Year 20 Capital Expansion Study. September 8, 2017

2034 Reference Case Draft Results 1 2034 Reference Case Draft Results Year20 Capital Expansion Study September 8, 2017 155 North 400 West, Suite 200 Salt Lake City, Utah 841031114

2034 Reference Case Draft Results 1 Disclaimer WECC receives data used in its analyses from a wide variety of sources. WECC strives to source its data from reliable entities and undertakes reasonable efforts to validate the accuracy of the data used. WECC believes the data contained herein and used in its analyses is accurate and reliable. However, WECC disclaims any and all representations, guarantees, warranties, and liability for the information contained herein and any use thereof. Persons who use and rely on the information contained herein do so at their own risk.

2034 Reference Case Draft Results 1 Table of Contents 1. Introduction... 1 2. Summary of Results... 2 3. Background & Objectives... 2 4. Reference Case... 3 5. Optimization Goals... 5 6. Load... 6 7. Generation... 15 7.1 Generation resources modeled within the RC... 15 7.1.1 Existing Generation... 15 7.1.2 Generation Additions... 16 7.1.3 Generation Results... 17 7.2 Annual Energy Production... 18 7.3 Added Resources (British Columbia and Alberta assumed as self supplied, not shown)... 28 7.4 Change in Resource Mix... 30 7.5 Generation Region Driver Mix... 31 7.6 CO 2 Production... 32 7.7 Water Usage (Thermal cooling only)... 33 7.8 Binding Fuel Constraints... 34 7.9 Levelized Cost of Energy... 36 8. Transmission... 37 9. Economics... 46 9.1 Levelized Cost of Energy... 46 9.2 Generation Capital Cost Model... 47 9.3 Generation Capital Cost Model... 50 10. Environmental Risk Category Data... 55 11. Pool Constraints... 58 12. Study Methodology... 58 Appendix A Glossary of Terms... 1 Appendix B WIEB 2024 StateProvince Load Distributions... 1 Appendix C Transmission Path Parameters... 1

2034 Reference Case Draft Results 2 Grid Component of LCOE and Optimal Transmission Expansion... 4 Appendix D Power Transfer Distribution Factors of Interfaces... 1

2034 Reference Case Draft Results 1 1. Introduction KnownKnowns, KnownUnknowns, and UnknownUnknowns Reports that say that something hasn't happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns the ones we don't know we don't know. And if one looks throughout the history of our country and other free countries, it is the latter category that tends to be the difficult ones. Donald Rumsfeld (Defense Department Briefing; February 12, 2002) Nothing is certain as to the future of the Western Interconnection. There is no absolute certainty as to what the load and generation portfolios across the Western Interconnection will look like twenty years into the future. Likewise, there is no absolute certainty as to what the transmission infrastructure will look like or how it will be utilized, or what reinforcements may be needed. These are some of the knownunknowns for which a better understanding is needed. Further, a ten year study horizon is not far enough in the future to guide investment decisions regarding transmission expansions, due to the lead time required for a new transmission expansion element. This is a knownknown that needs to be addressed. Obviously, the evolution of the Western Interconnection twenty years into the future cannot be predicted with absolute certainty. Yet potential futures can be better understood through scenario development. Identifying and studying knowns and unknowns with regard to trends and drivers that may influence the future of the Western Interconnection and performing scenario analysis from a capital expansion perspective serves to provide guidance to decision makers and to other study processes performed within WECC and across the Western Interconnection. Through scenario analysis, unknownunknowns are uncovered as an added benefit. The purpose behind the long term (Year 20) studies performed by WECC is not to predict the future, but rather to gain a better understanding of how the future of the Western Interconnection may unfold and of the strategic choices that may need to be made during the next 20 years. WECC is working to accomplish these goals by identifying and studying trends and drivers that may shape the energy future of the Western Interconnection twenty years into the future. Long term reliability analyses performed at WECC are meant to complement other analyses at WECC to screen for reliability risks and opportunities that warrant further detailed analysis. Results obtained from long term studies are meant to guide other study processes within WECC and to enable WECC stakeholders to make more informed decisions. Through stakeholder engagement and scenario development, study narratives are developed to conceptually explore different trajectories of possible energy futures. These scenario narratives are then used to craft data models that are studied as a way to identify and better understood the drivers that shape the energy future of the Western Interconnection and to find answers to questions required by stakeholders to make informed decisions regarding the energy future of the Western Interconnection. The Year20 Reference Case (Y20RC) is the foundation upon which Year20 (Y20) studies are performed. The assumptions and trajectories of the Year10 Common Case (Y10CC) are projected out another 10 years from the

2034 Reference Case Draft Results 2 Y10CC to derive the Y20RC. Whereas the Y10CC uses a production cost model (PCM) for analysis, the Y20RC uses a capital expansion model (CXM). Whereas the focus of the Year10 (Y10) studies is on system utilization given a set of assumptions about installed generation and transmission, the focus of the Y20 studies is on new generation portfolio mixes and transmission infrastructure needs given a set of assumptions and narratives about the drivers that may influence the future of the energy grid. 2. Summary of Results The following observations are summaries of the results presented in detail in this report: 1. The penetration of renewable resources, such as wind and solar, are not only economically viable but often preferred over other conventional resources, such as natural gas. The biggest drivers behind the optimal selection of new resource additions appear to be CO 2 price, natural gas prices, and the annual production of energy that can be realized from a resource. 2. With a CO 2 cost of $58/metricton for CO 2 emissions, existing coal plants were displaced and no new coal plants were optimally selected. This compared to a $37/metricton CO 2 cost in 2013 for the 2032 Reference Case studies where existing coal plants were kept (not displaced). 3. Total system CO 2 production decreased dramatically from 310 MMT in the 2024 CC to 66 MMT in the 2034 RC, primarily due to higher CO 2 and gas prices. 4. The 2034RC shows about 70% less water for thermal cooling of conventional, nonrenewable resources than the 2024 Common Case, also a result of higher CO 2 and gas prices displacing conventional resources where water is used for thermal cooling. 5. After cooptimization of generation portfolios with transmission utilization by performing security constrained optimal power flows for the various load duration block conditions, the Y20RC results suggest that the Western Interconnection proves to be very robust in accommodating the optimized generation portfolio consisting of a higher penetration of renewables from that of the Y10CC. The only interface paths that showed a possible need for transmission expansion was from Montana to the Pacific Northwest, primarily due to higher concentrations of wind selected in Wyoming and Montana. 3. Background & Objectives Recognizing that a ten year study horizon is not far enough in the future to guide investment decisions associated with transmission infrastructure reinforcements, WECC embarked upon an initiative to develop a Y20 Long Term Study Program (LTSP) in July of 2010. The LTSP effort was envisioned to be a screening process to provide insights regarding energy futures and to inform decision makers of capital expansion opportunities and risks. As stated earlier, the LTSP is not meant to predict with absolute certainty the future needs or state of the Western Interconnection, but rather provide decision makers with a range of plausible futures that could occur to inform strategic choices that may guide investment decisions in the next 20 years. Through stakeholder engagement and scenario development, study cases and data models are created and studied to answer key questions posed by stakeholders to gain a better understanding of the evolving energy future of the Western Interconnection.

2034 Reference Case Draft Results 3 WECC has used a scenariobased study approach because of uncertainties inherent with a 20year study horizon. This entails collaborating with the diverse stakeholder community in the Western Interconnection to develop plausible futures for the Western Interconnection, and then using those scenario narratives to define discrete study cases for further analysis. Scenariobased studies are effective method to understand the long term energy future of the electrical grid in terms of transmission utilization, expansion needs, and generation portfolio mixes, given the realm of uncertainties that may influence the energy future. The Y20 study program looks at the energy future from a capital expansion perspective where capital investments are large, infrastructure lead times are long, and the industry is at the mercy of future economic and technological conditions that are impossible to predict. Scenariobased analysis offers a tool for describing various plausible futures (scenarios), analyzing and comparing those futures, and drawing valuable insight into how the drivers identified in and among those futures affect transmission expansion and generation build out. The Y20RC provides a point of reference for this comparison. The Y20RC is based on the Y10CC, as the assumptions and modeling trajectories of the Y10CC are carried forward another 10 years. An important distinction between the Y10 study case model and process as compared to the Y20 study case model and process is that the focus of the Y10 studies is on system utilization given a set of assumptions about installed generation and transmission. The focus of the Year 20 studies is new generation portfolio mixes and transmission infrastructure needs given a set of assumptions about the drivers that may influence the future of the energy grid. The Y20 studies are performed using tools and techniques that cooptimize future generation portfolios and transmission infrastructure enhancements enable decision makers to make better and more informed decisions regarding capital investment needs given a set of future policy, economic, environmental and other considerations. The Y20 long term study methodology is described in detail in the Study Methodology section of this report. 4. Reference Case The Year20 Reference Case (Y20RC) is a capitalexpansion model (CXM) that represents the load, resource and transmission topology characteristics that may be needed in the Y20 energy horizon if the assumptions used to create the Y10CC were projected out another 10 years (see Figure 4.1). The Y10CC is the starting point for the Y20RC because it anchors the analysis to a common foundation of assumptions. Unlike the Y10CC, which is an expected future, the Y20RC represents a potential future given Y10CC assumptions and assumptions regarding energy drivers. The Y20RC serves as a point of reference to navigate and understand potential Y20 energy futures.

2034 Reference Case Draft Results 4 Figure 4.1 Extrapolating the Y20RC from the Y10CC A majority of the data contained within the Y20RC is derived from the Y10CC. Additional data associated with potential new transmission and generation candidates, economic parameters, and geospatial data layers are used to further augment the data derived from the Y10CC. Two different approaches are used to analyze the Y10 and Y20 study horizons. The Y10 analysis is a bottomup process aimed at evaluating the robustness of the current BES based on the assumptions of the Y10CC and the impact of various alternative futures. A production cost tool and model (PCM) is used to analyze the Y10 study horizon. The Y20 analysis, alternatively, uses a topdown process aimed at understanding investment decisions in transmission and generation infrastructure that may be required to realize energy futures under various alternative conditions as framed by stakeholders in scenario and study request narratives. Whereas a PCM is used to analyze the Y10 study horizon, a CXM is used to analyze the Y20 study horizon. The reader also should be aware of the different reference years used in this report and in other reports that comprise WECC s 2017 study program. WECC s 2016 and 2017 study programs are based on the 2026 Common Case. That model and data set is considered the most likely future 10 years in the future using 2016 as the reference year. However, the 2034 Reference Case is based instead on 2014 as the reference year. The difference in reference years is due to the time required to implement changes in the assessment tools used for the Y20 analyses. These changes, begun in 2014, were not completed until 2017. As a result, Y20 study cases that were intended to be complete by 201415 were not complete and it was necessary to defer their completion until 2017. The time required to update the assumptions on which the Y20RC is based to reflect the 2026 Common Case would have delayed study completion unacceptably. As a result, the reliability assessments

2034 Reference Case Draft Results 5 included in WECC s 2017 study program are based on different reference years 2016 in the case of the 2026 Common Case and 2014 in the case of the 2034 Reference Case. 5. Optimization Goals Both generation goals and transmission goals are cooptimized, subject to various constraints, to arrive at a solution of the long term CXM. In summary, this means that the CXM attempts to identify the leastcost mix of both generation and transmission, based on assumptions about generation and transmission costs Grid costs are calculated as reinforcements that may be needed to reinforce transmission path interfaces. At this point, it is necessary to define Levelized Cost of Energy (LCOE) and grid cost, the cost functions on which generation and transmission are cooptimized. LCOE is defined as the net present value of the unitcost of electricity over the lifetime of a generating asset. LCOE considers capital, operating, maintenance, fuel and other costs incurred in generating electricity. Grid costs are the capital investment costs associated expansion reinforcements to the transmission grid. Grid costs are levelized as a component of generation LCOE that is proportional to the incremental power transfer distribution factors between a generator and a transmission path requiring reinforcement. Figure 5.1 below shows general locational grid costs within the Western Interconnection. Generation optimization goals are characterized as either energy goals (MWh) or capacity goals (MW) defined as functions of load. An example of an energy goal is a state level Renewable Portfolio Standard (RPS) requirement, as state RPS requirements are specified in terms of annual renewable energy (MWh) which must be procured as a function of load. An example of a capacity and dispatch goal is a security constrained optimal dispatch of generation for each of the seasonal/load level load duration load blocks, as securityconstrained dispatch is usually specified in terms of the MW that must be dispatched. Multiple goals are defined and are processed using a cascading optimization where more restrictive and dependent goals (e.g., RPS carveouts) that roll up to more generic and less restrictive goals (e.g., general RPS) that are optimized further down the optimization cascade. Generation optimization goals have targets values defined as percentage values of system, state, or BA load energy or demand forecasts. The generation optimization goals include: Renewable Portfolio Standards (RPS) RPS classes, tiers, carveouts, etc. RPS Earmarks (Y10CC generating units explicitly defined to be part of the installed generation portfolio base to meet RPS requirements). Mustrun requirements Units required to meet reliability constraints Units required to meet flexibility constraints

2034 Reference Case Draft Results 6 Specific dispatch requirements associated with load demands (MW). For example, a dispatch requirement could be that generators capable of regulation must make up a percentage of the overall generation dispatch. Energy goals are specified annually whereas demand goals are based on a load duration block (season and load level). Many state programs promote particular technology types by establishing subtargets known as carve outs or setasides. Where states define carve outs or setasides, in addition to meeting the overall RPS targets, energy supply companies need to show that they have acquired a specified percentage of their power sales from the designated technology type. In some instances, multiple technology types are bundled together in tiers or classes with a similar effect. Not all states have setasides or tiers (some preferring to promote particular technologies through credit multipliers) and each state that groups technologies together in a tier does so differently. For instance, RPS carveouts are processed before generic RPS goals since carveouts are more specific and restrictive than generic RPS goals and generally count toward generic RPS goals. 6. Load The WECC Load Forecasting Tool (WLFT) was used to forecast Y20RC loads. Input data to the WLFT, and the Y20RC load forecasts produced were approved by TEPPC. WLFT forecasts were produced for each WECC Balancing Authority (BA; see Figure 6.1).

2034 Reference Case Draft Results 7 Figure 6.1 WECC Balancing Authorities Hourly load profiles from the Y10CC PCM were used to derive annual load energy (MWh) and eight equally probable seasonal heavy and light load duration blocks (MW) for each BA. The BA forecasts were further parsed by state boundaries using state percentage breakdowns obtained from the RPS REQUIREMENTS FOR WESTERN STATES/PROVINCES summary published by the Western Interstate Energy Board (WIEB), as provided in Appendix B, since RPS goals are defined by state/provinces and the service territories of some BAs span across multiple states. An added benefit of modeling load by state and BA is that statearea boundaries align well with WECC transmission interface paths as defined within the CC and the power supply assessment (PSA) model (see Figure 6.2).

2034 Reference Case Draft Results 8 Figure 6.2 Monitored WECC Transmission Topology Interface Paths Aggregation hubs are created for each statearea and nodal buses within the CC nodal power flow model, with Common Case Transmission Assumptions (CCTA), are also parsed by and associated with the statearea boundaries and aggregation hubs. The transmission interface paths delineated by these hubs are defined within the Y20RC power flow and are monitored as security constraints in optimal power flow analysis as part of the cooptimization of generation resource portfolios and transmission utilization and expansion. Parsing loads by states and areas provides a great deal of flexibility in the modeling and analysis of studies by allowing for layered adjustments and/or inspection of models and results by BA, state/province, and by region. Figure 6.3 presents the annual Y20RC energy forecast (GWh) produced by the WLFT, aggregated by state/province.

2034 Reference Case Draft Results 9 Figure 6.3 2034 WLFT System Load Energy Forecast (GWh) In addition to annual energy forecasts for each statearea, eight equally probable load duration blocks are modeled for four seasons at heavy and light load levels for each statearea. Each load duration block is modeled with a load demand value (MW) and a duration value (percent) where the duration represents the percentage of time over the course of a year that load demand occurs at the corresponding seasonal load demand level. The load duration block values are derived by applying the monthly load forecasts produced by the WLFT to the historical hourly load profiles for each BA to derive the equal probable seasonal/heavylight loads demand values. Figure 6.4 and Table 6.4 present the load energy and demand forecasts by state produced by the WLFT. Please note that the demand presented in Figure 6.4 and Table 6.4 represents the range that the eight load duration block demand values span (e.g., max to min).

2034 Reference Case Draft Results 10 Figure 6.4 2034 WLFT Forecasted Load Energy and Demand State or Province Table 6.4 2034 WLFT System Load Energy Forecast (GWh) 2024 Energy (MWh) 2024 Heavy Demand (MW) 2024 Light Demand (MW) 2024 Average Demand (MW) AB 113,234 13,785 12,064 12,926 AZ 104,688 14,431 9,470 11,951 BC 68,154 8,760 6,797 7,780 CA 318,837 41,979 30,805 36,397 CO 62,077 7,947 6,225 7,086 ID 30,284 3,965 2,949 3,457 MT 15,554 1,971 1,579 1,776 MX 14,985 2,011 1,411 1,711 NM 20,594 2,651 2,051 2,351 NV 44,827 6,008 4,227 5,117

2034 Reference Case Draft Results 11 State or Province 2024 Energy (MWh) 2024 Heavy Demand (MW) 2024 Light Demand (MW) 2024 Average Demand (MW) OR 62,694 8,117 6,194 7,157 TX 8,579 1,143 815 979 UT 31,396 4,010 3,157 3,584 WA 108,245 14,091 10,618 12,357 WY 22,279 2,803 2,283 2,543 Total 1,026,426 133,673 100,646 117,172 Figure 6.5 and Table 6.5 present forecasted load energy and demand compound annual growth rates (CAGR) by state. Again, the demand CAGR presented in Figure 6.5 and Table 6.5 represents the range that the eight load duration block demand CAGRs span (e.g., max to min). Figure 6.5 2034 WLFT Forecasted Load Compound Annual Growth Rate

2034 Reference Case Draft Results 12 Table 6.5 2034 WLFT Forecasted Load Compound Annual Growth Rate State or Province 2034 Energy CAGR (%) 2034 Heavy Demand CAGR (%) 2034 Light Demand CAGR (%) 2034 Avg Demand CAGR (%) AB 2.5% 3.1% 2.0% 2.5% AZ 2.6% 4.3% 2.6% 2.6% BC 1.5% 2.9% 0.0% 1.5% CA 0.6% 2.0% 0.3% 0.6% CO 1.4% 2.2% 1.2% 1.4% ID 0.0% 1.6% 1.0% 0.0% MT 0.1% 0.6% 0.5% 0.1% MX 1.0% 2.9% 0.0% 1.0% NM 2.7% 3.4% 2.8% 2.7% NV 1.2% 1.6% 1.7% 1.2% OR 0.8% 0.4% 1.6% 0.8% TX 1.5% 1.1% 2.2% 1.5% UT 0.4% 1.4% 1.7% 0.4% WA 0.3% 1.0% 1.2% 0.3% WY 2.0% 3.8% 14.7% 2.0% Weighted Average 0.9% 2.3% 0.4% 0.9% The WLFT is very complex and considers many factors including: Historical Load Data (Monthly Energy & Peak) Historical Actual & Normal Weather Data Weather Response Functions Economic Data & Forecasts EndUse Saturation and Efficiency data Peak Model Inputs Due to its complexity, explaining the tool s functions is beyond the scope of this report. The use of the WLFT to produce Y20RC load forecasts is a departure from the traditional method which was previously used to produce Y20RC load forecasts based on the same assumptions and trajectories of the Y10CC as illustrated in Figure 6.6.

2034 Reference Case Draft Results 13 Figure 6.6 Load Forecast Methodology Based on Y10CC Trajectory and Assumptions As a followup, the load forecasts C produced by the WLFT that were included in the Y20RC were compared with forecasts produced using the traditional method. A sidebyside comparison between the energy forecasts produced by the WLFT and that using the traditional method as illustrated in Figure 6.7. Figure 6.7 Comparison of 2034 System Load Energy Forecasts (GWh) System Total 1,091,682 (GWh) System Total 1,101,556 (GWh) As observed in Figure 6.7, the energy forecasts produced by both methods are similar in scale but differ slightly across state/provinces. A sidebyside comparison between the energy/demand forecasts produced by the WLFT and the Y10CC GAGR trajectory is presented in Figure 6.8.

2034 Reference Case Draft Results 14 Figure 6.8 Comparison of 2034 System Load Energy/Demand Forecasts (GWh / MW) It is observed that while the energy forecasts produced by both methods are similar, the range of seasonal/load duration block demands are quite different. Whereas the WLFT produced wide ranges of demand levels, with some appearing to be anomalous (e.g., California), the CC trajectory method produced flatter and better behaved demand level ranges. A sidebyside comparison between the CAGRs for the energy/demand forecast produced by the WLFT and the Y10CC GAGR trajectory is presented in Figure 6.9. Figure 6.9 Comparison of 2034 System Load Energy/Demand CAGRs (%) The CAGRs resulting from both methods are dramatically different. As noted before, an anomalous forecast was produced for Wyoming by the WLFT where a disconnect between the energy and load forecasts produced resulted in extremes in demand for Wyoming (i.e., 14.7% light load CAGR). WECC also observed that the WLFT CAGRs for heavy load are higher than the WLFT CAGRs for light load, whereas the CAGRs produced by the traditional method for heavy load are less than the CAGRs for light load. Efforts made by load serving entities to better manage their load profiles through energy efficiency and demandside management are reflected in the Y20RC load CAGRs (derived from the Y10CC load CAGRs) where hourly load profiles are flattening out with higher load factors due to faster light load CAGRs relative to heavy load CAGRs. It is reasonable to assume that

2034 Reference Case Draft Results 15 this trend will continue into the Y20RC study horizon and that a reversal in this trend as reflected in the CAGRs for the loads forecasted by the WLFT is unlikely to occur. Although WECC believed that using the WLFT would produce better load forecasts, doing so introduced a disconnect in continuity with the Y10CC. Further, the WLFT produced anomalies (e.g., CA & WY CAGRs) that are attributed to the inherent complexity of the WLFT. In particular, a twenty year study horizon does not afford the level of precision required of the input data to the WLFT which makes the WLFT less effective at producing long term load forecasts. So, upon reflection and further detailed review of the WLFT itself, WECC has concluded that the WLFT is better suited for short and nearterm load forecasting, but less effective for longterm load forecasting where complexities and uncertainties outweigh the benefits of using the WLFT for long term forecasting. As such, WECC has concluded that the previously used (traditional) method of using Y10CC assumptions and trajectories, as illustrated in Figure 6.6, is better suited to forecast Y20RC loads for the following reasons: Continuity of load models between the Y20RC and the Y10CC are lost with the use of the WLFT. Complexity of the WLFT introduces forecast anomalies that are difficult to understand or explain, adding more uncertainty to the long term load forecasts. Lack of precision required of the input data to the WLFT. The WLFT is better suited for short and nearterm load forecasting. The need for this level of complexity and granularity diminishes for longer term study horizons, such as that of the Y20RC, where uncertainties associated with the input parameters to the WLFT increase. The complexity of the WLFT and the added expense and burdens to manage and vet the data and the quality of the forecast results are not justified by the current forecasted results. The forecasts of the WLFT are anomalous and the trends are suggested by the forecasts are not reasonable. 7. Generation 7.1 Generation resources modeled within the RC Generation assumptions include existing and additional resources. Existing resources come from the Y10CC. Generation additions are represented by the resource options that the model selects when adding additional generation. 7.1.1 Existing Generation All generators included in the Y10CC are also included within the Y20RC as existing generation with sunk investment costs, meaning they have a capital cost of zero (since they already exist). Thus, when the model selects resources to add in the Y20RC (for load or RPS requirements), existing resources are likely to be selected before similar new resources since existing resources have a zero capital cost component of the overall levelized cost of energy (LCOE). It is important to note that retirements were not modeled in the Y20RC.

2034 Reference Case Draft Results 16 The model does not determine which units should be retired, but rather, the model may simply not select a particular generator to be included in a generation profile for a given case study due to study case modeling criteria. The benefit of not subjectively modeling generator unit retirements within the context of long term studies is that generator selection is subject only to the study case criteria assumptions. For example, existing coal generators were not selected for inclusion in the generation profile in some study cases where ere policy criteria included high CO 2 prices. In this example, we learn how CO 2 policy may impact the viability of existing resources, which we would otherwise not be able to glean if existing resources were subjectively retired. The Y10CC generators were aggregated at regional load hubs by type and area. The regional load hubs served as a proxy for internal TEPPC load areas and interconnected to other load hubs, gas hubs and potential renewable generation sources (at WREZ hubs) through the overall reduced transmission network. Interties between TEPPC load areas were preserved in the reduced network as were Y10CC Transmission Assumptions. Reliability generation is generation that is needed to ensure system reliability, for example, generators that must be running during certain periods to maintain local system voltage or to serve load in constrained areas. Reliability requirements are explicitly modeled as proxy goals. The goal targets for reliability goals may be defined in percentages of load energy, load demand, or capacity of another fuel type (e.g., flex requirement for wind or solar fuel types). Reliability generation is modeled using a flexible rulebased approach. Reliability rules can be defined to designate specific generating units for automatic selection in an optimized generation profile as reliability units, regardless of cost. The type and quantity of resources designated for reliability is determined as a modeling proxy. This reliability resource proxy can be defined in any number of ways. How the reliability resource proxy is defined should be vetted through stakeholder review. In the context of the current study program, all Y10CC combustion turbines (CT) are designated as reliability units within the model. CTs are typically the primary resources used to serve the final load block during high load periods and respond when additional generation is needed quickly, which makes them the best choice for the reliability designation. The Y10CC CTs are the least expensive CTs because they have no capital costs (i.e., they are assumed to already exist in 2034). Designation of Y10CC CTs as reliability units enables the model to balance the optimized generation with the demand goal without being too costly. Notably, the reliability units are still subject to economic dispatch considerations in the optimization when satisfying the System Peak Demand Goal. In that regard, they are not mustrun units. 7.1.2 Generation Additions The model selects incremental generation (generation in addition to those units that will exist in the Y10CC) according to each resource s LCOE. Detailed information on the capital costs used to calculate the LCOE is located in the Study Methodology section of this report. A brief list of additional parameters governing the model s selection of additional generation includes: The model does not perform an hourly unit commitment and dispatch, so resource capacity factors are derived from the PCM to estimate annual energy production for each unit. When the transmission expansion is performed, this generation can be adjusted such that load and generation match. Detailed information on these assumptions is located in the Study Methodology section of this report.

2034 Reference Case Draft Results 17 Gas price differentials are relative to Henry Hub statebystate. Each resource s contribution to peak is the same as in the Y10CC. New renewable generation with generation potential derived from data supplied by the National Renewable Energy Laboratory (NREL). Incremental gas resources are added at regional hubs. Incremental conventional generation expected capacity factors (annual energy) are provided by the Capital Cost tool. Onpeak capacity is the same as in the Y10CC assumptions. 7.1.3 Generation Results Generator results are a key component for the Y20RC. Additions in the generation results represent those resources that were added in the 20242034 timeframe. Existing generation is any generation assumed in the Y10CC. When reviewing these results, recall that the model also has the ability to represent transmission investment decisions in the resource selection process by calculating and applying grid costs. This approach allows for transmission needs and costs to influence, and in some cases determine, which resources are selected during the 20242034 timeframe. Fuel Percentage of Total Capacity (GW) Table 7.1 Percentage by Fuel 2024CC Percentage of Total Energy (GWh) Percentage of Total Capacity (GW) 2034RC Percentage of Total Energy (GWh) Biomass 1% 2% 1% 2% Coal 10% 16% 0% 0% Gap 0% 0% 5% 7% Gas 38% 34% 23% 25% Geothermal 2% 3% 2% 3% Nuclear 3% 6% 3% 5% Other 8% 2% 5% 2% Solar 5% 4% 12% 10% Water 24% 26% 24% 23% Wind 10% 7% 27% 24% Total 100% 100% 100% 100% Total Capacity and Additions Interconnectionwide installed capacity in 2034, by fuel, is shown above. The Y20 RC shows a 10% decrease in coal (all coal in the system) and 16% decrease in gas, as well as a 6% increase in solar, and an 18% increase in wind generation compared to the Y10CC.

2034 Reference Case Draft Results 18 Renewable resources in the Y20RC represented 42% of the Western Interconnection s installed resource capacity, compared to 18% in the Y10CC. In several instances renewable resources, rather than more conventional gas and coal resources, were the most economic choice to meet the system energy requirement. 7.2 Annual Energy Production The charts below show the annual energy production by fuel type in GWh comparing the Y10CC and the Y20 RC. Depicted in the annual energy results, the Y20RC consists of a 12% increase in total generation capacity additions compared to the Y10CC. Also, energy production from wind and solar more than doubled which offset coal and gas generation resources. Coal, specifically is shown as a very mininal resource due to the CO 2 price and availability of more economic renewables. Note, about half of the Y10CC resource mix is made up of coal and gas resources. Figure 7.1 2024 Fuel Mix (GWh)

2034 Reference Case Draft Results 19 Figure 7.2 2034 Fuel Mix (GWh) Table 7.2 Resource Mix Annual Energy (GWh) Fuel 2024CC (GWh) 2034RC (GWh) Biomass 17,767 17,767 Coal 153,554 174 Gap 0 78,840 Gas 329,010 269,860 Geothermal 29,842 29,842 Nuclear 56,081 56,081 Other 23,053 22,481 Solar 34,236 113,609 Water 250,966 250,966 Wind 71,609 257,964 Total 966,117 1,097,583 Table 7.3 lists the regions for which generation data is reported. Table 7.3 List of Reporting Regions Region Mnemonic Alberta British Columbia NWPC NWIN Basin Long Name Alberta British Columbia Pacific Northwest (OR, WA, Northern ID) Inland Nortwest (MT) Basin (Southern ID, Northern NV, Utah)

2034 Reference Case Draft Results 20 RMPA Rocky Mountains (CO, WY) CANorth California North (North of Path 26) CASouth California South (South of Path 26) AZNMNV Desert Southwest (AZ, NM, Southwest NV) Mexico Mexico Figure 7.4 below shows the Y10CC annual energy resource mix by region. Note, the locations and amounts of coal and gas resources used in this model compared to the Y20RC to follow.

2034 Reference Case Draft Results 21 Figure 7.3 2024 Generation Resource Mix by Region Table 7.4 2024 Resource Mix by Region British CA CA AZNM Fuel Alberta NWPC NWIN Basin RMPA Mexico Columbia North South NV Bio 2,247 146 4,814 0 248 0 6,177 3,854 279 0 Coal 15,874 0 0 14,512 29,810 52,399 681 553 39,724 0 Gap 0 0 0 0 0 0 0 0 0 0 Gas 52,249 0 32,329 655 13,656 18,345 67,199 70,721 58,992 14,865

2034 Reference Case Draft Results 22 British CA CA AZNM Fuel Alberta NWPC NWIN Basin RMPA Mexico Columbia North South NV Geothermal 0 0 234 0 6,140 0 9,587 8,552 0 5,329 Nuclear 0 0 8,312 0 0 0 16,903 0 30,865 0 Other 23 0 945 407 369 2,163 5,122 10,724 3,298 2 Solar 0 0 61 0 1,662 200 6,594 19,324 6,384 12 Water 2,632 73,692 110,64 7 7,540 5,699 2,618 32,372 6,929 8,838 0 Wind 7,414 1,504 21,674 1,950 4,093 11,549 5,366 11,524 5,577 959 Total 80,440 75,342 179,01 6 25,064 61,678 87,274 150,00 1 132,181 153,955 21,167 Below, the Y20RC annual energy resource mix by region is shown. Notice that the coal has all but dissapeared. The coal in RMPA, Montana, AZNMNV, and Alberta regions are gone, and have been replaced by wind. Coal in the Basin region is replaced by solar resources. Also notice the amount of gas in Mexico that has been replaced with soalr resoures. The Western Interconnection shows a considerable transition toward renewable resources.

2034 Reference Case Draft Results 23 Figure 7.4 2034 Generation Resource Mix by Region

2034 Reference Case Draft Results 24 Fuel Alberta British Columbia Table 7.5 2034 Resource Mix by Region NWPC NWIN Basin RMPA CA North CA South AZNM NV Mexico Bio 2,247 146 4,814 0 248 0 6,177 3,854 279 0 Coal 0 0 0 0 0 0 174 0 0 0 Gap 78,840 0 0 0 0 0 0 0 0 0 Gas 16,895 0 42,367 0 11,300 0 82,751 74,820 37,839 3,888 Geothermal 0 0 234 0 6,140 0 9,587 8,552 0 5,329 Nuclear 0 0 8,312 0 0 0 16,903 0 30,865 0 Other 0 0 945 407 368 1,884 5,105 10,721 3,051 0 Solar 0 0 61 0 65,205 200 6,594 19,324 6,384 15,842 Water 2,632 73,692 110,647 7,540 5,699 2,618 32,372 6,929 8,838 0 Wind 45,693 1,504 28,072 41,144 14,766 31,647 27,945 16,255 41,292 9,648 Total 146,307 75,342 195,451 49,091 103,726 36,348 187,609 140,456 128,546 34,707 When comparing the Y10CC with the Y20RC, we notice that throughout the Western Interconnection, there is a substantial substitution of renewable resources for coal and gas from 2024 to 2034. Shown below, we see that selection of solar resources is very specific to certain locations, with a great amount in the Basin region, while wind selection tends to be more spread out across the Western Interconnection. The figure below shows that 57% of the solar energy production in 2034 comes from the Basin region, while the 73% of the hydroelectricity comes from British Columbia and NWPC regions. Figure 7.5 2034 Fuel by Region

2034 Reference Case Draft Results 25 Shown below is the annual energy fuel mix by state. Clearly, California generates the most electriciy of any state or province in West, with about half coming from gas. Figure 7.6 2034 Fuel by State Table 7.6 2034 Fuel by State GWh (ABMX) Fuel AB AZ BC CA CO ID MT MX Biomass 2,247 279 146 10,032 0 630 0 0 Coal 0 0 0 174 0 0 0 0 Gap 78,840 0 0 0 0 0 0 0 Gas 16,895 19,557 0 157,571 0 2,624 0 3,888 Geothermal 0 0 0 18,139 0 127 0 5,329 Nuclear 0 30,865 0 16,903 0 0 0 0 Other 0 2,602 0 15,826 1,835 135 407 0 Solar 0 4,794 0 25,918 200 0 0 15,842 Water 2,632 6,719 73,692 39,301 1,522 10,117 7,540 0 Wind 45,693 2,643 1,504 44,200 7,286 12,738 41,144 9,648 Total 146,307 67,460 75,342 328,064 10,843 26,371 49,091 34,707 Table 7.6 2034 Resource Mix by State GWh (NMWY) Fuel NM NV OR TX UT WA WY Biomass 0 94 1,431 0 0 2,907 0 Coal 0 0 0 0 0 0 0 Gap 0 0 0 0 0 0 0 Gas 209 15,697 28,290 4,226 7,710 13,192 0 Geothermal 0 5,312 234 0 700 0 0 Nuclear 0 0 0 0 0 8,312 0

2034 Reference Case Draft Results 26 Fuel NM NV OR TX UT WA WY Other 231 333 202 0 128 732 49 Solar 241 2,110 61 2 64,442 0 0 Water 111 2,050 31,336 0 104 74,746 1,096 Wind 37,718 2,385 17,850 0 715 10,080 24,360 Total 38,511 27,981 79,404 4,228 73,799 109,970 25,505 The visual shown below depicts the generation to load comparison by region in the Y20RC. Notice the surplus and deficits across the Western Interconnection. Although RMPA has a lot of wind replacing the coal present in the Y10CC, there is still a deficit due to the amount of coal that was not selected in 2034. However, Mexico, Montana, and Basin have suprlus due to the amount of added renewables, even though they replaced coal and gas in the Y10CC.

2034 Reference Case Draft Results 27 Figure 7.7 2034 Generation/Load Table 7.7 2034 Generation/Load Region Generation (GWh) Load (GWh) Alberta 146,307 144,990

2034 Reference Case Draft Results 28 Region Generation (GWh) Load (GWh) British Columbia 75,342 78,904 NWPC 195,451 168,534 NWIN 49,091 15,418 Basin 103,726 66,911 RMPA 36,348 74,966 CANorth 187,609 150,368 CASouth 140,456 187,877 AZNMNV 128,546 187,104 Mexico 34,707 16,608 Total 1,097,583 1,091,682 7.3 Added Resources Added resources are resources that have been added (additional to the Y10CC) to meet the load demand of the Y20RC. Notice how the additional resources are only in three fuel types: Gas, Wind, and Solar. Solar comes from Basin and Mexico. While Wind is more spread out, the majority comes from Montana. Gas primarily comes from California. Additional generation comprises 418,018 GWh. Of that, wind was the primary contributor, making up 45% of the additional generation. The additional renewable resources shown below replace much of the fossil fuels shown in the Y10CC resource mix. It was observed that the Basin region added more solar resources than the AZNMNV region. Upon further investigation, it was discoved that the Capacity Factor for Utah is 0.518, and the Capacity factor for AZNMNV is 0.358 for solar, which accounts for the increase in solar resources in Basin over AZNMNV. The capacity factors for new renewable resources are derived from data supplied to WECC by NREL. Since the time of this study, the wind and solar data has been update. The wind and solar model in subsequent and followup Y20RC studies will be updated with the new wind and solar data. In interesting take away from this observation, regardless of whether the data needs to be updated or not, is that new solar resources approach the marginal LCOE resource price in the model with an annual capacity factor upwards of 0.4 (40%). With a capacity factor over 0.5 (50%), new solar resources are preferred over other resource technology types.

2034 Reference Case Draft Results 29 Figure 7.8 2034 Added Resources (GWh) 2034RC Alberta British Columbia Table 7.8 2034 Added Resources (GWh) NWPC NWIN Basin RMPA CA North CA South AZ NMNV Mexico Bio 0 0 0 0 0 0 0 0 0 0 Coal 0 0 0 0 0 0 0 0 0 0 Gap 78,840 0 0 0 0 0 0 0 0 0 Gas 14,717 0 10,714 0 0 0 20,372 27,647 0 0

2034 Reference Case Draft Results 30 2034RC Alberta British Columbia NWPC NWIN Basin RMPA CA North CA South AZ NMNV Mexico Geothermal 0 0 0 0 0 0 0 0 0 0 Nuclear 0 0 0 0 0 0 0 0 0 0 Other 0 0 0 0 0 0 0 0 0 0 Solar 0 0 0 0 63,543 0 0 0 0 15,830 Water 0 0 0 0 0 0 0 0 0 0 Wind 38,278 0 6,398 39,195 10,673 20,098 22,579 4,731 35,715 8,689 Total 131,835 0 17,111 39,195 74,216 20,098 42,951 32,378 35,715 24,519 7.4 Change in Resource Mix The chart and table shown below describe the change in resource mix from the Y10CC to the Y20RC. Generation that was used in the Y10CC that was included in the Y20RC is known as existing generation, or CC Existing. Generation that was used in the Y10CC, that was not included in the Y20RC is known as displaced generation or CC Displaced. Generation that was not in the Y10CC, but has been added to the Y20RC is known as added generation, or New Added. Most of the displaced resources in these results are coal fired resources. Figure 7.10 2034 Displaced Generation (GWh)

2034 Reference Case Draft Results 31 Table 7.9 2034 Displaced Generation (GWh) Report Region New Added (GWh) CC Existing (GWh) CC Displaced (GWh) Alberta 131,835 14,472 65,968 AZNMNV 35,715 92,831 61,124 British Columbia 0 75,342 0 CASouth 32,378 108,077 24,104 CANorth 42,951 144,658 5,343 RMPA 20,098 16,251 71,023 NWPC 17,111 178,340 676 Basin 74,216 29,510 32,168 NWIN 39,195 9,897 15,167 Mexico 24,519 10,188 10,979 Total 418,018 679,565 286,551 As shown above, we see that other than Alberta, Basin has the most added generation, although it doesn t have the most overall generation. Which consisted mainly of Solar as shown on the Added Resources visual. 7.5 Generation Region Driver Mix The chart below shows the drivers for allocated generation. As generation is selected, it is allocated toward as a particular goal. Generation falls under the following allocations: Gap (GWh) Units that aren t specified Instate RPS Driven (GWh) Renewable Portfolio Standard units that count toward a designated state goal System RPS Driven (GWh) Renewable Portfolio Standard units that count toward the system goal Instate NonRPS Driven (GWh) NonRenewable Portfolio Standard units that count toward a designated state goal System NonRPS Driven (GWh) NonRenewable Portfolio Standard units that count toward the system goal Shown below is the way generation was allocated for the Y20RC. We see that in NWPC, the generation allocation is mainly System NonRPS driven, while Basin is mainly System RPS driven. NWIN (Montana) was allocated by mainly by System NonRPS. This region consists mainly of renewables, which indicates that the renewable resources were chosen based on economics, rather than to meet RPS requirements.

2034 Reference Case Draft Results 32 Figure 7.11 2034 Generation Region Driver Mix Report Region Gap (GWh) Instate RPS Driven (GWh) Table 7.10 2034 Generation Region Driver Mix System RPS Driven (GWh) Instate NonRPS Driven (GWh) System NonRPS Driven (GWh) Alberta 9,691 7,451 0 22,833 0 AZNMNV 0 2,619 38,076 36 69,137 British 0 72,399 0 4 1,073 Columbia CASouth 0 6,430 24,333 0 50,931 CANorth 0 3,481 22,129 0 37,627 RMPA 0 6,271 1,931 52 1,056 NWPC 0 8,501 3,622 238 101,050 Basin 0 10,649 55,605 37 4,575 NWIN 0 456 401 127 35,245 Mexico 0 0 7,758 1,422 14,010 Total 9691 118,257 153,855 24,750 314,704 System NonRPS drivers show the highest energy contribution, suggesting that economics could may be the key driver. 7.6 CO2 Production The visual below shows the CO 2 production comparison between the Y10CC and the Y20RC. There is a considerable reduction in CO 2 production from 2024 to 2034 with the offset of coal and gas to renewables. Due to the drastic decrease in fossil fuels and the increase in renewables, the Y20RC has about 20% of the CO 2 production of the Y10CC.

2034 Reference Case Draft Results 33 Figure 7.12 CO 2 Production Table 7.11 CO 2 Production Report Region CC CO 2 (MMTon) RC CO 2 (MMTon) Alberta 78 7 AZNMNV 61 19 British Columbia 1 0 CASouth 51 22 CANorth 0 13 RMPA 59 0 NWPC 17 0 Basin 25 1 NWIN 12 0 Mexico 7 4 Total 310 66 7.7 Water Usage (Thermal cooling only) Water usage is defined as the amount of water needed to provide cooling for thermal power plants (e.g., gas, coal). Across the Western Interconnection, since renewable resources have increased and the amount of fossil fuels has decreased, the water usage for cooling has decreased significantly. The water usage shown below pertains

2034 Reference Case Draft Results 34 to thermal cooled resources only (not to hydroelecricity production). The Y20RC shows the use of about 31% of the water used in the Y10CC. Figure 7.13 Water Usage (Thermal Cooled Resources only) Region 2024CC 2034RC Alberta 93,588 10,947 British Columbia 4,091 0 NWPC 57,591 14,235 NWIN 31,740 0 Basin 162,812 26,897 RMPA 142,596 0 CANorth 192,778 112,414 CASouth 136,741 87,459 AZNMNV 231,539 100,178 Mexico 77,431 1,988 Total: (afy) 1,130,907 354,118 7.8 Binding Fuel Constraints Binding fuel constraints limit the amount of fuel that can be used in a case. Due to a finite amount of resources on the system, fuel limits are modeled to represent how much fuel will be available in the system. When a particular fuel is exhausted, it will hit its limit, and the model won t allow any more of that resource to be allocated. Notice that the binding constraint of solar in the Basin region indicates that all available solar modeled in the Basin region was selected due to a modeled capacity factor of.518 which translates to a higher energy production potential that yields a lower LCOE. Similarly, the high production potential of wind in New Mexico resulted in a binding constraint. As mentioned earlier, the wind and solar profiles in the Y20RC will be updated for followup and subsequent studies since the production potential of renewables, as defined through capacity factors, is critical to the study results. Other resources needed to be selected as fuel constraints were reached in Utah and Arizona.

2034 Reference Case Draft Results 35 Figure 7.14 2034 Fuel Constraints Table 7.12 2034 Fuel Constraints State Fuel Area UT solar pv utility tracking UT_PAUT AZ wind onshore AZ_WALC

2034 Reference Case Draft Results 36 7.9 Levelized Cost of Energy Figure 7.15 2034 LCOE by Fuel Levelized Cost of Energy includes more factors than just the fuel price. As shown above, the capital cost of wind is very close to the fuel price of gas. However, the CO 2 cost of gas makes wind a more economical option. Figure 7.15 shows the impact that each LCOE component costs have on the system. For example, the CO 2 cost of coal drives the price margin more than the fuel price. This makes renewables a more economic option than coal, even though the capital cost is usually higher. Note, also, that the fixed O&M cost of wind and solar are very similar. However, the capital cost of solar is nearly double that of wind. As mentioned before, the LCOEs for renewables are largely dependent on the energy production potentials which are locational in nature. The system marginal LCOE was roughly between 93 $/MWh and 95 $/MWh (where a distinct supply stack was generated for each goal the cascading optimization of resources). With a 2027

2034 Reference Case Draft Results 37 (midway between Y10 and Y20) gas price of 7.09 $/MMBtu as compared to a 2.50 $/MMBtu price used in the 2032 studies. In the 2032 studies, the LCOE price spread between new additions of gas and wind resources was very tight and optimal selection usually depended on locational variations of underlying parameters used in the calculation of LCOE. In this, the 2034 Y20RC, the price spread between new additions of gas and wind resources is roughly $40/MWh due to higher CO 2 costs and natural gas prices. The higher CO 2 and gas costs also resulted in some existing gas plants to be displaced (primarily gas turbine), despite the absence of a capital component of LCOE. Subsequently, gas resources were primarily selected to satisfy reliability goals (e.g., flex generation) whereas renewable resources were selected to satisfy RPS and adequacy goals. As mentioned before, the LCOE for renewables, and subsequently the optimal selection of renewables to satisfy an optimization goal is largely dependent on energy production potential. The proxy used to estimate annual production for a generation resource is the annual capacity factor (ACF). The levelized cost of energy (LCOE) is the cost function upon which cooptimization of generation and transmission is performed. Component costs of LCOE are levelized by the energy production of a resource over its economic life. The higher the ACF, the more energy produced by a resource over its economic life and subsequently, a lower LCOE. The lower the ACF, the less energy produced by a resource over its economic life and subsequently, a higher LCOE. As a result, LCOE varied widely for renewables depending on location and ACF as a measure of energy production potential. For new candidate renewable resources, the ACF was derived from data provided by NREL. Given the factors discussed above, the Y20RC results yielded a high concentration of wind in Wyoming and Montana and a high concentration of solar in the Basin and in Mexico. Gas resources were added in California and the Pacific Northwest primarily to satisfy reliability goals and/or due to lower grid costs (e.g., closer to load centers). 8. Transmission The focus of the LTSP, with regard to transmission, is on the utilization and expansion needs of major WECC transmission paths, which are generally interregional, spanning across multiple states. Interface paths captured within the Y10CC power flow case and as transfer paths in the Power Supply Assessment (PSA) zonal model as reflected in Figure 8.1 are also modeled in the Y20RC and monitored for security violations as part of the transmission optimization.

2034 Reference Case Draft Results 38 Figure 8.1 2034 Transmission Path Interfaces British Columbia 800 0 Alberta 2000 0 2000 250 MRO Pacific Northwest 2000 400 Montana 500 200 600 325 1800 200 4200 300 350 0 1400 Idaho Wyoming 3675 300 185 2200 1400 100 680 300 Northern California 2600 100 775 100 2858 Northern Nevada 2750 360 235 2750 650 Utah Colorado 1920 650 1400 BANC 800 3675 800 140 3000 250 350 614 300 664 2300 Southern LADWP Nevada 3883 4727 3750 692 4785 3750 468 1700 250 Southern 2814 250 California 692 New Mex ico 600 468 600 1273 1273 IID Arizona 150 255 150 163 1655 2400 San Diego 1168 2400 SPP 408 0 Mex ico Legend For each pair of numbers, the top or left number is the transfer capability (MW) in the direction of the arrow. The bottom or right number is the transfer capability in the opposite direction of the arrow. The regions used in this study are described below in Figure 8.2. Within subregions of the Western Interconnection, it is assumed that necessary reinforcements will be in place. The interregional transmission interfaces are delineated from the power flow network across major WECC interface paths.

2034 Reference Case Draft Results 39 Figure 8.2 WECC Path Transfer Limits WECC Path Transfer Limits Table 8.2 contains the existing transmission path transfer limits modeled within the Y20RC. Thickness of the path arrows represents the relative transfer capability of a path relative to all paths as a whole. Direction of arrow represents the direction of the dominate transfer capability (not to be confused with path flow). Bubbles represent aggregation hubs of load and generation. Bubbles are color coded by regional aggregations. Path equivalent impedances are generally inversely proportional with transfer limits (e.g., the higher the path limit, and the lower the resistance to path flow). The transfer limits of the interface paths were monitored according to seasonal capacity limits and direction of flow (see Figure 8.2 and Table 8.2). Table 8.2 WECC Path Transfer Limits From>To Limit (MW) To>From Limit (MW) Name Summer Winter Summer Winter BCAB 800 800 0 0 ABMT 0 0 250 250 BCPCNW 2,000 500 2,000 1,950 MTPCNW 2,000 2,000 500 500

2034 Reference Case Draft Results 40 From>To Limit (MW) To>From Limit (MW) Name Summer Winter Summer Winter MTWY 400 400 200 400 MTID 325 250 200 250 PCNWID 500 600 1,800 2,100 PCNWCANO 4,200 4,800 3,675 3,675 PCNWNVNO 300 300 300 300 IDNVNO 350 350 185 185 IDWY 0 0 2,200 2,200 IDUT 680 680 775 785 WYCO 1,400 1,400 1,400 1,450 WYUT 400 400 400 400 CANONVNO 100 100 100 100 UTNVNO 360 360 235 235 NVNONVSO 800 800 800 800 UTCO 650 650 650 650 UTNVSO 140 260 250 265 AZUT 250 250 250 250 CONM 614 614 664 664 AZNVSO 4,727 4,634 4,785 4,785 NMAZ 5,660 5,660 5,660 5,660 PCNWCASO 2,600 2,900 2,858 2,858 CANOCASO 4,000 4,000 3,000 3,000 CASOMX 408 408 0 800 NVSOCASO 4,000 6,637 6,697 6,697 AZCASO 3,595 4,379 2,876 2,876 UTNM 530 530 600 600 There a seven transmission expansion candidate technology types that can be selected to enhance a transmission interface path. These transmission technology types are those defined in the TEPPC Transmission Capital Cost Tool developed for WECC by Black & Veatch as listed in Table 8.3. Table 8.3 Transmission Technology Types Transmission Technology Voltage Type Capacity Rating (MW) 230 KV Single Circuit 230 KV AC 400 230 KV Double Circuit 230 KV AC 800 345 KV Single Circuit 345 KV AC 750 345 KV Double Circuit 345 KV AC 1500 500 KV Single Circuit 500 KV AC 1500 500 KV Double Circuit 500 KV AC 3000 500 KV HVDC BiPole 500 KV DC 3000

2034 Reference Case Draft Results 41 Enhancement MW capacities increase with higher voltages and the number of circuits, so too does the cost. Please note that in addition to transmission line costs, substation costs are also captured. Please also note that while 500 KV HVDC BiPole and 500 KV Double Circuit transmission technology types have the same enhancement MW capacity, the optimal selection choice is dependent on the combination of both transmission line costs and substation costs. 500 KV HVDC BiPole transmission line costs are less than 500 KV Double Circuit lines costs since the 500 KV HVDC BiPole technology type requires a single circuit, whereas the 500 KV Double Circuit requires two circuits. Conversely, 500 KV HVDC BiPole substation costs are more than 500 KV Double Circuit substation costs since 500 KV Double Circuit technology types require the addition of station convertors/invertors. The optimal choice of 500 KV HVDC BiPole over 500 KV Double Circuit occurs when the transmission costs of the 500 KV Double Circuit outweighs the substation cost of the 500 KV HVDC BiPole. Each interface path is associated with a transmission corridor that is optimized to minimize environmental impact. These corridors serve as proxies for the interface enhancement parameters (e.g., line mileage, permile costs). Corridor proxies are further optimized to choose the most cost effective transmission configuration according to different levels of capacity enhancement (see Appendix C). The optimal LCOE component grid costs appropriate to the level of expansion needs are used on the cooptimization of generation and transmission. A LCOEGC function is derived for each of the transmission interface paths modeled in the Y20RC. COEGCs are calculated for each interface path by associating an optimized corridor with the interface path as a proxy for calculating LCOEGCs and line parameters. The transmission corridors themselves are geospatially optimized to follow a path that minimizes environmental impact while capturing other geospatial considerations such as the effects of terrain difficulty (e.g., slope), land cover, and rightofway (ROW) costs. In addition, transmission parameters and LCOEs of seven different transmission technology types are calculated for each corridor and optimally selected for each corridor interface path as a function of expansion MW capacity need while minimizing cost. Appendix C contains charts providing optimal technology types and corresponding LCOE s as a function of MW expansion capacity for each transmission path interface. Figure 8.3 is an example of a chart found within Appendix C that illustrates the LCOE and MW enhancement values by technology type for the CANOCASO (bidirectional) interface path. For each technology type, there are three values of LCOE calculated with a high value representing new transmission construction, a low value representing reconductored transmission construction, and a median value of the two. The red diamond markers in the chart indicate the enhancement capacity for each technology type. Please note that the LCOEGC for the 500 KV Double Circuit technology type is slightly less than the 500 KV HVDC BiPole technology type. So, the optimal technology type for a 3000 MW capacity enhancement would be 500 KV Double Circuit.

2034 Reference Case Draft Results 42 Figure 8.3 Proxy Technology Type LCOEs for CANOCASO Interface Path Figure 8.4 illustrates the LCOE cost of the CANOCASO interface path as a function of enhancement MW capacity. In addition, the optimal technology type is provided at each level of capacity need. As mentioned earlier, the optimal technology type for a 3000 MW capacity enhancement on this interface path would be a 500 KV Double Circuit technology type. Figure 8.4 CANOCASO LCOE by Path Enhancement MW Capacity

2034 Reference Case Draft Results 43 Figure 8.5 illustrates the LCOE and MW enhancement values by technology type for the CANOPCNW (bidirectional) interface path. Please note that, for this path, the LCOEGC for the 500 KV Double Circuit technology type is more than the 500 KV HVDC BiPole technology type. So, the optimal technology type for a 3000 MW capacity enhancement would be 500 KV HVDC BiPole. Figure 8.5 Proxy Technology Type LCOEs for CANOPCNW Interface Path Figure 8.6 illustrates the LCOE cost of the CANOPCNW interface path as a function of enhancement MW capacity. In addition, the optimal technology type is provided at each level of capacity need. As mentioned earlier, the optimal technology type for a 3000 MW capacity enhancement on this interface path would be a 500 KV HVDC BiPole technology type, due to higher transmission line costs.

2034 Reference Case Draft Results 44 Figure 8.6 CANOPCNW LCOE by Path Enhancement MW Capacity Cooptimization of generation with transmission grid costs resulted in a 2034 generation portfolio that was further optimized using security constrained dispatch methods across eight equally probable load durations blocks representing seasonal heavy and light load conditions. The dispatch limits of generators in the optimized portfolios were constrained according to factor ratios of each unit s nameplate capacity where the lower limit factors were equated to the annual capacity factors and the upper limits were equated to the effective load carrying capability (ELCC) ratios as illustrated in Figure 8.7. Please note that the limits reflected in Figure 8.7 reflect those of the optimized portfolio and not that of the candidate pool of generating resources.

2034 Reference Case Draft Results 45 Figure 8.7 Regional Weighted Optimal Dispatch Limits by Region After cooptimizing generation portfolios with transmission expansions and applying security constrained dispatch of generation across eight seasonal load levels, dominate power flows were derived as equally probable weightings across all eight load conditions (Figure 8.8). Upon convergence to a final solution, the Western Interconnection grid was observed to be very robust in being able to support the generation portfolios resulting from the LTSP optimization with increased penetration of renewables. The only interface paths that showed a possible need for transmission expansion was from Montana to the Pacific Northwest. In looking at the power transfer distribution factors for the Montana to the Pacific Northwest interface, the main driver of the power flow on this path is due to the wind allocated in Wyoming and Montana as previously illustrated in Figure 7.8. Power transfer distribution factors (PTDF) calculated for each of the interfaces are provided in Appendix D.

2034 Reference Case Draft Results 46 Figure 8.8 2034 Dominate Path Flows Y20 Dominant Path Flows Dominant path flows across the Western Interconnect were calculated from eight equally probable load durations. Generation portfolio and dispatch were cooptimized to meet goals, minimize cost, and alleviate transmission security violations. The current network Infrastructure appears to be robust for Y20RC energy needs after portfolio and dispatch optimizations. 9. Economic Methodologies Used In This Report Economic parameters and assumptions are obtained from the WECC Capital Cost Calculator tools that went through peer review and were approved by TEPPC. The generation economic data was developed for WECC by Energy + Environmental Economics (E3) and the transmission data was developed for WECC by Black & Veatch. The optimization cost functions were based on LCOE. 9.1 Levelized Cost of Energy Levelized cost of energy (LCOE) is a measure of the overall cost of different generating technologies considering all costs incurred in generating power. Key inputs to calculating LCOE include capital costs, fuel costs, fixed and variable operations and maintenance (O&M) costs, financing costs, an assumed utilization rate for each plant type and transmission expansion costs. LCOE represents the permegawatt hour cost (in real dollars) of building and operating a generating plant over an assumed financial life and duty cycle.

2034 Reference Case Draft Results 47 E3 provided WECC with a simplified model to represent LCOE without using a pro forma cash flow structure. Capital costs are levelized using a capital recovery factor (CRF) formula developed by the National Renewable Energy Laboratory (NREL): Where: CRF = {i(1 + i )^n} / {[(1 + i)^n]1} n = economic life i = WACC WACC = weighted average cost of capital Annual costs (or benefits) are levelized using a levelization factor formula developed by the National Energy Technology Laboratory (NETL): Where: A=D*(1+D)^LP/(1+D)^LP 1 K=1+N/1+D LF=A*(1K^LP)/DN LF=Levelization Factor LP=Levelization Period D=Discount Rate N=Nominal Escalation Rate. 9.2 Generation Capital Cost Model The WECC Generation Capital Cost Model was developed for WECC by E3. E3 has been providing WECC with periodic updates to the Generation Capital Cost Mode since 2009. Included in these periodic updates are recommendations on resource cost and performance to the underlying data and assumptions to ensure continued currency and accuracy of these inputs to WECC s modeling processes. The generation technologies modeled in the Y20RC are listed in Table 9.2.1. Table 9.2.1 Y20RC Resource Technologies Technology Bio Combined Heat & Power (CHP) Subtype Biomass Biogas Landfill Biogas Other CHP (<5 MW) CHP (>5 MW)

2034 Reference Case Draft Results 48 Technology Coal Gas Geothermal Hydro Nuclear Solar Storage Wind Subtype Coal PC Coal IGCC with CCS Gas CT Aero Gas CT Frame Gas CCGT Conventional, Wet Cooled Gas CCGT Conventional, Dry Cooled Gas CCGT Advanced, Wet Cooled Gas CCGT Advanced, Dry Cooled Gas Reciprocating Engine Geothermal Geothermal EGS Hydro Small Hydro Large Nuclear Solar Thermal Parabolic Trough, 7.5hrs Storage Solar Thermal Solar Tower, 9hrs Storage Solar PV Residential Rooftop Solar PV Commercial Rooftop Solar PV Fixed Tilt (120 MW) Solar PV Tracking (120 MW) Solar PV Fixed Tilt (>20 MW) Solar PV Tracking (>20 MW) Storage 4hr LiIon Battery Storage 8hr LiIon Battery Storage 4hr Flow Battery Storage 8hr Flow Battery Storage CAES Storage Pumped Storage Wind Onshore (Interior) Wind Onshore (West) Wind Offshore The assumptions upon which the generation capital costs are based include: Presentday capital costs correspond to existing systems and/or plants. All resource costs are expressed in 2016 dollars. Capital costs represent allin plant costs and include all engineering, procurement, and construction (EPC); owner s costs; and interest during construction (IDC).

2034 Reference Case Draft Results 49 Fixed Operations and Maintenance (O&M) costs include labor and administrative overhead. Fixed O&M costs do not include property tax and insurance. All costs are intended to represent the average costs for new generation. Regional multipliers are used to estimate plant capital costs for each state Table 9.2.2 lists base resource financing parameters included in the Y20RC: Table 9.2.2 Base Resource Financing Parameters and Cost Data Inputs include: Financing Inputs IPP Units Value IOU Equity Share % 50% Debt Share % 50% Debt Cost % 6.0% Equity Return % 11.0% Financing Inputs IPP Units Value POU Debt Cost % 6.3% Financing Inputs IPP Units Value IPP AfterTax WACC % 8.25% Equity Floor % 20.0% Debt Cost % 7.0% Target DSCR 1.4 Debt Period (relative to PPA term) yrs 2 Tax Rates Units Value Federal % 35.00% State % 7.00% Combined % 39.55%

2034 Reference Case Draft Results 50 Fuel Cost Inputs Units Value Fuel Cost Bio $/MMBtu $2.22 Coal $/MMBtu $1.62 Gas $/MMBtu $7.09 Uranium $/MMBtu $0.60 Waste $/MMBtu $5.00 Wood $/MMBtu $2.34 Escalation Bio %/yr 2.0% Coal %/yr 2.0% Gas %/yr 2.0% Uranium %/yr 2.0% Waste %/yr 2.0% Wood %/yr 2.0% CO 2 Cost Inputs Units Value CO 2 Allowance Cost $/ton $0.00 Escalation %/yr 2.0% Levelized Cost Escalation Units Value Annual Cost Escalation %/yr 2.0% Property Tax and Insurance Inputs Units Value Property Tax %/yr 1.0% Insurance %/yr 0.5% Please note that while the resource financing parameters listed in Table 9.2.2 are not all inclusive, they do represent the financial basis upon which generator LCOE values are calculated. Table 9.2.3 lists other component inputs that are captured in the calculation of generator LCOE. Table 9.2.3 Component Inputs of Generator LCOE Performance Inputs Parameter Units Characteristics Installed Capacity MW Degradation %/yr Capacity Factor % Plant Cost Inputs Parameter Units Capital Costs US Avg Installed Cost $/kwac Regional Multiplier % Progress Multiplier %

2034 Reference Case Draft Results 51 Plant Cost Inputs Parameter Units Total Installed Cost $/kw Fixed O&M US Avg Unit Cost $/kwyr Regional Multiplier % Total Unit Cost $/kwyr Annual Escalation %/yr Variable O&M US Avg Unit Cost $/MWh Annual Escalation %/yr Fuel Costs Fuel Type Unit Fuel Cost $/MMBtu Annual Escalation %/yr Heat Rate Btu/kWh CO 2 Costs CO 2 Cost $/ton Unit Fuel Cost $/MMBtu Fuel CO 2 Content lb/mmbtu Property Tax & Insurance Property Tax % Insurance % Financing Selection Parameter Units Financing Choice IPP Inputs Financing Lifetime yrs Debt Term yrs AfterTax WACC % Equity Floor % Debt Cost % Target DSCR % Equity Share % Implied Equity Return % IOU Inputs Financing Lifetime yrs (Utility rate base) Equity Share % Debt Share % Debt Cost % Equity Return % POU Inputs Financing Lifetime (Utility rate base) Debt Cost Tax Assumptions Parameter Units Tax Eligibility ITC Credit % Capital Costs Eligible % PTC Unit Credit $/MWh Duration yrs MACRS Term yrs

2034 Reference Case Draft Results 52 Calculated Levelized Costs Parameter Units Levelized Cost of Energy Capital $/MWh (by component) Property Tax & Insurance $/MWh ITC $/MWh Fixed O&M $/MWh Variable O&M $/MWh Fuel $/MWh CO 2 $/MWh PTC $/MWh Total $/MWh Levelized Fixed Cost Capital $/kwyr (by component) Property Tax & Insurance $/kwyr ITC $/kwyr Fixed O&M $/kwyr PTC $/kwyr Total $/kwyr Project Finance Details Financing Type Equity Share % Debt Share % Equity Return % Debt Return % AfterTax WACC % Levelized Cost Escalation %/yr DSCR (>1.4 desired for IPP) Cost Multipliers Parameter Units Regional Multipliers US Average % Alberta % Arizona % British Columbia % California % CFE % Colorado % Idaho % Montana % New Mexico % Nevada % Oregon % Texas % Utah % Washington % Wyoming % Regional Capacity Factors US Average % Alberta % Arizona % British Columbia %

2034 Reference Case Draft Results 53 Cost Multipliers Parameter Units California % CFE % Colorado % Idaho % Montana % New Mexico % Nevada % Oregon % Texas % Utah % Washington % Wyoming % Capital Cost Multiplier By Study Horizon Year % Tax Credits Parameter Units Investment Tax Credit By Study Horizon Year % Production Tax Credit By Study Horizon Year $/MWh 9.3 Transmission Capital Cost Model The WECC Transmission Capital Cost Model was first developed for WECC by Black & Veatch in 2012. A subsequent update to the model was provided by Black & Veatch in 2014 which included an annual inflation multiplier based on the commodity prices of raw materials, engineering records of construction costs, and overall Consumer Price Index (CPI) data. Transmission costs are assigned to generation buses using power flow generation shift factors. As mentioned earlier, there are seven transmission technology types modeled within the Y20RC as listed in Table 9.3.1. Table 9.3.1 Transmission Technology Types Transmission Technology Voltage Type Capacity Rating (MW) 230 KV Single Circuit 230 KV AC 400 230 KV Double Circuit 230 KV AC 800 345 KV Single Circuit 345 KV AC 750 345 KV Double Circuit 345 KV AC 1500 500 KV Single Circuit 500 KV AC 1500 500 KV Double Circuit 500 KV AC 3000 500 KV HVDC BiPole 500 KV DC 3000 Further, both new and reconductored line costs are considered.

2034 Reference Case Draft Results 54 Transmission line costs assumptions include: Equipment: o Voltage Class o Conductor Type o Structure o Length Category o New or Reconductor? o Average Terrain Multiplier o Average Slope Multiplier Terrain o Desert/Barren Land o Desert/Barren Land / Wetland o Farmland o Farmland / Wetland o Forested o Forested / Wetland o N/A o N/A / Wetland o Open Water o Open Water / Wetland o Scrubbed/Flat o Scrubbed/Flat / Wetland o unknown o Urban o Urban / Wetland o Wetland Slope o Flat (<2) o Rolling Hills (28% Slope) o Mountain (>8% Slope) RightofWay (ROW) o BLM ROW Zones Allowance for Funds Used During Construction (AFUDC) Losses Substation costs assumptions include: Base Substation o Includes land, substation fence, control building, ground grid, etc. o Excludes breakers, transformers, etc. o Assumes flat, barren land with relatively easy site access Line/Transformer Positions o Includes switches, bus work, and circuit breakers Transformer o Includes foundation and oil containment

2034 Reference Case Draft Results 55 DC Converter Station o Includes DC Converter, harmonic filtering equipment, 500 kv DC and AC switchyard, reactive equipment, etc. o Excludes ground electrode Static VAR Compensator, Shunt Reactors and Series Capacitors o Turnkey installation costs 10. Environmental Risk Representation A geospatial data layer of environmental risk categories is utilized within the LTPT to optimize candidate transmission corridor paths such that risk of encountering environmental sensitivities is minimized. The WECC Environmental Data Work Group (EDWG) collects, manages and makes available environmental and cultural data in raw or aggregated form to provide value to utilities, developers, public agencies and other interested stakeholders, as well as WECC s transmission planning processes, throughout the Western Interconnection. The EDWG then applies this environmental data to describe various land types within the Western Interconnection according the likelihood that an entity pursuing an infrastructure development project might encounter environmentallyrelated risks. The geospatial environmental risk category layer developed by EDWG is illustrated in Figure 10.1.

2034 Reference Case Draft Results 56 Figure 10.1 Environmental Risk Category Geospatial Layer The Environmental Data Layer is a single GIS layer that identifies planninglevel risk to transmission development based on environmental sensitivities and constraints, as defined by four risk categories shown below in Table 10.1). Table 10.1 Environmental Risk Categories Environmental Risk Category 1 Description Least Risk of Environmental l Resource Sensitivities and Constraints Detail Areas with minimal identified environmental resource constraints and/or with existing land uses or designations that are compatible with or encourage transmission development. These areas would present few or minimal environmental and cultural mitigation requirements and are least likely to result in project delays. 2 Low to Moderate Areas where development would likely be allowed, but may

2034 Reference Case Draft Results 57 Environmental Risk Category 3 4 Description Risk of Environmental Resource Sensitivities and Constraints Moderate to High Risk of Environmental Resource Sensitivities and Constraints Areas Where Development is Currently Precluded by Law or Regulation Detail encounter one or more environmental or cultural resource sensitivity areas or constraints. Development could require low to moderate permit complexity or mitigation costs. This category also includes areas in the Protected Areas Database of the United States (PADUS) dataset that have an unknown land use designation or degree of restriction to transmission development. Transmission development would likely be allowed, but is likely to encounter one or more environmental or cultural resource sensitivities or constraints that would increase permitting complexity and could result in project delays and environmental mitigation costs. Areas where transmission development is currently precluded by federal, state, or provincial law, policy, or regulation, as well as areas where development would represent a fatal flaw likely to preclude successful project completion (e.g., National Parks, wilderness areas, identified Native American Traditional Cultural Properties and sacred sites). This geospatial data layer forms the basis upon which candidate transmission corridor are optimally routed to minimize environmental impact as illustrated in Figure 10.2. As mentioned previously in this report, these optimized corridors are used as proxies to estimate transmission interface parameters and costs. Figure 10.2 Geospatially Optimized Transmission Corridor

2034 Reference Case Draft Results 58 11. Pool Constraints Pool constraints are used to place limits on production of a group of units as a function of various constraint types. Pool constraints may be defined geographically. The following pool constraints are currently defined: Water Availability (acre feet per year) Fuel Availability (MW) CO 2 Emission Limit (tons) Individual generating units may be assigned to a pool for which they will be subject to a constraint. These pool assignments are based on quantitative constraint metrics. Incremental water availability along with generating unit water consumption rates was provided to WECC by Sandia National Labs. From this, total water availability was derived geographically at the generation hub buses across the Western Interconnection, forming the basis of the water availability pool constraints. Similarly, renewable energy production potential was provided to WECC by the National Renewable Energy Laboratory (NREL), upon which the Fuel Availability Constraints are modeled. Finally, CO 2 emission constraints are calculated from the production of CO 2 in the Y10CC at each generation bus using the CO 2 emission rates defined within the WECC Generating Unit Capital Cost tool. CO 2 emission constraints in the Y20RC are based on the assumption that CO 2 emission levels in Y20 will not exceed the emission levels of Y10. During the course of generating unit optimization, once a pool constraint limit has been exhausted, generating units from that pool are no longer candidates for optimization. Water availability data was provided to WECC by Sandia National Labs, including water consumption rates by generating unit technology type. Water availability pool constraints are geographically defined. Fuel availability data is defined for renewable resources representing the generation potential for each renewable technology type. Generation potential was derived from data provided to WECC by NREL. 12. Study Methodology As mentioned earlier, scenario development is a key component of The Long Term Study Program (LTSP). The LTSP studies are comprised of study requests and scenarios. In both cases, narratives are developed to describe the studies upon which a study case is developed and studied as illustrated in Figure 12.1

2034 Reference Case Draft Results 59 Figure 12.1 WECC LTSP Study Narrative Process Workflow Transforming the study narratives into data models is a complex exercise involving the creation modeling proxies and metrics from a wide array of data inputs as illustrated in Figure 12.2.

2034 Reference Case Draft Results 60 Figure 12.2 WECC LTSP Modeling Process Workflow There are many different generation optimization goals that must be captured ranging from policy driven goals to reliability goals. Another important characteristic of the generation goals is their interdependence with one another. That is to say, satisfying the requirement for any given goal will also count toward satisfying the requirements of other goals. For instance, an RPS carveout for a given state RPS requirement will also count toward that state s general RPS requirement which, in turn, will count toward the system adequacy requirement. A waterfall optimization is therefore used to capture this variety of generation goals and their interdependencies with one another with more restricted goals being optimized first (e.g., mustrun, RPS carveout and tiers), followed by dependent goals (e.g., system adequacy). Further, only a subset of resources may qualify toward satisfying a given. As such, each generation goal has associated with it a filtering rule which parses the subset of resources that qualify to meet the requirements of a goal (e.g., only instate solar resources will qualify toward a state s instate solar carveout). A resource supply stack is then built based on this qualified subset of resources from which resources are selected to satisfy the goal, as illustrated in Figure 12.3. Once a resource is selected for a given goal, it is no longer available for selection by subsequent goals in the optimization goals waterfall.

2034 Reference Case Draft Results 61 Figure 12.3 WECC LTSP Development of a Generation Supply Stack The final step in the study process is to review the results with stakeholders and to identify and answer the questions that are of interest to stakeholders and to refine the models where appropriate for followup studies.

2034 Reference Case Draft Results 62 Figure 12.4 WECC LTSP Analytics

2034 Reference Case Draft Results 1 Appendix A Glossary of Terms Acronym Term Definition ACF Annual Capacity Factors The net Annual capacity factor of a power plant is the ratio of ratio of its actual MWh output over the year, to its potential output if it were possible for it to operate at full nameplate capacity. AFUDC Allowance for Funds Used During Construction AFUDC is an accounting practice whereby the costs of debt and equity funds used to finance plant construction are credited on the statement of income and charged to construction in progress on the balance sheet. AFY AcreFeet Per Year AcreFeet is unit of volume used to measure a volume of water on a large scale. AcreFeet Per Year is a unit of measure often used to measure the annual availability or consumption of water on a large scale. BA Balancing Authority The responsible entity that integrates resource plans ahead of time, maintains loadinterchangegeneration balance within a Balancing Authority Area, and supports Interconnection frequency in real time. There are 38 BAs within the Western Interconnection. BES Bulk Electric System All Transmission Elements operated at 100 kv or higher and Real Power and Reactive Power resources connected at 100 kv or higher. This does not include facilities used in the local distribution of electric energy. BLM Bureau of Land Management The United States Bureau of Land Management (BLM) is an agency within the United States Department of the Interior that administers more than 247.3 million acres (1,001,000 km 2 ) of public lands in the United States which constitutes oneeighth of the landmass of the country. CF Capacity Factor Capacity Factor or Annual Capacity Factor is the ratio of the maximum capacity of a generating resource that represents the average annual energy production capacity of the resource. The capacity factor is obtained from the

2034 Reference Case Draft Results 2 Acronym Term Definition annual energy production as: CF = [Annual Energy Production (MWh)] / [8760 (hr/year)] / [Maximum Capacity (MW)] Conversely, knowing the capacity factor, one can estimate the annual energy production as: [Annual Energy Production (MWh)] = CF * [Maximum Capacity (MW)] * [8760 (hr/year)] CAGR Compound Annual Growth Rate The compound annual growth rate (CAGR) is a useful measure of growth over multiple time periods. It can be thought of as the growth rate that gets you from the initial investment value to the ending investment value if you assume that the investment has been compounding over the time period. CC Common Case WECCs Year 10 Production Cost Model Case that represents the most likely mix of loads, resources and transmission topology 10 years into the future relative to the specified reference year. CCTA Common Case Transmission Assumptions Transmission projects currently under development that, according to criteria agreed to by Regional Planning Groups, are likely to be completed within the next 10 years. Projects included in the CCTA are included in the transmission topology used in the Y10CC. CRF Capital Recovery Factor A capital recovery factor is the ratio of a constant annuity to the present value of receiving that annuity for a given length of time. CT Combustion Turbine A gas turbine, also called a combustion turbine, is a type of internal combustion engine. It has an upstream rotating compressor coupled to a downstream turbine, and a combustion chamber in between. Combustion turbines are used to electric power generators. Corridor A corridor is a geographic path upon which a transmission line exists or may be built. A corridor has associated with it a width that is dependent on the

2034 Reference Case Draft Results 3 Acronym Term Definition number of transmission lines in the corridor and the technology type of those lines (e.g., voltage levels and capacities). CXM Capital Expansion Model A study model that identifies potential needs for transmission infrastructure based on model inputs. CXMs typically use a levelized cost of energy (LCOE) cost function. D Discount Rate The interest rate used in discounted cash flow (DCF) analysis to determine the present value of future cash flows. Delaunay Triangulation Delaunay Triangulation Delaunay Triangulation, in the context of transmission line candidate creation, is a technique for creating a mesh of contiguous, nonoverlapping triangles from a dataset of points such that an optimal triangulation of a set of data points is created where every data point in the set of data points is interconnected by at least three line alternatives (triangulated) while minimizing the total line mileage. DER Distributed Energy Resources Distributed energy resources (DER) are smaller power sources, typically connected to the electrical grid a lower (distribution) voltages, that can be aggregated to provide power necessary to meet regular demand. DG Distributed Generation Power that is generated at the point of consumption DOE Department of Energy The United States Department of Energy (DOE) is a Cabinetlevel department of the United States Government created to ensure America s security and prosperity by addressing its energy, environmental and nuclear challenges through transformative science and technology solutions. DR Demand Response The ability for an electricity consumer to change its consumption based on instructions or incentives from its electricity service provider. DSIRE Database of State Incentives for Renewables & Efficiency DSIRE is the most comprehensive source of information on incentives and policies that support renewable energy and energy efficiency in the United States. Established in 1995, DSIRE is operated by the N.C. Clean Energy

2034 Reference Case Draft Results 4 Acronym Term Definition (http//www.dsireusa.org/) Technology Center at N.C. State University and is funded by the U.S. Department of Energy. Follow the navigation above to read about the history of DSIRE, the partners on the project, and the research staff that maintains the policy and incentive data in DSIRE. DSM DemandSide Management Energy demand management, also known as demandside management (DSM) or demandside response (DSR), [1] is the modification of consumer demand for energy through various methods such as financial incentives [2] and behavioral change through education E3 Energy and Environmental Economics Consultant to WECC providing energy industry related services and products. EDWG Environmental Data Work Group A work group created by WECC s Transmission Expansion Planning Policy Committee (TEPPC) to develop recommendations on the type, quality, and sources of data on land, wildlife, cultural, historical, archaeological, and water resources, exploring ways to transform that data into a form usable in WECC s study cases, 10year and longterm study models. EHV Extra High Voltage A bulk electrical power delivery system at a voltage level of typically 230 kv and above. ELCC Effective Load Carrying Capability The ratio of a generating unit s maximum nameplate capacity (MW) that, when multiplied by the maximum nameplate capacity, yields the net dependable generating unit capacity (MW) that can reliably serve load demand for a given period of time. FERC Federal Energy Regulatory Commission The Federal Energy Regulatory Commission (FERC) is a United States government agency, established in 1977 to oversee the country's interstate transmission and pricing of a variety of energy resources, including electricity, natural gas and oil. Flex Flex Generation A generation resource that is able to respond to and compensate for variable generation due to fluctuation is resources where generation is subject to

2034 Reference Case Draft Results 5 Acronym Term Definition external conditions, such as that of wind and solar renewable generation. Gap Resources Generation Optimization Goal Resources that are yet to be determined or specified that are required to meet a specific expectation or directive, usually policy based. For example, gap resources added to a specific region that has a policy directive to selfsupply generation from unspecified resources. A generation optimization goals must be met as part of the solution process. Generation optimization goals have associated with them a goal target that may be defined in terms of load energy, load demand, or flex target based on energy production from a dependent generation resource type requiring flex generation. Generation goals are optimized to meet their goal targets while minimizing LCOE. Generation optimization goals are cooptimized with transmission expansions to minimize LCOE cost. This is accomplished by formulating transmission expansion costs as a component of generating unit LCOE using generation shift factors to proportion transmission expansion costs to the generation buses. GSF Generation Shift Factor A Generation Shift Factor is a ratio of incremental generation at a power flow generation bus node that flows on a specific transmission line in the power flow model. For example, if generation at a power flow bus increased incrementally by 100 MW and the power flow on a given line in the power flow model subsequently increases by 75 MW, then the GSF of that generation bus relative to the line would be.75 or 75%. GIS Geographic Information System A geographic information system or geographical information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of spatial or geographical data. Grid Grid Costs Bulk Electrical Generation Production and Transmission Production Delivery System. The interconnected electricity system used to generate, transmit and distribute electricity The capital investment costs associated expansion reinforcements to the transmission grid. The costs of capital investments in the transmission grid

2034 Reference Case Draft Results 6 Acronym Term Definition system, that may be needed, are captured in the form of Grid Costs. Grid costs are calculated as reinforcements that may be needed to reinforce a transmission path interface which are levelized as a component of LCOE that is applied to the generation cost function in proportion to the incremental power transfer distribution factors between a generator and a transmission path requiring reinforcement. IOU InvestorOwned Utility An investorowned utility or IOU is a business organization, providing a product or service regarded as a utility (often termed a public utility regardless of ownership), and owned as private enterprise rather than a function of government or a utility cooperative. Itron Itron A technology and service provider company whose load forecasting tool is used by WECC. kv KiloVolts 1000 volts. LAR Loads and Resources Loads and Resources (LAR) data is collected annually under the direction of the North American Electric Reliability Corporation (NERC) and encompasses data, models, methods, and assumptions used by WECC to perform a Long Term Reliability Assessment of the Western Interconnection. LCOE Levelized Cost Of Energy The levelized cost of electricity (LCOE) is the net present value of the unitcost of electricity over the lifetime of a generating asset. LCOE considers capital, operating, maintenance, fuel and other costs incurred in generating electricity. LF Levelization Factor The term "levelized" arises from the recognition that the levelized cost of energy (LCOE) calculations establish a single present value of overall cost that can be transformed into a series of uniform (level) annual values through the use of so-called "levelization factors." By common practice in LCOE calculations, the levelization factors are termed differently when applied to different cost elements (e.g., fixed O&M versus variable O&M versus investment capital, etc.)

2034 Reference Case Draft Results 7 Acronym Term Definition LP Levelization Period The economic book life period over which levelized cost of energy (LCOE) is calculated. LSE Load Serving Entities Load Serving Entity (LSE) is the entity responsible for providing electricity to customer, often a utility, or an electric company. LTSP Long Term Study Program Long Term Study Program at WECC, through stakeholder engagement and scenario development, attempts to identify and better understand the potential energy futures of the Western Interconnection that may occur under various possible conditions as defined and framed by scenario narratives. These scenario narratives are crafted to identify and understand drivers that may influence how the energy future of the Western Interconnection may evolve. From these narratives, Y20 data sets are constructed to study potential long term energy futures of the Western Interconnection and to assess long term strengths and weaknesses of the Western Interconnection and identify where reinforcements may be needed from a capital expansion perspective. N Nominal Escalation Rate Used in the calculation of levelized cost of energy (LCOE) representing the nominal escalation rate (growth rate) associated with a component cost of LCOE. NERC North American Electric Reliability Corporation The North American Electric Reliability Corporation (NERC) is a notforprofit international regulatory authority whose mission is to assure the reliability and security of the bulk power system in North America. NERC develops and enforces Reliability Standards; annually assesses seasonal and longterm reliability; monitors the bulk power system through system awareness; and educates, trains, and certifies industry personnel. NERC s area of responsibility spans the continental United States, Canada, and the northern portion of Baja California, Mexico. NERC is the electric reliability organization for North America, subject to oversight by the Federal Energy Regulatory Commission and governmental authorities in Canada. NERC s jurisdiction includes users, owners, and operators of the bulk power system, which

2034 Reference Case Draft Results 8 Acronym Term Definition serves more than 334 million people. NgMkt Hubs Natural Gas Market Trading Hubs Market hubs in which Natural gas is priced and traded. NREL National Renewable Energy Laboratory NREL is a national laboratory of the U.S. Department of Energy that focuses on current energy challenges from breakthroughs in fundamental science to new clean technologies to integrated energy systems. O&M Operations and Maintenance The costs of a generating unit associated with operating and maintaining the unit, for example, fuel and maintenance costs. OTC Once Through Cooling A power generation facility that cools its heat source by passing water through the unit once and then discharging the heated water. PCM Production Cost Model A modeling tool that analyzes congestion on the bulk power system based on the generators economic dispatch costs. PEC Portfolio Energy Credit Synonymous with REC, applies in Nevada PRC Path Rating Catalog WECCs Catalog that defines the designed MW flows of each WECC Path PSA Power Supply Assessment The Power Supply Assessment (PSA) produced annual by WECC is an evaluation of generation resource reserve margins for the summer and winter peak hours over a 10 year forecast horizon. Loads and Resources (LAR) data modeled within the PSA is obtained from that submitted by the individual WECC Balancing Authorities (BA). PTDF Power Transfer Distribution Factors Power Transfer Distribution Factors (PTDF) indicate the incremental change in real power that occurs on transmission lines due to real power transfers between two regions. PV Photovoltaic Solar photovoltaics (PV) generators convert light into electricity using semiconducting materials that exhibit the photovoltaic effect, a phenomenon studied in physics, photochemistry,

2034 Reference Case Draft Results 9 Acronym Term Definition and electrochemistry. RC Reference Case WECCs Year 20 Capital Expansion Model Case REC Renewable Energy Certificate A tradable, nontangible energy commodity that represents proof that 1 megawatthour (equivalently, 1,000 kilowatthours) of electricity was generated from an eligible renewable energy resource. This term is interchangeable with Renewable Energy Certificate, Renewable Energy Credit, Green Tag, Green Ticket, or Renewable Certificate. REC, Bundled Bundled REC A bundled power purchase agreement for both the RECs and energy associated with an eligible RPS facility. REC, Unbundled Unbundled REC A transaction for the REC only and not the associated electricity. Once the RECs are unbundled from the energy, the energy is considered null (nonrenewable) power and no green claims can be made for use of this null electricity. RES Renewable Energy Standard Also known as a renewable portfolio standard (RPS) is a regulatory mandate to produce a specified amount of energy from renewable sources such as wind, solar, biomass and other alternatives to fossil and nuclear electric generation. REST Renewable Energy Standard and Tariff Similar to RES, however, it applies to Arizona ROW Right of Way The legal right, established by usage or grant, to pass along a specific route through grounds or property belonging to another RPCG Regional Planning Coordination Group The RPCG is made up of a member from each TEPPCrecognized Regional Planning Group. The purpose of the group is to coordinate planning activities between and among the Regional Planning Groups and TEPPC. RPG Renewable Portfolio Goal Similar to an RPS, however, the law requires that utilities only need to pursue

2034 Reference Case Draft Results 10 Acronym Term Definition renewable energy to the extent that it is "costeffective" to do so. RPS Renewable Portfolio Standard. A renewable portfolio standard (RPS) is a regulatory mandate to increase production of energy from renewable sources such as wind, solar, biomass and other alternatives to fossil and nuclear electric generation. It's also known as a renewable electricity standard. SAP System Adequacy Planning WECC's System Adequacy Planning Department exists to identify potential reliability risks to the Bulk Electric System stemming from changes in load, resource and transmission topology in the next 10 to 20 years. SCDT Study Case Development Tool Part of the LTPT that creates technicallyfeasible transmission segments needed to meet load with available resources, based on input parameters. SCED Security Constrained Economic Dispatch In optimal power flow analysis, a security constrained economic dispatch is used to economically dispatch generation resources to serve load while alleviating security violations such as circuit overloads. SNL Sandia National Laboratories Sandia National Laboratories is a multiprogram laboratory managed and operated by Sandia Corporation. TAS Technical Advisory Subcommittee TAS advises TEPPC on the data, models and studies used in WECC s reliability assessments. TEPPC Transmission Expansion Planning Policy Committee Transmission Optimization Goals TEPPC has four main functions: 1) oversee and maintain public databases for transmission planning; 2) develop, implement, and coordinate planning processes and policy; 3) conduct transmission planning studies; and 4) prepare Interconnectionwide transmission plans. The goals of transmission optimization is to serve load while mitigating circuit overloads by optimally dispatching generation and minimizing transmission expansion costs. This accomplished by performing a security constrained economic dispatch (SCED) after a generation portfolio is optimally produced. Transmission expansions are added where the SCED is unable to alleviate security violations and the cost of the transmission

2034 Reference Case Draft Results 11 Acronym Term Definition expansion is assigned to the generating buses contributing to the violation in the form of a grid cost component of LCOE. TEPPC Study Program The set of studies, developed annually, intended to assess economic and operational analyses and modeling of the Western Interconnection through transmission utilization and planning studies. TREC Tradable Renewable Energy Credit Synonymous with REC, applies in Colorado WACC Weighted Average Cost of Capital Weighted average cost of capital (WACC) is the average rate of return a company expects to compensate all its different investors. WGA Western Governors Association The Western Governors' Association represents the Governors of 19 Western states and 3 U.S.flag islands. The association is an instrument of the Governors for bipartisan policy development, information exchange and collective action on issues of critical importance to the Western United States. WIEB Western Interstate Energy Board The Western Interstate Energy Board is an organization of 11 western states and three western Canadian provinces, which are associate members of the Board. The Board seeks to promote energy policy that is developed cooperatively among member states and provinces and with the federal government. WLFT WECC Load Forecasting Tool The WECC Load Forecasting Tool (WLFT) is a tool licensed by WECC from Itron, Inc. for use in producing long term load forecasts. The WLFT is a complex tool that captures numerous factors to produce a load forecast including historical load, historical weather, weather response functions, economic data and forecasts, enduse saturation and efficiency, and peak modeling, etc. WREZ Western Renewable Energy Zones Areas throughout the Western Interconnection that feature the potential for large scale development of renewable resources in areas with low environmental impacts. WREZ was a joint initiative by the Western

2034 Reference Case Draft Results 12 Acronym Term Definition Governors Association and the U.S. Department of Energy. Y10 Year10 Year10 study horizon. The focus of Year10 studies is on system utilization given a set of assumptions about installed generation and transmission. Y10CC Year10 Common Case The production cost model study case dataset used as a baseline from which all Year10 studies are performed (e.g., individual study case datasets are built from where modeling parameter adjustments are made based on the narratives of the studies.). Y20 Year20 Year20 study horizon. The focus of the Year20 studies is on new generation portfolio mixes and transmission infrastructure needs given a set of assumptions about the drivers that may influence the future of the energy grid. Y20RC Year20 Reference Case The capital expansion model study case dataset used as a baseline from which all Year20 studies are performed (e.g., individual study case datasets are built from where modeling parameter adjustments are made based on the narratives of the studies.)

2034 Reference Case Draft Results 1 Appendix B WIEB 2024 StateProvince Load Distributions State/Province % BA within state/prov. BA Load, NEL (GWh) Load, Sales (GWh) Transmission Loss & Rate, Default = Balancing Area 2024 6% Alberta AESO Alberta Electric System Operator 100.00% 113,234 106,440 AB Total 113,234 106,440 6,794 Arizona APS Arizona Public Service (APS) 100.00% 37,184 34,953 SRP Salt River Project 100.00% 40,246 37,831 TEP Tucson Electric Power 100.00% 16,624 15,627 WALC WAPA Lower Colorado Region 82.15% 10,634 9,996 AZ Total 104,688 98,407 6,281 British Columbia BCHA British Columbia Hydro 100.00% 68,154 64,065 BC Total 68,154 64,065 4,089

2034 Reference Case Draft Results 2 State/Province % BA within state/prov. BA Load, NEL (GWh) Load, Sales (GWh) Transmission Loss & Rate, Default = Balancing Area 2024 6% California CISO California ISO 100.00% PG&E_BAY Pacific Gas & Electric Bay 100.00% 52,214 48,559 7% PG&E_VLY Pacific Gas & Electric Valley 100.00% 65,452 60,871 7% SCE Southern California Edison 100.00% 114,706 106,676 7% SDGE San Diego Gas & Electric 100.00% 25,819 24,012 7% IID Imperial Irrigation District 100.00% 4,646 4,321 7% LDWP SMUD Los Angeles Dept. of Water and Power 100.00% 33,317 30,985 7% Sacramento Municipal Utility District 100.00% 18,492 17,197 7% TIDC Turlock Irrigation District 100.00% 3,078 2,863 7% PACW PacifiCorp West 4.99% 1,113 1,035 7% BPA Bonneville Power Administration 0.00% 0 0 7% CA Total 318,837 296,518 22,319 Colorado PSC Public Service Colorado 100.00% 40,016 37,615 WACM WAPA Colorado Missouri Region 66.00% 22,061 20,737 CO Total 62,077 58,352 3,725 Idaho IPC Idaho Power Co. 92.60% 17,290 16,252 AVA Avista Corp. 38.41% 5,619 5,282 PACE_ID PacifiCorpEastIdaho 100.00% 4,299 4,041 BPA Bonneville Power Administration 0.61% 389 366 ID Total 27,596 25,941 1,656 Montana NWMT NorthWestern Energy 100.00% 11,923 11,208 WAUW WAPA Upper Great Plains West 100.00% 750 705 BPA Bonneville Power Administration 4.52% 2,878.40 2,706 MT Total 15,551 14,618 933

2034 Reference Case Draft Results 3 State/Province % BA within state/prov. BA Load, NEL (GWh) Load, Sales (GWh) Transmission Loss & Rate, Default = Balancing Area 2024 6% Nevada NEVP Nevada Power Company 100.00% 28,852 27,121 New Mexico SPP Sierra Pacific Power 100.00% 15,426 14,500 PNM NV Total 44,278 41,621 2,657 Public Service Company New Mexico 100.00% 16,076 15,112 EPE El Paso Electric Company 20.46% 2,207 2,074 WALC WAPA Lower Colorado Region 17.85% 2,311 2,172 NM Total 20,594 19,358 1,236 Oregon PGE Portland General Electric 100.00% 25,014 23,513 PACW PacifiCorp West 72.50% 16,165 15,195 BPA Bonneville Power Administration 32.47% 20,697 19,455 IPC Idaho Power Co. 7.40% 1,382 1,299 OR Total 63,258 59,463 3,795 Texas EPE El Paso Electric Company 79.54% 8,579 8,065 TX Total (in Western Interconnection) 8,579 8,065 515 Utah PACE_UT PacifiCorpEastUtah 100.00% 31,396 29,512 UT Total 31,396 29,512 1,884 Washington BPA Bonneville Power Administration 62.41% 39,788 37,400 AVA Avista Corp. 61.59% 9,010 8,469 PACW PacifiCorp West 22.51% 5,019 4,718 PSE Puget Sound Energy 100.00% 27,072 25,448 SCL Seattle Department of Lighting 100.00% 10,636 9,998 TPWR Tacoma Power 100.00% 5,566 5,232 CHPD PUD No. 1 of Chelan County 100.00% 4,22 3,973 DOPD PUD No. 1 of Douglas County 100.00% 1,892 1,778 GCPD PUD No. 2 of Grant County 100.00% 5,036 4,734 WA Total 108,245 101,750 6,495

2034 Reference Case Draft Results 4 State/Province % BA within state/prov. BA Load, NEL (GWh) Load, Sales (GWh) Transmission Loss & Rate, Default = Balancing Area 2024 6% Wyoming PACE_WY PacifiCorpEastWyoming 100.00% 10,914 10,259 WACM WAPA Colorado Missouri Region 34.00% 11,365 10,683 WY Total 22,279 20,942 1,337 MEX CFE Commission Federal de Electricidad 100.00% 14,985 14,086 Mexico Total 14,985 14,086 899 WI TOTAL 1,023,751 959,137

2034 Reference Case Draft Results 1 Appendix C Transmission Path Parameters The Levelized Cost of Energy component of grid cost (LCOEGC) is provided for each of the transmission interface paths modeled in the Y20RC. The LCOEGCs were calculated for each interface path by associating an optimized corridor with the interface path as a proxy for calculating LCOEGCs and line parameters. Further, depending on the transmission interface expansion capacity needs, an optimal transmission configuration was chosen. The transmission corridors themselves were geospatially optimized to follow a path that minimized environmental impact while capturing other geospatial considerations including the effects of terrain difficulty (e.g., slope), land cover, and RightofWay (ROW) costs. In addition, transmission parameters and LCOEs of seven different transmission technology types were calculated for each corridor and optimally selected for each corridor interface path as a function of expansion MW capacity while minimizing cost. The charts provided for each transmission path interface consist of a chart of LCOEGC by transmission technology type and a chart of LCOE and optimal transmission technology type by expansion MW capacity. Table App.C.1 Transmission Path Limits From>To Limit (MW) To>From Limit (MW) Name Summer Winter Summer Winter BCAB 800 800 0 0 ABMT 0 0 250 250 BCPCNW 2000 500 2000 1950 MTPCNW 2000 2000 500 500 MTWY 400 400 200 400 MTID 325 250 200 250 PCNWID 500 600 1800 2100 PCNWCANO 4200 4800 3675 3675 PCNWNVNO 300 300 300 300 IDNVNO 350 350 185 185 IDWY 0 0 2200 2200 IDUT 680 680 775 785 WYCO 1400 1400 1400 1450 WYUT 400 400 400 400 CANONVNO 100 100 100 100 UTNVNO 360 360 235 235 NVNONVSO 800 800 800 800 UTCO 650 650 650 650 UTNVSO 140 260 250 265 AZUT 250 250 250 250 CONM 614 614 664 664 AZNVSO 4727 4634 4785 4785 NMAZ 5660 5660 5660 5660

2034 Reference Case Draft Results 2 From>To Limit (MW) To>From Limit (MW) Name Summer Winter Summer Winter PCNWCASO 2600 2900 2858 2858 CANOCASO 4000 4000 3000 3000 CASOMX 408 408 0 800 NVSOCASO 4000 6637 6697 6697 AZCASO 3595 4379 2876 2876 UTNM 530 530 600 600

2034 Reference Case Draft Results 3 Figure App.C.1 Transmission Limits

2034 Reference Case Draft Results 4 Grid Component of LCOE and Optimal Transmission Expansion

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