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WECC 2026 Common Case Capacity Assessment with RECAP
August 16, 2016 Nick Schlag, Managing Consultant Arne Olson, Partner Ryan Jones, Evolved Energy Research
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WECC Capacity Planning Exercise
WECC and E3 used a tool called the Renewable Energy Capacity Planning (RECAP) model to assess capacity needs in the 2026 common case RECAP model is open-source and will be an in-house resource for WECC RECAP showed that with modest resource sharing, all regions exceed reliability targets in the common case Marginal capacity value for renewables changes substantially between now and 2026 based on the expected renewable build
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2026 Common Case RECAP Study Methodology
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E3 Renewable Energy Capacity Planning Model (RECAP)
E3 has developed a non-proprietary model for evaluating power system reliability and resource capacity value within high penetration renewable scenarios Initially developed to support CAISO renewable integration modeling Used by a number of utilities and state commissions CPUC, SMUD, FPL, PGE, EPE, CAISO
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RECAP Overview and Functionality
RECAP is a convolution-based loss of load probability (LOLP) model Outputs include basic reliability statistics, the target PRM, and the capacity value of variable generation 20,000 21,000 22,000 LOLP comes from the chance that net load exceeds net thermal generation Net dispatchable generation distribution Net load distribution Net load Net dispatchable generation LOLP
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Generation Adequacy Planning
Accepted industry practice for adequacy planning is a two-step process: Develop reliability study to determine quantity of resources needed to meet defined reliability target We use a normalized expected unserved energy (Norm-EUE) standard of 0.001% (99.999% load is served) The stringency of this Norm-EUE target is in between the two most common interpretations of “1 day in 10 years” [1] Translate required quantity of resources into a Planning Reserve Margin (PRM) heuristic for easy application to multiple processes PRM defined as % of typical year (1-in-2) peak load [1]
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Study Assumption Summary
Alberta Eight WECC regions with imports/exports Monthly capacity and outage rates from 2026 common case Historical hydro used for hydro 4-hr peaking capability Multi-year load and renewable profiles WECC Flexibility Study British- Columbia NWPP Basin CA-North RMPA CA-South AZ-NM-NV
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Import & Export Assumptions
1,525 MW 0 MW 0 MW 0 MW Summer Net Imports Winter Net Imports -4,625 MW -1,525 MW 2,255 MW 250 MW 500 MW 0 MW 625 MW 464 MW 5,054 MW 2,556 MW -3,445 MW -1,942 MW Imports/exports assumptions for planning are more constraining than path ratings Negative values indicate net exports
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The Impact of High Renewable Penetrations
A resource’s contribution towards reliability depends on the other resources on the system The diminishing marginal peak load impact of solar PV is illustrative of this concept While the first increment of solar PV has a relatively large impact on peak, it also shifts the “net peak” to a later hour in the in day This shift reduces the coincidence of the solar profile and the net peak such that additional solar resources have a smaller impact on the net peak
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2026 Common Case RECAP Results
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Regional Capacity Needs in the 2026 Common Case
All regions except for Alberta and AZ-NM-NV shown to have surplus capacity Capacity shortfalls in Alberta and AZ-NM-NV can be eliminated without new capacity by adjusting import & export assumptions Allowing imports within path ratings clears capacity needs Negative values indicate surplus capacity Reducing unnecessary exports clears capacity shortage
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Renewable Capacity Value
Alberta AZ-NM-NV Basin British-Columbia CA-North CA-South NWPP RMPA Wind Capacity 2,385 2,513 3,068 849 2,390 5,974 10,670 2,797 PV Capacity 3,954 1,332 9,935 15,228 312 1,156 CSP Capacity 967 132 6 1,520 Wind ELCC 414 606 718 308 1,137 1,039 1,232 679 PV ELCC 1,668 696 2,279 4,192 74 524 CSP ELCC 654 86 1 847 Portfolio Value 2,929 1,499 3,417 6,078 1,307 1,203 Wind ELCC (%) 17% 24% 23% 36% 48% 12% PV ELCC (%) 42% 52% 28% 45% CSP ELCC (%) 68% 65% 21% 56% Initially, solar PV has a capacity value of 5%, but after 11 GW of wind, summer peaks in the NW become more critical and the capacity value of solar increases CA-North wind has very high marginal capacity value using NREL wind toolkit while the common case profile gives value of 18% Marginal capacity value of 51%, when the first MW of solar is installed, drops to 6% after 15,000 MW are installed
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Target Planning Reserve Margins
Each target planning reserve margin results in equivalent power system reliability Normalized Expected Unserved Energy = 0.001% Large amount of ‘firmed’ imports reduces PRM Hydro dominated systems show higher reliability at lower PRM
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Conclusions & Recommendations
No need for ‘generic’ resources in 2026 Common Case for reliability purposes Small capacity shortages in AZ-NM-NV and Alberta can be solved by reducing unnecessary exports or allowing imports, respectively Wind and solar production on peak varies greatly by region and changes with resource penetration LOLP modeling is necessary to accurately evaluate renewable resource capacity value Renewable profiles of different origins show large differences Recommendation to use actual renewable production data where possible and to gather multiple years of data necessary for reliability assessments
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Thank You! Energy and Environmental Economics, Inc. (E3) Montgomery Street, Suite San Francisco, CA Tel Web Arne Olson, Partner Nick Schlag, Sr. Consultant Ryan Jones
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RECAP MODEL METHODOLOGY
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RECAP Model flow chart Generator Module Net Load Module
Hourly load Hydro NQC Hourly solar Hist. imports Thermal fleet Hourly wind Import limits Generator Module Net Load Module Transmission Module Outage Probability Table Net Load Distributions Import Probability Distributions LOLP Module LOLP/LOLE LOLF & EUE Target PRM MW of Need ELCC
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RECAP state based model overview
Convolution based approach for determining loss of load probability in month/hour/day-type slices Using LOLP, the model calculates LOLE, annual LOLP, EUE Four-step LOLP calculation: Step 1: calculate hourly net load distributions Step 2: calculate capacity outage probability table Step 3: calculate distribution for imports Step 4: calculate probability that supply < net load in each time period
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Reliability Metrics The RECAP model calculates conventional power system reliability metrics: Loss of Load Probability (LOLP) Loss of Load Expectation (LOLE) Expected Unserved Energy (EUE) RECAP also calculates effective capacity of renewables, demand response, and other dispatch-limited resources: Effective Load Carrying Capability (ELCC)
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Creating gross load distributions
Using the large load sample size, probability distributions are created for each month/hour/day-types (576 total distributions) Weekday 12 Months 24 Hours Example Load Probability Distribution September HE 13 PST Weekend 12 Months 24 Hours 12 x 24 x 2 = 576 Total Distributions
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Calculating LOLP The result is a unique pair of distributions (thermal generation and net load) for all 576 month/hour day-types Then it is possible to calculate the loss of load probability (LOLP) for each pair of distributions 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
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Adding Renewables After adding renewables to the system, the net load distribution shifts to the left LOLP decreases (or at least stays the same) in every hour 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
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Additional load to return to original system LOLE
Calculating ELCC The amount of load that can be added to the system while maintaining reliability is the effective load carrying capability (ELCC) of a generator ELCC was established in the 1960s and has been a common metric for conventional generation for decades. In the past 10 years it has been adopted for variable generation. Original system LOLE Additional load to return to original system LOLE = ELCC LOLE after renewables Garver, L.L., "Effective Load Carrying Capability of Generating Units," Power Apparatus and Systems, IEEE Transactions on , vol.PAS-85, no.8, pp.910,919, Aug. 1966
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Load Regression Steps Load can be predicted using fundamental drivers
Temperature Daylight/solar Day-of-week and holidays Economic conditions Historical weather can put recent load record in historical context and allow backcasting using current economic conditions Load shapes are developed for each load area (BA) independently and are aggregated to produce a regional hourly load profile Historical Hourly Load [ ] Historical Weather Data [ ] Fit NN Regression on Daily Energy Simulated 2012 Daily Load [ weather] Scale historical hourly load shapes by daily energy Simulated 2012 Hourly Load [ weather] Scale simulated hourly load shape to forecast annual demand & peak Simulated 2026 Hourly Load [ weather]
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Example Simulation of Hourly Load
Neural network regression is used to create a predicted load shape based on weather indicators from Example: Pacific Northwest,
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Wind and solar profiles
Wind ( ) and solar ( ) are from NREL Wind & Solar Toolkits and were previously used in the E3/NREL WECC Flexibility Study Multiple years of data are necessary for a robust reliability assessment Northern-CA August Wind Profile Significant differences have been identified between profiles of different sources and more study is necessary for a full reconciliation Not a focus of this study once profile source was shown to have no directional impact on results
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Alberta resource assessment
Net Imports - Hydro 942 Bio 377 CC 4,461 ST 5,068 CT 4,696 DC-Intertie DR EE ES Geo ICE 19 MotorLoad PS Wind ELCC 414 PV ELCC CSP ELCC sum 15,976 peak load 14,472 starting PRM 10% starting LOLE 18.35 capacity shortage 555 Alberta
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AZ-NM-NV resource assessment
Net Imports (3,445) Hydro 1,808 Bio 47 CC 16,370 ST 11,816 CT 7,943 DC-Intertie 200 DR 759 EE - ES Geo 32 ICE MotorLoad PS 207 Wind ELCC 606 PV ELCC 1,668 CSP ELCC 654 sum 38,666 peak load 34,482 starting PRM 12% starting LOLE 4.75 capacity shortage 602 AZ-NM-NV
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Basin resource assessment
Net Imports 2,255 Hydro 3,475 Bio 65 CC 2,613 ST 7,007 CT 1,735 DC-Intertie - DR 1,035 EE ES Geo 911 ICE 160 MotorLoad PS Wind ELCC 718 PV ELCC 696 CSP ELCC 86 sum 20,754 peak load 15,800 starting PRM 31% starting LOLE E-05 capacity shortage (2,800) Basin
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British-Columbia resource assessment
Net Imports 1,525 Hydro 17,227 Bio 789 CC 265 ST 23 CT 148 DC-Intertie - DR EE ES Geo ICE MotorLoad PS Wind ELCC 308 PV ELCC CSP ELCC sum 20,285 peak load 13,064 starting PRM 55% starting LOLE capacity shortage (5,803) British- Columbia
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CA-North resource assessment
Net Imports 500 Hydro 7,527 Bio 887 CC 9,512 ST 165 CT 4,483 DC-Intertie - DR 971 EE ES Geo 1,034 ICE 303 MotorLoad (812) PS 2,096 Wind ELCC 1,137 PV ELCC 2,279 CSP ELCC 1 sum 30,081 peak load 24,911 starting PRM 21% starting LOLE 0.27 capacity shortage (1,467) CA-North
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CA-South resource assessment
Net Imports 5,054 Hydro 1,916 Bio 559 CC 12,738 ST 2,308 CT 8,105 DC-Intertie - DR 1,297 EE 180 ES 1,094 Geo 1,895 ICE 2 MotorLoad (1,263) PS 1,448 Wind ELCC 1,039 PV ELCC 4,192 CSP ELCC 847 sum 41,409 peak load 37,018 starting PRM 12% starting LOLE 0.54 capacity shortage (1,344) CA-South
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NWPP resource assessment
Net Imports (1,525) Hydro 30,470 Bio 722 CC 7,276 ST 3,320 CT 1,440 DC-Intertie 200 DR 222 EE - ES Geo ICE 300 MotorLoad (282) PS 500 Wind ELCC 1,232 PV ELCC 74 CSP ELCC sum 43,950 peak load 33,562 starting PRM 31% starting LOLE E-07 capacity shortage (6,247) NWPP
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RMPA resource assessment
Net Imports 625 Hydro 1,366 Bio 4 CC 3,349 ST 6,609 CT 2,897 DC-Intertie 730 DR 525 EE - ES Geo 10 ICE 218 MotorLoad PS 554 Wind ELCC 679 PV ELCC 524 CSP ELCC sum 18,090 peak load 14,823 starting PRM 22% starting LOLE 0.07 capacity shortage (1,156) RMPA
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