Western Interconnection Flexibility Assessment Update to TEPPC May 7, 2015 Arne Olson, Partner

About the Study WECC and WIEB are interested in understanding power system reliability and flexibility needs under higher renewable penetration There have been many stakeholder requests for studies to help understand the operational issues associated with higher renewables WECC is interested in understanding and gaining experience with new reliability planning tools WECC and WIEB engaged E3 and NREL to study operational needs Funding for E3 work from WECC and WIEB (through ARRA) Funding for NREL work from DOE

“Institutional” solutions 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 dispatch as an operational strategy Regional coordination Flexible supply and demand-side resources Energy storage Transmission Learn about how to do flexibility modeling and planning “Institutional” solutions “Physical” solutions

Project Team Partnership between WECC, WIEB, NREL and E3 WECC and WIEB provide general project oversight role E3 directs technical work NREL provides data, HPC resources, technical support, and insights from previous experience E3 Arne Olson, Partner Nick Schlag, Project Manager Dr. Elaine Hart Ryan Jones Ana Mileva NREL Bri-Mathias Hodge, Project Manager Carlo Brancucci Martinez-Anido Greg Brinkman WECC Dan Beckstead Vijay Satyal Donald Davies Branden Suddeth WIEB Tom Carr Maury Galbraith

Project Elements Renewable energy capacity value analysis Effective load-carrying capability (ELCC) method using E3’s Renewable Energy Capacity Planning Model (RECAP) Power system flexibility analysis Regional model using E3’s Renewable Energy Flexibility (REFLEX) model for PLEXOS run on NREL’s High Performance Computing environment Stakeholder participation opportunities Technical review committee Executive advisory group

Cases Studied 2024 Common Case 2024 High Renewables Case Few reliability or flexibility issues anticipated Primary purpose of case is calibration 2024 High Renewables Case Renewable penetration that is high enough to show interesting operational challenges Composition of case now being determined in consultation with technical review committee

Regional Focus Study focuses on five regions Explicitly control interactions between regions through modeling of interties Most regions share characteristics appropriate for a resource planning study: Similar weather and load patterns across the region Limited internal transmission constraints Some degree of regional coordination already Limited reliance on other regions

RECAP MODEL RESULTS

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

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

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

Additional load to return to original system LOLE 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 Additional load to return to original system LOLE = ELCC LOLE after renewables

Portfolio vs. Marginal ELCC Values 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 Individual Solar Capacity Value Individual Wind Capacity Value Combined Capacity Value 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

Reliability Metrics LOLE 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) Marginal ELCC Cumulative ELCC

Common Case Renewable Mix 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

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 Type Target PRM Common Case PRM Basin 14% 17% California 13% 25% Northwest 15% 32% Rockies 19% Southwest

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

Marginal ELCC Curves by Technology and Region (Cont.) Basin Rockies Northwest

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

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

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

REFLEX MODEL STATUS

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

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: Economic tradeoff between upward (loss of load) and downward (curtailment) flexibility violations Endogenous determination of load following reserves as function of expected within-hour flexibility deficiencies Stochastic sampling of load, wind, solar, and hydro conditions Sub-regional study footprints with specified boundary conditions Import/export limitations and maximum ramp rates

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 Renewable Curtailment Strategy to Minimize Downward Violations Strategy to Minimize Upward Violations

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

Capturing Transmission in Flexibility Assessment Multiple options for representing interregional power flows have been tested Original project plan Model Complexity 1 2 3 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 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 Final project plan

Zonal Topology Zonal topology chosen based on aggregations of interregional WECC paths 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 Northwest BS to NW P14 P18 P76 P80 Basin Rocky Mountain BS to RM P30 P38 NW to CA P65 P66 CA to BS P24 P28 P29 BS to SW P35 P78 P79 California RM to SW P31 Southwest SW to CA P46

Regional Ramping Constraints Production simulation models tend to overstate ramps on interties compared to historical levels Constrained case limits intertie ramps based on historical levels Example: Historical vs. Modeled Flows over Path 46 Upward Ramps Duration (hrs) Downward Ramps Source: http://www.wecc.biz/Lists/Calendar/Attachments/5076/130124_DWGMeeting_E3_Presentation-PCM.pdf

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

Strawman Limited-Draw Results: California April Day 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 Curtailment due to solar oversupply Strawman High Renewables All dispatchable plants reduce output to minimum levels

Strawman Limited-Draw Results: Northwest April Day 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 Curtailment due to simultaneous high wind & hydro conditions Strawman High Renewables

Strawman Limited-Draw Results: Northwest April Day #2 During lower hydro conditions, high wind may not result in curtailment 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 Strawman High Renewables

Strawman Limited-Draw Results: Southwest April Day 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 Curtailment due to solar oversupply Strawman High Renewables Large coal ramps require further investigation

Strawman Limited-Draw Results: Rocky Mountains April Day 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 Large coal ramps require further investigation

Strawman Limited-Draw Results: Basin April Day 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 Large coal ramps require further investigation

NEXT STEPS

General Project Progress Develop RECAP input data Assess reliability of the 2024 Common Case Develop REFLEX input data Implement 2024 Common Case in REFLEX for PLEXOS Develop assumptions for High Renewables Case Conduct flexibility assessment of 2024 Common Case Assess reliability of High Renewables Case Conduct flexibility assessment of High Renewables Case Investigate flexibility solutions for High Renewables Case

Updated Timeline Updated timeline based on progress made to date in Common Case modeling

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