1. Document number DNV KEMA KERMIT Overview ERCOT Joint Regional Planning Group / Long Term Study Task Force Austin, TX October12, 2012 1

2. Agenda  Project Goals  Calibrating KERMIT to ERCOT  Building Scenario 1  Results  Discussion 2

3. Agenda  Project Goals  Calibrating KERMIT to ERCOT  Building Scenario 1  Discussion 3

4. Overview of Project Requirements  Deliver a tool to ERCOT capable of analyzing ERCOT’s system resources to: - Ensure adequate grid reliability; - Maintain system frequency within current NERC standards; - Provide for timely replacement of lost resources due to unit outages or unit variability; - Adequate control for risks due to unforeseen future occurrences in the real-time operations time-frame  DNV KEMA will develop: - A calibrated version of KERMIT for ERCOT’s system - Two future scenarios that ERCOT can use for the Long Term Study - New demand response modules for KERMIT to reflect potential future market participants  In addition, DNV KEMA will help support ERCOT in their KERMIT analyses to help ensure the objectives of ERCOT’s Long Term Study are met 4

5. Study Benefits of Utilizing KERMIT  The primary benefit will be to support the long term transmission planning process  This includes: - The ability to examine and verify adequate ancillary service requirements - Ensuring transmission plans are able to maintain stability in the event of a generator outage or significant system event - Ensuring dispatch solutions for transmission networks are feasible and reliable - Testing future alternative market products or policy requirements for their effect on transmission flows  The results of KERMIT analyses help ensure efficient and necessary investments are made in transmission paths and upgrades for future system conditions 5 KERMIT helps ensure efficient investments are made that reduce future costs and minimize risks

6. Overview of KERMIT  Developed using two software platforms: - Matlab / Simulink for performing simulations - Microsoft Excel for entering data  What is KERMIT? - KEMA’s Renewable Market Integration Tool - Originally developed to study how integrating large penetrations of renewable power affects sub-hourly operations  KERMIT has expanded in scope and is now a tool for systems to examine operational strategies for handling variability in their system  This includes: - Renewable integration studies - Automatic generation control design and development - Evaluating the benefits of increased storage deployment - Analysis support for federal and ISO/RTO policy development 6

7. KERMIT Time Scales of Focus 7

8. Agenda  Project Goals  Calibrating KERMIT to ERCOT  Building Scenario 1  Discussion 8

9. Overview of KERMIT Architecture 9 Inputs: Load Plant Schedules Generation Portfolio Grid Parameters Market/Balancing Outputs: Power Plant MW Outputs Area Interchange Frequency Deviation Scenarios: Increasing Wind Adding Reserves Storage Parameters Test AGC Parameters Trip Events KERMIT 24h Simulation Generation Conventional Renewable Inter- connection Frequency Response Real Time Market Generator trip Load rejection Wind power forecast versus actual Volatility in renewable resources

10. Grid Modeling – Calibrating KERMIT 10 Inputs: Load (PI Historian) Plant Schedules (SCED) Generation Portfolio (RARF) Grid Parameters Wind Production Outputs: Power Plant MW Outputs Frequency Deviation Scenarios: Test AGC Parameters Trip Events KERMIT 24h Simulation Generation Conventional Renewable Frequency Response Generator trip We simulated a generator trip in KERMIT to replicate observed 780 MW generator trip on Feb 15

11. Guide to Calibration  Step 1 – Estimate inertia - Inertia estimated by observations of system frequency deviations from 60 Hz after large trip events - Feb 15 – At 16:40 MLSES_Unit3 tripped thereby removing 780 MW in a 4s period - Inertia (M) estimated via the following formula:  Initial estimate for M during hour 16 is 13,448 MWs/MVA - This provides a starting point for setting inertia multipliers for generators on system 11

12. Guide to Calibration  Step 2 – Refine Inertia and Load response feedback loop gain - Examine hour when unit tripped offline - Seek for inertia and load response values that give the correct maximum frequency decline  Step 3 – Turn AGC on and iteratively adjust - Transport delays - Integral control gain (minimize sustained frequency offset errors) - Smoothing of ACE signal - Previous parameters (mainly load response) - Goal is to match maximum frequency decline and then rate of recovery of frequency  Step 4 – Analyze results for other periods of time - Ensure calibration settings for one day are sufficient for most days - Measure results based on ability to replicate frequency deviation and recovery time 12

13. KERMIT Calibration - Details  KERMIT Setup - Used 4-sec PI historian data for system load, DC tie flows, and wind generation - Used SCED 5-min base points generator dispatch to take care of generator dispatch - DC tie flows were modeled as MW source points and kept on a fixed schedule - Integral gain set to zero for AGC, other parameters (deadzone, etc) set to data received  KERMIT Modifications - ERCOT is, in the view of KERMIT, an island system - KERMIT needs to have two areas, even for island systems - As a result, used a trick - Introduce a second balancing area with very low load and interconnection (1 MW) - Set load and generation within area equal to each other to limit flows across 1 MW interconnection  Calibration - We calibrate to a large generator trip to replicate severe frequency deviations and recovery times - We introduce generator trips through generator base point adjustments 13

14. KERMIT Calibration Results 14 We were able to sufficiently replicate historical large events both in terms of frequency excursion and in terms of recovery time

15. Frequency for Nov 29, 2011 Event 15

16. Agenda  Project Goals  Calibrating KERMIT to ERCOT  Building Scenario 1  Discussion 16

17. Scenario 1 Information  We were tasked with developing two scenarios in KERMIT  The build for Scenario 1 is complete though currently in the debugging and verification of results stage  Summary of new generation - Expansion CC: 700 MW - Expansion CT: 2380 MW - New Wind: 6,968.2 MW - Administrative CT: 13,940 MW - Solar (PV): 2,500 MW - New DR: 500 MW 17

18. Selection of Study Days  KERMIT runs approximately 100x real time and is designed to run for a 24 hour simulation period  Need to select a representative sample of study days to examine for each scenario  We examined 2011 wind and conventional generation production as well as 5-min wind ramps to develop classification categories for a given day  We then classified each day by the categories and determined the sample size based on number of days in each category and the standard deviation of net load  The sample size was selected to give a 90% confidence interval 18

19. Density of Daily Generation 19

20. Distribution of Daily 5-min Ramps by Generation Level 20

21. Categorization and Draft Sampling Plan 21

22. Load Flow Model  Implemented a pipe and bubble model in KERMIT to to take into account transmission limits on major transmission interfaces.  Monthly Available Transfer Capability (ATC) values for each “pipe” were used as constraints in KERMIT  Monthly ATCs were calculated based on detailed AC model of the network.  First implementation - Assign regulation capacity to generators with sufficient headroom and not based on location - Control ERCOT frequency system wide - Observe if any “pipe” flows violated ATC constraints - Readjust regulation awards and rerun  Second implementation (currently designing) - Assign regulation capacity to generators with sufficient headroom taking into account which bubble a generator is within - Control ERCOT frequency system wide while ensuring ATC constraints are enforced 22

23. Pipe and Bubble Model for KERMIT 23 SanAnt North Dallas East Houston Coast North Central South Central Austin South West

24. SCED  We created a simplified version of SCED in KERMIT  Operates every 5 minutes  Designed to - Adjust generation levels to account for intra hourly variability - Alleviate regulation deployment (reset AGC deployments to zero)  SCED is based on hourly marginal cost curves developed by - Using the heat rate curves and fuel prices from the PROMOD simulations to develop hourly variable production cost for each generator - Estimating SCED hourly capacity by estimating generator HDL and LDL  HRUC was not modeled explicitly - Instead, variability HRUC designed to handle was included in the wind forecasts fed into PROMOD - We may change this approach and choose to model HRUC at a later stage 24

25. Demand Response Module  Responsive Reserve load service will be rolled into DR module - Triggered when frequency dips below set level, 59.7 Hz  KERMIT DR Modules input - DR schedules for price responsive DR determined outside of KERMIT (DA commitments) - Price signals from KERMIT SCED to capture price-based responses (can be used to investigate different scenario deployments of DR) - System frequency to trigger RR and frequency responsive DR (EV car chargers for example)  DR responses include ability to model - Imperfect response and probabilistic response - Real time DR will include minimum sustained deployment - May need to implement a cap on maximum deployments 25

26. Modeling Wind and Load Variability  Load, wind and solar data provided are 1-hr time resolved  The purpose of KERMIT is to study intra-hour variability - Therefore need to replicate intra-hour variability of load, wind, and solar - Need to upsample each data set to 1-s time resolution  Method for upsampling data set - Observe historical variability by examining power spectral density (PSD) of each data set - For wind, need to examine rate of change in PSD as more wind plants are added - This captures the smoothing effect of geographic diversity - Same goes for solar - Create a filter that replicates the PSD of each data set - Add white noise to each data set - Run noise-added data sets through filter to obtain high-resolution data sets  Will walk through a PSD for wind to explain further 26

27. Modeling Wind Variability - PSD 27

28. Agenda  Project Goals  Calibrating KERMIT to ERCOT  Building Scenario 1  Discussion 28

29. Next Steps  Simulate all 22 days and summarize results  Build Scenario 2 into KERMIT  Assist ERCOT in using KERMIT and in the analysis of results 29

30. Global presence DNV KEMA 30 HEAD OFFICE Arnhem, The Netherlands

31. Appendix 31

32. 2011 Generation 32

33. Wind Generation and 5-min Ramps 33