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WECC 2038 Scenarios Reliability Assessment DraftMichael Bailey, PE

2/3/2020

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1. PrefaceThis report presents the results and analysis of potential risks to the reliability of the Western Interconnection (WI) associated with potential futures as described by WECC 2038 Scenarios developed by the WECC Scenario Development Subcommittee (SDS) and, as the name suggests, focuses on a 20-year study horizon.

The creation of the WECC 2038 Scenarios was a collaborative effort between WECC and its stakeholders to imagine plausible energy futures and the drivers that shape them. The crafting of the scenarios began with a focus question developed by WECC and its stakeholder. Namely:

How might customer demand for electric services in the Western Interconnection evolve as new technologies and policies create more market options, and with that, what risks and opportunities may emerge for the power industry in sustaining electric reliability?

Scenarios cannot capture all aspects of the complexity of interrelationships and interdependencies of the real world. However, scenarios are a powerful tool for sensitizing decision makers to emergent key factors which can influence the outcome of their decisions. When used as a tool for guiding long-term capital investment decisions, scenarios can help managers to more effectively assess both the timing and scale of investments. This is the context upon which the WECC 2038 Scenarios Reliability Assessment was performed.

2. AcknowledgementsWECC would like to acknowledge and thank the participants on the WECC Scenario Task Force (WSTF), listed in Table 1, whose efforts were instrumental in crafting the scenarios and providing guidance in the assessment process. Special thanks to Amy Mignella, Bryce Freeman, Thomas Carr, Carl Zichella, Gerald Harris, Peter Mackin, and Richard Marrs for their leadership, contributions, and long-term commitment to the Scenario Development effort at WECC.

Table 1: WECC Scenarios Task Force (WSTF) Participants

Member OrganizationFrank Afranji Northwest Power PoolRavi Aggarwal Bonneville Power AssociationJamie Austin PacifiCorpThomas Carr Western Interstate Energy BoardTyler Cooper Black Hills CorporationTaylor Cramer Mitsubishi Electric Corporation

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Member OrganizationBryce Freeman Wyoming Office of Consumer AdvocateTessa Haagenson City of BurbankGerald Harris The Quantum Planning GroupRobyn Kara PacifiCorpYara Khalaf Puget Sound EnergyHarris Lee SRPPeter Mackin GridBright, Inc.Richard Marrs The Quantum Planning GroupAmy Mignella Amy T. Mignella, Esq.Gayle Nansel Western Area Power AdministrationMichael Reynolds SRPApril Spacek Avista CorporationLei Xiong Alberta Electric System OperatorXiaofei (Sophie) Xu Pacific Gas and Electric CompanyJanice Zewe Sacramento Municipal Utility DistrictWenjuan (Wendy) Zhang Pacific Gas and Electric CompanyCarl Zichella Natural Resources Defense CouncilJulia Prochnik Natural Resources Defense CouncilKate Maracas Western Grid Group

3. List of Acronyms and AbbreviationsAcronyms and abbreviations used in this report are listed in Table 2.

Table 2: List of Acronyms and Abbreviations

Acronym Definition2028 ADS P2v2.0

2028 Anchor Data Set (Phase 2 version 2.0)

ADS WECC Anchor Data Set

BPS Bulk Power System

BTM Behind the meter

CAGR Compound Annual Growth Rate

DER Distributed Energy Resources are energy producing resources located on the distribution system such as solar PV and battery storage.

DER-EV Distributed Energy Resources represented by high Electric

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Acronym DefinitionVehicle (EV) penetration

EFS The NREL Electrification Futures Study

LMP Locational Marginal Price ($/MWh)

NEV No dispatchable DER-EV enabled

NREL National Renewable Energy Laboratory

NTC No Transmission Path Constraints enforced

PCM Production Cost Model

TOU Time-of-Use (related to diurnal energy usage)

WEV With dispatchable DER-EV enabled

WI Western Interconnection

WSTF WECC Scenario Task Force

WTC Transmission Path Constraints enforced

4. Executive SummaryState the assessment’s purpose, scope, methods, findings, conclusions and recommendations, and present a summary of how results were obtained for the reasons for the recommendations.

Write the executive summary so it can be read and understood independently of the main body of the white paper. Use footnotes, figures, and tables sparingly and only if the information is essential to the summary. Do not refer to figures, tables, or references contained elsewhere in the document.

Though it appears at the beginning of the white paper, because abstract is a condensed version of the rest of the document, it should be the last thing the author writes.

Revisit after done with meat of report.

Motivation

Key Findings

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Context

20 year horizon

Limitations

What we were able to study.

What we weren’t able to study.

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5. Table of Contents1. Preface..............................................................................................................22. Acknowledgements...........................................................................................2

WECC Scenario Task Force..............................................................................................2Study Program Collaboration...........................................................................................2National Lab Partnerships................................................................................................2

3. List of Acronyms and Abbreviations.................................................................24. Executive Summary..........................................................................................2

Motivation......................................................................................................................... 3Key Findings..................................................................................................................... 3

5. Table of Contents..............................................................................................46. Introduction......................................................................................................77. Scenario Design................................................................................................7

Scenario Development Process........................................................................................7Focus Question.................................................................................................................8Scenario Matrix................................................................................................................8Event/Pattern/Structure...................................................................................................9

8. Assessment Approach.....................................................................................10Tools, Models, Methods, Data........................................................................................10Load 10Generation Resources.....................................................................................................10Transmission...................................................................................................................11Economics....................................................................................................................... 11Key Drivers.....................................................................................................................11Customer........................................................................................................................ 11Policy.............................................................................................................................. 12Market............................................................................................................................ 12Reporting Metrics...........................................................................................................12Limitations......................................................................................................................12

Results & Observations.........................................................................................12Focus Question...............................................................................................................12

Customer..................................................................................................................... 13

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Services.......................................................................................................................13Technologies...............................................................................................................13Policies........................................................................................................................ 13Market-options............................................................................................................13Industry....................................................................................................................... 13Reliability.................................................................................................................... 13

2038 Reference Case (RC)..............................................................................................13RC Modeling Components...........................................................................................13RC Unserved Load.......................................................................................................14RC System Energy Production....................................................................................17RC Inter-Regional Observations..................................................................................19RC Seasonal Variations...............................................................................................23RC Key Takeaways......................................................................................................25

Scenarios 1..................................................................................................................... 25SC1: Key Questions.....................................................................................................25SC1: Early Indicators and Metrics..............................................................................25Notable Observations..................................................................................................26

Scenario 2.......................................................................................................................26Scenario 3.......................................................................................................................27Scenario 4.......................................................................................................................27Cross Comparison of Scenarios......................................................................................27Economics....................................................................................................................... 27Sensitivities....................................................................................................................27

9. Conclusions.....................................................................................................2810. Recommendations..........................................................................................28Appendix A - References.......................................................................................29Appendix B – Assumptions, Tools, Models, Methods, & Data...............................29

Tools, Models, Methods, Data........................................................................................29Load Models...................................................................................................................30Generation Resource Models..........................................................................................30Transmission Models......................................................................................................34Key Scenario Drivers......................................................................................................34

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6. IntroductionThis assessment investigates and analyzes potential risks to the reliability of the Western Interconnection (WI) associated with each of four potential futures developed by the WECC Scenario Development Subcommittee (SDS). WECC and its stakeholders developed the four future scenarios for the Western Interconnection based on a “focus question” developed during a scenario development workshop held March 27-28, 2018 at WECC’s headquarters offices in Salt Lake City, Utah. The focus question for the WECC 2038 Scenarios is:

How might customer demand for electric services in the Western Interconnection evolve as new technologies and policies create more market options, and with that, what risks and opportunities may emerge for the power industry in sustaining electric reliability?

This report is organized with the following sections:

Section 7. Scenario Design: describes the process upon which the WECC 2038 Scenarios were crafted and the key components that need to be studied.

Section 8. Assessment Approach: describes the process upon which the WECC 2038 Scenarios were scoped and studied.

Section 9. Results & Observations: presents the results and reporting metrics of the analysis.

Section 10. Conclusions: a compilation of key findings from the analysis.

Section 11. Recommendations: a compilation of recommendations and follow-up steps.

Section Appendixes: Information provided in more detail than what is presented in the body of the report.

More to added as needed after analysis part of report is complete.

7. Scenario Design

Scenario Development ProcessScenario-based planning is a technique for managing uncertainty in decision making. It is especially useful when long-term investments must be evaluated despite the human inability to accurately predict the distant future. Scenarios offer a tool for imagining plausible and well-researched futures thereby enabling planning across a wider range of potential futures. When used well, this approach can spur learning and help in identifying emerging risks and opportunities.

Scenarios cannot capture all aspects of the complexity of interrelationships and interdependencies of the real world. However, scenarios are a powerful tool for sensitizing decision makers to emergent key factors which can influence the outcome of

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their decisions. When used as a tool for guiding long-term capital investment decisions, scenarios can help managers to more effectively assess both the timing and scale of investments.

The WECC 2018-2038 Scenarios emerged from a scenario development workshop held March 27-28, 2018 at WECC’s headquarters offices in Salt Lake City, Utah. The purpose of that workshop was to gather ideas, facts, and suggestions for new long-term scenarios from a diverse group of SDS members and stakeholders. The meeting was attended by 25 people from a wide range of WECC member organizations and WECC staff members, and was facilitated by a team of consultants hired by WECC from the Quantum Planning Group, Inc.

This report uses the WECC Scenarios as qualitative input into a quantitative modeling process. The resulting scenario narratives were used to guide the selection of key quantitative inputs (for example the NREL Electrification data inputs) which approximate and meet the intent of expressed in the narrative. This creates a process that is useful for approximating impacts and showing the direction of effect key variable have on one another. The process is the essential aspect of using modeling and scenarios in tandem as a learning process. In this light, as underlying data and modeling capabilities improve over time, additional scenario-based analysis can provide longer term opportunities for learning, and, allowing WECC to assess potential effects of the data for electric system reliability.

Focus QuestionScenario planning, as a tool for managing future uncertainty, enables stakeholders to create and test strategic responses given a diverse range of plausible future conditions. Good scenarios are based on a clear enunciation of the decisions and uncertainties at play: the “focus question” that ensures scenarios are developed with a clear sense of the issues at hand. Leading up to and during the scenario development workshop, the SDS agreed on the focus question detailed in Table 3 below.

Table 3: WECC 2038 Scenarios Focus Question

How might customer demand How do we define customers, in what segments or categories? With DER? Grid connected?

for electric services What services beyond commodity supply of electricity? What kinds of enhanced services?

in the Western Interconnection evolve as new technologies

Technology innovation that impacts distributed energy as well as utility scale supply and delivery systems.

and policies Policies at all levels.

create more market options, and with that,

Markets: regulated and unregulated.

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Options: Power supplies.

what risks and opportunities may emerge for the power industry

Who and what players will be in it?

in sustaining electric reliability? What risks to the reliability of the Bulk Power Systems in the Western Interconnection?What standards and requirements apply?

Scenario MatrixWhile scenario analysis does not allow accurate predictions of the future, it does provide a tool for rigorously imagining alternative plausible futures in which important decisions may play out. The most useful scenarios derive these imagined futures from a studied consideration of factors and trends (“key drivers”) that will most likely and powerfully influence future conditions. These key drivers are discussed in greater detail in Section 8. Assessment Approach Key Drivers under the heading Key Drivers and in Appendix B – Assumptions, Tools, Models, Methods, & Data under the heading Key Scenario Drivers of this report. From this list of drivers, a “scenario matrix” was created. A scenario-matrix is a tool for organizing and distinguishing ideas when creating scenarios. To create a matrix, the key drivers are first prioritized using the consensus or majority vote of the SDS to select the two drivers that are simultaneously the most uncertain and the most important. Additionally, the top two drivers should be independent of one another. These two drivers are then given a range of uncertainty, represented as an arrow with ends pointing in opposite directions to indicate polar extremes. Crossing these arrows represent two vectors (axes) and creates four quadrants that function as “scaffolding” for developing distinctive scenarios. This process was used during the SDS workshop to create the scenario matrix shown in Figure 1 below.

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Figure 1: WECC 2038 Scenarios Matrix

Event/Pattern/StructureThe relevance of the key drivers used to craft the narratives for each of the four scenarios described in the WECC 2038 Scenario Matrix (Figure 1) are tracked within the WECC Event/Pattern/Structure (EPS) tracking system. The EPS tracking system is a methodology used in WECC Scenario Development to track, organize, and analyze current events in a way that focuses on how the constituent parts of an event interrelate and how a collection of events interrelates in the broader context of the Western Interconnection as a whole and the WECC scenarios. EPS tracking system serves as a portal for WECC staff and stakeholders to submit and classify current events as they relate to the energy future of the Western Interconnection and the WECC scenarios. The constituent parts of the methodology are:

Event Level - What happened or what did you observe?

Pattern Level - What pattern or trend is indicated?

Structure Level - What might be driving this on a core or structure level?

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On a quarterly basis, these EPS submissions are compiled into a trends report which is posted on the WECC Scenario Planning Trends Reports portal. Together, the WECC EPS tracking system and trend reports serve to inform the scenarios development process and stakeholders of events that are shaping the energy future of the Western Interconnection.

8. Assessment ApproachTo be added later in combination with Appendix B – Assumptions, Tools, Models, Methods, & Data where this section will provide a high level overview and Appendix B will provide more detail.

Tools, Models, Methods, DataTo be added later in combination with Appendix B – Assumptions, Tools, Models, Methods, & Data where this section will provide a high level overview and Appendix B will provide more detail.

LoadTo be added later in combination with Appendix B – Assumptions, Tools, Models, Methods, & Data where this section will provide a high level overview and Appendix B will provide more detail.

Generation ResourcesThe generation resource model used in the 2038 scenarios was derived from the 2028 ADS PCM model and the NREL Mid-Case Standard Scenario. The construction of the generation resource portfolio used in the studies was essentially done by augmenting the 2028 ADS PCM resource model with additional resource types such that the result had the same resource mix as the NREL 2038 Mid-Case Standard Scenario resource portfolio. The resulting generation resource portfolio is representative of resource candidates that are available for commitment and dispatch by the PCM tool. From this point forward, this derived candidate portfolio is referred to as the 2038 Scenarios Candidate Resource Portfolio. The derivation of this portfolio is described in greater detail in Appendix B – Assumptions, Tools, Models, Methods, & Data under the heading Generation Resource Model. The 2038 Scenarios Candidate Resource Portfolio is a candidate resource portfolio baseline. With exception to the Reference Case, additional hourly resources representing flexible distributed energy resources from electrical vehicles (DER-EV), unique to each scenario, are added to the 2038 Scenarios Candidate Resource Portfolio.

TransmissionTo be added later in combination with Appendix B – Assumptions, Tools, Models, Methods, & Data where this section will provide a high level overview and Appendix B will provide more detail.

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EconomicsTo be added later in combination with Appendix B – Assumptions, Tools, Models, Methods, & Data where this section will provide a high level overview and Appendix B will provide more detail.

Key DriversAs discussed earlier, the most useful scenarios derive these imagined futures from a studied consideration of factors and trends (“key drivers”) that will most likely and powerfully influence future conditions. For the WECC Scenarios, the SDS agreed on the following initial set of key drivers shown below. 1. Changes in State and Provincial electric energy market policies 2. Changes in Federal electric energy market policies 3. Evolution of customer-side energy supply technology and service options 4. Changes in the character and shape of customer demand for electric power 5. Changes in utility-scale power supply options 6. Changes in State, Provincial, and Federal electric system regulations for reliability 7. Evolution of climate change and environmental issues on electric power service 8. Evolution of fuel markets in the electric power sector 9. Shifts in the cost of capital and financial markets 10. Economic growth within the Western Interconnection 11. Worldwide developments in the electric power industry

From this list of key drivers, the SDS derived thematic drives used to create the scenario matrix as discussed in Section 7. Scenario Design under the heading Scenario Matrix. The thematic drivers form the axis of the scenario matrix as shown in Figure 1. The thematic drivers are:

1. Direction of state and provincial energy policy.2. Customer adoption of energy service options.

It is important to note that while that while the thematic drivers from the axis of the scenario-matrix, the remaining key drivers identified are still used in the formulation of the scenario narratives and model construction. The modeling of these key drivers are discussed in greater detail in Appendix B – Assumptions, Tools, Models, Methods, & Data under the heading Key Scenario Drivers of this report.

CustomerTo be added later in combination with Appendix B – Assumptions, Tools, Models, Methods, & Data where this section will provide a high level overview and Appendix B will provide more detail.

PolicyTo be added later in combination with Appendix B – Assumptions, Tools, Models, Methods, & Data where this section will provide a high level overview and Appendix B will provide more detail.

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MarketTo be added later in combination with Appendix B – Assumptions, Tools, Models, Methods, & Data where this section will provide a high level overview and Appendix B will provide more detail.

Reporting MetricsTo be added later in combination with Appendix B – Assumptions, Tools, Models, Methods, & Data where this section will provide a high level overview and Appendix B will provide more detail.

Load Levels and Growth Rates Resource Mix Path Utilization Unserved Load CO2 Emissions LCOE LMP Capital Investment Cost

LimitationsTo be added later in combination with Appendix B – Assumptions, Tools, Models, Methods, & Data where this section will provide a high level overview and Appendix B will provide more detail.

9. Results & ObservationsThe Western Interconnection is undergoing transformation change and there is a great deal of uncertainty surrounding its energy future. Considering this uncertainty, the goal of Scenario Planning is not to predict the future, but to gain a better understanding of plausible futures and underlying drivers. A guiding principle behind the WECC 2038 Scenarios Assessments is to shine a light on how underlying drivers may influence the energy future of the Western Interconnection. The scenario assessments are not meant to be comprehensive, but rather a learning process that can be built upon. The trick to scenario planning is to ask the right questions. In this regard, there are questions that we know we can answer, there are questions that we know we can’t answer, and there are questions yet to be discovered. As such, an attempt is made to answer those questions we can answer, identify those questions that we aren’t able to answer, and suggest future work to uncover key questions yet to be posed.

2038 Reference Case (RC)A reference case for 2038 was created by extending the area load trajectories of the 2028 ADS PCM another ten years and is meant to be representative as a “business-as-usual”

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case in 2038. The 2038 Reference Case serves as a basis to compare scenarios in the analysis.

RC: Modeling ComponentsLoad Models: That extended from the 2028 ADS PCM using the CAGRs of the 2028 ADS PCM as described in Appendix B – Assumptions, Tools, Models, Methods, & Data under the heading Load Models.

Generation Resource Portfolio: 2038 Scenarios Candidate Resource Portfolio as described in Appendix B – Assumptions, Tools, Models, Methods, & Data under the heading of Generation Resource Models with no DER-EV additions.

Transmission Topology: The transmission topology is that contained within the 2028 ADS PCM with interface paths monitored as described in Appendix B – Assumptions, Tools, Models, Methods, & Data under the heading of Transmission Models.

RC: LoadUnserved load in the Reference Case simulation was observed to be in the amount 306 GWh as shown in Figure 2. While no unserved load was observed in the 2028 ADS PCM, it is informative to compare the diurnal aspects of unserved load in the Reference Case to that of the 2028 ADS PCM.

Figure 2: Unserved Load for 2038 Reference Case WTC NEV

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The bulk of the unserved load shown in Figure 2 occurs primarily in the Basin, Rocky Mountain, and Southwest regions of the Western Interconnection. Transmission path utilizations in these regions are also observed to be heavily utilized as will be discussed later in this section under the heading of RC: Inter-Regional. The load and generation balance for a ten-day period where unserved load occurs in the 2038 Reference Case in comparison to that of the 2028 ADS PCM is shown in Figure 3.

Figure 3: Load/Gen Comparison - 2038 Reference Case to 2028 ADS PCM

Note that the load profile between the 2038 Reference Case and the 2028 ADS PCM is very similar since the load model for the 2038 Reference Case was derived from the 2028 ADS PCM by applying the CAGRs of area loads in the 2028 ADS PCM to extrapolate the load model out another ten years. For the week shown, the peak demand of the Reference Case is approximately 200,000 MW as compared to a peak demand of approximately 168,000 MW in the 2028 ADS PCM corresponding to a yearly CAGR of approximately 1.76%. Despite the load profiles being similar, unserved load is observed in the 2038 Reference Case whereas no unserved load is observed for the same period in the 2028 ADS PCM. This can be explained by comparing the generation mix being committed and dispatch to serve load. An envelope between demand peaks and valleys, as illustrated by the horizontal red dashed lines in Figure 3, where unserved load occurs when the peaks and valleys of load occur outside of this envelope. Note that there is a greater percentage mix of solar generation in the 2038 Reference Case than there is in the 2028 ADS PCM where the portfolio of candidate resources that the PCM can commit and dispatch is the 2038 Reference Case is the 2038 Scenarios Candidate Resource

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Portfolio. The bold blue arrows point to where the greatest amounts of unserved load occur in the 2038 Reference Case. Note that unserved load occurs after the energy supply from solar drops off abruptly, as indicated by the vertical dashed blue arrows, which leads to operational challenges from a resource flexibility standpoint. The unserved load occurs on the downward slope after the peak demands as indicated by the bold green arrows. As energy supply from solar resources decrease, energy supply from DG/DR/EE resources, which includes electrical storage, increase up to peak demand, but drops off on the downward slope after peak when unserved load occurs. Solar also contributes to dump energy (spillage) when energy from solar is at its peak. The operational challenge that solar energy introduces is that it peaks when demand is low and disappears when demand is high. An operational partnership between solar and energy storage so that the combined net energy supply would be relatively constant would greatly reduce the operational challenges introduced by solar and the reliability risk of unserved energy. This could be accomplished in several ways that could include policy, market mechanisms, and/or technology advancements.

A comparison of the diurnal energy profiles of energy storage between the 2038 Reference Case and the 2028 ADS PCM is shown in Figure 4.

Figure 4: Energy Storage Comparison - 2038 Reference Case to 2028 ADS PCM

Note that the commitment and dispatch of storage in the 2038 Reference Case is spikier than that of the 2028 ADS PCM, which suggests that there is a greater reliance on storage for flexibility during demand peaks. The availability of storage in the PCM is modeled by hourly profiles. By shifting these profiles a few hours in time, which is a sensitivity beyond the scope of this study, there may be an opportunity to mitigate the unserved load. In practice, there may be opportunities to accomplished this in several ways that could include policy, market mechanisms, and/or demand-side technology advancements.

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The net effect of variable generation on the net load demand is shown in Figure 5. As Figure 5 shows, the effect of wind and solar on net demand is to make it spikier and more variable which introduces operational challenges requiring greater dependence on resource flexibility.

Figure 5: Load Snapshot Comparison - 2038 Reference Case to 2028 ADS PCM

Locational marginal price (LMP) for load is shown in Figure 6. The spikes in LMP occur when unserved load occurs as previously described and are highest when peak demand is outside the envelope shown in Figure 3 as previously discussed. Not to that the LMPs spikes are highest for the APS, PNM, PSC, SCE areas. LMP price differential spikes between areas occur when congestion is encountered requiring more expensive local generation to be dispatched and/or load to be shed (unserved). As Figure 6 shows, the price spikes peak at 4000 $/MWh in the 2038 Reference Case, which is the maximum allowed LMP in the PCM, which results when load is shed. Conversely, the price spikes peak under 100 $/MWh in the 2028 ADS PCM which occurs from congestion and the dispatch of various different resources in different locations at different commitment and dispatch costs.

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Figure 6: LMP Snapshot Comparison - 2038 Reference Case to 2028 ADS PCM

Correlating LMP to the load versus generation balance indicates that unserved load and LMP price spikes occur uniformly when outside of this envelope and that value of the LMPs correlate relatively in proportion to the extent that load demand exceeds the upper limit of the envelope. This observation for the Reference Case will be used later as a comparison to what is observed for the scenarios.

Figure 7: Correlation of LMP to Load/Gen Balance - 2038 Reference Case

A comparison of dispatched generation at peak hour between the 2038 Reference Case and the 2028 PCM is shown in .Figure 8. As Figure 8, there is a proportionally greater amount of wind, solar, and gas fired generation in the 2038 Reference Case mix than that of the 2028 ADS PCM. Figure 8 further illustrates the increased dependence on gas fired generation for flexibility as more variability is introduced by increased amounts of wind and solar.

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Figure 8: Generation at Peak Hour Comparison - 2038 Reference Case to 2028 ADS PCM

2028 ADS PCM 2038 Reference Case

RC: GenerationThe annual resource energy production mix for the 2038 Reference Case as compared to the 2028 ADS PCM is shown in Figure 9. The nomenclature used in Figure 9 is given below:

Ref: 2038 Reference Case.

WTC: With transmission constraints enforced.

NEV: No flexible (dispatchable) distributed energy resources in the form of electric vehicle storage (DER-EV).

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Figure 9: Annual Resource Energy Production Mix (GWh) – 2038-Ref-WTC-NEV

2028 ADS P2v2.0 2038-Ref-WTC-NEV

Coal DER-EV DG/DR/EE/BTM Geo/Bio HydroNuclear Other Solar - CSP Solar - PV Storage - ESNG-CC NG-CT/OGS Storage - PS Wind Load

The change in energy production is shown in Figure 10.Figure 10: 2038-Ref-WTC-NEV – System Annual Generation Difference by Type (GWh)

Generation Comparison Added/Displaced Generation

As Figure 9 and Figure 10 show, coal fired generation is observed to be completely displaced due to retirements based upon stated policy and industry decisions and a $50/ton CO2 cost modeled within the simulation. Energy production from gas fired generation, solar, and wind are observed to increase significantly. It is important to note that the dependence on gas fired generation, both combined cycle and combustion turbines, increase to offset the displacement of coal, beyond that

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which renewables provide. In the PCM simulations, renewable resources have low production costs and are largely committed and dispatched as “price takers” whereas gas resources are committed and dispatched largely based on marginal price signals, just as they are today. This emphasizes the continued future dependence on gas fired generation both in terms of adequacy and operational flexibility.

RC: Inter-RegionalIn this section, path utilizations between the 2038 Reference Case and the 2028 ADS PCM case are compared. The most heavily utilized paths in the 2028 ADS PCM case simulation are shown in Figure 11. The most heavily utilized paths in the 2038 Reference Case simulation are shown in Figure 12.

Figure 11: Most Heavily Utilized Paths - 2028 ADS P2V2.0

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Figure 12: Most Heavily Utilized Paths - 2038-Ref-WTC-NEV

A cross correlation of heavily utilized paths between the 2038 Reference Case and the 2028 ADS PCM is shown in Table 4 where those paths highlighted in yellow are in the top fifteen heavily utilized paths of both cases, those paths highlighted in blue are in the top fifteen of the 2028 ADS PCM only and those paths highlighted in orange are in the top fifteen of the 2038 Reference Case only.

Table 4: Correlation of Heavily Utilized Paths to Regions - 2038-Ref-WTC-NEV

Path Region(s)

P01 Alberta-British Columbia Alberta, British Columbia

P05 West of Cascades-South P08 Montana to Northwest NorthwestP15 Midway-LosBanos California

P19 Bridger West Basin, Rocky Mountain

P20 Path C Basin, NorthwestP25 PacifiCorp/PG&E 115 kV Interconnection California, Northwest

P26 Northern-Southern California CaliforniaP28 Intermountain-Mona 345 kV BasinP29 Intermountain-Gonder 230 kV

P30 TOT 1A Basin, Rocky Mountain

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P32 Pavant-Gonder InterMtn-Gonder 230 kV P36 TOT 3 P42 IID-SCE P45 SDG&E-CFE California, MexicoP47 Southern New Mexico (NM1) P55 Brownlee East Basin, NorthwestP61 Lugo-Victorville 500 kV Line P65 Pacific DC Intertie (PDCI) California, NorthwestP66 COI California, NorthwestP75 Hemingway-Summer Lake P80 Montana Southeast NorthwestP83 Montana Alberta Tie Line Alberta, NorthwestHeavily Utilized in both the 2038 Reference Case and the 2028

ADS PCMHeavily Utilized in the 2028 ADS PCM Only

Heavily Utilized in the 2038 Reference Case Only

From Figure 11, Figure 12, and Table 4, it is observed that path utilizations for the 2038 Reference Case noticeably increase around the Basin Region and exports to Southern California from the Northwest. This observation is further illustrated in a comparison of transmission path utilizations of the Reference Case to that of the 2028 ADS as shown in Figure 13.

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Figure 13: Path Flow Comparison of 2038 Ref-WTC NEV to 2028-ADS-P2V2.0

2028 ADS P2v2.0 2038-Ref-WTC-NEV

It is observed in Figure 13 that:

Energy production from coal is observed to be completely displaced, primarily in the Basin, Northwest, Rocky Mountain, and Southwest regions.

Energy production from gas fired resources increase noticeably in the Basin, Northwest, Rocky Mountain, and Southwest regions.

Energy production from solar resources increase noticeably in the Basin, California, Rocky Mountain, and Southwest regions.

Energy production from wind resources increase noticeably in the Alberta, Basin, Northwest, and Southwest regions.

Annual Imports into the Alberta region decrease with net neutral resource adequacy modeled for Alberta based on the Alberta Integrated Resource Plans.

Annual exports from the British Columbia region to Alberta region decrease due to net neutral resource adequacy modeled for Alberta.

Annual exports from the Basin region increase, primarily in the direction of the Northwest and Rocky Mountain regions with increased energy production from wind, solar, and gas resources outpacing load growth.

Annual imports into the California region increase noticeably as load growth outpaces increased energy production which primarily comes from solar resources.

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Annual net exports from the Northwest region increase slightly with slight increases in gas, wind, and solar offset slightly by a displacement of coal.

Annual imports into the Rocky Mountain region increase noticeably as the Rocky Mountain region goes from a net exporter to a net importer due to a large displacement of coal not being entirely offset from increases in wind, solar, and gas.

Annual exports from the Southwest region increase noticeably with noticeable increases in energy production from wind, solar, and gas fired resources, despite the displacement of coal fired resources.

RC: Seasonal VariationsThe seasonal variations of path flows is shown in Figure 14. It is observed in Figure 14 that:

The Alberta region is a net importer in Summer and Spring (to a lesser extent) and a net exporter during Winter and Autumn (to a lesser extent) with the energy production mix relatively constant proportionally across all seasons.

The British Columbia region is a net exporter across all seasons where exports to the Northwest region are greatest during Autumn and Winter and exports to Alberta are greatest during the Summer, supplemented by loop flow from the Northwest in Summer.

The Basin region is a net exporter across all seasons where exports out of the Basin to other regions are uniform across all seasons with the majority of exports going to the Rocky Mountain Region with loop flow coming from the Southwest region.

The California region is a net importer across all seasons where imports are greatest in Winter and Autumn and are least in Spring and Summer. This seasonal variation in imports are largely due to the increased amount of solar in the resource mix with energy production from solar peaking in Summer.

The Northwest region is a net exporter across all seasons where exports are greatest in Summer and are least in Winter.

The Rocky Mountain region is a net importer across all seasons where imports are greatest in Winter and are least in Summer. This seasonal variation is largely due to higher load demand in the Rocky Mountain region relative to the rest of the system and commitment of cheaper resources outside of the Rocky Mountain region to meet the system needs.

The Southwest region is a net exporter across all seasons where exports are greatest in Winter and are least in Summer. This seasonal variation is largely due to increased hydro production from the Northwest region in Summer and decreased hydro production in Winter when the load demand in the Northwest is at its peak.

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Figure 14: Season Path Flow Variations for 2038 Ref-WTC NEV

Winter Spring

Summer Autumn

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RC: Key TakeawaysThe key takeaways from the 2038 Reference Case simulations are:

Unserved energy in the amount of 306 GWh was observed primarily during Summer and in the Basin, Rocky Mountain, and Southwest regions.

The risk of unserved load is greatest at peaks above roughly 180 GW and is exacerbated by the sharp drop-offs energy from solar at peak.

A greater dependence on gas fired generation for adequacy and flexibility will occur with the displacement of coal and as increased variability that is introduced from wind and solar.

Energy storage is a useful tool to mitigate unserved load but will require mechanism to shape the diurnal availability of energy storage when it is needed most, which could be realized in several ways that could include policy, market mechanisms, and/or technology advancements.

An operational partnership between solar and energy storage so that the combined net energy supply would be relatively constant would greatly reduce the operational challenges introduced by solar and the reliability risk of unserved energy.

Transmission path utilizations were heavily utilized in the Basin, Rocky Mountain, and Southwest regions which correlates with the unserved energy observed in those regions.

Scenarios 1Scenario 1 is characterized in the Scenario Matrix as having open markets with limited customer adoption of new service options. The following factors further describe the characterization of Scenario 1 followed by the modeling approach used to capture the factor (highlighted in red):

Regulations are open and flexible to allow a range of energy service options.Captured primarily by assumptions about technology advancement in terms of cost and performance. In other words, how will regulations impact technology innovation and customer adoption? In the case of this scenario, the NREL Moderate assumptions for technology advancement is chosen. The Moderate advancement case is intended to reflect a moderate increase in technology trends beyond current levels in terms of innovation, research and development, deployment, cost reductions, and performance improvements 2050.

Customer adoption of new energy options is limited by new products and not meeting customer needs (benefits don’t justify costs.)The NREL Reference trajectory for end-use technology adoption was used. The Reference electrification adoption scenario represents a business-as-usual outlook where only incremental changes with respect to electrification occur. In particular, the Reference scenario includes policies that existed in 2017 only. It also excludes any dramatic technological, societal, or behavioral shifts as they relate to the adoption of end-use equipment. It reflects a future in which the rate of adoption of

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electric technologies roughly follows current trends. In other words, an electrification transition that remains in the earliest stages even by 2050.

The bulk transmission system is maintained to back up reliability for the interconnection.Scenario 1 is modeled with the same reliability requirements as that of the 2028 ADS PCM including transmission path limits, reserve, ramping, and other operational security constraints.

SC1: Modeling ComponentsLoad Models: Derived from the NREL Demand-side Scenario with the Reference Customer Adoption of new service options and with Moderate Technology Advancement assumptions as described in Appendix B – Assumptions, Tools, Models, Methods, & Data under the heading Load Models.



SC1: LoadScenario 1 was observed to have a total of 10 GWh of unserved load, primarily in the Basin and Rocky Mountain regions and, to a lesser extent, in the Southwest region as shown in Figure 15. Unserved load in Scenario 1 was much less than the 306 GWh of unserved load observed in the 2038 Reference case. Unserved load in Scenario 1 was concentrated in the Summer Peak month of August, where unserved load occurred across the Summer season in the 2038 Reference Case as shown in Figure 16. While the annual load energy requirements of Scenario 1, Scenario 3, and the Reference Case are very close, as shown in Figure 44, the reason that Scenario 1 has much less unserved energy is due largely in part because Scenario 1 has the benefit of flexible DER in the form of electric vehicle storage (flexible DER-EV), which will be discussed in greater detail later, whereas the Reference Case does not.

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Figure 15: Unserved Load for 2038 Scenario 1 WTC WEV


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Figure 17: Load/Gen Comparison - 2038 Scenario 1 to 2038 Reference Case

Figure 18: Energy Storage Comparison - 2038 Scenario 1 to 2038 Reference Case

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Figure 19: Load Snapshot Comparison - 2038 Scenario 1 to 2038 Reference Case

Figure 20: LMP Snapshot Comparison - 2038 Scenario 1 to 2038 Reference Case

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Figure 21: Generation at Peak Hour Comparison - 2038 Scenario 1 to 2038 Reference Case

2038 Reference Case 2038 SC1-WTC-WEV

SC1: Generation

SC1: Inter-Regional

SC1: Seasonal Variations

SC1: Key Questions1. How might customer and other behind the meter energy products and services

be captured for planning purposes? a. What key categories of energy services and products might be selected for

scale?2. What potential reliability risks should we be thinking about if the world of

Scenario 1 comes to pass?

SC1: Early Indicators and Metrics

Early Indicators for Scenario 1Indicator Definition/RationaleChange in State/Provincial Energy Policies Designed to provide open markets for products,

services, and customer choice options

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Early Indicators for Scenario 1Indicator Definition/RationaleCustomer Adoption of Energy Service Options Wide differences in and moderate speed of adoption

of new energy product and service options across the Western Interconnection

Limited success of pilot projects in new energy services and products that enable more customer choice options

Delays the proliferation and availability of customer choice options

Fracturing of electric energy markets across the Western Interconnection away from current patterns

Customer choice driven, and supported through state and provincial regulations and policies

SC1: Key TakeawaysWhy not much difference from that of Reference and Scenario 3. Draw from NREL conclusions. Model limitations. Focus on where we need to deep dive going forward.

NREL narrow focus.

A lot of stuff going on at a customer level that we can’t identify.

Make sure to emphasize

NREL chart with category other and paragraph on limitations of storage and DER.

Results look the same because of . . .

What we learned is known unknowns which is the story. Limitations of data, models, tools. Scenarios are too complex for models. Suspend disbelief. Don’t constrain thinking by knowledge of model and tool limitations.

Flexible DER in the form of electric vehicle storage (flexible DER-EV) is effective at lower levels of electrification load. While flexible DER-EV represents less than 2% of the total energy production of the resource portfolio, it is very effect at reducing unserved energy at lower levels of electrification.

Scenario 2Scenario 2 is characterized in the Scenario Matrix as having open markets with high levels of customer choice and adoption of new service options. The following factors further describe the characterization of Scenario 2 followed by the modeling approach used to capture the factor (highlighted in red):

Regulations are open and flexible to allow a range of energy service options. Captured primarily by assumptions about technology advancement in terms of cost and performance. In other words, how will regulations impact technology innovation and customer adoption? In the case of this scenario, the NREL Rapid assumptions for technology advancement is chosen. The Rapid advancement case

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is intended to reflect a rapid increase in technology trends beyond current levels in terms of innovation, research and development, deployment, cost reductions, and performance improvements 2050.

Customers are willing to try new energy options with varied levels of success and benefits versus costs. The NREL High trajectory for end-use technology adoption was used. The High scenario assumes a more favorable set of conditions for electrification—including a combination of technology breakthroughs, policy support, and underlying societal and behavioral shifts that yield an electrification transition. As a result, the High scenario reflects an increase in the degree of electrification across all end-use sectors comprised of Transportation, Commercial, Residential, and Industrial. The High trajectory assumes that the electric technologies generally experience earlier saturation.

The bulk transmission system is maintained as reliability is increasing met by distributed energy options.Scenario 2 is modeled with the same reliability requirements as that of the 2028 ADS PCM including transmission path limits, reserve, ramping, and other operational security constraints. Scenario 2 has a higher degree of flexible DER in the form of electric vehicle energy storage than the other scenarios.

SC2: Modeling ComponentsLoad Models: Derived from the NREL Demand-side Scenario with High Customer Adoption of new service options and with Moderate Technology Advancement assumptions as described in Appendix B – Assumptions, Tools, Models, Methods, & Data under the heading Load Models.



SC2: LoadScenario 2 was observed to have a total of 2,860 GWh of unserved load across all regions but primarily in the California, Basin and Southwest regions and, to a lesser extent, in the Rocky Mountain region as shown in Figure 22. Unserved load in Scenario 2 was much more than the 306 GWh of unserved load observed in the 2038 Reference case, in large part because. The occurrence of unserved load in Scenario 2 is similar to that of the 2038 Reference Case, shown in Figure 23, but occurred in more regions. Scenario 2 has the highest annual load energy requirement than all the Scenarios. Much more so than

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that of Scenario 1, Scenario 3, and the Reference Case, as shown in Figure 44. Subsequently, Scenario 2 also has the highest amount of unserved load, much more so than all the other scenarios and even the Reference Case which doesn’t have flexible DER-EV, which will be discussed in greater detail later. It should be noted that while the annual load energy requirement of Scenario 2 is slightly higher than that of and Scenario 4, the resulting difference in unserved energy is great. An inflection point between Scenario 2 and Scenario 4 exists where the 2038 Scenarios Candidate Resource Portfolio does not have enough resource flexibility to support high levels of electrification even with higher levels of flexible DER-EV. While flexible DER-EV scales up with increased electrification, the need for resource flexible increases faster than the increase in flexible DER-EV as electrification load increases. While flexible DER-EV represents less than 2% of the total energy production of the resource portfolio, it is very effect at reducing unserved energy at lower levels of electrification.


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Figure 24: Load/Gen Comparison - 2038 Scenario 2 to 2038 Reference Case

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SC2: Generation

SC2: Inter-Regional

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SC2: Key Questions


SC2: Key TakeawaysModels are telling us that flexibility on supply and demand side.

Market or policy provides mechanisms to incentivize TOU and flexibility.

We know that policies: rate structures.

Go back to EPS system. Example, PGE getting with car vendors to broadcast price signals. LMP and EPS.

An inflection point between Scenario 2 and Scenario 4 exists where the 2038 Scenarios Candidate Resource Portfolio does not have enough resource flexibility to support high levels of electrification even with higher levels of flexible DER-EV. While flexible DER-EV scales up with increased electrification, the need for resource flexible increases faster than the increase in flexible DER-EV as electrification load increases.

Scenario 3Scenario 3 is characterized in the Scenario Matrix as policy driven by lower costs while maintaining reliability while customer service option choices are restricted. The following factors further describe the characterization of Scenario 3 followed by the modeling approach used to capture the factor (highlighted in red):

Regulations are made to hold control over the system to assure environmental goals are met and community-wide reliability is sustained. Captured primarily by assumptions about technology advancement in terms of cost and performance. In other words, how will regulations impact technology innovation and customer adoption? In the case of this scenario, the NREL Moderate assumptions for technology advancement is chosen. The Moderate advancement case is intended to reflect a moderate increase in technology trends beyond current levels in terms of innovation, research and development, deployment, cost reductions, and performance improvements 2050.

Customer demand for new energy options are constrained to assure cost sharing and overall system integrity. The NREL Reference trajectory for end-use technology adoption was used. The Reference electrification adoption scenario represents a business-as-usual outlook

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where only incremental changes with respect to electrification occur. In particular, the Reference scenario includes policies that existed in 2017 only. It also excludes any dramatic technological, societal, or behavioral shifts as they relate to the adoption of end-use equipment. It reflects a future in which the rate of adoption of electric technologies roughly follows current trends. In other words, an electrification transition that remains in the earliest stages even by 2050.

The bulk transmission system is protected and maintained to assure reliability for the interconnection. Scenario 3 is modeled with the same reliability requirements as that of the 2028 ADS PCM including transmission path limits, reserve, ramping, and other operational security constraints.

SC3: Modeling ComponentsLoad Models: Derived from the NREL Demand-side Scenario with the Reference Customer Adoption of new service options and with Slow Technology Advancement assumptions as described in Appendix B – Assumptions, Tools, Models, Methods, & Data under the heading Load Models.



SC3: LoadScenario 3 was observed to have a total of 98 GWh of unserved load, primarily in the Basin and Rocky Mountain regions and, to a lesser extent, in the Southwest region as shown in Figure 29. Unserved load in Scenario 3 was much less than the 306 GWh of unserved load observed in the 2038 Reference case but not as much as that observed in Scenario 1. The occurrence of unserved load in Scenario 3 is like that of the 2038 Reference Case, shown in Figure 29, in that it occurs across Summer. While the annual load energy requirements of Scenario 1, Scenario 3, and the Reference Case are very close, as shown in Figure 44, the reason that Scenario 3 has much less unserved energy is due largely in part because Scenario 3 has the benefit of flexible DER in the form of electric vehicle storage (flexible DER-EV), which will be discussed in greater detail later, whereas the Reference Case does not.

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43

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Figure 31: : Load/Gen Comparison - 2038 Scenario 3 to 2038 Reference Case


44

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45

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SC3: Generation

SC3: Inter-Regional


SC3: Key Questions


SC3: Key Takeaways Flexible DER in the form of electric vehicle storage (flexible DER-EV) is effective at

lower levels of electrification load. While flexible DER-EV represents less than 2% of the total energy production of the resource portfolio, it is very effect at reducing unserved energy at lower levels of electrification.

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Scenario 4Scenario 4 is characterized in the Scenario Matrix as policy driven with high levels of customer service option choices. The following factors further describe the characterization of Scenario 4 followed by the modeling approach used to capture the factor (highlighted in red):

Regulations are put in place to assure standards are met for reliability and system integrity purposes. Captured primarily by assumptions about technology advancement in terms of cost and performance. In other words, how will regulations impact technology innovation and customer adoption? In the case of this scenario, the NREL Moderate assumptions for technology advancement is chosen. The Moderate advancement case is intended to reflect a moderate increase in technology trends beyond current levels in terms of innovation, research and development, deployment, cost reductions, and performance improvements 2050.

Customers are directed towards new service options based on regulatory approval to assure reliability. The NREL Medium trajectory for end-use technology adoption was used. The Medium scenario is intended to reflect an electrification future that is plausible but not transformational. It includes accelerated adoption of electric technologies serving end uses in all sectors; however, electric technologies are not ubiquitous in this scenario, where technical, economic, and consumer preference obstacles remain for certain end users. Even for services where increased electrification is assumed to occur, adoption of end-use technologies often remains in the diffusion stage or saturates at somewhat modest levels by 2050. For other services, electrification is assumed to still be at the early stages with uptake occurring only in limited markets and by early adopters..

The bulk transmission system is protected and maintained to assure reliability for the interconnection. Scenario 4 is modeled with the same reliability requirements as that of the 2028 ADS PCM including transmission path limits, reserve, ramping, and other operational security constraints.

SC4: Modeling ComponentsLoad Models: Derived from the NREL Demand-side Scenario with Medium Customer Adoption of new service options and with Moderate Technology Advancement assumptions as described in Appendix B – Assumptions, Tools, Models, Methods, & Data under the heading Load Models.


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SC4: Load149 GWh


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Figure 38: : Load/Gen Comparison - 2038 Scenario 4 to 2038 Reference Case

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SC4: Generation

SC4: Inter-Regional

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SC4: Key Questions


SC4: Key Takeaways Flexible DER in the form of electric vehicle storage (flexible DER-EV) is effective at

lower levels of electrification load. While flexible DER-EV represents less than 2% of the total energy production of the resource portfolio, it is very effect at reducing unserved energy at lower levels of electrification. An inflection point between Scenario 2 and Scenario 4 exists where the 2038 Scenarios Candidate Resource Portfolio does not have enough resource flexibility to support high levels of electrification even with higher levels of flexible DER-EV. While flexible DER-EV scales up with increased electrification, the need for resource flexible increases faster than the increase in flexible DER-EV as electrification load increases.

Cross Comparison of Scenarios

Economics

Sensitivities Transmission Constraints Flexible DER-EV Retirements Storage

To fully understand potential future reliability risks associated with the four WECC futures, this assessment defined sensitivities around the Reference Case and each of the four Scenario cases:

Case No Transmission Path Constraints (NTC) and No dispatchable DER-EV (NEV);

Case No Transmission Path Constraints (NTC) and With dispatchable DER-EV (WEV);

Case With Transmission Path Constraints (WTC) and No dispatchable DER-EV (NEV); and

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Bailey, Michael, 01/22/20,

These are metrics.

Assessment Title

Case With Transmission Path Constraints (WTC) and With dispatchable DER-EV (WEV).

The analyses and figures that follow investigate in detail the implications of these varying sensitivities.

10. ConclusionsAs the team reviewed analytical results, what did you observe that addresses the primary reliability question? Were there any unexpected results? What challenges did the team encounter and how were these challenges managed? Did you identify new reliability risks or observe mitigation or disappearance of previously identified risks?

Focus Question (Revisited)Complexity goes beyond the modeling.

Anticipation of long term . . .

CustomerContext

What able to model and study.

What were not . . .

What we observed (salient to key word).

Services

Technologies

Policies

Market-options

Industry

Reliability

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Other General For a given portfolio mix of installed generation, there appears to be a uniform

correlation between unserved energy and load demand across all cases (scenarios and Reference Case) where bounds on load demand exist such that if load demand exceeds the upper bound (180,000 MW observed in this study), unserved load will uniformly occur. The greater the difference between the peak and valley bounds (envelope) further exacerbates the risk of unserved energy.

The greater the mix of solar in the resource portfolio, the greater this risk of unserved at peak and the spillage of energy when solar is at its peak (dump energy). Large amounts of solar generation in the portfolio mix greatly increases the challenges of commitment and dispatch, often resulting in unserved energy and high LMPs at peak load demand and dump energy off-peak load demand.

Flexible DER in the form of electric vehicle storage (flexible DER-EV) is effective at lower levels of electrification load. While flexible DER-EV represents less than 2% of the total energy production of the resource portfolio, it is very effect at reducing unserved energy at lower levels of electrification. An inflection point between Scenario 2 and Scenario 4 exists where the 2038 Scenarios Candidate Resource Portfolio does not have enough resource flexibility to support high levels of electrification even with higher levels of flexible DER-EV. While flexible DER-EV scales up with increased electrification, the need for resource flexible increases faster than the increase in flexible DER-EV as electrification load increases.

Mechanisms to smooth diurnal load profiles to reduce demand at peak and the envelopes between peaks and valleys of load demand could greatly reduce the risk of unserved energy, high LMPs at peak, and the need for additional levels of flexible generation. These mechanisms could orginate from policy, market, technology innovation, etc. Some examples of mechanism could be time-of-use rate design, incentives, smart technology, etc.

11. RecommendationsBased on the team’s observations and conclusions, do you have recommendations for generation or transmission developers, policy makers, utilities or others to consider? Are there specific risk mitigation strategies that one or more entities should consider?

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Appendix A - References1. Mai, Trieu, Paige Jadun, Jeffrey Logan, Colin McMillan, Matteo Muratori, Daniel

Steinberg, Laura Vimmerstedt, Ryan Jones, Benjamin Haley, and Brent Nelson. 2018. Electrification Futures Study: Scenarios of Electric Technology Adoption and Power Consumption for the United States. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-71500. https://www.nrel.gov/docs/fy18osti/71500.pdf

2. WECC 2028 Scenarios Assessment – Supplemental Information

3. Arizona Public Service 2017 Integrated Resource Plan.4. BC Hydro Integrated Resource Plan.5. Cole, Wesley, Nathaniel Gates, Trieu Mai, Daniel Greer, and Paritosh Das. 2019.

2019 Standard Scenarios Report: A U.S. Electricity Sector Outlook. Golden, CO: National Renewable Energy Laboratory. NREL/TP-TP-6A20-74110. https://www.nrel.gov/docs/fy20osti/74110.pdf.

6. NREL (National Renewable Energy Laboratory). 2019. 2019 Annual Technology Baseline. Golden, CO: National Renewable Energy Laboratory. https://atb.nrel.gov/electricity/2019

7. NERC (North American Electric Reliability Corporation) February 2017. Distributed Energy Resources – Connection Modeling and Reliability Considerations. Atlanta, GA: NERC. https://www.nerc.com/comm/Other/essntlrlbltysrvcstskfrcDL/Distributed_Energy_Resources_Report.pdf

8. WECC 2019 Generator Capital Cost Tool developed for WECC by E3.9. Quantum Planning Group, Inc. 250 Point Lobos Avenue, Suite 304, San Francisco

CA 94121 US.10. WECC Event/Pattern/Structure (EPS) tracking system.11. WECC Scenario Planning Trends Reports.12. WECC 2018-2019 Draft Scenarios for Horizon Year 2038 v0.1.13. Hale, Elaine, Henry Horsey, Brandon Johnson, Matteo Muratori, Eric Wilson, et al.

2018. The Demand-side Grid (dsgrid) Model Documentation. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-71492. https://www.nrel.gov/docs/fy18osti/71492.pdf.

14. Alberta Integrated Resource Plan.

Appendix B – Assumptions, Tools, Models, Methods, & Data

Tools, Models, Methods, Data

https://www.alberta.ca/integrated-resource-plans.aspx

https://www.nrel.gov/docs/fy18osti/71492.pdf

https://www.wecc.org/RAC/Pages/SDS.aspx#DiscussionItems

https://www.wecc.org/SystemAdequacyPlanning/Pages/Scenario-Planning.aspx#TrendReports

https://www.wecc.org/SystemAdequacyPlanning/Pages/Scenario-Planning.aspx#EPSCurrentEvents

https://www.artofquantumplanning.com/home.html

https://www.wecc.org/SystemAdequacyPlanning/Pages/Scenario-Planning.aspx#ApprovedDocuments

https://www.nerc.com/comm/Other/essntlrlbltysrvcstskfrcDL/Distributed_Energy_Resources_Report.pdf

https://www.nerc.com/comm/Other/essntlrlbltysrvcstskfrcDL/Distributed_Energy_Resources_Report.pdf

https://atb.nrel.gov/electricity/2019


https://www.bchydro.com/content/dam/BCHydro/customer-portal/documents/corporate/regulatory-planning-documents/integrated-resource-plans/current-plan/0002-nov-2013-irp-chap-2.pdf

https://www.aps.com/-/media/APS/APSCOM-PDFs/About/Our-Company/Doing-business-with-us/Resource-Planning-and-Management/2017IntegratedResourcePlan.ashx?la=en&hash=150046DC45ABF903ADA3C458B97A3AED

https://www.wecc.org/RAC/Pages/StS.aspx#WECC/SDSScenariosTaskForce(WSTF)


Assessment Title

Load ModelsThe modeling assumption that went into this report were vetted through the WECC Scenario Task Force (WSTF). The effort of which was to transform the Scenario narratives into data and models that can be studied. Models and methods chosen were focused on the Scenario Matrix themes. Tools and models used to perform this study included a production cost models, power flows, capital expansion tools, the WECC Anchor Data Set ADS P2v2.0 and data provided by the National Renewable Energy Laboratory (NREL) as part of their Electrifications Futures Study (EFS).

To evaluate potential reliability risks associated with various futures for the Western Interconnection, it was necessary to define the load profiles that were representative of various levels of customer adoption of new service options. To do so, WECC and the WSTF turned to the National Renewable Energy Laboratory (NREL). The four future scenarios described in Figure 1 define different loads to be served, based on the drivers defined by the scenario matrix. NREL has defined a series of potential future load profiles, referred to as the Demand-side Scenarios, as part of the Electrifications Futures Study (EFS). These demand-side scenarios are based on the rate of future technology advancement and the rate of customers’ adoption of new technologies. Within that range of potential future load profiles, the SDS has identified four that would be expected to correspond closely to the load profiles associated with WECC’s future scenarios. The NREL demand-side scenarios and their correlation with WECC’s future scenarios are shown below in Figure 43.

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Figure 43: NREL Demand-side Scenarios Matrix

Slow Technology Advancement

Moderate Technology Advancement

Rapid Technology Advancement

Reference Customer Adoption

Reference Adoption, Slow Technology Advancement

Reference Adoption, Moderate Technology Advancement

Reference Adoption, Rapid Technology Advancement

Medium Customer Adoption

Medium Adoption, Slow Technology Advancement

Medium Adoption, Moderate Technology Advancement

Medium Adoption, Rapid Technology Advancement

High Customer Adoption

High Adoption, Slow Technology Advancement

High Adoption, Moderate Technology Advancement

High Adoption, Rapid Technology Advancement

Figure 44: 2038 Annual Load Requirements by Case and State

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SC3 SC1

SC4

SC2

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Figure 45: 2038 Annual Load CAGRs by Case and State

The capability of this flexible DER-EV varies hourly as it was derived by NREL from a bottoms-up approach using the DSGRID .tool designed to create comprehensive load data sets at a sufficient temporal, geographic, sectoral, and end-use resolution to enable detailed analyses of current patterns and future projections of end-use load. DSGRID uses a bottom-up methodology that allows highly resolved analysis of “what-if” scenarios. dsgrid leverages detailed sectoral energy models to provide

hourly time series of load by subsector, end use, and county covering a full year (see Figure ES1). Although dsgrid currently emphasizes electricity load data, its component sector models for

x

This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

residential buildings, commercial buildings, and industry provide information on other fuel use,

including natural gas. The data sets can thus be leveraged to support analysis of numerous

demand-side technology-driven changes, such as energy efficiency, electrification, and

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operational flexibility (i.e., demand response). The electricity use data are time-synchronized

with solar and wind data sets so as to be suitable for use in power systems analysis.

Generation Resource ModelsThe generation resource model used in the 2038 scenarios was derived from the 2028 ADS PCM model and the NREL Mid-Case Standard Scenario. NREL developed 36 forward-looking resource portfolios referred to as the Standard Scenarios. These scenarios are designed to capture a range of possible generation resource portfolio futures considering a variety of factors that may impact these futures. The resource portfolio chosen for the scenario studies was the Mid-case which represents a reference portfolio that uses policies that are in place as of July 31, 2019 and include other default assumptions derived from the NREL’s annual technology baseline. The Mid-case scenario represents a reference case and provides a useful baseline for comparing scenarios and evaluating the trends. The 2028 ADS PCM generation portfolio and its underlying performance and economic parameters were preserved. It was discovered that, in initial simulations, there was a large amount of resource retirements between 2028 and 2038 amounting to 26,813 MW, most of which was hydro located in the British Columbia (BC) region. It was decided by the WSTF that the BC retirements were suspect. BC Hydro was contacted regarding these retirements and confirmed that the retirement dates were not correct and. WECC will work with the appropriate WECC Subcommittees to review retirement dates for all resources and make corrections to the ADS. In the interim, the retirement dates for BC hydro resources were pushed out to 2050 for all scenarios, in order to maintain a level of resource adequacy consistent with the most recent integrated resource plan (IRP) for BC Hydro.

New resource types were add such that the 2038 generation candidate portfolio had the same resource mix as that of the NREL Mid-Case standard Scenario as shown in Figure 46. The resulting 2038 Scenarios Candidate Resource Portfolio serves as a baseline of candidate resources, new and existing, that the PCM can choose from for commitment and dispatch across all scenario PCM simulations.

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Figure 46: NREL Mid-Case Standard Scenario

The resource additions to the 2028 ADS PCM such that the portfolio mix of the 2038 Scenarios Candidate Resource Portfolio was equivalent to the NREL Mid-Case Standard Scenario is shown in Figure 47.

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Figure 47: 2038 Resource Additions to 2028 ADS PCM

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000

Biopower

Coal

CSP

Curtailment

Geothermal

Hydro

Imports

Land-based Wind

NG-CC

NG-CT

Nuclear

Offshore Wind

Oil-Gas-Steam

Rooftop PV

Storage

Utility PV

MW

Candidate Generation Portfolio Capacity Additions: 2038 vs 2028(121 GW)

The total capacity of the 2038 Scenarios Candidate Resource Portfolio is 395 GW as compared to 274 GW for the 2028 ADS PCM as shown in Figure 48.

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Figure 48: Total Generation Total Capacity Comparison, 2028 vs 2038

In addition to the Mid-Case resource additions described above, additional resources were added to the 2038 Scenarios Candidate Resource Portfolio for each scenario to represent the flexible DER in the form of electric vehicles (DER-EV) as previously discussed in this appendix under the heading Load Models. The capability of this flexible DER-EV varies hourly as it was derived by NREL from a bottoms-up approach using the DSGRID. While flexible DER-EV was modeled in all of the Scenarios, it was not modeled in the reference case as since the load derived for the reference case was derived from the 2028 ADS PCM load growth patterns which and not from the NREL EFS demand-side loads selected for each of the scenarios. The DER-EV modeled for each scenario is unique depending on the NREL demand-side load selected for the scenario. The DER-EV additions for Scenario 2 corresponding to the NREL “High Customer Adoption, Moderate Technology Advancement” is shown in Figure 49.

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Figure 49: Scenario 2: Flexible DER-EV Additions by Region

While Scenario 2 had the largest amount of flexible DER-EV modeled, it still represented less than 2% of the total 2038 candidate resource portfolio capacity. DER-EV capacity for the other scenarios were closer to or less than 1%.

Transmission Models

Key Scenario DriversFor the WECC Scenarios, the SDS agreed on the following initial set of key drivers shown below. Accompanying each key driver presented below is a description (highlighted in red) as to the approach, extent and/or limitation of how the key driver is captured in the modeling. A detailed discussion of these drivers can be found in the WECC 2018-2019 Draft Scenarios for Horizon Year 2038 v0.1 report.

1. Changes in State and Provincial electric energy market policiesWith the exception of a CO2 cost of $55/ton, all state and provincial are captured exogenously in NREL’s Regional Energy Deployment System Model (ReEDS) which was used to construct the Mid-Case generation resource portfolio, as part of the NREL standard scenarios, used in the WECC 2038 scenarios study.

2. Changes in Federal electric energy market policies.Federal policy is captured exogenously only to the extent that it is captured within

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NREL’s Regional Energy Deployment System Model (ReEDS) which was used to construct the Mid-Case generation resource portfolio, as part of the NREL standard scenarios, used in the WECC 2038 scenarios study.

3. Evolution of customer-side energy supply technology and service options.The generation resource candidates used in the 2038 scenario studies was constructed by augmenting the 2028 ADS resource portfolio with additional resources, by type, such that the final candidate portfolio had the same resource mix as that of NREL’s Mid-Case standard scenario. Customer-side energy supply is captured in the 2028 ADS includes DG/DR/EE/BTM, rooftop solar, and electrical energy storage modeled on the supply-side is also included in the 2038 generation resource portfolio. New rooftop solar and energy storage modeled on the supply-side within the NREL Mid-Case standard scenario was added the 2038 scenario candidate portfolio. Further, flexible DER-EV derived from the NREL Electrification Futures Study (EFS) was also added to the 2038 scenario candidate portfolio. The candidate portfolio was then presented to the production cost model (PCM). The PCM then committed and dispatched resources from the pool of candidates in the simulations.

4. Changes in the character and shape of customer demand for electric power.Changes in customer demand were captured exogenously in the demand-side scenarios obtained from NREL and used to derive the 2038 scenario load profiles. The demand-side scenarios created by NREL as part of the Electrifications Future Study (EFS) were derived by a bottoms-up approach based on four customer classes consisting of Residential, Commercial, Industrial, and Transportation. The bottoms-up modeling approach is further discussed in the Assessment Approach of this report.

5. Changes in utility-scale power supply options Changes in utility-scale power supply options were captured exogenously in NREL’s Regional Energy Deployment System Model (ReEDS) which was used to construct the Mid-Case generation resource portfolio, as part of the NREL standard scenarios, used in the WECC 2038 scenarios study.

6. Changes in State, Provincial, and Federal electric system regulations for reliability Changes in State, Provincial, and Federal policy are captured exogenously only to the extent that it is captured within NREL’s Regional Energy Deployment System Model (ReEDS) which was used to construct the Mid-Case generation resource portfolio, as part of the NREL standard scenarios, used in the WECC 2038 scenarios study5.

7. Evolution of climate change and environmental issues on electric power service State and provincial policies in the west are included in the modeling in the form of RPS, coal retirements, and a CO2 cost of $55/ton. Other climate change considerations, such as water availability and drought, were studied separately in the WECC 2038 Energy-Water-Climate Change Assessment be studied by a consortium of National Labs (Sandia, PNNL, NREL) on behalf of WECC and will be published in separate report forthcoming.

8. Evolution of fuel markets in the electric power sector The evolution of fuel markets were not explicitly modeled in this study beyond that of the inclusion of new resource types captured as part of the NREL Mid-Case standard scenario.

9. Shifts in the cost of capital and financial markets Shifts in capital and financing are captured only to the extent that they are captured in WECC Generation Capital Cost tool which includes all the financing

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parameters necessary to generate LCOE, LFC, and yearly cash flows. Included in these financing parameters are Capital Investment Costs, WACC, Progress Multipliers, Locational Adjustments, Tax Credits, etc.

10.Economic growth within the Western InterconnectionEconomic growth primarily translates to load models directly and generation models indirectly. Economic growth is captured in the scenario studies only to the extent that it is modeled exogenously in the NREL EFS demand-side scenarios selected for the scenario studies. In this regard, the NREL EFS demand-side scenarios consist of nine different load profiles representative of nine different levels of electrification, including load growth, based on customer adoption and technology advancement.

11.Worldwide developments in the electric power industryWorldwide developments are captured in the scenario studies only to the extent that the are modeled exogenously in the NREL EFS demand-side scenarios and Mid-Case standard scenario selected for the scenario studies. Worldwide developments are captured primarily as technology advancement and climate change.

Distributed Energy ResourcesRecognizing that there are various industry definitions for distributed energy resources (DER), the North American Electric Reliability Corporation (NERC) developed a working definition to create context for discussion. NERC’s working definition for DER is:

A Distributed Energy Resource (DER) is any resource on the distribution system that produces electricity and is not otherwise included in the formal NERC definition of the Bulk Electric System (BES).

In recent years, the penetration and role of DER has grown considerably and is transforming the way the BES is managed and operated. It can be easily argued that we are in the early stages of the DER evolution. The rate of growth in DER is causing planners and researches to scramble to gain a better understanding of how DER is impacting the BES today, what technology innovations may occur, how it will evolve in the future, how should planners respond, and what policy and market mechanisms may be needed. In risk management, there is a mechanism for classifying risk as shown in Figure 50.

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Figure 50: Risk Management Domain

In the context of DER, examples of risk management domain factors may include:

Unknown-Knowns – For industry, how do we leverage the work of the National Laboratories?

Unknown-Unknowns – Scenario Planning is useful in this pursuit in terms of trying to ask the right questions.

Known-Knowns – We know that the role of rooftop solar and behind-the-meter (BTM) energy storage is growing and we are already aware of some of the impacts that it is having on the BES.

Known-Unknowns – We know that DER and the technologies of DER will evolve over time, but we don’t have definitive knowledge of how and to what extent.

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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.

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