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Valuing Capacity for Resources with Energy Limitations Independent Assessment Kevin Carden 2-15-2019 Executive Summary Capacity value of 4-hour+ resources is high in the planning window To accurately capture capacity value, accurate

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Valuing Capacity for Resources with Energy Limitations – Independent Assessment

Kevin Carden 2-15-2019

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Executive Summary

§ Capacity value of 4-hour+ resources is high in the planning window § To accurately capture capacity value, accurate load and resource representation critical in study framework

§ Wide range of weather years § Economic commitment and dispatch § Realistic diversity between regions

§ Capacity value changes as penetration and composition of energy limited resources change, and as renewable resources are added

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3

Overview

§ Base Case Results Update

§ Astrapé Neighbor Modeling Review § Results § High Renewable Scenarios

§ 2025 with 37% Renewable § 2030 with 50% Renewable

§ GE Input and Framework Simulation Comparison

§ Load Shapes § Commitment Methodology § Transmission Limitations

§ Single Zone Scenario

§ Conclusions

§ Study Framework Requirements § Study Update Frequency

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Base Case Results Update

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Astrapé Neighbor Modeling Review

§ Neighbor Modeling in SERVM

§ Neighbors are modeled at target reliability (0.1 LOLE) § Neighbors are modeled with existing energy limited and emergency resources

§ Neighbors are not allowed to sell from emergency resources

§ Load data was further reviewed for historical correlation

§ An error was corrected in PJM load data

Peak Load Load Diversity (MW) (% below non-coincident 50/50 peak) Non-Coincident Peak Load At System Coincident Peak At NYISO Coincident Peak New Old New Old NYISO 36,427

  • 5.9%
  • 10.7%

0.0% 0.0% PJM 163,597

  • 0.9%
  • 4.1%
  • 3.8%
  • 16.9%

ISONE 26,762

  • 7.9%
  • 12.9%
  • 3.3%
  • 3.2%

IESO 24,404

  • 9.1%
  • 10.2%
  • 14.2%
  • 14.5%

System 291,297 0.0% 0.0%

  • 2.3%
  • 6.6%

Astrapé Load Summary

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4 Hour Duration Results

*All energy limited resource portfolios include 1408 MW of 8-hour PSH. 60% 65% 70% 75% 80% 85% 90% 95% 100% 1000 2000 3000 Capacity Value (%) Additional ELR above existing PSH (MW)

Approximate 2019 ELR Penetration Potential 2025 ELR Penetration with 2025 Potential Renewable (37%) Potential 2030 ELR Penetration with 2030 Potential Renewable (50%)

Fractional Capacity Value

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2019 Resource Mix: 1-8 Hour Duration Results

*All SERVM energy-limited resource portfolios include 1408 MW of 8-hour PSH *All results from 2019 resource mix *Potomac results converted to represent average capacity value 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2 4 6 8 10 Capacity Value (%) Duration (Hours)

PSH + 1000 MW PSH + 2000 MW Potomac PSH + SCR + 1000 MW

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Renewable Shapes and Capacity Amounts

Study Year 2019 2025 2030 Hydro Energy (GWh) 27,721 27,721 27,721 Solar Energy (GWh) 42 13,234 24,245 Wind Energy (GWh) 4,384 16,297 26,436 Total Renewable (GWh) 32,147 57,252 78,402 Total Renewable (% of Load) 21% 37% 50%

§ Astrapé constructed a renewable portfolio that reached 50% penetration by 2030.

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Net Load Shape Comparison

*Net Load = Gross Load – Solar Energy – Wind Energy – Hydro Energy 5000 10000 15000 20000 25000 30000 5 10 15 20 25 MW Hour of Day Gross Load 2019 Net Load 2025 Net Load 2030 Net Load *August 2016 Load Data

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2025 Renewables

§ Addition of renewable energy steepens daily net load shape, shortening the need for duration.

*All energy limited resource portfolios include 1408 MW of 8-hour PSH.

Penetration (MW) 4 Hour Fractional Capacity Value for 2019 Resources (%) 4 Hour Fractional Capacity Value for 2025 Resources (%) PSH + 2000 MW 86.1% 100.0% PSH + 3000 MW Not Studied 94.8%

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2030 Renewables

§ Further additions continue to steepen the daily net load shape, further reducing the need for duration.

Penetration (MW) Capacity Value (%) 4 Hour Duration 6 Hour Duration PSH + 2000 MW 100.0% 100.0% PSH + 3000 MW 100.0% 100.0%

*All energy limited resource portfolios include 1408 MW of 8-hour PSH.

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GE Input and Framework Simulation Comparison

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GE MARS Comparison – Load Shapes

§ SERVM simulations were performed with IRM Load Shapes § IRM load shapes show lower value for all durations simulated

Penetration (MW) Capacity Value (%) 4 Hour Duration 6 Hour Duration Astrapé Load Shapes IRM Load Shapes Astrapé Load Shapes IRM Load Shapes PSH + 1000 MW 97.8% 87.7% 100.0% 96.6% PSH + 2000 MW 86.1% 80.6% 97.6% 94.5%

*All energy limited resource portfolios include 1408 MW of 8-hour PSH.

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GE MARS Comparison – Commitment Method

§ SERVM simulations were performed using must-run commitment to mimic GE MARS § Must run commitment does not capture correct shape of generator

  • utages

Penetration (MW) Capacity Value (%) 4 Hour Duration 6 Hour Duration Economic Commitment Must Run Commitment Economic Commitment Must Run Commitment PSH + 1000 MW 97.8% TBD 100.0% TBD PSH + 2000 MW 86.1% TBD 97.6% TBD

*All energy limited resource portfolios include 1408 MW of 8-hour PSH.

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Transmission Limit Scenario

§ The IRM process requires artificial movement of generators across zones

§ This surfaces unrealistic reliability events, but still uses the original transmission constraints

§ Astrapé relaxed constraints slightly instead of moving generators § Results were still very similar

Penetration (MW) Capacity Value (%) 4 Hour Duration 6 Hour Duration Relaxed Constraints Transmission Limited Relaxed Constraints Transmission Limited PSH + 1000 MW 97.8% 95.4% 100.0% 99.3% PSH + 2000 MW 86.1% 85.8% 97.6% 92.7%

*All energy limited resource portfolios include 1408 MW of 8-hour PSH.

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Single Zone Scenario

§ Zone J modeled with all energy-limited capacity § Little difference between single zone analysis and control area results

Penetration (MW) Capacity Value (%) 4 Hour Duration 6 Hour Duration Zone J NYISO Zone J NYISO PSH + 1000 MW 97.4% 97.8% 97.5% 100.0% PSH + 2000 MW TBD 86.1% TBD 97.6%

*All energy limited resource portfolios include 1408 MW of 8-hour PSH.

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Drivers of Differences from GE Study

Driver Astrapé Approach GE Approach Treatment of Load Uncertainties Use 38 Years of Historical Weather Patterns; 5 Economic Load Forecast Uncertainties Scale Weather Shapes Using the Same Multiplier Every Hour; 3 Weather Shapes; 7 Load Forecast Uncertainties Diversity with Neighbors 38 Years of Historical Diversity Artificial Diversity for Top 3 Load Days Treatment of Resource Interactions Endogenous Treatment

  • f all Interactions

Post-Processing of Energy Limited Dispatch Commitment Method Economic Commitment and Dispatch Must-Run Commitment Internal Transmission Constraints IRM Base Case with Slight Relaxation IRM Base Case with Generator Relocation

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Conclusions

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Conclusions

§ Capacity value of 4-hour+ resources is high in the planning window § To accurately capture capacity value, accurate load and resource representation critical in study framework

§ Wide range of weather years § Economic commitment and dispatch § Realistic diversity between regions

§ Capacity value changes as penetration and composition of energy limited resources change, and as renewable resources are added

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Appendix

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EFOR vs EFORd

!"# = !"% !"% + '% !"#( = !"%( !"%( + '%

NYCA SERVM EFOR 12.9% SERVM EFORd 7.2%

!"%( = Hours forced out AND unit would have been operated

FOH = 12 FOHd = 6

!"# =

)* )*+),, = 10.7%

!"#( =

2 2+),, = 5.6%

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Astrapé Resource Adequacy Clients

Southern Company Astrapé Clients – Economic/Physical TVA Duke MISO Astrapé Clients –Physical Reliability CPUC PG&E ERCOT PNM Entergy CLECO Santee Cooper NCEMC SPP AESO

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SERVM Framework

§ Capture Uncertainty in the Following Variables § Weather (38 years of weather history) § Impact on Load and Resources (hydro, wind, PV, temp derates on thermal resources) § Economic Load Forecast Error (distribution of 5 points) § Unit Outage Modeling (100s of iterations) § Multi-Area Modeling – Pipe and Bubble Representation § Total Base Case Scenario Breakdown

x =

190 Load Scenarios

x

100 Unit Outage Draws

=

19,000 8760 Hour Simulations 38 Weather Years (Equal Probability) 5 LFE Points (Associated Probabilities) 190 Load Scenarios (Associated Probabilities)

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Incorporating Weather Uncertainty for Load

  • Collect Recent Hourly Loads
  • Collect Recent Weather Data
  • Normalize to Single Base Year
  • Train using Neural Network Software
  • Collect 1980-2017 Temperature
  • 1. Develop

Load/Weather Relationship

  • 2. Apply

Relationship to Create Synthetic Shapes

  • 3. Scale Loads

from Base Year to Future Study Year

  • 4. Simulate Study

Year with Each Shape

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Peak Load Variability by Weather Year

  • 15.0%
  • 10.0%
  • 5.0%

0.0% 5.0% 10.0% 15.0% 1992 2004 1996 2014 2000 1984 1982 1985 1989 1990 1987 2017 1986 2009 1998 1994 1995 2003 2007 1983 2005 1997 1988 2008 1991 2015 2002 2016 1980 1981 1993 2006 2012 2010 1999 2001 2011 2013 Percentage from Normal Peak Weather Year

12.9%

  • 9.5%
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26 25000 27000 29000 31000 33000 35000 37000 39000 20 40 60 80 100 Load (MW) Hours Per Year IRM Loads Compared to Historical Load Shapes

IRM Historical Astrapé Modeled

2 4 6 8 10 12 14 16 18 25000 30000 35000 40000 Days per Year Load Threshold (MW) Frequency of Days with >4 Hours Above Load Threshold

Astrapé IRM

Effect of Load Scaling for Uncertainty

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Load Forecast Uncertainty and Forward Period

§ Non-weather load forecast error increases with forward period § Each weather shape simulated with each LFE and associated probabilities

3-Year Forward LFE

Discrete LFE Error Points Modeled

Non-Weather Forecast Error

With Increasing Forward Period

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Unit Outage Modeling

§ Full Outages

§ Time to Repair § Time to Failure

§ Partial Outages

§ Time to Repair § Time to Failure § Derate Percentage

§ Startup Failures § Maintenance Outages § Planned Outages § Created Based on Historical GADS Data

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Cumulative MW Offline % of time Solving by Convolution Actual History

§ Multi State Frequency and Duration Modeling vs Convolution SERVM’s multi state modeling is designed to capture the tails which is essential to risk based

  • studies. Simple

convolution methods do not capture these risks.

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Multi-Area Modeling

§ Pipe and Bubble Representation with import and export constraints § Constraints can be constants, distributions, tied to load level, or input by month § Ties can be modeled with random outages § Areas will share resources based on economic pricing and physical constraints § Load/Wind/Hydro diversity is embedded in each region’s input data

G A H B F E D C

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Energy Limited Duration Approach

§ Study Steps

§ Model all loads and resources in NYCA, ISO-NE, PJM, IESO, HQ

§ Include existing PSH with constraints in NYCA § Include energy limited resources (DR and PSH) in neighboring regions

§ Calibrate reliability in NYCA and neighboring regions to 0.1 LOLE § Add energy limited capacity § Remove perfect (no duration limit and no forced outage rate) conventional capacity until NYCA reliability again meets 0.1 LOLE § Fractional capacity value = Perfect capacity removed / energy limited capacity added

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Key Assumptions

§ Simulated at criterion for NYCA and neighbors § Reserves fully exhausted before shedding firm load § Capacity value instead of ELCC § Energy limited resources compared to perfect capacity § Endogenous simulations § 2019 resource mix § Existing pumped storage hydro always modeled with 8-hour duration § Magnitude of each portfolio directly comparable to GE portfolios, although composition is different due to PSH treatment.

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Forced Outage Rate Discussion

§ Astrapé modified transmission constraints rather than shifting generators because of the forced-outage rate effect asymmetry present in the GE MARS simulations.

Figure Source: “Valuing Capacity for Resources with Energy Limitations” Slide 43

https://www.nyiso.com/documents/20142/3698135/09242018%20Capacity%20Value%20of%20Resources%20with%20Energy%20Limitations.pdf/c271ef4f-6378-72ac-203e-c59ff3884ef8

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4 Hour Duration Results

*All energy limited resource portfolios include 1408 MW of 8-hour PSH. Additional ELR Above Existing PSH (MW) Capacity Value (%) 100 100.0% 250 100.0% 500 100.0% 1000 97.8% 2000 MW with 2025 Renewable 100.0% 3000 MW with 2030 Renewable 100.0%

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2019 1-8 Hour Duration Results

*All SERVM energy-limited resource portfolios include 1408 MW of 8-hour PSH *All results from 2019 resource mix *Potomac results converted to represent average capacity value Duration (Hours) Capacity Value (%) PSH + 1000 MW PSH + 2000 MW Potomac PSH + SCR + 1000 MW 1 54.1 38.4 2 75.4 60.7 67.3 4 97.8 86.1 97.1 6 100.0 97.6 8 100.0 100.0