advanced, Wet Cooled
Basic, Dry Cooled
Advanced, Dry Cooled
Aero derivative
Frame
Large
Small
Upgrade
Nuclear
Solar PV
Residential Rooftop
Commercial Rooftop
Distributed Utility (Fixed Tilt)
Distributed Utility (Tracking)
Large Utility (Fixed Tilt)
Large Utility (Tracking)
Solar Thermal No Storage
Six-Hour Storage
Wind
Onshore
Page 95 of 121
Offshore
Capital and Fixed O&M Costs
Future capital cost and fixed O&M cost estimates were developed in a two-step
approach. First, present-day capital costs were developed through a literature review of
NREL work, National Energy Technology Laboratory work, EIA data, IRPs, and actual
data on installed cost of generation technologies.26 Then, future costs were extrapolated
from present-day costs using one of two methods, depending on the type of technology,
mature or emerging. (All capital costs are expressed in 2010 dollars.) For mature
technologies (e.g., coal, gas, hydro) it was assumed that capital costs remain stable in
real terms over time, so the present-day estimated capital and fixed O&M costs were
used for future costs. Table 31 shows the assumed future capital and fixed O&M costs
for the mature generation technologies considered in TEPPC’s analysis.
Table 31: Assumed Capital and Fixed O&M Costs for Mature Technologies
Technology
Capital Cost ($/kW)
Coal
Pulverized
$3,600
IGCC w/ CCS
$8,000
Combined Heat & Power27
Small (<5 MW)
$3,700
Large (>5 MW)
$1,600
Gas – Combined Cycle Gas Turbine28
Basic, Wet Cooled
$1,100
Advanced, Wet Cooled $1,200
Basic, Dry Cooled
+ $75
Advanced, Dry Cooled + $75
Gas – Combustion Turbine
Aero derivative
$1,150
Frame
$800
Fixed O&M ($/kW-yr)
$30
$60
$0
$0
$10
$10
$12
$6
26
A full list of the sources considered in the literature review is available in Section 8.2 of the report
prepared for WECC by E3. See “Cost and Performance Review of Generation Technologies”, Energy &
Environmental Economics, October 23, 2012, available at <<insert link from website>>
27
Small CHP is presumed to be used primarily to meet on-site loads but may export to the grid if the
relative thermal load is large enough; large CHP is presumed to be developed to export substantial
amounts of electricity to the grid while serving a large thermal load.
28
Combined cycle gas turbine (CCGT) technologies include both basic and advanced designs. Basic
CCGTs typically utilize two F-class combustion turbines (CT), whereas advanced CCGTs typically employ
one G- or H-class CT. The default assumption is that both designs use wet cooling, but the heat rate
penalty, incremental cost increase, for dry cooling are provided.
Page 96 of 121
Nuclear
Biogas
Landfill
Other
Biomass
Geothermal
Hydro
Large
Small
Upgrade
$7,500
$70
$2,750
$5,500
$4,250
$5,800
$130
$165
$155
$150
$3,000
$3,500
$1,500
$30
$30
$23
It was assumed that capital and fixed O&M costs for emerging technologies (e.g., wind,
solar PV, and solar thermal) decline over time, so a cost reduction potential was
developed for each technology, using one of two approaches. For emerging
technologies in the commercial phase with some installed global capacity, forwardlooking learning curves were used to quantify estimated future capital costs. Learning
curves describe a commonly observed empirical relationship between the cumulative
experience in the production of a resource and the cost to produce it. A learning rate,
expressed as the percentage reduction in cost that accompanies a double in cumulative
production experience, can be established through an evaluation of historically
observed capital cost trends for a specific technology.29 The learning rate for a specific
technology, together with information on that technology’s forecasted global installed
capacity, create the learning curve for that technology. Learning curves for emerging
generation technology capture the trend that the costs of emerging technologies often
drop rapidly as production scales up.30
For nascent emerging technologies with a very small installed global capacity whose
commercialization is just beginning, it is not possible to rely on a learning rate that is
well supported by the available literature. In these cases forecasted cost reductions can
be established through a survey of projected point estimates of future costs, using the
same types of sources used to evaluate present-day costs, including utility IRPs,
engineering assessments of potential cost reductions, and consulting reports.
Present-day and future capital and O&M costs are provided for three emerging
generation technologies: 1) solar PV, 2) solar thermal, and 3) wind. Table 32 shows the
29
Learning curves are most often expressed as the percentage reduction in cost that accompanies a
doubling in cumulative production experience, a metric known as the learning rate. A key parameter
needed to establish a future learning curve for a specific technology is a forecast of global installed
capacity. Where a consensus learning rate for a specific technology has been established, that rate can
be applied; however, in cases where no consensus rate exists, a review of literature on historically
observed capital cost trends can be conducted to determine an approximate learning rate.
30
In addition, learning curves capture the trend that capital costs remain stable for mature technologies.
Page 97 of 121
assumed present-day capital and O&M costs for six types of solar PV technology. The
values in the table include a conversion factor to convert the DC nameplate rating to AC
rated output. The projected capital cost reduction for solar PV technologies was
determined using learning curves and applying separate learning rates to photovoltaic
modules and balance-of-systems (BOS) components.31 Figure 34 shows the learning
curves developed for the modules and BOS components, as well as the projected
capital cost reductions. This approach results in a 26 percent reduction in solar PV
capital costs relative to 2012 levels by 2022, and a 34 percent reduction by 2032.
Table 32: Assumed Present-Day Capital and Fixed O&M Costs for Solar PV Technologies
Technology
Residential Rooftop
Commercial Rooftop
Small Utility Scale (fixed tilt)
Large Utility Scale (fixed tilt)
Small Utility Scale (single-axis
tracking)
Large Utility Scale (single-axis
tracking)
31
Capital Cost
($/kW)32
$5,300
$4,500
$2,825
$2,400
Fixed O&M ($/kWyr)
$65
$55
$50
$50
Capacity
(kW)
<10
10-1,000
1-20
100
$3,225
$50
1-20
$2,800
$50
100
Point to more complete description of the method in the E3 paper Section 4.5.3.
Capital costs shown for solar PV technologies are expressed relative to the system’s DC nameplate
rating. However, TEPPC’s modeling requires the capital cost inputs expressed relative to the system’s AC
rated output. The values in Table 32 are converted from DC to an AC basis assuming an inverter
efficiency of 85 percent.
32
Page 98 of 121
Figure 34: Projected Capital Cost Reductions for Solar PV Based on Learning Curves
Solar PV Capital Cost Index
(% of 2012 Capital Costs)
120%
100%
80%
82%
74%
69%
66%
60%
40%
20%
0%
2012
Blended Index
2017
2022
Modules Index
2027
2032
BOS Index
Present-day and future capital and O&M costs for solar thermal technology are
comprised of estimated costs for parabolic trough and power tower technologies.
Parabolic trough thermal solar technology is used predominantly; however, commercial
interest in power tower solar thermal technology is growing. Table 33 gives the
assumed present-day costs for solar thermal technology with and without storage.
Neither present-day costs nor cost reduction estimates separate out specific costs for
trough and tower technologies.
Table 33: Assumed Present-Day Capital and Fixed O&M Costs for Solar Thermal Technology
Technology
Capital Cost ($/kW) Fixed O&M ($/kW-yr)
Solar Thermal with storage33
$7,100
$60
Solar Thermal without storage
$4,900
$60
Solar thermal generation technologies are in a very early stage of commercialization, so
cost trajectories for solar thermal technology were created by surveying engineering
studies and IRPs for point estimates. Figure 35 and Figure 36 compare the point
estimates with the assumed future costs for solar thermal with and without storage. The
33
Costs were estimated using information on parabolic trough systems with six-hour storage from CPUC,
Black & Veatch, NREL, and APS; and a tower system with nine hours of storage described by Sandia.
Page 99 of 121
estimated cost reduction potential for solar thermal technology in the short-term (five
years) is 15 percent, and 30 percent in the long term (20 years).
Figure 35: Comparison of Assumed Costs and Point Estimates for Solar Thermal with 6-hour
Storage
Assumed
Trough
Tower
All-In Capital Cost (2010 $/kW)
$10,000
$8,000
$6,000
$4,000
$2,000
$2010
2015
2020
2025
Installation Vintage
Page 100 of 121
2030
2035
Figure 36: Comparison of Assumed Costs and Point Estimates for Solar Thermal without Storage
Assumed
Trough
Tower
All-In Capital Cost (2010 $/kW)
$6,000
$5,000
$4,000
$3,000
$2,000
$1,000
$2010
2015
2020
2025
Installation Vintage
2030
2035
Wind technologies include onshore wind (a mature technology) and offshore wind
(emerging). Assumed present-day costs are shown in Table 34. The learning curve
approach was applied to determine appropriate cost reductions, shown in Figure 37.
Table 34: Assumed Present-Day Capital and Fixed O&M Costs for Wind Technology
Technology
Capital Cost ($/kW) Fixed O&M ($/kW-yr)
Onshore wind
$2,000
$60
Offshore wind
$6,000
$100
Page 101 of 121
Figure 37: Projected Capital Cost Reductions for Wind Based on Learning Curves
In addition to capital costs, the effect that potential performance improvements might
have on solar and wind costs was evaluated. Wind performance improvements have the
potential to reduce capital costs and increase plant capacity factors. For this reason,
wind turbine improvements were included in the 20-year study. Improvements were not
included in the 10-year study due to the complexity involved with adding capacity factor
improvements for all plants across 8,760 hours of the year. No performance
improvements were included for both solar PV or solar thermal technologies. Solar
performance improvements might have some effect on plant costs but not on plant
capacity factors.
All capital and fixed O&M costs represent the U.S. average costs for new generation.
Regional multipliers were developed to account for regional differences in costs. Statespecific construction cost indices were developed using information from the cost
indices in the US Army Corps Civil Works Construction Cost Indexing System
(CWCCIS).34 The input costs for the CWCCIS are approximately 37 percent labor, 37
percent materials, and 26 percent equipment. Assuming that 100 percent of labor costs
and 50 percent of material costs are variable by region, the information from the
CWCCIS, variable cost indices were created. The indices were applied to technologyspecific regional adjustment assumptions on the relative contribution of labor, materials,
34
US Army Corps of Engineers (USACE) (2011), Civil Works Construction Cost Index System (CWCCIS),
Department of the Armey, Washington DC.
Page 102 of 121
and equipment. Table 35 shows the resulting technology-specific regional multipliers,
which are applied only to capital costs. A separate multiplier is applied to fixed O&M
costs.
Colorado
Idaho
Montana
Nevada
New Mexico
Oregon
Texas
Utah
Washington
Wyoming
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
CFE
0.98
0.96
0.98
0.98
0.98
0.98
0.96
0.98
0.96
0.95
0.96
0.98
0.97
British
Columbia
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
California
Biogas
Biomass
CHP
Coal – PC
Coal – IGCC
Gas CCGT
Gas CT
Geothermal
Hydro–Large
Hydro–Small
Nuclear
Solar PV
Solar
Thermal
Wind
Fixed O&M
Arizona
State/
Province
Alberta
Table 35: Technology-Specific Regional Multipliers
1.11
1.17
1.08
1.11
1.10
1.08
1.19
1.11
1.18
1.21
1.20
1.07
1.13
0.94
0.91
0.96
0.94
0.95
0.96
0.90
0.94
0.90
0.88
0.89
0.96
0.93
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.90
0.99
0.99
0.97
0.95
0.98
0.97
0.97
0.98
0.95
0.97
0.95
0.94
0.95
0.98
0.96
0.98
0.97
0.99
0.98
0.98
0.99
0.97
0.98
0.97
0.97
0.97
0.99
0.98
1.05
1.08
1.03
1.05
1.04
1.03
1.08
1.05
1.08
1.09
1.09
1.03
1.06
0.97
0.95
0.98
0.97
0.98
0.98
0.95
0.97
0.95
0.94
0.95
0.98
0.96
1.04
1.07
1.03
1.04
1.04
1.03
1.07
1.04
1.07
1.08
1.08
1.03
1.05
0.92
0.88
0.94
0.92
0.93
0.94
0.87
0.92
0.87
0.85
0.86
0.95
0.91
0.97
0.95
0.98
0.97
0.97
0.98
0.95
0.97
0.85
0.94
0.95
0.98
0.96
1.04
1.07
1.03
1.04
1.04
1.03
1.07
1.04
1.07
1.08
1.08
1.03
1.05
0.94
0.91
0.96
0.94
0.95
0.96
0.90
0.94
0.90
0.88
0.89
0.96
0.93
1.00 0.99 1.00 1.07 0.96 0.99 0.98 0.99 1.03 0.98 1.03 0.95 0.98 1.03 0.96
1.00 0.97 1.00 1.13 0.93 0.99 0.96 0.98 1.06 0.96 1.05 0.91 0.96 1.05 0.93
Table 36 shows the final capital cost inputs to the 10-year and 20-year studies.
Table 36: Capital Cost Inputs to the 10-Year and 20-Year Studies
Technology
Biogas
Biomass
Coal
CHP
Gas CCGT
Subtype
Landfill
Other
PC
IGCC w/ CCS
Small (<5 MW)
Large (>5MW)
Basic, Wet
Cooled
Advanced, Wet
Cooled
Basic, Dry Cooled
Present-Day
Capital Cost
($/kW)
Input Cost for 10- Input Cost for 20Year Study ($/kW) Year Study ($/kW)
$2,750
$5,500
$4,250
$3,600
$8,000
$3,700
$1,600
$1,100
$2,750
$5,500
$4,250
$3,600
$8,000
$3,700
$1,600
$1,100
$2,750
$5,500
$4,250
$3,600
$8,000
$3,700
$1,600
$1,100
$1,200
$1,200
$1,200
$1,175
$1,175
$1,175
Page 103 of 121
Technology
Gas CT
Subtype
Advanced, Dry
Cooled
Aero derivative
Frame
Geothermal
Hydro
Large
Small
Upgrade
Nuclear
Solar PV
Residential
Rooftop
Commercial
Rooftop
Distributed Utility
(Fixed Tilt)
Distributed Utility
(Tracking)
Large Utility
(Fixed Tilt)
Large Utility
(Tracking)
Solar
No Storage
Thermal
Six Hour Storage
Wind
Onshore
Offshore
Present-Day
Capital Cost
($/kW)
Input Cost for 10- Input Cost for 20Year Study ($/kW) Year Study ($/kW)
$1,275
$1,275
$1,275
$1,150
$800
$5,500
$3,000
$3,500
$1,500
$7,500
$6,250
$1,150
$800
$5,500
$3,000
$3,500
$1,500
$7,500
$5,480
$1,150
$800
$5,500
$3,000
$3,500
$1,500
$7,500
$4,340
$5,250
$4,600
$3,650
$3,325
$2,910
$2,310
$3,800
$3,330
$2,640
$2,850
$2,500
$1,980
$3,300
$2,890
$2,290
$4,900
$7,100
$2,000
$6,000
$4,460
$6,460
$1,950
$5,850
$3,675
$5,325
$1,830
$5,490
Levelization of Costs
Because the 10- and 20-year studies are “snapshot” analyses – that is, they evaluate
the infrastructure requirements and operations of the grid during a single year in the
future – the capital costs must be translated into levelized annual costs. The generation
tool has four models that calculate magnitude of the costs that would be borne by
ratepayers to fund a project’s construction, on an annual basis. There are three cash
flow models each representing project financing costs assuming the project is funded by
an IPP, IOU or POU. The fourth model is a simple algebraic levelized cost calculator
that was developed for inclusion in the LTPT.
Several assumptions were included in the development of the four annualization tools.
These are discussed below.
Weighted average cost of capital (WACC): The WACC assumptions used in each of the
cash flow models are as follows: IPP project WACC is 8.25 percent; IOU project WACC
Page 104 of 121
is 7.31 percent; and POU WACC is 6.30 percent. For the IOU- and POU-financed
projects, whose capital costs are recovered through the rate base, a fixed utility capital
structure was assumed. POU projects differ from IOU projects in that they are exempt
from income tax and entirely debt-financed.
Capital Recovery Factor (CFR): A simplified approach to annualizing the capital and
fixed O&M costs was required in order to integrate into the LTPT. To accomplish this,
WECC staff used the CFR calculation developed by NREL.35 The approach
approximates the cash flows calculated by the cash-flow models.
Financing Lifetime: The financing lifetime of a plant is an assumption of the period over
which the costs of the plant would be recovered and passed on to ratepayers. Table 37
shows the default financing entity and assumed financing lifetime for some resource
types.
Table 37: Assumed Financing Entities and Lifetimes for Generation Technologies
Technology
Default Financing Entity Assumed Financing Lifetime
Biogas
IPP
20 yrs.
Biomass
IPP
20 yrs.
Coal – PC
IOU
40 yrs.
Coal – IGCC
IOU
40 yrs.
CHP
IPP
20 yrs.
Gas – CCGT
IPP
20 yrs.
Gas – CT
IPP
20 yrs.
Geothermal
IPP
20 yrs.
Hydro – Large
IOU
40 yrs.
Hydro - Small
IPP
20 yrs.
Nuclear
IOU
40 yrs.
Solar Thermal
IPP
20 yrs.
Solar PV
IPP
20 yrs.
Wind
IPP
20 yrs.
Federal tax policies: Assumptions about three major federal tax incentives—accelerated
depreciation, Production Tax Credit (PTC), and Investment Tax Credit (ITC)—were
included in the annualization calculation.
35
For more information on the approach visit Short, W., et al. (1995) A Manual for the Economic
Evaluation of Energy Efficiency and Renewable Energy Technologies, NREL, Golden, CO (Short, 1995)
Page 105 of 121
Table 38 summarizes the eligibility of each generation technology for these tax credits
or tax benefits. For the 10-year Common Case, the impacts of current PTC and ITC (30
percent) were included for levelized resource costs for applicable resources. For the 20year Reference Case, PTC impacts were not included and ITC impacts were included at
10 percent.
Page 106 of 121
Table 38: Federal Generation Tax Policy Summary
Technology
Accelerated Depreciation PTC
ITC
Biogas
$11
10 yrs.
Biomass
$22
10 yrs.
Coal – PC
20 yrs.
Coal – IGCC
20 yrs.
CHP
10 yrs.
Gas – CCGT
20 yrs.
Gas – CT
20 yrs.
Geothermal
$22
5 yrs.
Hydro – Large
$11
20 yrs.
Hydro - Small
$11
20 yrs.
Nuclear
20 yrs.
Solar Thermal
30% 5 yrs.
Solar PV
30% 5 yrs.
Wind
$22
5 yrs.
Property Tax and insurance: Fixed O&M costs for biogas, biomass, geothermal, solar
thermal, solar PV and wind include property tax and insurance. For all other
technologies, the annual property tax is calculated at one percent of the plant’s
remaining value; annual insurance is calculated as one half percent of the initial capital
cost and escalates at two and a half percent per year.
Capital cost vintages: A single vintage of capital costs was applied to all projects of a
specific type in both the 10- and 20-year studies. Based on an assumption that
renewable development will happen early in the 10-year cycle due to the expiration of
tax incentives, all 2022 resource costs are based on the capital costs of a plant installed
in 2015. For resources added after 2022 (20-year study), the assumed capital cost
vintage is 2027.
Table 39 shows the levelized cost of energy (LCOE) calculated by both the cash flow
models and the simple calculation for each generation resources. The cash flow-derived
LCOE is used in the 10-year analysis. It was calculated using the cash flow model that
corresponds to the default financing entity listed in Table 37 for each technology. The
LCOE derived from the simple calculation is used in the 20-year analysis.
Table 39: Summary of LCOE from cash flow and simple analyses
Technology
Biogas
Biomass
Subtype
Landfill
Other
LCOE - Cash
Flow
($/MWh)
2015 vintage
$91.79
$113.04
$89.20
Page 107 of 121
LCOE - Simple 2027
vintage
($/MWh)
($/kW-yr)
$97.50
$116.32
$107.34
$652.38
$761.18
$794.95
Technology
Coal
CHP
Gas CCGT
Gas CT
Geothermal
Hydro
Nuclear
Solar PV
Solar
Thermal
Wind
Subtype
PC
IGCC w/ CCS
Small (<5 MW)
Large (>5MW)
Basic, Wet Cooled
Advanced, Wet Cooled
Basic, Dry Cooled
Advanced, Dry Cooled
Aero derivative
Frame
Large
Small
Upgrade
Residential Rooftop
Commercial Rooftop
Distributed Utility (Fixed
Tilt)
Distributed Utility
(Tracking)
Large Utility (Fixed Tilt)
Large Utility (Tracking)
No Storage
Six Hour Storage
Onshore
Offshore
LCOE - Cash
Flow
($/MWh)
$90.32
2015
vintage
$184.86
LCOE - Simple 2027
vintage
$206.89
$93.95
$87.46
$87.03
$90.64
$90.22
$106.63
$108.82
$106.24
$106.42
$196.17
$49.99
$160.19
$267.27
$224.79
$122.57
$71.89
$144.32
$168.13
$83.24
$81.96
$81.09
$84.78
$83.92
$101.73
$105.14
$133.97
$87.68
$164.53
$46.70
$121.83
$295.00
$248.08
$134.26
$507.28
$1,012.43
$681.83
$559.78
$600.66
$593.33
$621.06
$613.74
$749.44
$777.29
$824.02
$290.47
$417.99
$155.41
$835.70
$482.59
$405.84
$274.54
$117.22
$128.80
$305.52
$109.14
$105.05
$161.63
$153.47
$61.25
$195.42
$119.11
$115.05
$183.44
$175.76
$81.82
$209.39
$243.57
$272.91
$420.13
$575.06
$247.61
$685.09
10-Year
Capital costs are not modeled in the 2022 TEPPC datasets; however, TEPPC used the
capital cost tool to calculate the applicable capital costs of incremental generation and
transmission for comparison purposes.36
20-Year
Capital costs are critical to the 20-year study process. The LTPT requires capital costs
to perform the generation optimization function done by the SCDT and the transmission
expansion done by the NXT. For each future scenario to be studied, the LTPT selects
and optimized set of generation resources based on their capital cost. The LTPT then
36
Data for the capital cost tool and in this report was gathered by Black & Veatch and E3 from publicly
available resources.
Page 108 of 121
applies incremental transmission, the capital costs of which are then added to the
appropriate generation. The result is a recalculation of the incremental generation cost
that includes grid costs. Based on these new generation costs, the LTPT optimizes the
generation again, and the process repeats until the LTPT reaches convergence. For a
detailed description, see the Tools and Models section.
Reliability
Reserve Margins
10-Year
The reserve assumptions used for the 2010 TEPPC 2020 studies have reserves
modeled at the subregional level. The data includes four percent reserves (three
percent for spinning and one percent for contingency) for each of the eight regions,
which approximates 50 percent of the WECC reserve requirement (after forced
outages) – 7 percent for thermal and five percent for hydro. Reserves are enforced
during the dispatch phase of the model and units may be backed down at times to meet
reserves.
20-Year
Planning reserves are not explicitly modeled within the LTPT as an optimization goal,
but rather determined as a solution result. The system reserve value is determined by
comparing the generation capacity available for peak load demand after optimization of
energy and system peak goals with the peak load demand. Over the course of
performing the long term studies, it was discovered that ample system reserve was
generally available after satisfying the energy goals (see the study write-ups). The
reserve model within the LTPT will be refined for future study cycles by applying what
we have learned during this study cycle, consistent with 10-year planning processes.
Flexibility Reserves
10-Year
In addition to the load-based reserve requirement, a new reserve component was
implemented to model the balancing needed for variable generation. The flexibility
reserve is calculated using 10-minute wind and solar data, and then aggregated to an
hourly addition to the operating reserve discussed above. This hourly flexibility reserve
requirement is in addition to the 4 percent of daily peak load requirement that the model
caries as on operating reserve, as shown in Figure 38.
Page 109 of 121
Figure 38: Composite Reserve Requirement
An example of the composite reserve requirement, shown on an hourly basis, is
provided in Figure 39. The chart is for the California South region during a two day block
of time in the summer. As shown, the flex reserve portion of the composite reserve
requirement varies with the solar and wind penetration.
Figure 39: Hourly Reserve Example
Page 110 of 121
Flex reserve requirements are calculated for every hour of the year. The result of these
calculations are presented in Figure 40, where the composite hourly reserve
requirement is provided (by subregion) for every hour of the 2022 Common Case. As
shown, California North and California South both have reserve requirements that
exceed 18 percent of load in certain hours. This means that large penetrations of
renewables are on the system and could conceivably drop off at any moment, thus
necessitating a large operating reserve. Figure 41 shows the same data, sorted in
duration format.
Figure 40: Composite Hourly Reserve Requirements by Subregion
Page 111 of 121
Figure 41: Subregion Reserve Requirement - Duration Plot
20-Year
Flexibility reserves are not accounted for or modeled in the LTPT.
Page 112 of 121
Environmental Data
Considering environmental and cultural information in transmission planning will create
results (transmission alternatives) that more effectively limit environmental and cultural
risks and constraints that could affect transmission development. Transmission
alternatives that take these risks and constraints into account result in more realistic
potential transmission corridors, and also facilitate a more collaborative, stakeholderinclusion, and comprehensive transmission planning process. Further, considering
environmental and cultural risks at the planning level would be expected to expedite the
siting process when these factors are considered in greater detail. The use of
environmental and cultural information in transmission planning puts these real-world
considerations on par with demand, generation resources, energy policies, impacts on
transmission reliability and other factors that have traditionally been considered in past
planning cycles.
WECC, through its Environmental Data Task Force (EDTF), has identified, collected,
and processed geospatial information (data layers) on environmental and cultural
resource for use in transmission planning. These data layers were identified by EDTF
members and subject matter experts familiar with the current state of environmental
data. The environmental and cultural data layers identified by the EDTF provide a basis
for considering of these resources during transmission planning. WECC has aggregated
these data layers into a seamless “Risk Classification Data Layer” that depicts
environmental and cultural risks and constraints across the entire Western
Interconnection; the Risk Classification Data Layer has been incorporated into the longterm planning tools.
Environmental Data Used
Data Types
Incorporating environmental and cultural data into the transmission planning process
requires a clear understanding of what is considered “environmental and cultural data.”
For the purposes of WECC data collection effort, “environmental and cultural data”
included the following resource categories:







Land (including visual resources)
Wildlife
Cultural
Historical
Archaeological
Water Resources
Transmission and rights-of-ways
Page 113 of 121
WECC’s environmental and cultural data collection effort focused on free, publicly
available, data. However, to allow WECC to consider certain resource categories that
were not available from free public sources, exceptions were made to allow fee-based
and other non-public data, such as the inclusion of NatureServe’s Multi-Jurisdictional
Data Base of Species Occurrence, which describes rare and Threatened/Endangered
Species, and transmission line data.37
Data Sources
Data layers considered for use in transmission planning were identified by EDTF
members and subject matter experts. Identified data layers were collected and
categorized into the following categories based on the original data source:





Federal (including Canada) Data
State/Provincial Data
Non-Governmental Organization Data
Vendors’ Data
Cultural and Tribal Data
These five categories were chosen as an easily identifiable, base-level categorization
system to track and organize the large amounts of data being collected.
Federal
Federal data originates from a federal agency in the United States or Canada and
usually spatially represents multi-state or multi-provincial areas. Data from the following
U.S. federal agencies was collected and catalogued:







Department of the Interior
Fish and Wildlife Service
Federal Emergency Management Agency
Forest Service
Geological Survey
National Park Service
National Resources Conservation Service
State and Provincial
State and provincial data originates from a state and provincial level agency or from a
local government agency such as a county or city municipality. Only limited county and
37
Free publicly available transmission line data does not exist. Data used by WECC came from Platts
(http://www.platts.com/).
Page 114 of 121
city data were collected because the data was often too fine of a scale for use in
regional transmission planning. Data from the following state/provincial sources was
collected and catalogued:









Province of British Columbia
AltaLIS (Province of Alberta)
Cal-Atlas (California)
Wyoming Game and Fish Department
Colorado Division of Wildlife
Montana Fish, Wildlife and Parks
Oregon Department of Fish and Wildlife
Washington State Department of Natural Resources
Arizona Game and Fish Department
Non-Governmental Organization
For the purposes of data collection Non-Governmental Organizations (NGOs) were
defined as any organization that operates independently from the government and is not
a data vendor (seller). This definition can include both for-profit and non-profit
organizations, or data from other environmental documents. Data from the following
NGOs was collected and catalogued:






NatureServe
Conservation Biology Institute
West-Wide Energy Corridor Programmatic EIS
ConserveOnline
National Audubon Society
Western Renewable Energy Zones Phase 1 Report (Canada Only)
Vendors
Vendor data was defined as any for-profit company that is not government-related and
is in the business of selling data or software even though the data received from the
vendor may not have a fee associated with it. Data from the following vendors was
collected and catalogued:


Environmental Systems Research Institute
Platts
Cultural Resources Data
Cultural resources data is comprised of features of archaeological, anthropological,
historical, and tribal interest. Cultural resources data are typically collected from the
State and provincial government agency (e.g., the state historic preservation offices).
Cultural resources data sets are unique from most types of environmental data in that
they:
Page 115 of 121

Contain information of a sensitive or protected nature; release of such data to the
public could compromise the protection of resources;
 Are only released by the controlling government agency to authorized users;
 Are restricted for publication, distribution, and presentation;
 Are maintained in a variety of electronic formats that may or may not be
manageable in a GIS.
The list of preferred data available for transmission planning currently includes several
sources of cultural data, including data for National Historic Trails and National Historic
Monuments. In addition to these currently available data layers, WECC is in the process
of working with state agencies to acquire additional relevant cultural resource data,
including:


The locations of known cultural sites
The locations of cultural survey (inventory) areas
Data Quality
Following their identification and collection, WECC determined the quality of each
environmental and cultural data layers using a two-step process. First, all data layers
were examined using section 3.1 of the Data Quality Protocol. Data layers that passed
the Data Quality Protocol were added to the EDTF Data Inventory38, which details all the
environmental and cultural data layers considered for use in transmission planning.
Second, data layers in the EDTF Data Inventory, section 3.2, were reviewed by the
diverse stakeholder group that comprises the EDTF membership, and data found useful
for transmission planning were identified as “preferred data” for use in regional
transmission planning.
Data Quality Protocol
The purpose of the Data Quality Protocol39 is to provide data users and stakeholders a
standardized, structured/step-wise protocol for performing a fitness-for-use (data
quality) assessment of environmental and cultural data layers identified by WECC.
Ultimately, the Data Quality Protocol is intended to assist the EDTF in assessing
whether catalogued data layers should be considered “preferred data” (data that meets
quality standards and is potentially useful to consider in regional transmission planning).
Implementation of the Data Quality Protocol involves collaborations between GIS
analysts and stakeholders and other subject matter experts.
38
EDTF, Data Inventory:
http://www.wecc.biz/committees/BOD/TEPPC/External/EDTF_Data_Inventory.xlsx
39
EDTF, Data Quality Protocol:
http://www.wecc.biz/committees/BOD/TEPPC/External/EDTF_Data_Quality_Protocol.pdf
Page 116 of 121
Preferred Data Sets
Preferred data are those environmental and cultural data sets that stakeholders and
subject matter experts determined are useful to inform transmission planning. In
general, preferred data can be quantified and occur at a scale conducive to regional
transmission planning. The current list of preferred data40 for transmission planning may
change over time as new information becomes available (e.g., through routine data
updates or the biennial open season process).
Risk Classification System
The EDTF stakeholders assigned Risk Classification Categories to each data layer
identified as preferred. The EDTF’s Risk Classification System was developed to allow
the categorization of risk to transmission development from various environmental and
cultural features on the landscape. Because of the large area covered by the Western
Interconnection, a basic four-point scale was used for categorization. Under this system,
Risk Classification Category 1 represents the lowest risk and Risk Classification
Category 4 represents the highest risk (i.e., areas were transmission development is
precluded by law or regulation). The four-point Risk Classification System was based on
the suitability criteria used in other studies, (i.e., Electric Power Research InstituteGeorgia Transmission Commission [EPRI-GTC] Overhead Electric Transmission Line
Siting Methodology41, Renewable Energy Transmission Initiative42, and Arizona
Renewable Resource and Transmission Identification Subcommittee 43), and
professional judgment of stakeholders and subject matter experts. The EDTF
stakeholder group agreed to this approach because it allowed an easily understandable,
“planning level” method to aggregate risk and constraints from a variety of
environmental and cultural features.
The four Risk Classification Categories are described in detail below:
1.) Least Risk of Environmental or Cultural Resource Sensitivities and Constraints:
Areas with minimal identified environmental or cultural resource constraints and with
existing land uses or designations that are compatible with or encourage transmission
40
EDTF, Data Inventory:
http://www.wecc.biz/committees/BOD/TEPPC/External/EDTF_Data_Inventory.xlsx
Electric Power Research Institute-Georgia Transmission Commission, “EPRI-GTC Overhead Electric
Transmission Line Siting Methodology” (February 2006): http://my.epri.com/portal/server.pt
41
Renewable Energy Transmission Initiative, “Phase 1B Final Report” (January 2009):
http://www.energy.ca.gov/2008publications/RETI-1000-2008-003/RETI-1000-2008-003-F.PDF
42
Arizona Renewable Resource and Transmission Identification Subcommittee, “Final Report”:
http://www.westconnect.com/filestorage/ARRTIS%20Final%20Report.pdf
43
Page 117 of 121
development. These areas would present few or minimal environmental and cultural
mitigation requirements and are least likely to result in project delays.
2.) Low to Moderate Risk of Environmental or Cultural Resource Sensitivities and
Constraints: Areas where development may encounter one or more environmental or
cultural resource sensitivities or constraints that would require low to moderate permit
complexity or mitigation costs. This category also includes areas in the Protected Areas
Database of the United States (PAD-US) dataset that have an unknown land use
designation or degree of restriction to transmission development.
3.) High Risk of Environmental or Cultural Resource Sensitivities and Constraints:
Transmission development is likely to encounter one or more environmental or cultural
resource sensitivities or constraints that will substantially increase permitting complexity
and which could result in project delays and high mitigation costs. This category also
includes areas identified as avoidance areas (based on environmental and cultural
sensitivities) in Canada from the WREZ Phase 1 Report44.
4.) Areas Currently Precluded by Law or Regulation: Areas where transmission
development is currently precluded by federal, state, or provincial law, policy, or
regulation, and areas identified as exclusion areas (based on environmental and cultural
sensitivities) in Canada from the WREZ process.
The Risk Classification System was applied to the preferred data based on reviews of
applicable laws, regulations, policies as well as input from relevant subject matter
experts and the stakeholders. The full justification for the Risk Classification
assignments is available in the Environmental Recommendations for Transmission
Planning Report45 (also Appendix D).
The application of the Risk Classification to the preferred data was used to create a
seamless, GIS-based Risk Classification Data Layer that depicts environmental and
cultural risks and constraints across the entire Western Interconnection for use in
WECC’s long-term planning tool.
Limitations on Environmental and Cultural Data
Data used by WECC to describe environmental and cultural features through the
preferred data sets will change as new and updated information becomes available. To
remain relevant, a regular process of data review and update for preferred data has
been developed and is being implemented. Data providers are continuing to improve
Western Governors’ Association, “Western Renewable Energy Zones - Phase 1 Report” (June 2009):
http://www.westgov.org/rtep/219
44
WECC, “Environmental Recommendations for Transmission Planning”, (May 6, 2011):
http://www.wecc.biz/committees/BOD/TEPPC/External/Environmental_Recommendations_for_Transmiss
ion_Planning.pdf
45
Page 118 of 121
their existing data as well as adding new or previously unmapped data layers. Because
of the large amount of data providers who supplied data for the analysis and the varied
timelines of data updates by those providers, it is difficult to have the most current data
for all necessary layers simultaneously for analysis. Issues of data currency make it
important to explicitly label products with “current as of” dates.
WECC recognizes there is not currently data available for some of the environmental
and cultural features that EDTF stakeholders and subject matter experts identified as
having the potential to affect transmission development. Below are the primary data
gaps identified for environmental and cultural information.
Cultural Resources Data
Currently, cultural geospatial data that is seamless across administrative boundaries is
limited due to a variety of factors, including the sensitivity of the data for public release.
WECC is working closely with several state historic preservation offices to determine an
appropriate method and scale for acquiring and using the cultural resource location and
inventory (survey) data they collect. The goal is to develop a process and product that
respects the sensitivity of cultural data and the need to protect the locations of these
irreplaceable resources from public release, while providing sufficient information to
allow the consideration of cultural resources during regional transmission planning.
Tribal and First Nations data can also be sensitive. Other than reservation lands and
other legal boundaries, WECC has not been successful in identifying data for important
Tribal and First Nations cultural resources and traditional use sites.
Wildlife Data
WECC has collected wildlife data from state, NGO and federal data sources. However,
inconsistencies exist in naming conventions and the overall lack of seamlessness of
wildlife data across jurisdictional boundaries. As data from the Western Governors’
Association state Crucial Habitat Assessment Tool program become available across
the U.S. portion of the Western Interconnection, many of these issues should be
addressed.
Canadian Data
Currently, the bulk of the dataset catalogue consists of U.S. data. While the existing
seamless Risk Classification Data Layer contains information for Canada, additional
and updated data are needed from the Canadian portions of WECC in British Columbia
and Alberta. WECC is working to update and improve Canadian data by conducting
concentrated data outreach with Canadian data stewards and stakeholders.
Page 119 of 121
Appendix A: Hydro Data
Northwest Monthly Hydro Energy
Model
Type
Net Jan
(MWh)
Net Feb
(MWh)
Net Mar
(MWh)
Net Apr
(MWh)
Net May
(MWh)
Net Jun
(MWh)
Net Jul
(MWh)
Net Aug
(MWh)
Net Sep
(MWh)
Net Oct
(MWh)
Net Nov
(MWh)
Net Dec
(MWh)
HD
3296130
2751293
2849190
2988179
4496427
3623152
2918105
2605933
2265705
2439842
2962553
3308635
PLF
772784
613794
596074
642522
886513
781561
749151
640505
547975
576476
682568
724186
PLF
K=0
565145
454549
446125
620949
903762
896270
826312
696704
521988
507613
565531
587718
HTC
5966807
5415016
5266361
4590316
6876082
6866154
6584136
5196928
3766430
4710420
5260755
5541062
Total
10600866
9234652
9157750
8841966
13162784
12167137
11077704
9140070
7102098
8234351
9471407
10161601
California Monthly Hydro Energy
HD
1049811
1200660
1580895
1829247
2684502
2732327
2596800
2013813
1524696
1330445
1265316
1743867
PLF
0
0
0
0
0
0
0
0
0
0
0
0
PLF
K=0
169070
167790
183090
203788
267000
250081
244905
210303
166825
163699
162441
162249
HTC
1021973
979346
1321334
1539393
1954744
1800356
1613657
1410880
1065646
897122
702798
1091014
Total
2240854
2347796
3085319
3572428
4906246
4782764
4455362
3634996
2757167
2391266
2130555
2997130
East Monthly Hydro Energy
HD
517695
457784
561638
521789
677658
810376
830235
716156
494936
437216
413022
563372
PLF
13493
11929
12473
13336
19139
18115
18482
15856
13487
14035
12353
13477
PLF
K=0
246432
214724
219808
231066
305432
302329
287418
227429
191769
195135
197777
218131
HTC
190493
172144
214681
497807
475744
498394
535282
403570
315095
330339
453680
271665
Total
968113
856581
1008600
1263998
1477973
1629214
1671417
1363011
1015287
976725
1076832
1066645
British Columbia Monthly Hydro Energy
HD
0
0
0
0
0
0
0
0
0
0
0
0
PLF
0
0
0
0
0
0
0
0
0
0
0
0
PLF
K=0
1,430,778
995,016
1,186,734
1,275,877
2,204,981
2,498,568
2,677,247
2,261,320
1,856,198
1,703,579
1,571,774
1,519,228
HTC
6,529,619
5,419,326
5,408,477
3,363,302
3,333,432
3,298,617
3,862,729
4,348,720
4,188,403
5,047,461
5,330,881
6,083,217
Total
7960397
6414342
6595211
4639179
5538413
5797185
6539976
6610040
6044601
6751040
6902655
7602445
Alberta Monthly Hydro Energy
HD
4141
7385
4174
6161
49484
36775
52564
44217
31704
26667
19660
7952
PLF
0
0
0
0
0
0
0
0
0
0
0
0
PLF
K=0
41703
40894
48849
50732
61898
62784
49221
40946
35849
42221
44265
50122
HTC
141870
124597
133494
122955
152106
292007
254118
165077
161593
161263
150125
151256
Total
187714
172876
186517
179848
263488
391566
355903
250240
229146
230151
214050
209330
Interconnection-wide Monthly Hydro Energy
HD
4867777
4417122
49958967
5345376
7908071
7202630
6397704
5380119
4317041
4234170
4660551
5623826
PLF
786277
625723
608547
655858
905652
799676
767633
656361
561462
590511
694921
737663
PLF
K=0
2453128
1872973
2084606
2382412
3743073
4010032
4085103
3436702
2772629
2612247
2541788
2537448
HTC
13850762
12110429
12344347
10113773
12792108
12755528
12849922
11525175
9497167
11146605
11898239
13138214
Total
21957944
19026247
20033397
18497419
25348904
24767866
24100362
20998357
17148299
18583533
19795499
22037151
Page 120 of 121
Model
Type
Capacity
(MW)
HD
PLF
PLF K=0
HTC
Total
10808
2261
1938
17621
32628
HD
PLF
PLF K=0
HTC
Total
5985
0
705
3584
10274
HD
PLF
PLF K=0
HTC
Total
2678
88
629
2416
5810
HD
PLF
PLF K=0
HTC
Total
0
0
4370
13676
18046
HD
PLF
PLF K=0
HTC
Total
85
0
100
811
996
HD
PLF
PLF K=0
HTC
Total
19556
2349
7741
38109
67755
% of Region
Net Generation
Capacity
(GWh)
Northwest
33.12%
36505
6.93%
8214
5.94%
7593
54.01%
66040
118352
California
58.26%
21552
0.00%
0
6.86%
2351
34.89%
15398
39302
East
46.09%
7002
1.51%
176
10.82%
2837
41.59%
4359
14374
British Columbia
0.00%
0
0.00%
0
24.22%
21181
75.78%
56214
77395
Alberta
8.53%
291
0.00%
0
10.04%
569
81.43%
2010
2871
WECC Total
28.86%
65350
3.47%
8390
11.42%
34532
56.25%
144022
252295
Page 121 of 121
% of Region Net
Generation
30.84%
6.94%
6.42%
55.80%
54.84%
0.00%
5.98%
39.18%
48.71%
1.23%
19.74%
30.32%
0.00%
0.00%
27.37%
72.63%
10.13%
0.00%
19.84%
70.03%
25.90%
3.33%
13.69%
57.08%