1. System Flexibility Assessment for the Western Interconnection RAWG Meeting W ESTERN E LECTRICITY C OORDINATING C OUNCIL

2. “Flexibility” Wide-spread production simulation modeling Results from past TEPPC studies… 2013 Plan - Recommendation 3: Assess Future Operational Flexibility 2 W ESTERN E LECTRICITY C OORDINATING C OUNCIL

3. About the Study Need to understand power system flexibility needs under higher renewable penetration in planning timeframe – Stakeholder requests: Further integrate and expand planning tools WECC engaged E3 and NREL to study operational needs using E3’s Renewable Energy Flexibility Model (REFLEX) – Funding for E3 work from WECC and WIEB (through ARRA) – Funding for NREL work from DOE

4. Project Team Partnership between WECC, WIEB, NREL and E3 – WECC & WIEB provide project oversight and direction – E3 directs technical work – NREL provides data, HPC resources and technical support E3 NREL WECC WIEB Stakeholders

5. Stakeholder Input Technical Advisory Group provides input on data, methodologies and assumptions – Includes representatives of utilities, advocacy groups, National Labs, Northwest Power and Conservation Council, EPRI Executive Advisory Group helps ensure study’s relevance to Western decision-makers – Jim Robb (WECC), Mark Rothleder (CAISO), Rebecca Wagner (NVPUC), Doug Larson (WIEB), Kimberly Harris (PSE), Mike Hummel (SRP), Gregg Lemler (PG&E), Bill Gaines (TCPL), Stacey Kusters (NVE), Stephan Bird (PAC), Elliot Manzier (BPA), Tom Imbler (Xcel) Periodic reporting to WIEB/SPSC, TEPPC, and other WECC committees

6. Study Goals Assess the ability of the fleet of resources in the Western Interconnection to accommodate high renewable penetration while maintaining reliable operations Quantify the size, magnitude and duration of operating challenges resulting from high renewable penetration Investigate potential flexibility solutions, including: – Renewable curtailment as an operational strategy – Regional coordination – Diverse renewable portfolio – Flexible supply and demand-side resources – Transmission – Energy storage Learn about how to do flexibility modeling and planning “Institutional” solutions “Physical” solutions

7. Cases Studied 2024 Common Case – Few reliability or flexibility issues anticipated – Primary purpose of case is calibration 2024 High Renewables Case(s) – Want to study a case with renewable penetration that is high enough to show interesting operational challenges – Composition of case TBD in consultation with technical and executive groups – Sensitivities to understand how composition of case affects flexibility challenges Alternative levels of wind, solar, & baseload renewables

8. Strawman High Renewables Case High renewables case should have enough wind and solar generation to illuminate significant flexibility constraints Test simulations of this “Strawman” case, while preliminary, provide some useful insights into challenges at higher penetrations to be shared today

9. Flexibility Study Sequence 1.Identify flexibility constraints under conservative assumptions – Demonstrate the magnitude and frequency of potential flexibility violations under the worst case 2.Relax constraints and demonstrate the efficacy of solutions that are available in the absence of investment – Renewable curtailment, flexible ties, increase ramp rates, decrease Pmin 3.Additional studies, depending on time and resources, exploring the benefits of investments in power system flexibility – Transmission, flexible generation & loads, energy storage

10. Schedule 10 W ESTERN E LECTRICITY C OORDINATING C OUNCIL  Jul 2014: Project kickoff  Aug 2014: Common case review complete  Sep 2014: First technical and executive group meeting  Oct 2014: RECAP analysis of Common Case complete  Nov 2014: RECAP analysis of High Renewables Case  April 2015: REFLEX analysis of Common Case  Apr 2015: REFLEX analysis of High Renewables Case  May 2015: Final report X X X X X X

11. Current Status 11 W ESTERN E LECTRICITY C OORDINATING C OUNCIL Insights from Model Testing – REFLEX constraints can be applied to multiple regions simultaneously Platform for flexibility analysis has been expanded to allow multi-zone simulation “Boundary conditions” in each region are endogenous to the model – Running REFLEX using a full “nodal” transmission topology is not feasible Achieving reasonable computation time requires simplifications to transmission representation – Three-day optimization window is infeasible due to model runtime Study will use two sequential one-day optimizations—each with a one-day look- ahead period—to simulate unit commitment in the second day of each draw

12. Timeline for REFLEX Analysis 12 W ESTERN E LECTRICITY C OORDINATING C OUNCIL Completion of the first full simulations of the Common Case is the next step—the remaining pieces of analysis should follow directly (schedule is relative to completion of initial Common Case runs): TRC Meeting – Common Case results +2 weeks High Renewable Case complete +4 weeks TRC Meeting – High Renewable Case +6 weeks Analysis of solutions +8 weeks Draft report +10 weeks TRC Meeting – report review +12 weeks Final report +16 weeks Current goal is to meet this key milestone by Aug 15

13. Development of High Renewable Case Assumptions 13 W ESTERN E LECTRICITY C OORDINATING C OUNCIL Development of assumptions for the High Renewable Case has been shifted and will take place in parallel to work on the REFLEX Common Case Goal in developing High Renewable Case: establish renewable portfolios that are interesting—but not extreme— from the perspective of system flexibility Largest challenge is expected to be oversupply: when a region’s must- take generation exceeds its demand for energy E3 will analyze patterns in net load for a range of portfolios to help inform mix/penetrations; to be shared with TRC early April Example April Day

14. Questions 14 W ESTERN E LECTRICITY C OORDINATING C OUNCIL

15. RECAP MODEL RESULTS

16. E3 Renewable Energy Capacity Planning Model (RECAP) Flexibility Assessment utilizes RECAP, E3’s non-proprietary model for evaluating power system reliability and resource capacity value under high renewable penetration Initially developed to support CAISO renewable integration modeling Used by a number of utilities and state commissions Will be transferred to WECC as part of study process

17. Calculating LOLP LOLP is determined by comparing the distributions of potential load and resource states and calculating the probably that load exceeds generation Gross load distribution Net thermal generation distribution LOLP comes from the chance that net load exceeds net thermal generation Gross load Net thermal generation LOLP

18. Adding Renewables After adding renewables to the system, net loads are reduced—distribution shifts to left LOLP decreases in every hour (nearly) Gross load distribution Net thermal generation distribution Net load distribution with renewables Renewable net load Gross load Thermal generation Reduction in LOLP with increase in renewables

19. Calculating ELCC Since LOLE has decreased with the addition of renewables, adding load will return the system to the original LOLE The amount of load that can be added to the system is the effective load carrying capability (ELCC) Original system LOLE LOLE after renewables Additional load to return to original system LOLE = ELCC

20. Portfolio vs. Marginal ELCC Values 1.The cumulative portfolio capacity value is used for resource adequacy planning – Due to the complementarity of different resources the portfolio value will be higher than the sum of each individual resource measured alone – May need to attribute the capacity value of the portfolio to individual resources There are many options, but no standard or rigorous way to do this 2.The marginal capacity value, given the existing portfolio, is used in procurement Provides a measure of the value of the next resource to be procured This value will change over time with the mix of system needs & resources Individual Solar Capacity Value Individual Wind Capacity Value Combined Capacity Value

21. Reliability Metrics The RECAP model calculates conventional power system reliability metrics: – Loss of Load Probability (LOLP) – Loss of Load Expectation (LOLE) – Loss of Load Frequency (LOLF) – Expected Unserved Energy (EUE) RECAP also calculates effective capacity of renewables, demand response, and other dispatch-limited resources: – Effective Load Carrying Capability (ELCC) LOLE Marginal ELCC Cumulative ELCC

22. Renewable penetration in the Common Case is approximately 20% of load (U.S. portion); wind and solar serve approximately 13% of load: E3 and NREL have developed production profiles to reflect the operational characteristics of these resources Common Case Renewable Mix

23. Target Planning Reserve Margins RECAP estimates reserve margins needed to achieve a target reliability threshold – LOLF = 1 event in 10 years Target PRM needed to meet standard varies by region Common Case above Target PRM for all regions TypeTarget PRM Common Case PRM Basin14%17% California13%25% Northwest15%32% Rockies17%19% Southwest15%32%

24. Marginal ELCC Curves by Technology and Region SouthwestCalifornia Marginal ELCC = capacity contribution of next increment of capacity of a given type Curves are illustrative – they assume a single technology

25. Marginal ELCC Curves by Technology and Region (Cont.) BasinRockies Northwest

26. Observations on ELCC Values Marginal ELCC of solar PV at low penetrations is 50-60% of nameplate capacity (except in NW) – Aligns well with commonly used heuristics At low penetrations, marginal ELCC values for wind range from 15-30% of nameplate capacity – Slightly higher than common heuristics ELCC values exhibit significant diminishing returns to scale, particularly solar PV which shifts net load peak into the evening – As penetration increases, heuristics become increasingly inaccurate

27. Effect of Diversity on ELCC Values For a diverse portfolio, ELCC of combined portfolio is higher than individual ELCC values – At 20% of load: W = 3041, S = 6172, W+S = 12,861

28. Ongoing uses for RECAP by WECC The need for ELCC in WECC’s planning studies will increase as the penetration of variable generation increases As part of the flexibility assessment project, the RECAP model will be transferred to WECC staff to help support modeling efforts – TEPPC Common Case development – Summer load assessments – Section 111(d) impacts As an open-source tool, RECAP can also be shared with or modified by stakeholders

29. REFLEX MODEL STATUS

30. E3’s Renewable Energy Flexibility (REFLEX) Model REFLEX answers critical questions about flexibility need through stochastic production simulation – Captures wide distribution of operating conditions through Monte Carlo draws of operating days – Illuminates the significance of the operational challenges by calculating the likelihood, magnitude, duration & cost of flexibility violations – Assesses the benefits and costs of investment to avoid flexibility violations Implemented as an add-on to Plexos for Power Systems

31. WECC Flexibility Assessment – Distinguishing Characteristics The use of REFLEX for PLEXOS in this study is different from conventional production cost modeling of the WECC in several important respects: 1.Economic tradeoff between upward (loss of load) and downward (curtailment) flexibility violations 2.Endogenous determination of load following reserves as function of expected within-hour flexibility deficiencies 3.Stochastic sampling of load, wind, solar, and hydro conditions 4.Sub-regional study footprints with specified boundary conditions Import/export limitations and maximum ramp rates

32. Renewable Dispatch is Used to Solve Upward Ramping Shortages Model needs robust information on cost of upward vs. downward shortages – Cost of unserved energy due to ramping shortfall: very high ($5,000-50,000/MWh) – Cost of renewable dispatch: replace the lost production ($50-$150/MWh) Limited Ramping Capability Unserved Energy Limited Ramping Capability Renewable Curtailment Strategy to Minimize Downward ViolationsStrategy to Minimize Upward Violations

33. Stochastic Sampling From a Range of Conditions In order to ensure robust sampling results, RECAP and REFLEX sample from a broad range of load, wind, & solar conditions – Historical data matched up based on month of year, day type (i.e. load level) Range of available data

34. Capturing Transmission in Flexibility Assessment Multiple options for representing interregional power flows have been tested 123 Original project plan Single-Zone Models Each region modeled independently with no internal transmission Imports and exports captured through supply curves Offers simplest modeling framework, but difficult to represent interregional power exchange Single-Zone Models Each region modeled independently with no internal transmission Imports and exports captured through supply curves Offers simplest modeling framework, but difficult to represent interregional power exchange Zonal Model Loads and resources grouped together by region Regions linked together by transport model Provides macro level view of interregional power exchange, but ignores individual line and path flow limits Zonal Model Loads and resources grouped together by region Regions linked together by transport model Provides macro level view of interregional power exchange, but ignores individual line and path flow limits Nodal Model All nodes in WECC (25,000) represented Dispatch solution is constrained by DC OPF and enforced line limits Provides greatest fidelity of transmission system, but requires significant development and is computationally intensive Nodal Model All nodes in WECC (25,000) represented Dispatch solution is constrained by DC OPF and enforced line limits Provides greatest fidelity of transmission system, but requires significant development and is computationally intensive Model Complexity Final project plan

35. Zonal Topology Zonal topology chosen based on aggregations of interregional WECC paths California Northwest Basin Rocky Mountain Southwest NW to CA P65 P66 CA to BS P24 P28 P29 SW to CA P46 RM to SW P31 BS to SW P35 P78 P79 All Paths P14: ID to NW P18: NT - ID P24: PG&E - Sierra P28: Intermtn - Mona P29: Intermtn – Gonder P30: TOT 1A P31: TOT 2A P35: TOT 2C P38: TOT 4B P46: WOR P65: COI P66: PDCI P76: Alturas Project P78: TOT 2B1 P79: TOT 2B2 P80: MT SE BS to NW P14 P18 P76 P80 BS to RM P30 P38

36. Strawman High Renewables Case High renewables case should have enough wind and solar generation to illuminate significant flexibility constraints Test simulations of this “Strawman” case, while preliminary, provide some useful insights into challenges at higher penetrations to be shared today

37. Strawman Limited-Draw Results: California Curtailment: ~6% RG Overgeneration occurs regularly and periodically, especially in the spring months Overgeneration is solar-driven and occurs in the middle of the day Hydro, pumped storage, and imports help to meet nighttime load April Day Strawman High Renewables All dispatchable plants reduce output to minimum levels Curtailment due to solar oversupply

38. Strawman Limited-Draw Results: Northwest Curtailment: ~3% of RG Overgeneration conditions occur during high hydro and/or high wind conditions Curtailment may be concentrated during nighttime or could persist through day if wind output remains high More day-to-day variability in conditions within seasons compared to regions with high solar penetration April Day Strawman High Renewables Curtailment due to simultaneous high wind & hydro conditions

39. Strawman Limited-Draw Results: Northwest Curtailment: ~3% of RG Overgeneration conditions occur during high hydro and/or high wind conditions Curtailment may be concentrated during nighttime or could persist through day if wind output remains high More day-to-day variability in conditions within seasons compared to regions with high solar penetration April Day #2 Strawman High Renewables During lower hydro conditions, high wind may not result in curtailment

40. Strawman Limited-Draw Results: Southwest Curtailment: ~3% of RG Coal plants are cycled down the middle of the day to accommodate solar, but curtailment still occurs Steep morning down-ramp and evening up- ramp of coal, hydro, and gas are challenging operational conditions April Day Strawman High Renewables Large coal ramps require further investigation Curtailment due to solar oversupply

41. Strawman Limited-Draw Results: Rocky Mountains Curtailment: <1% of RG Frequent ramping of coal plants indicates system is stressed although large-scale curtailment is not observed in the region Suggests possible trade-off between curtailment and coal cycling Strawman High Renewables April Day Large coal ramps require further investigation

42. Strawman Limited-Draw Results: Basin Curtailment: <1% of RG Steep up- and down-ramps as coal is cycled down in the middle of the day to accommodate solar Basin has the least amount of curtailment of all regions Strawman High Renewables April Day Large coal ramps require further investigation