2013 Plan Tools and Models - WECC€¦ · Web viewIn the next planning cycle, WECC can build upon its early success with the LTPT by making improvements to the model to enhance the

Document name 2013 Interconnection-wide PlanTools 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

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TEPPC

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Previous name/number (if any)

Status ( ) in effect( ) usable, minor formatting/editing required( ) modification needed( ) superseded by _____________________( ) other _____________________________( ) obsolete/archived)

W E S T E R N E L E C T R I C I T Y C O O R D I N A T I N G C O U N C I L • W W W . W E C C . B I Z1 5 5 N O R T H 4 0 0 W E S T • S U I T E 2 0 0 • S A L T L A K E C I T Y • U T A H • 8 4 1 0 3 - 1 1 1 4 • P H 8 0 1 . 5 8 2 . 0 3 5 3 • F X 8 0 1 . 5 8 2 . 3 9 1 8

2013 Interconnection-wide Plan Tools and Models

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2013 Interconnection-wide PlanTools and Models

By

WECC Staff

Western Electricity Coordinating Council

September 19, 2013

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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 top-down 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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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 long-term 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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ContentsSummary and Key Findings.............................................................................................4Background......................................................................................................................7Production Cost Model (PCM).......................................................................................10

Model Description......................................................................................................10Key Results and Metrics............................................................................................15Model Limitations.......................................................................................................16

Long-Term Planning Tool (LTPT)...................................................................................18Model Description......................................................................................................20Key Results and Metrics............................................................................................33Model Limitations.......................................................................................................35

Capital Cost Calculators.................................................................................................37Model Description......................................................................................................38Key Results and Metrics............................................................................................46Model Limitations.......................................................................................................47

Steady-State and Dynamics Models..............................................................................48Model Description......................................................................................................48Key Results and Metrics............................................................................................48Model Limitations.......................................................................................................48

Supporting Models.........................................................................................................50Wind and Solar Modeling...........................................................................................50Hydro Modeling..........................................................................................................50Flexibility Reserves Modeling....................................................................................58

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BackgroundThe 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

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future network necessary to realize an "energy future" study case provided to the 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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(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

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

FocusCapacity 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 ModelThe production cost model (PCM), TEPPC’s primary analytic tool, 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 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 DescriptionA 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 StackGenerating 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 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

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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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

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47

Hypothetical Resource Availability - Friday/Saturday

CT - Dispatchable

Biomass - Dispatchable

CC - Dispatchable

Coal - Dispatchable

Nuclear - Dispatchable

Biomass - Must run

QF - Must run

Geothermal - Must run

Cogen - Must run

Hydro - Must take

Solar - Must take

Wind - Must take

Demand

Transmission ParametersTEPPC’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, load buses, and transmission lines with their associated physical characteristics. For the TEPPC datasets, the transmission topology is imported from a WECC-approved power

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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 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

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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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 non-dispatched 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 MetricsOne 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 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.

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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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 LimitationsThere 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 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

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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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.

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

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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 ToolUnlike 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. 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

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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.

Model DescriptionThe 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

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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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 least-cost 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 ModelThe 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.

Figure 3: LTPT Process Flow and Input Requirements

Given the complexity of the model iterations, the LTPT functionality and model is presented in six steps:

Step 1: Generation Optimization

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Inform Decision Makers

Inform Other Planning

Screen Alternatives

Compare Energy Futures

Measure Impacts

Identify High Value Projects

Analytics

ExpansionPlan

SeverityMeasures

Capital Costs

GridConstraints

Capital Costs

Alternative Candidates

Optimize Transmission

Tiered Optimization

Capacity Constraints

Energy Constraints

Operating Characteristics

Optimize Generation

Stakeholder Collaboration

Geospatial

Capital Costs

Transmission

Generation

Load

Federal & State Mandates

Cultural Considerations

Social Considerations

Policy Consideration

Environmental Considerations

Assertions

NarrativesCriteria


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.

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.

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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.

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 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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http://www.wecc.biz/committees/BOD/TEPPC/External/2032_ReferenceCase.xlsx


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 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

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considerations, assumptions and methods were used as guidance in performing the network reduction:

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 sub-transmission 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

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transmission expansion, irrespective of location, by interconnecting intermediate hubs with new expansion at least cost.

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 springo 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 fallo 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 wintero 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 FunctionalityThe 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 ConsiderationsThe 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 MetricsTEPPC’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 LimitationsThe 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-of-concept” 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..

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.

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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http://www.arb.ca.gov/cc/ab32/ab32.htm


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 CalculatorsGeneration 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.

Model DescriptionTEPPC uses two tools to calculate the estimated capital cost inputs to the 10- and 20-year studies: the TEPPC Generation Capital Cost Tool (generation tool) and the TEPPC Transmission Capital Cost Tool (transmission tool). Figure 10 depicts the capital cost

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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.

Generation ToolThe 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 SubtypesBiogas Landfill

8 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

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http://www.wecc.biz/committees/BOD/TEPPC/External/121101_TEPPC_GenCapCost_Calculator.xlsm

http://www.wecc.biz/committees/BOD/TEPPC/External/121101_TEPPC_TransCapCost_Calculator.xlsx


OtherBiomass

Coal PCIGCC w/ CCS

Combined Heat & Power Small (<5 MW)Large (>5MW)

Gas CCGT

Basic, Wet CooledAdvanced, Wet CooledBasic, Dry CooledAdvanced, Dry Cooled

Gas CT Aero derivativeFrame

Geothermal

HydroLargeSmallUpgrade

Nuclear

Solar PV

Residential RooftopCommercial RooftopDistributed Utility (Fixed Tilt)Distributed Utility (Tracking)Large Utility (Fixed Tilt)Large Utility (Tracking)

Solar Thermal No StorageSix-Hour Storage

Wind OnshoreOffshore

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.

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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 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

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Technology Default Financing EntityBiogas IPPBiomass IPPCoal – PC IOUCoal – IGCC IOUCHP IPPGas – CCGT IPPGas – CT IPPGeothermal IPPHydro – Large IOUHydro - Small IPPNuclear IOUSolar Thermal IPPSolar PV IPPWind 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 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 10-year 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.

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Figure 13: Simple calculator results screen

Transmission ToolThe 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 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 ToolTransmission Line Voltage Classes Substation Types230-kV Single Circuit 230 kV230-kV Double Circuit 230 kV345-kV Single Circuit 345 kV345-kV Double Circuit 345 kV500-kV Single Circuit 500 kV (ac)500-kV Double Circuit 500 kV (ac)500-kV HVDC Bi-pole 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 MetricsThe 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 LimitationsThe 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 DescriptionSteady-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 MetricsSteady-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 LimitationsThe 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. Steady-state and dynamic data are gathered and maintained by the TSS, under

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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-to-bus 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 ModelingSolar 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 meso-scale 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 ModelingHydro 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.

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 9 http://wind.nrel.gov/Web_nrel/

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http://wind.nrel.gov/Web_nrel/


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 PCM-based 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 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

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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 hydro-generation 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 hydro-generation 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 MethodThis 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 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

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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value as high as five. The proportionality constant is found by regressing scaled hourly plant generation against scaled load:

G−GG

=K L−LL

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 Dam11

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=G+ L−LLK G

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 G is therefore replaced withG0:

G=G 0+L−LLK G0

and is constrained by:

Gmin<G0<Gmax

Iterative adjustment of G0 forces the modeled average G to equal G 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 MethodAn 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 hydro-generation 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−LL−KG

Lmin−LL ]

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.

4 pC= A2→ p= A

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 ModelingFlexibility 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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Documents

2013 Plan Tools and Models - WECC€¦ · Web viewIn the next planning cycle, WECC can build upon its early success with the LTPT by making improvements to the model to enhance the