Document name
2013 Interconnection-wide Plan
Tools and Models
Category
( ) Regional reliability standard
( ) Regional criteria
( ) Policy
( ) Guideline
(X) 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( ) superseded by _____________________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)
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2013 Interconnection-wide Plan Tools and Models
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2013 Interconnection-wide Plan Tools and Models
2013 Interconnection-wide Plan
Tools and Models
By
WECC Staff
Western Electricity Coordinating Council
September 19, 2013
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2013 Interconnection-wide Plan Tools and Models
2013 Interconnection-wide Plan
Tools and Models
Summary
The purpose of this section is to describe the various tools and models used in the Plan
analyses. In addition to providing readers a description of how data and assumptions
(as described in the “Data and Assumptions” report) are turned into results via their use
in the tools and models, this section also creates the technical justification for others’
use of these analytical methods.
The 2013 Plan includes 10-year and 20-year transmission planning analyses. The two
time horizons were approached in different ways that required the development of tools,
models, and datasets that meet the individual needs of each. The 10-year analysis is a
bottom-up process aimed at evaluating the robustness of the Common Case and the
impact of various alternative futures. The 20-year analysis, alternatively, uses a topdown process aimed at understanding the performance of a specific generation and
transmission infrastructure package.
The models and data used for the two timeframes are diverse and complementary. One
serves to understand the performance of infrastructure choices while the other the
drivers of infrastructure choices. The 10-year study horizon (year 2022) was performed
in a production cost model (PCM), while the 20-year study horizon (year 2032) was
analyzed in a capital expansion model (LTPT).
The PCM is the primary analytic tool used in the Plan’s 10-year horizon analyses. In this
planning cycle, TEPPC made improvements to the PCM that allowed for enhanced
analysis. The PCM was improved to allow the consideration of cycling costs. In addition,
WECC coordinated with vendors to update the DC line model in the PCM. The PCM is
useful for economic evaluation, but does not evaluate capital costs, transmission
reliability or sub-hourly operational impacts. Additionally, TEPPC’s model does not
recognize the limitations of ownership or contractual rights on a generator’s ability to
access transmission. Of particular concern, the increasing amount of variable
generation analyzed in the 10-year planning studies indicate the need for sub-hourly
integration and stability analyses, and other evaluations outside the capacity of the
hourly PCM.
To meet the needs of the 20-year analysis, WECC developed the LTPT. This complex
capital expansion optimization tool is comprised of the Study Case Development Tool
(SCDT) and the Network Expansion Tool (NXT) that work together to co-optimize
generation and transmission expansions necessary to meet load at least-cost given a
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2013 Interconnection-wide Plan Tools and Models
set of stakeholder-derived decision factors (e.g., environmental, policy, economic,
reliability) and reliability-based constraints.
The LTPT promises to be a powerful tool in evaluating potential future resource and
transmission expansion decisions. This study cycle the “proof-of-concept” was a
resounding success and a useful addition to the suite of tools currently used in longterm planning. As with any new software tool with this level of complexity, a period of
maturity is needed before rigorous results can be expected. In the next planning cycle,
WECC can build upon its early success with the LTPT by making improvements to the
model to enhance the tool’s ability to address stakeholder study requests.
In addition to the previously mentioned major models that produce the results discussed
in the Plan, there are a plethora of additional models that describe the loads,
generation, environment and policy attributes. These include:

Capital costs are an important element of the 10-year analysis and a critical input
into the LTPT. During this planning cycle, WECC improved its generation capital
cost tool and created a new transmission cost tool that breaks transmission
capital costs down into transmission line and substation costs.

As the integration of variable generation increases in importance in transmission
planning, WECC has developed a tool to estimate “flexibility reserve”
requirements.

Wind and solar models – Create hourly shapes and assist with generation
selection based on resource availability data and stakeholder input.

Steady-state and dynamic models – Describe the physical attributes of the
generation and transmission system that determine how energy flows.

Hydro models – Determine the behavior of various hydro generators based on
water availability, environmental constraints, and operational factors.
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Contents
Summary and Key Findings ............................................................................................ 4
Background ..................................................................................................................... 7
Production Cost Model (PCM) ....................................................................................... 10
Model Description ..................................................................................................... 10
Key Results and Metrics ........................................................................................... 15
Model Limitations ...................................................................................................... 16
Long-Term Planning Tool (LTPT) .................................................................................. 18
Model Description ..................................................................................................... 20
Key Results and Metrics ........................................................................................... 33
Model Limitations ...................................................................................................... 34
Capital Cost Calculators ................................................................................................ 37
Model Description ..................................................................................................... 38
Key Results and Metrics ........................................................................................... 46
Model Limitations ...................................................................................................... 47
Steady-State and Dynamics Models ............................................................................. 48
Model Description ..................................................................................................... 48
Key Results and Metrics ........................................................................................... 48
Model Limitations ...................................................................................................... 48
Supporting Models ........................................................................................................ 50
Wind and Solar Modeling .......................................................................................... 50
Hydro Modeling ......................................................................................................... 50
Flexibility Reserves Modeling.................................................................................... 58
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2013 Interconnection-wide Plan Tools and Models
Background
The aim of TEPPC’s processes and analytics is to understand long-term
Interconnection-wide transmission needs, costs, and reliability impacts over a broad
range of potential energy futures. There is no singular model or tool available that can
meet all of TEPPC’s needs. As such, TEPPC and WECC rely on several models and
tools.

Production Cost Model (PCM) – The PCM is the primary analytic tool used in the
Plan’s 10-year horizon analyses. The PCM performs a security-constrained
economic dispatch of the electric system for every hour of the study year with the
goal of minimizing total operating costs at the Interconnection-wide level. By
emulating the hourly operation of the electric system, 10-year horizon studies are
able to compare system operational costs and transmission utilization and
congestion.

Long-Term Planning Tool (LTPT) – The LTPT, WECC’s newest planning model,
is a capital expansion optimization tool used for planning and screening. The
LTPT is comprised of the Study Case Development Tool (SCDT) and the
Network Expansion Tool (NXT), which work together to co-optimize generation
profiles and transmission expansions necessary to realize potential energy
futures as derived by stakeholders and which are also subject to various
stakeholder decision factors (e.g., environmental, policy, economic, reliability)
and engineering constraints.
The purpose of the SCDT is to create an optimized study case representing a
potential "energy future." The study case produced by the SCDT serves as input
data to the NXT. The study case produced by the SCDT is comprised of a
reduced nodal network model (e.g., area load and generation hub level),
potential transmission expansion candidate lines, and a minimum cost
optimization of generation. The minimum cost optimization of generation
performed by the SCDT is subject to stakeholder decision factors, engineering
constraints and economics (e.g., minimizing the levelized cost of energy
(LCOE)). Transmission capital costs are calculated within the SCDT using the
same formulations found within the TEPPC transmission capital cost tool
augmented by geospatial considerations (e.g., terrain difficulty cost factor).
The purpose of the NXT is to create an optimized transmission network
expansion that augments the existing network. This is done to produce a feasible
future network necessary to realize an "energy future" study case provided to the
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NXT by the SCDT. This is done while mitigating engineering constraints that
include overloaded lines and loss of load. In addition to creating an optimal
transmission expansion, the NXT also determines grid cost components
applicable to generators in the study case.
The LTPT was first implemented with trial runs in the fall of 2012. As such, the
20-year study results for the 2013 Plan are TEPPC’s inaugural LTPT studies.

Capital Cost Analysis – The roles of generation and transmission capital costs
are quite divergent in TEPPC’s different study horizons. In the 10-year study
horizon, the primary analytic tool is production cost modeling. The generation
portfolio and transmission topology are determined exogenously. WECC staff,
with assistance from stakeholders, develops assumptions for a 10-Year Common
Case (2022), representing the most likely load, resource, and transmission
topology configuration 10 years into the future if current patterns continue, as
well as a number of change cases that alter some of these assumptions. In this
context, the inclusion of resource capital costs in WECC’s study allows for a
more complete quantification of the relative costs of each change case relative to
the 2022 Common Case, or other base cases used for reference. This
information complements the changes in production costs that can be taken
directly from PCM result comparisons.
The role of capital costs in the 20-year studies is quite different. In this process,
the SCDT and the NXT—together, the LTPT—optimize the electric sector’s
expansion subject to a large number of constraints in order to minimize the cost
of delivered energy in 2032. The 2032 Reference Case represents load,
resource, and transmission topology configuration expected 20 years in the
future if current patterns continue an additional 10 years beyond the 2022
Common Case. Similarly, 20-year study cases alter some of these assumptions
and are compared to the 2032 Reference Case. Costs are a key input to the tool
as cost (more specifically levelized cost) is the decision method through which
the LTPT makes generation and transmission choices.

Reliability Studies – In addition to costs, capital expansion and hourly PCM
results, Interconnection-wide plans should be evaluated for their reliability
implications. This analysis is not designed to supplant reliability analyses
performed by other organizations or by WECC as part of other processes, which,
in combination, ensure future additions to the Western Interconnection are
designed to meet reliability requirements. Rather, the reliability studies performed
by TEPPC (with assistance from WECC’s Planning Coordination Committee
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2013 Interconnection-wide Plan Tools and Models
(PCC) and Technical Studies Subcommittee (TSS)) are designed to identify
where elements of the Plan, analyzed as a package, may have reliability criterion
violations or need additional investigation.
Figure 1 provides a graphical depiction of the two TEPPC study horizons and their
associated tools. TEPPC uses the results from these tools to inform the “Observations
and Recommendations” section in the Plan.
Figure 1: TEPPC Models and Study Horizons
Today
10-Yr
20-Yr
Tools
Tool
PCM
LTPT
Reliability
Studies
Given the distinct differences between the LTPT and PCM tools, a summary
comparison of the differences and the limitations between them is provided in Table 1.
Table 1: 10-Year and 20-Year Model Limitations and Differences
Attribute
10-Year Studies
20-Year Studies
Tool
Production Cost Model
Capital Expansion Model
Objective
Minimization
Production Cost
Capital Cost
Focus
Capacity additions and
specific projects, planned and
in progress
Understanding potential “energy futures”
and decisions needed to achieve those
futures
Model decides to
Dispatch Generation
Build Generation and Transmission
Load
From balancing authorities w/
stakeholder adjustment
From BAs w/ stakeholder adjustment
Resources
Stakeholder specific
Stakeholder specific and LTPT derived;
iterative
Transmission
Stakeholder specific
Stakeholder specific and LTPT derived;
iterative
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Attribute
10-Year Studies
20-Year Studies
Interdependence
Starting point of 20-Year
Informs 10-Year
In addition to these major models, there are a number of other models created to
address the analytical needs of specific technologies. Oftentimes these models are
located within the system-wide models described previously. These models are highly
advanced and in many cases represent the industry’s state-of-the-art techniques. These
supporting models include:

Hydro Modeling – TEPPC has extremely advanced hydro-generation modeling
that is used in the PCM. Hydro energy provides a large portion of the Western
Interconnection’s energy and is also a highly flexible resource. TEPPC’s hydro
models attempt to capture this energy as well as the flexible nature of the
generators.

Wind and Solar Modeling – Capturing the variability and diversity of hourly wind
and solar generation is a key to creating accurate PCM studies. TEPPC utilizes
National Renewable Energy Laboratory (NREL) data to model wind and solar
generation in the PCM studies.

Flexibility Reserve Calculator – The NREL flexibility reserves method calculates
the additional reserves required to manage the variability and uncertainty
associated with variable generation resources like wind and solar. Given the high
penetration of variable generation in the West, this is an important assumption for
the PCM studies. The process uses historical load and wind and solar data at a
10-minute resolution to derive equations that predict the variability based on
statistical analysis of that data.
Production Cost Model
The production cost model (PCM), TEPPC’s primary analytic tool, performs a securityconstrained economic dispatch of the electric system for every hour of the study year
with the goal of minimizing total operating costs of the Western Interconnection. Model
results on operational costs and transmission utilization and congestion are used to help
TEPPC evaluate the electric system in the 10-year study horizon.
Model Description
A PCM simulates the hourly operation of the Bulk Electric System (BES). The simulation
dispatches the generation to serve the load as it varies each hour during the study
period – usually a full year representing 8,760 hours. A model of transmission lines
simulates how energy moves from generators to load. The objective of the PCM is to
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reach a least-cost solution for each hour of the simulation, subject to both generation
and transmission constraints.
Generation Model Parameters
Several operating parameters act as generation constraints that limit the generator
dispatch levels and the hour-to-hour changes in output levels within the model. These
are designed to represent real-world operating constraints. Constraints for all
generators include:



Maximum Capacity – the maximum dispatch level; usually varies by month
Minimum Capacity – the minimum dispatch level for committed units
Ramping Rates – the maximum change in dispatch from one hour to the next
hour
Additional constraints for thermal generators include:







Minimum Downtime – the number of hours that a unit must remain off if taken off
line for economics or outage
Minimum Runtime – the number of hours that a unit must remain on once
committed
Must-run Status – designates that a unit has to be dispatched to at least its
minimum capacity level regardless of economics
Forced Outage Rate – the percentage of time that a unit is off for unplanned
outages
Forced Outage Duration – the number of hours that a unit must remain off after a
forced outage
Startup Energy Required – the energy (MMBtu) required to start a unit and ramp
it up to its minimum capacity
Startup Cost Adder – the other non-fuel costs associated with starting a unit
Other cost factors that affect the incremental cost are the fuel cost, heat rate, and
variable operations and maintenance (O&M) cost.
Additional constraints for hydro generators include:



Monthly energy limits
Load response factor
Price response factor
Additional constraints for fixed dispatch generators such as wind and solar include:


Monthly energy limits
Annual energy limits
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The generation constraints limit how the generation is dispatched from the moment a
generator is committed for operation. Importantly, it is the difference in constraints
among the different generation technologies that drive how the PCM operates. As an
example, the minimum downtime and minimum runtime for base-load generation (coal
and nuclear) are higher than a gas turbine since these units cannot be cycled as easily.
The incremental cost (next MWh of energy) of a particular generator is based on the
heat rate (thermal efficiency) and fuel cost. The total production cost is based on the
incremental cost and the startup, variable O&M, and emissions costs.
Hydro, wind and solar generation pose unique modeling challenges due to their fuel
sources. As such, the modeling methods for these resources have to be approached
differently. TEPPC has focused significant effort over the last five years to improve how
hydro, wind, and solar are modeled. Subsequent sections of this report detail how these
resources are modeled.
Generation Stack
Generating units are sorted in order of incremental cost from a PCM viewpoint; a slice
of any given hour is referred to as the “generation stack.” The rank order based on the
data assumptions used in the PCM dataset is represented in the generation stack with
the units committed first at the bottom and the units committed last at the top. Although
this Interconnection-wide depiction of the stack is not as meaningful as a stack for a
given TEPPC load area or pool,1 it does help visualize how generation is dispatched on
an Interconnection-wide level. Several generation types are split into a “minimum”
dispatch and an “above minimum” dispatch to model the minimum dispatch constraint.
The modeling assumptions incorporated into the PCM dataset dictate the order and
priority of the generation dispatch. This is often referred to as building the “generation
stack” and is based on non-economic considerations (e.g., reliability must-run,
Qualifying Facilities, or preference generators) and operating costs. The initial
commitment and dispatch of generation is determined by the cost assumptions, heat
rates, startup costs, and variable O&M costs.
In the 2022 Common Case dataset, these inputs set the preferred order illustrated in
Figure 2Error! Reference source not found.. At the beginning of the first day of the
study period, the unit types from the bottom of the order up to “nuclear” are contributing
1TEPPC
Load Area: the topology used to represent the most granular level of data in the TEPPC PCM
datasets is often referred to as a “TEPPC Load Area.” These load areas typically align closely with
Balancing Authorities. Loads are assigned via areas and also assigned to generators.
TEPPC Pool: The pool is an aggregation of TEPPC Load Areas. The pool level is the footprint in which
the security-constrained economic dispatch is performed.
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to serve the demand. As the demand increases, more coal is dispatched, and then
combined cycles (CC), more biomass, and finally, combustion turbines (CT) are used to
meet the remaining load. There are constraints that limit ramp-up rates, ramp-down
rates, minimum capacity, maximum capacity, minimum runtime, and minimum downtime
for these generators. These constraints tend to cause some complications during the
off-peak valleys. The coal units and some CC units can only be curtailed to their
minimum capacities and any thermal units that are shut down incur a startup cost when
they are brought back online.
Figure 2: Sample Resource Economic Order
Hypothetical Resource Availability - Friday/Saturday
5000
4500
CT - Dispatchable
4000
Biomass - Dispatchable
CC - Dispatchable
3500
Coal - Dispatchable
Nuclear - Dispatchable
3000
Biomass - Must run
2500
QF - Must run
Geothermal - Must run
2000
Cogen - Must run
1500
Hydro - Must take
Solar - Must take
1000
Wind - Must take
500
Demand
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47
Transmission Parameters
TEPPC’s PCM has the capability to perform the previously described economic
dispatch of generators in two types of transmission models: a transportation model
(zonal) and a nodal model. The zonal model is less detailed, solves faster, and is thus
better suited for situations where transmission model granularity is not as important. In a
nodal model, each bus and transmission line is explicitly modeled, which enables a
granular look at transmission system utilization. TEPPC utilizes the nodal transmission
model in its studies.
All major transmission equipment can be represented in the nodal transmission model.
This includes voltage transformers, phase-angle regulators, DC ties, generation buses,
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load buses, and transmission lines with their associated physical characteristics. For the
TEPPC datasets, the transmission topology is imported from a WECC-approved power
flow case from the PCC. This ensures data and results can be coordinated with
reliability analysis performed by the PCC. From this point, TEPPC alters the
transmission configuration based on study parameters (e.g., adding projects to
expansion cases).
In addition to the individual transmission lines and associated equipment, TEPPC also
models a series of interfaces (“paths”) and nomograms in the nodal model. The paths
are groupings of lines that have a bidirectional limit imposed on the aggregate flow of
the lines. TEPPC uses the “planned rating” from the WECC Path Rating Catalog2 as the
interface limits. Nomograms are reliability-driven transmission system operating
instructions used to impose a limit on transmission flows or generation dispatch.
Nomograms are used to ensure there is adequate voltage and frequency support
throughout the Western Interconnection. Taken together, the path limits and
nomograms assure that the transmission system is operated within established
reliability limits.
The PCM integrates the aforementioned transmission topology with the commitment
and dispatch steps such that the transmission constraints are observed when
generators are scheduled, started, and cycled. By monitoring transmission elements
and adhering to transmission system limits while performing the economic dispatch for
all 8,760 hours, the PCM provides an accurate depiction of the operation of the Western
Interconnection.
Importantly, it is common practice to not monitor all transmission elements within the
model. Essentially this feeds fewer constraints into the linear program and allows the
model to solve in a more reasonable timeframe. TEPPC’s datasets use the WECC
Paths (as interfaces) and major transmission lines as monitored transmission elements
that serve as constraints in the model optimization. Testing has proven that using this
reduced set of constraints does not decrease model accuracy when results are viewed
on an Interconnection-wide level.
Operational Reliability Parameters
In addition to transmission and generation constraints, TEPPC also includes
assumptions regarding the modeling of operating reserve requirements in its studies. In
the model, the reserve requirement is broken down into spinning reserve and quick-start
2
The WECC Path Rating Catalog contains information about all significant paths in the Western
Interconnection. Since the Catalog contains confidential information, it is not available to the public.
However, the description of each path and the path’s planned rating are public.
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reserve. TEPPC assumes that there are sufficient quick-start reserves in the Western
Interconnection and thus are not modeled. Therefore, the operating reserve requirement
in the TEPPC dataset is spinning reserve. Any dispatchable unit type can be designated
to contribute to spinning reserve. Furthermore, each generator can have the maximum
percentage of unloaded capacity that can contribute to specified spinning reserves. If a
unit is specified to contribute to spinning reserve and it has capacity remaining, the nondispatched capacity will be credited toward the reserve requirement.
The introduction of variable resources, such as wind and solar, creates the need for
additional operating reserves. Reserve requirements are a much-studied topic and
widely debated. TEPPC, along with our partners at NREL, have taken up the challenge
to determine what the necessary level of additional reserves is necessary for various
levels of variable generation. This modeling is described in the Flexibility Reserves
section below.
Key Results and Metrics
One purpose of the Plan is to evaluate transmission congestion and utilization under
alternative futures. It is important to have a clear understanding of what the term
congestion means in this report. It is possible to have reliable delivery of energy in a
congested system. In this context, system reliability can be characterized as “keeping
the lights on” while responding in a predictable fashion to both planned and unplanned
outages in generation and transmission. System congestion, on the other hand, is a
measure of the economic performance of the transmission system that answers the
question, “How well does the transmission system, while operating within the bounds of
reliable operation, perform to deliver the lowest cost3 energy to consumers?” If there is a
low-cost resource in the system that is underutilized because there is not enough
transmission capacity while operating within reliability standards, then the system is said
to be congested, meaning that one or more transmission lines are at their limit. This
forces the use of higher-cost resources to meet load than would have been used had
there not been a transmission system constraint. The load is still being served reliably,
albeit at a higher energy cost than without the transmission constraint and its apparent
congestion.
Several metrics are used to report levels of congestion and utilization for TEPPC
studies. Congestion is measured by the number of hours that a constrained
transmission facility operates at 99 percent of its limit or higher (referred to as the U99
3
Lowest cost on an Interconnection-wide basis noting that there are with a number of non-economic
assumptions internalized within simulations to capture the desirability of specific resources or types of
resources in the dispatch optimization.
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metric). Since the simulation does not allow constraints to be exceeded without
incurring a cost penalty, the 99 percent level represents effective full utilization of a
transmission line or path. This interpretation seems straightforward; however, in
interpreting the results some caution is required. It is important to understand the
limitations in the simulation ability to emulate actual operations to avoid drawing
mistaken conclusions from the studies.
Importantly, in order to compare costs across various study cases in the Plan, the
system variable production cost associated with serving load given a set of resource
and transmission assumptions must be added to the capital cost of any generation or
transmission system additions. TEPPC uses the PCM simulation to calculate operating
cost differences for different resource portfolios and transmission additions in order to
assist in addressing such comparisons. TEPPC then adds the estimated annualized
capital costs for any generation and transmission additions. The capital cost modeling is
described in a subsequent section.
Model Limitations
There are limitations in the PCM’s ability to emulate and accurately represent many
operational realities of the Western Interconnection. It is important to understand these
limitations to ensure mistaken conclusions from the TEPPC studies are avoided.
One of the known difficulties with PCM simulations is the difficulty of capturing the
effects of transactions made under long-term contracts. For instance, a take-or-pay
agreement for fuel may cause a given plant to run at full capacity when it would be
operated at a lower level if it were dispatched based on short-term fuel prices. This kind
of contract-specific information is generally regarded as commercially sensitive and thus
confidential. By design, the TEPPC database has been developed from publicly
available sources so that it can be used by stakeholders for their own studies. Having a
public database also allows TEPPC studies to be conducted in a transparent
environment. The inaccuracy introduced by using only public data is somewhat
minimized in long-range studies because, over time, contracts expire and replacement
contracts trend toward the actual fuel costs. However, there is always a set of legacy
agreements that alter hourly dispatch decisions (e.g., energy returns, peaking
commitments, preference customers) that a public database will not recognize.
Another difficult issue to address in production cost simulation is the impact of
scheduling rules on system dispatch. Outside of the California Independent System
Operator and the Alberta Electric System Operator areas, energy is scheduled between
Balancing Authority Areas in the Western Interconnection based on contract paths for
fixed blocks of energy. It is well understood that contract path schedules do not
correspond to actual system flows. Actual flows are a result of the set of specific,
locational injections and withdrawals of energy and the physical properties of the
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transmission system.4 System physics dictate actual flows without regard for facility
ownership or scheduling rights represented by contract paths. Yet, with the exception of
a centrally dispatched regional transmission organization or independent system
operator, scheduling based on flow distribution has not been adopted because of the
complexities of dealing with transmission ownership rights and with cross compensation
among transmission owners.
Under contract path scheduling, parties must obtain rights to submit a schedule using
the Transmission Service Request procedures outlined in open access tariffs. Available
Transfer Capability, made available in response to a transmission service request,
reflects firm commitments. If the holder of a right to transmission service on a particular
path does not schedule its use, unscheduled transmission capacity may be sold to
others as non-firm service. However, this occurs only on an hourly or daily basis. There
are also constraints imposed on scheduling and actual flow levels to avoid reliability
problems. For instance, if a path is fully scheduled, no additional power can be
scheduled over that path, even if the actual flow turns out to be below the path’s transfer
capability limit. This may leave some capacity apparently unused. However, the loop
flow (the difference between the scheduled and actual flows) created by these
schedules is flowing on other lines in the system. This scheduling rule is a decentralized
approach to keep a scheduling party within its contractual rights and avoiding reliability
problems elsewhere in the system. Another mismatch between actual and simulated
operation is a result of the timing requirements for the submission of schedules.
Schedules are submitted a day-ahead or with changes 30 or more minutes before the
next hour. It is impossible to schedule every last MW of transfer capability because
scheduling occurs at these discrete time intervals. All of the above factors are real
constraints on actual operations that cannot be explicitly modeled in TEPPC studies.
An additional limitation, more specific to generation, is hydro resource’s inability to limit
their contribution to reserves in the PCM. Operational constraints prevent the full
remaining capacity of hydro resources to count toward spinning reserve requirements.
Although hydro resources are extremely flexible and have quick ramp capabilities, there
are some constraints (e.g., environmental water flow issues) that prevent this sort of
ramping. Based on that, the full remaining capacity of some hydro plants were not
available for reserves. Currently, TEPPC’s PCM is not able to capture this real-world
behavior.
4
The primary physical property of an AC network is the impedance of its elements. Impedance is a
measure of the opposition to flow on an AC transmission line. The relative impedance of the lines
determines flow distribution. The relationship is inversely proportional, so lines with higher impedance
carry less power than lines with lower impedance.
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Finally, because their prime directive is to keep the lights on, system operators are
inherently conservative. There is a reluctance to operate transmission lines at their
maximum capacity hour-after-hour because of the increased risk such operation entails.
Operators must also deal with situations in which otherwise uneconomic generation
must be run to support local voltage. Operators also tend to deal with neighboring
systems rather than all parties in the Western Interconnection. This tends to produce a
local optimum rather than the global optimum dispatch that occurs in a production cost
simulation. The limitation in trading partners occurs because of the difficulty of dealing
with all possible Interconnection-wide generator and transmission usage options in the
limited time available for hour-to-hour decision making. These factors and other
practical limitations produce what might be called a de facto de-rating of transmission
paths in real world operations. The effects of such operational practices are very difficult
to estimate and even harder to simulate, so simulation results tend to overstate the
transmission system’s ability to economically move energy and understate the need for
transmission expansion.
Because of all these factors, finding congestion in a PCM simulation study provides an
indicator of the possibility of savings that can be achieved by transmission expansion,
but that information alone is not sufficient to justify investment. In fact, new transmission
investment is rarely justified based on the results of a congestion analysis alone. Still,
when transmission is added in a congestion study, the simulation can be repeated and
the results used to estimate the incremental production cost savings associated with the
transmission expansion. While the absolute operating results of a single simulation may
not precisely match actual system operating costs, the incremental energy cost
differences calculated between pairs of cases are more reliable because they represent
the cost difference between two operating conditions that are the result of applying a
single change. For example, the addition of a transmission line while all other assumed
infrastructure and operating rules are kept constant.
Long-term Planning Tool
Unlike the PCM, which performs a production cost simulation of a defined generation
and transmission system, the Long-term Planning Tool (LTPT) is a capital expansion
planning model that builds new generation and transmission assets based on a set of
model inputs. TEPPC uses the LTPT to analyze study cases with a 20-year study
horizon.
The 20-year studies are first defined as a set of scenarios based on policy, technology,
environmental, and other considerations – examples include Renewable Portfolio
Standards (RPS), population growth, changes in technology for consumption and
production, energy efficiency and demand-side management effects, regulatory policy
for greenhouse gases and other environmental issues, and overall economic conditions.
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The intent is to consider a broad range of possibilities rather than restricting
consideration to a narrow range of expectations. Looking across the 20-year studies
identifies strategic choices available for planning, including the identification of both
common needs (areas that need to be addressed in most, if not all study cases), but
also the occurrence of lower-probability conditions that could have high impacts on
future transmission needs, even if they were to occur in only limited scenarios.
Long-term transmission planning looks ahead to a horizon year that is 20 years ahead
of the year in which the studies are being conducted, in this case 2032. The purpose of
such a long-term view is not to accurately predict the shape of the network at a
particular point in time, but rather to understand the factors that may dictate future need
for transmission and to identify strategic choices that may need to be made by
considering possible end-states of the transmission system in the horizon-year. WECC
uses this information on possible future needs and possible network configurations as a
guide to current decision making that will have long-range effects on future network
design.
For instance, consider a situation where long-term planning indicates a high likelihood
that 5,000 MW of capacity will be needed on a given transmission segment 20 years in
the future and that the target plan indicates that the corridor can only accommodate
three lines. This information suggests that the next segment5 to be added should be at
500-kV or higher voltage, rather than at 345 kV, in order to make best long-term use of
the corridor. While simplistic, this example illustrates the value of long-term planning as
a guide to current decisions that require a choice among network design options that
will be implemented in the near future (e.g., 10 years or less from today). The goal is to
provide information that enables decision makers to make good network design choices
that will meet near-term needs and, to the extent possible, longer-term needs while
ensuring longer-term needs while ensuring flexibility in future expansion planning.
Analysis this far into the future is a complex task not only because of uncertainties
about environmental issues, policy, and future loads and resources to be served by the
transmission system, but also because the interconnected nature of the network results
in complex interactions among its parts as a function of system physics. The possibility
of technological change adds further uncertainties. To address this uncertainty, 20-year
planning studies must deal with a range of possible study cases or horizon end states.
5
As used here, a segment is any element of the transmission network connecting two places in the
transmission system (i.e., nodes or buses).
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Model Description
The LTPT is a complex model that iterates between two optimization tools in order to
arrive at an optimal least-cost generation and transmission expansion solution for a
given set of study assumptions.

Scenario Case Development Tool (SCDT) – the SCDT is responsible for adding
incremental resources so that load requirements and policy goals are met via a
least cost solution. The SCDT also identifies the catalogue of candidate
transmission expansion segments for consideration. The SCDT is the first step
in the iterative LTPT process.

Network Expansion Tool (NXT) – the NXT is run after the SCDT has developed
load and generation assumptions. The NXT evaluates candidate transmission
lines that could relieve overloaded lines in four system conditions, which are
hourly system dispatches intended to represent a variety of typical operating
conditions. The NXT also provides a least-cost solution.
The LTPT iterates between the SCDT and NXT until it converges on a feasible leastcost solution for a given energy future characterized by a set of study assumptions. For
each iteration, the SCDT produces an updated optimization of generation and a
corresponding study case as inputs to the NXT, while the NXT provides an updated
optimized network expansion and allocation of grid costs to generators as inputs to the
SCDT. Convergence is reached when no further updates to generation and
transmission expansion are needed between iterations. The end result of this iterative
process is a set of point-to-point transmission segments which, if added to the existing
transmission grid, would allow the resources selected in the study case to meet the load
used as an input to the study case.
LTPT Model
The LTPT Model is very complex and requires a large amount of input data and
captures a wide variety of considerations, as illustrated in Figure 3.
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Figure 3: LTPT Process Flow and Input Requirements
Optimize
Generation
Optimize
Transmission
Criteria
Narratives
Analytics
Assertions
Load
Environmental
Considerations
Generation
Operating
Characteristics
Alternative
Candidates
Measure
Impacts
Policy
Consideration
Transmission
Capital Costs
Capital Costs
Compare
Energy Futures
Social
Considerations
Capital Costs
Energy
Constraints
Severity
Measures
Screen
Alternatives
Cultural
Considerations
Geospatial
Capacity
Constraints
Grid
Constraints
Inform Other
Planning
Federal & State
Mandates
Stakeholder
Collaboration
Tiered
Optimization
Expansion
Plan
Inform Decision
Makers
Identify High
Value Projects
Given the complexity of the model iterations, the LTPT functionality and model is
presented in six steps:
Step 1: Generation Optimization
The SCDT performs the first step of the LTPT iterative process, which is shown visually
in Figure 4Error! Reference source not found.. The LTPT starts from a set of 10-year
assumptions on generation and transmission. For this study cycle, resources
(generation and transmission) assumed in the 2022 Common Case are included in the
model with zero capital cost, which typically results in those resources being selected
before new resources are added
The generation optimization may be comprised of multiple goals including those of
reliability, policy and system. The more restrictive goals are implemented first followed
by less restrictive goals, until all goals have been satisfied.
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Figure 4: LTPT Iterative Process
More restrictive policy goals, within the 20-year timeframe, that are implemented first
include local policy goals and reliability goals as shown in Figure 5Error! Reference
source not found.. Local policy goals implemented as first priorities include:

Forcing all simple-cycle combustion turbines contained within the 2022 Common
Case that are stipulated as reliability units into the generation optimization,
irrespective of cost.

Forcing renewable generation identified in the 2022 Common Case that have
already been earmarked as counting toward state RPS goals are into the
generation optimization, irrespective of cost.

Distributed generation (DG) set asides, as specified by state-enacted RPS. This
modeling goal is an energy requirement, so the tool would be required to
evaluate DG options in a given state and select the lowest-cost arrangement of
DG resources that could provide the required energy.
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Figure 5: SCDT Resource Selection Goals
Less restrictive goals that are satisfied later in the optimization include generic policy
goals, system energy goals, and system peak goals. As an example, in the 2032
Reference Case,6 state-enacted RPS requirements were the main generic policy goal.
Any RPS-eligible resource was available for selection and every resource was
compared bases on levelized cost. For generic policy goals, the LTPT does not make
any assumptions about state-preferences for in-state resources or particular technology
types as these have already been accounted for in meeting local policy goals. The tool
compares resources Interconnection-wide on a cost basis, assuming resources from
across the Western Interconnection can be procured without ensuring energy delivery
(i.e., firm transmission availability) for all 8,760 hours in the year. Furthermore, there are
no location-specific procurement assumptions associated with generic policy goals.
After satisfying generic policy goals, the SCDT selects resources needed to meet the
defined system energy goal. Notably, all resources selected for local policy and generic
policy are used to further reduce the system energy requirement since these resources
not only meet policy needs, but also serve load. The model then selects resources to
meet the remaining system energy needs by selecting any type of resource on a cost
basis. For example, in this step additional wind (in excess of RPS) is considered
alongside combined cycle gas units.
The last step is the system peak demand goal where the SCDT verifies that it has
selected enough resources to meet the summer system peak (i.e., the highest load
demand of the study year). Unlike the previous three steps, the fourth is based on an
hourly dispatch and not energy. Each resource selected in the first three steps has an
6
Information on the 2032 Reference Case is found at
http://www.wecc.biz/committees/BOD/TEPPC/External/2032_ReferenceCase.xlsx.
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expected dispatch in this hour. To the extent that the SCDT is resource deficient in this
hour, it will compare resources – based on levelized cost of energy (LCOE) – and select
enough to meet peak demand.
Once each of the successive goals in Figure 5 has been satisfied by the SCDT, the
generation optimization for the first iteration is complete and this infrastructure build out
is passed to the NXT.
Step 2: Transmission Expansion
The NXT receives the generation and load assumptions passed from the SCDT and is
tasked with creating a feasible transmission alternative for a particular system condition.
The NXT achieves this by performing a transmission expansion optimization for a given
study hour (snapshot in time).
Within the NXT, the expansion planning model is formulated as a mixed-integer linear
programming problem, solved using a Branch-and-Bound algorithm. This algorithm
implicitly enumerates circuit investment decisions that are represented as nodes of a
search tree, and can therefore reach and prove the optimality of the expansion plan. It is
important to notice that due to the complexity of the problem, the solution using the AC
power flow model is not applicable. Therefore, the DC power flow model is utilized for
the network optimization..
There are two different aspects to the combinatorial nature of the transmission
expansion planning optimization considering a no-load-loss planning criteria:
1. Deriving a feasible solution, meaning a set of candidate additions that guarantee
that the network equations are obeyed and circuit limits are enforced; and
2. Finding the optimal (least-cost) transmission expansion solution.
In the initial iteration, that hour is the summer peak hour. The necessary data to perform
this load flow are:




Physical representation of the system (e.g., power flow data)
Existing and incremental generator dispatch levels
Load levels
Candidate lines to consider for optimal network expansion
From a practical standpoint, with regard to the LTPT, it was necessary to reduce the full
network model of roughly 18,000 buses to that of roughly 1,000 buses to allow the
model to complete iterations in a reasonable amount of time. The following
considerations, assumptions and methods were used as guidance in performing the
network reduction:
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
The 2022 Common Case nodal model served as the starting point for the 2032
Reference Case network model.

The LTPT nodal network model is limited to a size of roughly 1,000 buses due
primarily to performance limitations inherent to the complexities of the
optimization algorithms used in the LTPT.

The use of hub buses was used to represent aggregations of load and
generation.

Since RPS requirements are defined state-by-state, generation hub buses were
defined for each Balancing Authority (BA) by state (i.e., if a BA had generators in
two separate states, then there would be two state generation hubs defined for
that BA).

Tie-lines at voltage levels of 230 kV and above, and lengths over 50 miles were
preserved.

Sub-transmission is considered to be lines below 230 kV that electrically connect
tie-line terminal buses to their corresponding TEPPC load area hubs.

Sub-transmission is assumed to have necessary reinforcements to support 2032
regional transmission “backbone” expansions.

No prescient knowledge was assumed about sub-transmission reinforcements or
how boundary flows between BAs might change due to such reinforcements.
Transmission lines between each TEPPC load area hub and any tie-line terminal
bus(es) in the TEPPC load area were used as proxies to represent the subtransmission network within that TEPPC load area. Modeling the parameters of
these transmission lines is a function of like voltage transmission parameters and
the distance between the tie-line terminal bus and TEPPC load area hub.
The primary goal of the NXT is to determine transmission expansion needs that may
need to take place before 2032, given various scenarios and conditions. Transmission
lines considered for expansion are chosen from predefined transmissions candidate
lines identified in the SCDT, as shown in Figure 6. The transmission candidate lines
available for consideration as part of an expansion were determined by:

First interconnecting all hubs to their nearest neighboring hubs. This assumes
that any generation hub may be interconnected to any load hub by new
transmission expansion, irrespective of location, by interconnecting intermediate
hubs with new expansion at least cost.
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
Running a few thousand power flows where the network model was stressed
under several extreme load and dispatch conditions. Under these stressed
network conditions, overloads were examined one-by-one and mitigated by
adding multiple lines to the candidate list until all circuit violations were mitigated
for all stress conditions.
Figure 6: NXT Transmission Candidates
The NXT relieves all overloaded lines by adding transmission. In adding this new
transmission, the NXT considers the straight-line cost of the incremental branches and
optimizes to a least-cost solution (with zero branch violations). At this point, the NXT
can calculate and assign grid costs to the generation that caused the additional lines to
be added.
To the extent possible, the following reliability considerations are captured within the
LTPT in performing the transmission expansions:




Generation adequacy is captured as part of system energy and capacity goals;
No loss of load;
No overloads of monitored circuits; and
Planning reserve enforcement.
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Step 3: Grid Costs
The novelty of the LTPT modeling and optimization approach lays in the tool’s ability to
calculate and assign grid costs. Grid costs are the cost of incremental transmission that
are divided and assigned to the incremental generators that caused line overloads and
thus transmission expansion. For example, if two generators of equal size were added
at Bus A and the NXT solution required that transmission be added from Bus A to Bus B
due to line overloads, the cost of that added transmission would be divided among the
two generators added to Bus A according to the relative capacity of each generator. Any
existing generation located at Bus A would not be assigned grid costs as the
assumption was that existing generation would have firm transmission rights and alone
would not have required additional lines.
Once grid costs are calculated and assigned to generators, these costs are then carried
over into the next iteration in the SCDT’s generation optimization. Because these costs
are now considered in the generation selection, a generator that required incremental
transmission to be added in iteration one will have a higher LCOE (due to grid costs)
when the SCDT begins its generation selection in iteration two.
Step 4: Model Iterations
As shown in Figure 4, the aforementioned process of selecting generation, evaluating
transmission needs, and assigning grid costs continues iteratively until the model
converges – that is, the answer does not change from one iteration to the next. With the
assignment of grid costs to generation for consideration in the SCDT, model
convergence is often challenging to achieve given the small cost spreads of resources.
Resource solutions often flip-flop between model iterations, sometimes responding to
price differences less than $1/MWh. Assigning grid costs to a particular resource may
make this resource less attractive to the SCDT, thereby leading the tool to select a
different resource in the second iteration. This resource may or may not require
additional transmission. If grid costs are assigned to this alternative resource, perhaps it
is now more expensive than the original resource that was selected given that they both
now have grid costs assigned. This is a rather simplistic example but demonstrates how
the models’ decision making ability is thorough, but often rather time intensive.
Step 5: Model Convergence
The model will have converged to a single study solution when the resource and
transmission selections are not changing from one iteration to the next. With this
solution, the tool does not need to further investigate the implications of grid costs and it
has arrived at its final solution (as shown in Figure 4). However, the study is not yet
complete. At this point, the transmission build out is based on only one system condition
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– heavy summer. It is well understood that there are many reasons to build
transmission, and reliability, or more explicitly summer reliability, is only one of many.
Step 6: System Conditions
The final analytical step is to consider additional system conditions in the NXT beyond
the annual peak demand (heavy summer) condition. Evaluating these conditions
provides a better understanding of what the transmission needs might be in other typical
operating times. In total, the NXT evaluates four system conditions, each with unique
load, hydro and variable generation (wind and solar) levels. Hours analogous to these
system conditions are identified within the 2022 Common Case simulation are used to
model them in the LTPT. A summary of the system conditions, their specifications, and
their analogous hours in the 2022 Common Case follows. These were then used to
make the system conditions for the 2032 Reference Case and other 2032 scenarios.



Heavy summer (captured in initial LTPT iterations)
o Characteristic: annual peak demand hour.
o 2022 analogous hour: July 21, 2022, 16th hour (“HS Hour”).
o Application to 2032 Scenarios: Solution from Steps 1-5.
Light spring
o Characteristic: the hour used for the 2022 Light Spring Scenario; a lightly
loaded hour with high renewable penetration in the Western
Interconnection.
o 2022 analogous hour: March 31, 2022, 14th hour (“LSP Hour”).
o Application to 2032 Scenarios:
 Demand: 2032 Scenario’s heavy summer load multiplied by the
LSP demand multiplier (ratio between the LSP Hour and HS Hour
loads)
 Hydro generation: Ratio of each area’s LSP Hour hydro dispatch to
its max capacity in the 2022 Common Case
 Variable generation (VG): Ratio of each area’s LSP Hour combined
solar and wind dispatch to their combined max capacity in the 2022
Common Case
Light fall
o Characteristic: lightly loaded hour between 11:00 p.m. and 5:00 a.m.,
Monday through Saturday or anytime Sunday within the months of
September, October and November which has the highest solar and wind
generation and relatively low hydro generation.
o 2022 analogous hour: November 4, 2022, 2nd hour (“LF Hour”).
o Application to 2032 scenarios:
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



Demand: 2032 scenario’s heavy summer load multiplied by the LF
demand multiplier (ratio between the LF Hour and HS Hour loads)
Hydro generation: Ratio of each area’s LF Hour hydro dispatch to
its max capacity in the 2022 Common Case
VG: Ratio of each area’s LF Hour combined solar and wind
dispatch to their combined max capacity in the 2022 Common
Case
Heavy winter
o Characteristic: peak demand hour in the months of December, January
and February.
o 2022 analogous hour: December 15, 2022, 19th hour (“HW Hour”).
o Application to 2032 scenarios:
 Demand: 2032 scenario’s heavy summer load multiplied by the HW
demand multiplier (ratio between the HW Hour and HS Hour loads)
 Hydro generation: winter peak contribution per the TEPPC PRM
Gap Tool.
 VG: winter peak contribution per the TEPPC PRM Gap Tool.
Figure 7 illustrates the diversity of load, hydro and VG levels among the 2032
Reference Case system conditions.
Figure 7: Summary of 2032 Reference Case system conditions
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The heavy summer expansion is determined as part of the LTPT iterations in Steps 1-5
and is not evaluated independently in this step. The other system conditions are created
from the heavy summer solution as follows:
1. The system condition demand is scaled from the demand in heavy summer
system condition.
2. The contributions (ratio of a resource’s dispatch to its maximum capacity) of the
hydro, wind, and solar generation in the heavy summer condition are revised to
those characteristic of each system condition. For example, the light fall condition
has high wind and solar contributions whereas the heavy summer condition has
lower wind and solar contributions, so the heavy summer wind and solar
contributions are replaced with values analogous to those in the LF Hour within
the 2022 Common Case).
3. Lastly, the total generation (with revised hydro, wind, and solar contributions) is
balanced to match the load of each system condition. The contributions of solar,
wind, and hydro are fixed characteristics of each system condition, so the other
resources are decremented (their dispatch is changed to 1 MW) until the
resource contribution and demand are balanced:
4. Gas-fueled resources are decremented first since they are traditionally the most
operationally flexible resource. The decrementing order is based on the
resource’s levelized cost of energy (LCOE) is done from highest to lowest LCOE.
5. Coal-fueled resources are decremented next, from highest to lowest LCOE, since
they are operationally flexible.
6. If decrementing all gas- and coal-fueled resources across the Western
Interconnection isn’t enough to balance the system condition’s resources and
load, then all other resources (regardless of resource type) are decremented,
from highest to lowest LCOE, with two exceptions: “earmarked” renewable and
DG resources are typically the least flexible (controllable) resources, so they are
left out of the decrementing process.
Once created, the heavy winter, light fall, and light spring system conditions are tested
with their own NXT runs to evaluate what the transmission needs are given the
resources selected in the prior LTPT optimization (with the heavy summer condition
providing grid cost information to resources). By considering what transmission may be
required over multiple system conditions, we better represent a broader set of reasons
for building transmission.
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LTPT – Geospatial Functionality
The goal of the LTPT GIS functionality is to capture geospatial considerations within the
transmission expansion planning process. Layers of geospatial information are modeled
within the LTPT to capture these considerations.
Key features of the LTPT GIS functionality include:





The ability to capture multiple layers of GIS data.
The ability to access the impact of transmission expansion on a GIS layer (e.g.,
environmental and cultural impact layer as in Figure 8).
The ability to access the impact of a GIS layer on transmission expansion (e.g.,
terrain difficulty).
The ability to traverse a geospatial landscape by bending transmission corridors
around undesirable artifacts of the geospatial landscape (e.g., protected
environmental or cultural areas, difficult terrain).
The ability to calculate capital costs and characteristic data for a transmission
expansion as a function of geospatial factors (e.g., terrain difficulty, land use, line
length).
The layers of geospatial information modeled within the LTPT include:

Environmental Risk Classification Categories – Used to assess the
environmental and cultural impacts of transmission expansion.

Rights-of-Way Costs – Used to assess the land usage costs associated with
obtaining transmission rights-of-way. These costs are based on the Bureau of
Land Management land usage cost data.

Terrain Difficulty – Used to assess the topographic costs associated with terrain
elevation, slopes and vegetation. Terrain difficulty costs are based on terrain
difficulty categories as defined by Black and Veatch.
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Figure 8: Environmental and Cultural GIS layer modeled within the LTPT
Other LTPT Modeling Considerations
The LTPT studies take water availability into account when selecting generation for
inclusion in the study. The water availability was provided by Sandia National
Laboratories. This data is used when the model is adding resources. Many potential
resources, such as combined cycle units that rely on water cooling, would have a
requirement of water to operate. By incorporating water availability into the LTPT
resource selection process as a constraint, the model will not select more resources
than what the available water allows. Because of this, there may be some situations
where air-cooled or waterless resources are favored over those that consume water.
The result of the LTPT’s iterative process is a set of point-to-point straight-line
transmission expansion segments. If these segments were built, they would not be built
as straight lines, due to physical and other constraints.
The Environmental Data Task Force created by the Scenario Planning Steering Group
(SPSG) in 2010 developed a Risk Classification System that represents a seamless,
GIS-based risk classification data layer that depicts environmental and cultural risks and
constraints across the entire Western Interconnection. Environmental and cultural risks
represented in this classification system include:
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
Land characteristics, such as National Parks, Areas of Critical Environmental
Concern, Wilderness Areas, Agricultural Lands and National Recreation Areas;

Wildlife characteristics, such as State Wildlife Management Areas, Critical Bird
Habitats, Migration Corridors and Ungulate Ranges;

Cultural characteristics, such as National Historic Trails and Heritage
Rangelands; and

Tribal lands, such as Native American reservations, First Nation lands and
Bureau of Indian Affairs allotments.
These and other environmental and cultural data have been used to create a four-tiered
Risk Classification System that represents the relative risk that a transmission project
would encounter environmental and/or cultural issues if it crossed specific lands.
Specific risk classifications are:

Category 1: Lowest risk of environmental or cultural issues. Category 1 lands
tend to be existing transmission corridors or rights of way.

Category 2: Low-to-moderate risk. Transmission segments crossing Category 2
lands may or may not be required to mitigate environmental/cultural impacts.

Category 3: High risk. Segments crossing Category 3 lands are possible, but
proponents should expect significant mitigation requirements.

Category 4: Exclusion areas. Development in Category 4 lands is precluded by
legal and/or regulatory constraints.
The LTPT incorporates this environmental/cultural data to create geospatial landscapes
that indicate those areas which would be preferred by nature of incurring the least
environmental/cultural risk. These landscapes allow planners to “bend” the straight-line
transmission segments produced by the LTPT to conform to the least-impactful
environmental/cultural corridors. This layer is incorporated into the “line bending” portion
of the LTPT.
Key Results and Metrics
TEPPC’s 10-year studies in the PCM focus on system utilization and production cost
results, while 20-year studies in the LTPT focus on capital expansion results. For each
scenario evaluated in the LTPT, the results are broken down into three categories.



Transmission expansion results
Generation expansion results
Environmental results
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The transmission expansion results typically consist of lines that were added to the
system based on NXT analysis and final generation solution. For each study, there are
four sets of transmission expansion results – one for each system condition evaluation.
It can be inferred that a transmission line that was added in all four system condition
NXT expansions is perhaps more interesting to further investigate than a line that was
added in only one system condition. A sample expansion result from the 2032
Reference Case is presented in Figure 9.
Figure 9: All Expansions
Unlike the transmission expansion results, there are only one set of generation results.
Since generation added through 2022 is assumed in the model as a starting point, the
most interesting results pertain to what generation was added in the 2022-2032
timeframe. Assumptions about load, policy, CO2 pricing, generation technology costs
and transmission costs can greatly influence what generation is added in this timeframe.
One of the goals of the LTPT is to identify corridors or transmission lines that were the
resulting expansion of multiple scenarios or studies. By looking across study results, we
can begin to identify corridors that indicate where transmission expansion is needed
regardless of study assumptions.
Model Limitations
The vision behind the LTPT and its development was to create a capital expansion
modeling tool for use in long-term planning. During this current study cycle, a “proof-ofconcept” has been established for the LTPT as a useful addition to the suite of tools
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currently used in long term planning. While the “proof-of-concept” for the LTPT has
been established, there are also a number of areas for improvement with the LTPT that
have been identified through stakeholder and staff review. As with any new software
tool with this level of complexity, a period of maturity is needed before rigorous results
can be expected.
Despite these weaknesses, the results of the LTPT for the current study program have
value in a number of ways, including:





Offering insights as to the effect of various scenario drivers on the optimized
solution.
Lessons learned in terms of model refinements and the crafting of scenario
narratives and metrics.
Educating stakeholders and staff as to what the LTPT is and is not. What can be
reasonably expected of the LTPT? What is within the scope of the LTPT
capabilities (e.g., the LTPT is not a production cost tool).
Informing the 10-year planning process of notable considerations for the next
study cycle.
Understanding how to craft exogenous proxies to capture and represent scenario
narrative drivers and metrics.
In short, while the results of the LTPT during the current study cycle are not rigorous
enough to pass peer review at a level characteristic of WECC planning processes, the
results have merit in that they have established the LTPT as a viable addition to the
current suite of tools used in planning and offers meaningful lessons and insights that
can be applied to future planning studies.
Weaknesses in the LTPT need to be remedied for future study programs, and where
possible within the scope of the LTPT intrinsic modeling capabilities. Some of the
limitations of the LTPT include:

The LTPT is not a PCM nor does it perform an hourly dispatch simulation. Some
of the common results that stakeholders are accustom to seeing (e.g.,
transmission line utilization) are exogenous study inputs and assumptions in the
LTPT, rather than model outputs. Production cost functionality is not within the
intrinsic scope of the LTPT.

Much of the modeling elements within the LTPT are dependent on the scenario
narratives and metrics provided by stakeholders (e.g., exogenous proxies). If
there are flaws in how these scenarios are crafted, then the corresponding
exogenous proxies modeled within the LTPT will be in error. So there is a
symbiotic relationship between the modeling elements (exogenous proxies)
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within the LTPT and the scenario narratives and metrics developed by
stakeholders.

Since the LTPT only evaluates transmission needs for four typical system
conditions in the study year, it does not capture and suggest the need for all of
the necessary future transmission projects. Transmission is built for a number of
reasons – policy, economics, and reliability being some key reasons. The tool
does not capture the need for all of these projects.

Twenty-year forecasting tools are limited, so Reference Case assumptions are
generally rough and a best guess that is intended to serve as a reference point,
not a prediction.

The LTPT assumes a dispatch for generators and asset utilization for added
transmission lines because there is not an 8,760 hourly dispatch.

State-level energy delivery policies, such as California’s AB32 CO2 policy,7
cannot be evaluated specifically since energy is dispatched at an
Interconnection-wide level.

The purpose of the LTPT is not to recommend transmission expansion plans, but
rather to better understand transmission expansion needs under various
scenarios and conditions. This will enhance the ability of analysts to produce,
evaluate and describe findings for the bodies (SPSG and TEPPC) that review
and oversee the study processes and apply study findings in developing
transmission plans.

While the LTPT allows “line bending” to avoid the most environmentally sensitive
corridors at the planning level, this is not currently a co-optimized solution that
balances capital costs and environmental risks. The capability exists, however,
but is contingent upon having environmental mitigation costs. Within the current
study program, geospatial environmental data is in the form of risk categories.
The “line bending” that takes place is done to minimize environmental impact.
The resulting transmission expansion costs are therefore subject to the result of
“bending lines” around environmentally sensitive areas and not co-optimized with
transmission expansion costs..
7
CA AB32 restricts the carbon intensity of energy sold into California. For more information on AB32, see
http://www.arb.ca.gov/cc/ab32/ab32.htm.
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
DC lines are currently excluded in the LTPT as transmission expansion
candidates. This capability exists in the current LTPT; however, it was not fully
explored due to time restrictions.

Due to time restrictions, some LTPT modeling (e.g., reliability criteria, network
reduction methodology and assumptions) were not reviewed by stakeholders as
thoroughly as others (e.g., capital costs). Such modeling will need refinement in
the future.

The LTPT utilizes many robust analytic metrics and indicators to perform its
optimization; however, better analytic metrics and indicators (e.g., those used to
identify which of the optimized transmission expansions are the most likely and
most beneficial) will need refinement in the future.
It is important to note the paradigm shift associated with the 20-year planning activity as
compared to the 10-year planning activity. Unlike the 10-year process, the 20-year
planning process is a top-down approach initiated by TEPPC with information flowing
back down to other planning processes. The focus of the 20-year planning process is
also different. Rather than focusing on understanding the performance of specific
transmission additions, the 20-year process focuses on identifying future needs so that
existing transmission corridors can be best utilized and future transmission corridors
can be best identified. The long view taken for the 20-year plans provides input to the
10-year plans. The 10-year plan then adjusts the 20-year plan based on more recent
and realistic conditions.
Capital Cost Calculators
Generation and transmission capital cost estimates are used for analysis in both of the
Plan’s study horizons:

The inclusion of resource and transmission capital costs in TEPPC’s PCM allows
for a more complete quantification of the relative costs of each change case
relative to the 2022 Common Case, or other base case used for reference.

Capital costs for transmission and generation are a key input to the LTPT and
20-year studies as they allow the model to compare resource and transmission
expansion options based on levelized cost.
Based on this need, TEPPC developed capital cost tools that calculate an annual
levelized fixed cost for a given resource or transmission project. Note that this section
focuses on the cost calculator models themselves, with special attention on how the
capital cost data reviewed in the “Data and Assumptions” section are implemented in
the models.
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Model Description
TEPPC uses two tools to calculate the estimated capital cost inputs to the 10- and 20year studies: the TEPPC Generation Capital Cost Tool (generation tool) and the TEPPC
Transmission Capital Cost Tool (transmission tool). Figure 10 depicts the capital cost
components and required inputs for each of the capital cost tools. Both tools are
spreadsheet-based calculators that rely on default inputs and inputs provided by the
user. The default inputs for each tool are described in detail in the Data and
Assumptions section. The tools are available for public use on the TEPPC website.8
Figure 10: Capital Cost Analysis - Components, Tools, and Data
The generation and transmission tools work in different ways. The transmission tool
provides an estimate of the components that comprise a transmission project, including
a number of line elements and substations. The user defines the components and the
tool provides an overall capital cost for the project based on the user input. For the
generation tool, the components that make up a particular type of resource technology
are “hard-wired” into the default present-day capital costs that are inputted into the
model. The generation tool uses the present-day capital costs to extrapolate future
capital costs and then calculates levelized costs. The function of the generation and
transmission tools is discussed in more detail below.
WECC, “Transmission Capital Cost Tool”:
http://www.wecc.biz/committees/BOD/TEPPC/External/121101_TEPPC_TransCapCost_Calculator.xlsx
WECC, “Generation Capital Cost Tool”:
http://www.wecc.biz/committees/BOD/TEPPC/External/121101_TEPPC_GenCapCost_Calculator.xlsm
8
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Generation Tool
The generation tool takes assumed values for future capital and fixed O&M costs for
each of the generation technologies considered in the TEPPC study work, listed in
Table 2, and calculates an annual levelized fixed cost. It also takes into account capital
cost reductions based on anticipated technological advancements.
Table 2: Technologies included in the generation tool
Technology
Biogas
Subtypes
Landfill
Other
Biomass
PC
IGCC w/ CCS
Small (<5 MW)
Combined Heat & Power
Large (>5MW)
Basic, Wet Cooled
Advanced, Wet Cooled
Gas CCGT
Basic, Dry Cooled
Advanced, Dry Cooled
Aero derivative
Gas CT
Frame
Geothermal
Large
Hydro
Small
Upgrade
Nuclear
Residential Rooftop
Commercial Rooftop
Distributed Utility (Fixed Tilt)
Solar PV
Distributed Utility (Tracking)
Large Utility (Fixed Tilt)
Large Utility (Tracking)
No Storage
Solar Thermal
Six-Hour Storage
Onshore
Wind
Offshore
Coal
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To use the tool, the user must select provide input on generation type, location, and installation
vintage from drop-down menus in the tool. (Figure 11Figure 11: Screenshot of generation
calculator user interface
) User input on resource location controls the regional multiplier value that is applied to
the calculation. The tool default is to report the U.S. average cost for a resource
technology; however, the user can select from any of the 12 states within the Western
Interconnection, as well as Alberta, British Columbia and Mexico. When a location is
selected, regional multipliers are applied to the cost calculation to reflect differences in
labor and material costs. User input on installation vintage affects cost as well. The
calculator can calculate the levelized cost for any installation vintage from 2012 to 2032.
The values used for the 10- and 20-year studies are 2015 and 2027, respectively.
Figure 11: Screenshot of generation calculator user interface
In addition to the input provided to the three mandatory fields, the user may override a
number of the default inputs to the tool. These default inputs cover plant performance
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and capacity, capital costs, fixed O&M costs, financing options, taxes and tax
incentives, and insurance.
Once the user input is complete, the generation tool applies one of four analysis
methods to translate the assumed capital and fixed costs into levelized costs for the
generation in the selected vintage year. The first three methods are based on project
owner-dependent financing options for Investor-Owned Utility (IOU), Independent
Power Producers (IPP), and Publicly-Owned Utility (POU) ownership scenarios. Cash
flow models were developed for each of the financing options. If the user does not
override the financing option, the tool will use the default financing options shown in
Table 3.
Table 3: Financing option default assumptions
Technology
Default Financing Entity
Biogas
IPP
Biomass
IPP
Coal – PC
IOU
Coal – IGCC
IOU
CHP
IPP
Gas – CCGT
IPP
Gas – CT
IPP
Geothermal
IPP
Hydro – Large
IOU
Hydro - Small
IPP
Nuclear
IOU
Solar Thermal
IPP
Solar PV
IPP
Wind
IPP
The cash flow model calculations take into account installation vintage, cost and
recovery rates, financing lifetime, federal tax policy, property tax and insurance, fuel
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costs, carbon costs, and O&M costs. Figure 12 is a screenshot of an output from one of
the cash flow models.
Figure 12: Screenshot of output fields from cash flow models
The cash flow models rely on complex calculations to estimate levelized costs of
energy. The complex calculations allow the cash flow models to provide estimates that
mimic real-world financing and cost components, making these models good for the 10year analysis or individual user inquiry; however, the complexity of the cash flow models
does not lend itself well to the 20-year study, where the calculation of resource capital
costs must be integrated into the LTPT directly. A simplified algebraic calculation was
developed to provide capital cost information to the LTPT. The simplified calculation
uses a capital recovery factor and other levelization factors to account for the cost
components inputs used in the cash flow analyses. Figure 13 shows the results screen
for the simplified calculator.
Figure 13: Simple calculator results screen
Transmission Tool
The transmission tool estimates separate capital costs for transmission lines and
substations in a single tool. Capital cost estimates were developed for each of the
elements using a “bottom-up” approach, detailing the component and land costs and
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then adjusting these to take into consideration potential cost variations such as location
and terrain. Table 4 shows the transmission line voltages and substation types for which
cost estimates were developed.
Table 4: Transmission & Substation Facilities Included in the Transmission Tool
Transmission Line 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 HVDC Bi-pole
Substation Types
230 kV
230 kV
345 kV
345 kV
500 kV (ac)
500 kV (ac)
500 kV (dc)
Transmission line capital costs are divided into equipment cost components (conductor,
structure, and line length) and location cost components (right of way and terrain).
Baseline costs were developed for each of the equipment cost components. Then cost
multipliers were developed to account for cost variations based on specific design
considerations, terrain, and location. Figure 14 shows the user input fields (yellow).
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Figure 14: Screenshot of transmission tool line costs user input interface
The user must first define the project by voltage, conductor, etc. Next the user enters
information on line routing, including terrain type and miles per Bureau of Land
Management (BLM) zone. The routing information is not calculated within the tool itself
and takes a separate GIS analysis. For the 20-year studies, WECC developed a GIS
tool that “bends” straight-line transmission expansions into more realistic routes. The
line bending tool provides information on terrain and BLM zones that is inputted into the
transmission tool, as illustrated in Figure 15.
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Figure 15: Screenshot of LTPT line bending and resulting terrain difficulty multiplier
The user input for substation costs is much simpler because it does not include terrain
or location information (see Figure 16).
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Figure 16: Screenshot of transmission tool substation cost user input interface
The transmission tool takes the information for transmission line and substation costs
and adds an allowance for funds used during construction (AFUDC) and overhead
costs, expressed as percentages of the overall transmission and substation costs. The
AFUDC and overhead cost is set to the 17.5 percent shown in Figure 16, but can be
adjusted by the user.
Transmission costs are calculated in the exact same way as that of the transmission
calculator, except that BLM rights-of-way cost and terrain costs are determined
geospatially (see Figure 17). The capital costs of the candidate transmission lines are
then passed to the NXT as input data. The NXT creates an optimized transmission
expansion and allocates the capital cost of the expansion “grid costs” to new generation
proportionally to each generator’s contribution to the need for the transmission
expansion. For example, if a given new generating unit contributes 10 percent of the
total line utilization of a new transmission expansion line, then that generating unit is
allocated 10 percent of the capital cost of the line. Grid costs assigned to generating
units are then levelized as a component cost of the total levelized capital cost energy for
each generating unit.
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Figure 17: Screenshot - terrain slope difficulty and transmission expansion
Key Results and Metrics
The results of the transmission and generation capital cost tools were thoroughly
reviewed by stakeholders and were dependent on their feedback to the following
questions:

Do the costs reported by the tools reflect the most current understanding of
expected costs to build new generation and transmission today?

Do the performance parameters of the facilities (e.g., capability, O&M, included
structures) appropriately pair with the costs?
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
Do the tools adequately model the cost trends expected to occur over the next
two decades?
In addition, outputs from the tools were compared with publicly-available sources:

Government-contracted engineering studies: National Renewable Energy
Laboratory, National Energy Technology Laboratory, U.S. Energy Information
Administration

Regional studies: California Energy Commission, California Public Utilities
Commission, Northwest Power and Conservation Council

Integrated resource plans (IRPs) of western utilities: NV Energy, Arizona Public
Service Company, Portland General Electric, Xcel Energy, Avista, Idaho Power,
PacifiCorp
Lastly, the tools are consistent with regard to:


Dollars values are in 2012 dollars
Inputs are base, U.S. average values
Model Limitations
The costs produced by the capital cost tools represent the cost to develop generation
and transmission projects in the Western Interconnection based on some key factors.
However, every facility is unique and the actual cost of a specific project will be
determined by other factors not considered in the development of these tools. As a
result, the costs calculated by the transmission and generation capital cost tools should
not be used to measure the actual cost or cost effectiveness of a specific generation or
transmission project. Actual costs of a specific project should be determined at the siting
level, which is outside of the planning level scope of WECC’s tools.
Development of the capital cost tools relied on public data and a combination of several
methods, including literature review, contractor knowledge input, and actual cost
information where available, and peer group review. These methods are only as good
as the information on which they rely. Where no data or poor data exists, a robust
characterization of cost is difficult. This was especially true for nascent generation
technologies and technologies in a state of rapid change. In these cases, the
development of present-day costs relied on expert judgment based on experience in the
electric sector.
In terms of transmission and substations, most new facilities interconnect to the existing
grid and include some new equipment and some upgrades to existing equipment. In
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addition, transmission facilities are developed not only to transmit incremental power
generation, but also to provide additional system reliability and serve load. Separating
out capacity costs from the cost to provide reliability or serve load can be very difficult, if
not impossible.
Steady-State and Dynamics Models
Model Description
Steady-state and dynamic models define a power system transmission network’s
stable/equilibrium and time-varying behavior. WECC uses the General Electric (GE)
Positive-Sequence Load Flow (PSLF) software, which utilizes the Newton-Raphson
method to solve for the power system’s state. The thermal and voltage analysis module
of the GE Energy Steady-State Analysis Tools (SSTools) is utilized to automate the
process of subjecting the power system to numerous contingencies.
Key Results and Metrics
Steady-state contingency analysis involves subjecting the power system to the loss of
one or more system elements and determining whether or not the system’s resulting
state is desirable. North American Electric Reliability Corporate (NERC) Transmission
Planning (TPL) standards categorize contingencies based on severity and guide
whether the result is desirable. The following properties of the power system are
monitored during steady-state contingency analysis:



Bus voltage
Branch (transmission lines and transformers) loading transformers) loading
Change in bus voltage (pre- vs. post-contingency)
If one or more of these properties is beyond its applicable rating(s) and/or limit(s) before
or after the system is subjected to a contingency, then that contingency is reported as a
potential problem. After exposing the system to numerous contingencies, the
contingencies which lead to potential problems are investigated to evaluate their system
impact and used to propose specific solutions that will improve the operational reliability
of the system.
Model Limitations
The steady-state models, dynamic models, GE PSLF software, and SSTools have
limitations including:

All steady-state and dynamic models are approximations of real-life equipment
and are most accurate at the time they were created. The models are dependent
on the data submitter’s diligence in reevaluating and updating the models.
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Steady-state and dynamic data are gathered and maintained by the TSS, under
the PCC, and undergo thorough review by stakeholders; however, they are still
subject to data entry errors.

Not all equipment can be represented well with the currently available GE PSLF
model templates.

The GE PSLF will try to solve and report a solution with what it is given. It will not
directly report what is impeding the solution, so it is up to the user to evaluate the
issue.

The GE PSLF will not give modeling corrections or recommendations, especially
regarding: generation dispatch, maximum capacity and reactive capability; line
and transformer impedance, ratings, and flows; or load amount and distribution.

The accuracy of dynamic models is dependent on whether the equipment is
tested properly during the development of the model. In the case of large
generators (10 MVA for a single unit or 20 MVA for a single plant), their models
are typically reevaluated every five years as part of the WECC Generator Testing
Program.

The SSTools create transmission line contingencies assuming that each bus-tobus branch in the steady-state model corresponds to a real-life electrical path
with a breaker at each end; however, there are real-life transmission lines that
have multiple bus-to-bus sections and only two breakers – one at each of the
endpoint buses. As a result, SSTools’ contingencies involving such lines will be
inaccurate (i.e., it will only evaluate the result of taking out each section of the
line individually and not the result of taking out all sections of the line).

For contingencies involving the loss of generation, SSTools has a redispatch
feature that increases the dispatch of all other generators to make up for the lost
generation. Each generator’s increase in dispatch is proportional to its size and
ignores the generator’s maximum capacity and whether or not the increased
generation overloads the generator’s step-up transformer or tie-line that connects
it to the Western Interconnection. As a result, this redispatch may be significantly
different than the real-life actions taken in response to the loss of generation.

The SSTools reports violations and potential problems based on the applicable
ratings and limits that the user inputs. It is up to the user to investigate and
evaluate whether a reported problem is, in fact, a real issue with which to be
concerned.
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Supporting Models
Wind and Solar Modeling
Solar and wind generation are modeled as fixed shape resources in TEPPC’s 10-year
PCM studies. This means that solar and wind generation is forced into the model as
must-take generation as these units have no production costs. The 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.9 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, which then can be
converted to power output.
TEPPC profiles are used to capture a much larger region and are used to represent a
shape that would be more characteristic of a typical 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. The methods by which this data is created are
detailed in the Data and Assumptions section of the Plan.
The LTPT and 20-year studies also rely on the NREL data to model wind and solar
generators. However in this instance, hourly profiles are not needed. Only annual
capacity factors for existing and potential Western Renewable Energy Zone (WREZ)
wind sites are required by the model. The annual capacity factors are generated by the
same methodology used to create the 10-year PCM wind and solar shapes.
Hydro Modeling
Hydro generation differs from thermal generation in that it is not only limited by plant
capacity, but also by water supply and therefore energy availability. Hydro-generation
plants also have dispatchability constraints due to environmental or other operational
factors (e.g., irrigation water deliveries, flood control, environmental release) that are
sometimes unpredictable. However, in many cases hydropower plants have significant
generation flexibility arising from their particular operating regime. This may include
reservoir storage, consistency of resource, and minimal environmental constraints.
9
http://wind.nrel.gov/Web_nrel/
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Representing the flexibility of a hydro plant in a PCM simulation allows it to better
respond to load variations and transmission constraints, resulting in a more realistic
transmission system. Currently, the PCM has limited hydro-modeling capabilities.
TEPPC, utilizing stakeholder input through the Hydro Modeling Task Force (HMTF), has
worked to develop methods to better present hydro-plant flexibility within the constraints
of the PCM operating limitations. There are numerous methods available for modeling
hydrogenation. These methods vary in data, computing and manpower requirements. In
general, the most accurate hydro-generation models require large sets of data to
account for multiple variables and multiple water availability scenarios, which lead to the
need for extensive computing resources. However, TEPPC may have limited individual
plant hydro-generation data available due to proprietary issues, or may not have
sufficient computing power or personnel to carry out detailed hydro-generation
modeling.
Modeling of hourly hydro output is often done outside the PCM, in which case the hydro
generation dispatch is not optimized within the constraints of the operating system, as is
the thermal generation. Commonly, historical data modeling of hydro generation is
used, where the 8,760 hourly chronological time series from a representative year is
input for each hydro-generation plant. This method assumes that future generation will
be similar to past generation given similar loads. Historical generation patterns reflect
constraints on the hydropower system in the year from which the data is taken.
However, they do not reflect constraints that may be present in future operating
conditions. If loads and generation are correlated, then generation must be updated
whenever loads change. In addition, load or generation discrepancies in the data year
are carried forward as predictions of future discrepancies. In a 10- or 20-year forecast
timeframe, the lack of accuracy is an issue, particularly if the flexibility of certain hydro
plants is not properly factored in.
In order to produce effective studies of long-term transmission expansion planning
needs, TEPPC requires a hydro-generation model that uses minimal data, minimal
modeling manpower, but satisfactorily represents a hydro plant’s flexibility. The HMTF
developed and utilized two methods for better integrating hydro generation into PCMbased transmission planning. One of these, proportional load following (PLF), is a
method for improving the modeling of hydro generation for plants where operation is
primarily governed by load variability. The PLF model uses as inputs: monthly plant
minimum and maximum operating capacity, the allowable monthly energy, and an
assigned proportionality constant (“K” value) determined by the plant’s ability to follow
load. This greatly reduces the hydro plant data requirements compared to the historical
data.
The second method is the hydrothermal co-optimization (HTC) method within the PCM.
The HTC method allows for a portion of the generation capacity of suitable hydro plants
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to be cost optimized within the system constraints, as are the thermal generators. In this
way, the flexible portion of the plant capacity is at the disposal of the system, and more
accurate transmission simulation results can be obtained. The HTC method relies upon
a PLF-modeled hydro shape and a “p” factor, which describes the fraction of a plant’s
capacity that is able to be cost-optimized within the full system constraints in the PCM.
Historical Data Hydro Generation
Historical data modeling uses a representative year’s hourly chronological hydrogeneration profile, and alters it with a correction factor to produce the forecasted HCHP.
The correction factor is system dependent; transmission study planners determine how
the hourly shape needs to be modified. This may include temperature corrections,
weather forecast corrections, day shifts, and others. The historical data forecast is input
into the PCM and is subtracted from the hourly load forecast. The resulting adjusted
load is used in the thermal dispatch optimization algorithm. This method of hydrogeneration modeling is data intensive; an entire year of hourly plant generation is
required (8,760 data points).
Additionally, the hydro-generation profile is hard-wired, that is, it cannot be adjusted in
the model due to future load variations since it is based purely on the hydro-generation/load relationship present in the model year used. This presents problems in terms of
accuracy; hydro-generation/-load anomalies present in the model year will be carried
forward to the forecast year while the probability of future anomalies will not be
accounted for. This deficiency can be seen in Figure 18Error! Reference source not
found., where the historical data modeled hydro generation of a hydro plant is plotted
with the operating area load. There are several instances where the hydro shape
deviates significantly from the load shape, indicating that the load pattern the generation
was modeled with is different than that of the current load. In the red box, there appears
to be a time-shift difference, while in the red ellipse, a shape difference is present.
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Figure 18: Historical data – modeled hydro generation10
Finally, the hydro generation is not cost or security optimized, therefore not dispatched
in a real-time situation when these would be honored. A considerable drawback for
modeling hydro generation with historical data is that it prevents any available flexibility
in a hydro plant to system changes, such as renewable generation, system outages,
market pricing, or transmission congestion from being utilized in the PCM. This is of
particular importance when modeling future scenarios with significant penetrations of
renewables, as hydro generation is sometimes an important factor in their integration,
and because higher penetrations of renewables have a significant impact on system
energy prices.
PLF Hydro-Generation Modeling Method
This particular model is sensitive to the data and task constraints under which TEPPC is
operating, namely limited access to specific hydro-generation data and a need to
recognize and utilize any inherent flexibility a hydro plant may have in a years-ahead
transmission study. The PLF approach to modeling hydro generation assumes that
generation is proportional to load, subject to minimum and maximum capacity and an
energy limit. An additional variable, the proportionality constant (K), quantifies the ability
of the plant to follow load (i.e., how flexible it is in ramping up or down). The K value
describes hydraulic and fisheries/environmental constraints as one number, which
characterizes the plant’s ability to adjust to load. It only models transmission constraints
and impacts of other generators on plant hydro generation – to the extent they limit
10
Historical data modeled hydro generation for John Day Power Plant in Washington compared with the
Bonneville Power Administration (BPA) load for the first 72 hours of July. The historical data generation is
based on the 2006 profile, and the load is a 2019 forecasted load profile.
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historical plant flexibility. As a measure of the K value, plants without any operating
flexibility have a K equal to zero, while plants with a high level of flexibility may have a K
value as high as five. The proportionality constant is found by regressing scaled hourly
plant generation against scaled load:
𝐺 − 𝐺̅
𝐿 − 𝐿̅
=𝐾
𝐺̅
𝐿̅
In some cases one K is suitable for all water conditions; in others it is not.
Figure 19 provides an example of a monthly regression, resulting in a proportionality
constant (K value) and a correlation coefficient (R2). From the regression, the correlation
coefficient (R2) value provides a measure of how well the plant generation correlates to
the load demand. An example plot of all monthly K values and correlation coefficients is
shown in Figure 20Error! Reference source not found., providing illustration of the
need for using month-specific K values in PLF-modeling scenarios in certain plants.
Figure 19: Regression of hourly generation at Grand Coulee Dam 11
11
Regression between scaled hourly generation at Grand Coulee Dam and scaled load in the BPA
control area during December 2009.
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Figure 20: 2009 monthly K values and correlation coefficients for Grand Coulee Dam
In order to produce an hourly generation profile, the PLF model uses the algorithm:
𝐺 = 𝐺̅ +
𝐿 − 𝐿̅
𝐾𝐺̅
𝐿̅
where G is generation, L is load, K is the PLF constant, and over bars denote averages.
The second term on the right hand side can be viewed as the plant flexibility, which
modulates variability. It is a function of both average generation and the K value. This
implies that in periods of high generation, plant flexibility increases. To assess whether
this is true, one can investigate correlations between K and generation. As K values and
loads increase, the equation could yield a generation value greater than plant capacity.
Similarly, large K and low generation could drive generation below a plant minimum or
even negative. The 𝐺̅ is therefore replaced with 𝐺0 :
𝐺 = 𝐺0 +
𝐿 − 𝐿̅
𝐾𝐺0
𝐿̅
and is constrained by:
𝐺𝑚𝑖𝑛 < 𝐺0 < 𝐺𝑚𝑎𝑥
Iterative adjustment of 𝐺0 forces the modeled average G to equal 𝐺̅ within a
convergence criterion. As average generation approaches plant capacity, plant
variability decreases. In the extreme case of average generation equaling plant
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capacity, the plant runs at its capacity rating during all hours regardless of the K value.
A root finding approach such as Newton’s or bisection determines 𝐺0 .
In using the PLF to model appropriate plants, TEPCC gains an advantage over
historically derived generation profiles. First, the use of the PLF reduces the data
storage and processing time, since the hydro-generation values needed include only the
monthly plant minimum and maximum, monthly allowable energy, and monthly K
constant for a total of 48 numbers versus the 8,760 needed for historical data. Often,
general knowledge of a plant’s operation allows assignment of a K value, for cases in
which hourly generation data are not available. This can often be assessed by obtaining
minimal information from plant operators. For example, if the plant does not vary its
generation over a 24-hour period, then the K value will be zero. In the case where plant
generation routinely goes to zero at night, then K is equal to four. If the plant generation
sometimes, but not routinely, goes to zero, the K value is considered to be
approximately three.
Second, the PLF-calculated generation can be applied to forecasted loads, since the
hydro-generation shape is not coupled with the year of data load, as in historical data.
Additionally, loads may be adjusted for non--dispatchable generation. By subtracting
must-run resources, such as wind and solar, from the load, the PLF model can generate
a profile incorporating these resources. In this way, the flexibility of the hydro plant
compensates for the must-run resources. When a PLF-modeled generation profile
incorporating renewables is used as input in a PCM, the resulting locational marginal
price (LMP) in effect, accounts for the renewables.
Disadvantages in using the PLF model are that it does not take into account non-load
operational constraints, and does not cost-optimize the hydro generation in the PCM’s
dispatch. Still, it can be a valuable tool for accounting for inherent hydro-plant flexibility
in long-term transmission studies. In addition, the PLF model can be a useful interim
solution until more progress is made enhancing capabilities for modeling hydraulic
constraints and interaction of hydro and non-hydro resources in the PCM.
HTC Hydro Modeling Method
An additional method available to TEPPC is HTC, a model within the PCM that modifies
the scheduling of hydro energy into the thermal commitment and dispatch algorithm as
warranted by energy prices (LMP). As used by TEPPC, the HTC method adjusts hydrogeneration profiles created by the PLF, dispatching a portion of the available resources
based on price during the thermal unit dispatch.
Because HTC modifies the generation curve produced by the PLF, it uses all the same
monthly inputs with the addition of monthly “p” factors. The p factor represents the
fraction (between 0 and 1) of a plant’s dispatchable capacity that can adjust its output
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based on market price. One approach to calculating the p factor comes from noting that
the PLF K values and their R2 values provide a measure of plant flexibility. Flexibility
can then be allocated between the PLF and the HTC. The range of plant generation, A,
is estimated using the PLF equation.
𝐴 = [𝐾𝐺
𝐿𝑚𝑎𝑥 − 𝐿̅
𝐿𝑚𝑖𝑛 − 𝐿̅
− 𝐾𝐺
]
𝐿̅
𝐿̅
Adjustments of a plant’s flexibility designated to the HTC can be made up to 4pC where
C is the plant maximum capacity minus plant minimum generation and the 44 relates to
the flexibility of moving up or down in each of two PCM security-constrained unit
commitment and security-constrained economic dispatch Security-Constrained Unit
Commitment (SCUC)/Security-Constrained Economic Dispatch (SCED) iterations.
Assuming the PLF dispatches half of a plant’s flexibility to follow the load and the HTC
dispatches the other half to react to market prices, an equation to obtain p can be
derived as follows.
4𝑝𝐶 =
𝐴
𝐴
→𝑝=
2
8𝐶
It should be noted that because half of the plant’s flexibility is now assumed by the HTC,
the calculated PLF K value should be halved when used in the PLF model. This is just
one approach to calculating the p factor; other techniques may be used depending on
the information available on a given hydro plant. Regardless of the techniques used,
modeling experience has shown that p factors should not exceed approximately 0.11.
Once the p factors have been determined for specified hydro plants, the HTC modeling
proceeds as follows:
1. First, a PLF-modeled generation curve for a hydropower generator is created
using the PLF input parameters. The PLF generation curve, maximum/minimum
generator capacity, monthly energy, ramp rates, and monthly p factors are all
then used as inputs into the HTC model linked to the SCUC/SCED algorithm.
2. Next, based on these input parameters, the PCM will redispatch a portion of the
hydro generation determined by the PLF hydro schedule in concert with the
thermal generators to optimize both the thermal and hydro generation.
3. Finally, the PCM produces an optimized, revised monthly generation time-series
for each hydro plant for which the HTC is applied.
A PCM simulation utilizing the HTC results is not only an optimized hydro dispatch
schedule, but also more accurate LMP valuations in the system. Figure 21 shows the
dispatch of a hydro generator using HTC. The node price and the dispatch of the same
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generator using PLF is also shown. It can be seen that the HTC generation increases
over the PLF when the price is high, and decreases below the PLF when the price is
low (black circles).
Figure 21: Hydro plant hourly dispatch schedule
The HTC does not work well with all hydro plants. For example, the HTC would not be
an appropriate modeling method if the majority of a plant’s generation is determined by
run of the river, environmental controls or flood control. Another limitation experience
with the HTC method is that the energy available for response to changes in price (the p
factor fraction), is dispatched as a block, regardless of the magnitude of the price
change. These results in overly large generation ramps since there are currently no
ramp restrictions available in the PCM. This issue is currently being evaluated by the
HMTF, and a method to dispatch a generation value that is proportional to the price
change (triangle method) is being tested and slated for potential development within the
PCM.
Flexibility Reserves Modeling
Flexibility reserves are defined as the additional reserves required to manage the
variability and uncertainty associated with variable generation resources like wind and
solar. Given the high penetration of variable generation in the West, including this
additional reserve requirement is an important assumption for the PCM studies. The
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process uses historical load, wind and solar data at a 10-minute resolution to derive
equations that predict the variability based on statistical analysis of that data.
Flexibility reserves are only applicable to TEPPC’s 10-year production cost model runs
as they have an hourly dispatch with an operating reserve requirement. The LTPT has
no such operating reserve requirement as it is a capital expansion tool. The traditional 4
percent of daily peak spinning reserve is combined with the predefined hourly flexibility
reserve to create a composite hourly reserve requirement, as shown in Figure 22Error!
Reference source not found..
Figure 22: Composite Hourly Reserve Requirement
An example of the composite reserve requirement is shown in Figure 23Error!
Reference source not found. for the California south subregion. Load, solar and wind
generation, and the reserve requirement components are shown for each two day
summer period. Note how the four percent of daily peak reserve requirement is based
on the single day peak and changes from the first day to the second. This reserve
requirement is combined with the hourly flexibility reserve requirement, which is
noticeably higher for hours in which there are high penetrations of variable generation.
These two reserves are additive and their total represents the total composite reserve
requirement.
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Figure 23: Flexibility Reserve Example
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