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DNV KEMA KERMIT OverviewERCOT Joint Regional Planning Group / Long Term Study Task ForceAustin, TXOctober12, 2012

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Agenda

Project Goals

Calibrating KERMIT to ERCOT

Building Scenario 1

Results

Discussion

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Agenda

Project Goals

Calibrating KERMIT to ERCOT

Building Scenario 1

Discussion

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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

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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

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KERMIT helps ensure efficient investments are made that reduce future costs and minimize risks

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

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KERMIT Time Scales of Focus

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1 msecond 1 day 1 month 1 year1 hour10 minutes1 minute1 second1 cycle

HarmonicsProtection

Stability

Frequency responseRegulation

Balancing

Capacity

Economics

Transient and harmonics analysisShort circuit analysis

· PSS/E· DigSILENT

AGC and balancing· KERMIT

Production costingMarket simulationSystem planning

· ProMod· GE MAPS

Agenda

Project Goals

Calibrating KERMIT to ERCOT

Building Scenario 1

Discussion

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Overview of KERMIT Architecture

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Inputs:LoadPlant SchedulesGeneration PortfolioGrid ParametersMarket/Balancing

Outputs:Power Plant MW OutputsArea InterchangeFrequency Deviation

Scenarios:Increasing Wind Adding ReservesStorage ParametersTest AGC ParametersTrip Events

KERMIT 24h Simulation

Generation•Conventional•Renewable

Inter-connection

FrequencyResponse

Real Time Market

Generator trip

Load rejection

Wind power forecast versus actual

Volatility in renewable resources

Grid Modeling – Calibrating KERMIT

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Inputs:Load (PI Historian)Plant Schedules (SCED)Generation Portfolio (RARF)Grid ParametersWind Production

Outputs:Power Plant MW OutputsFrequency Deviation

Scenarios:Test AGC ParametersTrip Events

KERMIT 24h Simulation

Generation•Conventional•Renewable

FrequencyResponse

Generator trip

We simulated a generator trip in KERMIT to replicate observed 780 MW generator trip on Feb 15

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

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tfP

M

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

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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

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Date TimeMagnitude of Event

(MW)Mininum Frequency Deviation from 60 Hz

Duration (s)

Magnitude of Event (MW)

Mininum Frequency Deviation from 60 Hz

Duration (s)

2/15/2011 16:40 780 0.23 54 780 0.232 545/19/2011 14:08 1163 0.28 212 1163 0.276 208

11/29/2011 3:29 1365 0.268 25 1365 0.268 23

Actual KERMIT ModelEvent

KERMIT Calibration Results

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We were able to sufficiently replicate historical large events both in terms of frequency excursion and in terms of recovery time

Frequency for Nov 29, 2011 Event

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3.48 3.5 3.52 3.54 3.56 3.58

-0.25

-0.2

-0.15

-0.1

-0.05

Hour of the Day

Fre

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from

60

Hz

(Hz)

ActualSimulated

Agenda

Project Goals

Calibrating KERMIT to ERCOT

Building Scenario 1

Discussion

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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

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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

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Density of Daily Generation

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Distribution of Daily 5-min Ramps by Generation Level

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Categorization and Draft Sampling Plan

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Total Generation 5-MinuteWind Ramps Days to Sample

Low Low 12/25/2011, 9/10/2011, 9/23/2011, 10/28/2011 Low Medium 12/18/2011, 4/6/2011, 4/8/2011, 5/5/2011, 10/15/2011 Low High 12/22/2011, 3/21/2011, 3/22/2011, 4/4/2011 High Low 8/27/2011, 8/2/2011 High Medium 9/14/2011, 7/2/2011, 7/3/2011, 7/12/2011 High High 8/15/2011, 6/8/2011, 6/13/2011

Total Generation 5-MinuteWind Ramps Days Standard Deviation Sample Size

Low Low 16 89,485 4 Low Medium 199 95,406 5 Low High 32 77,044 4 High Low 5 49,322 2 High Medium 100 84,398 4 High High 13 65,835 3

Load Flow Model

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Pipe and Bubble Model for KERMIT

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SanAnt

North

Dallas

East

Houston

Coast

North Central

South Central

Austin

South

West

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

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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

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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

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Modeling Wind Variability - PSD

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10-7

10-6

10-5

10-4

10-3

10-4

10-3

10-2

10-1

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Frequency (Hz)

Po

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5 Days

2 Days

24 Hours

6 Hours

1 Hour

1 Wind Plant4 Wind Plants20 Wind Plants

Agenda

Project Goals

Calibrating KERMIT to ERCOT

Building Scenario 1

Discussion

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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

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Global presence DNV KEMA

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HEAD OFFICEArnhem, The Netherlands

Appendix

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2011 Generation

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Wind Generation and 5-min Ramps

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