Analysis of Target Reserve Margin for ERCOT

January 12, 2007

Generation Adequacy Task Force Meeting

Analysis of Target Reserve Margin for ERCOT

Warren LasherRegional Planning

Generation Adequacy Task Force MeetingJanuary 12, 2007 2

Background

• The most recent study of the target reserve margin for ERCOT was completed in March 2002.

• The generation fleet in ERCOT has changed significantly since that time:– More combined-cycle gas generation

– More wind generation

– Mothballing and retirement of older gas generation

– Proposals for new coal generation

• The expectation at the time was that the 2002 study was completed was that it would be updated for 2005

• It was time for a new Target Reserve Margin Study


NERC Standard – Under Review

• There is currently a NERC standard under development on this issue; the summary of this standard is:– Each region shall establish probability-based

methodology to evaluate whether projected resources will be adequate to serve load, given relevant uncertainties

– Each region shall periodically assess the resource adequacy of the region using that methodology


Global Energy Decisions, Inc.

• ERCOT issued an RFP and selected Global Energy Decisions (GED) to complete the new Target Reserve Margin Study

• GED used their unit commitment and dispatch software, MarketSym, to analyze the impact of load volatility, wind generation, unit maintenance, and unit forced outages on expected unserved energy (EUE), Loss of Load Probability (LOLP) and Loss of Load Events (LOLE).


A Few Definitions

• EUE – Expected Unserved Energy – the expected number of megawatt-hours of load that will not be served in a given year

• LOLP – Loss of Load Probability – the probability that there will be a loss of load event in a given year

• LOLE – Loss of Load Events – the number of events in which some system load is not served in a given year. A Loss of Load Event can last for one hour or for several contiguous hours, and can involve the loss of one megawatt of load or several hundred megawatts of load.

• Generally-accepted criteria: a target reserve margin that results in one expected Loss of Load Event every ten years, or 0.1 Loss of Load Events every year.


Study Methodology

1) Establish the base generation fleet for simulations– Unit parameters for 2008 from GED proprietary generation database were

used– Unit additions and retirements were matched to 2005 CDR

2) Develop an hourly load profile for target year (2008)– Distribution was checked against internal ERCOT load forecast model

3) Develop probability distributions for volatile input parameters (system load, wind generation, unit outages)

4) Run simulations to serve load using volatility from previous step. Multiple simulations allow analysis of unlikely events.

5) Record the number of loss of load events and associated unserved energy

6) Combine results from multiple runs (with different levels of proxy new generation) to establish the target reserve margin

This method provides an evaluation of the interaction between load volatility, unit outages, and wind generation patterns, and considers off-peak hours rather than just annual peak hour


Stochastic Input Parameters

• Unit outages were simulated using Monte Carlo draws. – Each unit was assigned a forced outage rate, and

random draws were made to determine if specific units were out of service in any given hour.

• Wind volatility was developed using AWS Truewind wind generation patterns (from the recently completed Analysis of Competitive Renewable Energy Zones). – Software randomly selected hourly aggregate wind

generation patterns in 24-hour increments.– Random selections were made only from the energy

patterns developed by AWS Truewind for the month being simulated.


Stochastic Input Parameters

• Hourly load volatility was developed using a two-parameter normal mean-reverting model.– The two parameters define the volatility

(variance) of the data compared to the expected load and how quickly the hourly load patterns revert back to the expected load

– Mean reversion parameters were developed using ERCOT load data from 1995 – 2004.

• One factor (α) represents short-term load deviations and the second factor (σ) represents longer-term deviations.


ERCOT Load Distributions – 100 Iterations

30,000

35,000

40,000

45,000

50,000

55,000

60,000

65,000

70,000

75,000

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145 151 157 163

Hours of a Week

MW

h


Wind Generation

• In order to calculate the reserve margins in each simulation, the effective load-carrying capacity of the wind generation was needed.

• Process: – Run the model with the base set of generating

units and a generic thermal generator (550 MW). Determine the expected unserved energy.

– Remove the generic thermal generator and add new wind generation until the same expected unserved energy is achieved.

– The amount of new wind generation will have the same effective load-carrying capability as the 550-MW generic thermal generator


Wind Generation

• Result: 6,300 MW of wind had the same load carrying capacity as 550 MW of thermal generation.

• Based on these results, the effective load-carrying capability (ELCC) of wind would be 8.7%.


Reserve Margin Simulations

• 100 model simulations were run at each of the following reserve margins: – 10%, 12%, 14%, 16%, 18%, and 20%.– Reserve margin calculation was based on

ERCOT GATF 2005 methodology, except for ELCC of wind

• Result: Target reserve margin of one Loss of Load Event every 10 years was reached at a reserve margin of 12.9%.


Reserve Margin Simulation Results

0

1

2

3

4

5

6

10 11 12 13 14 15

Reserve Margin (%)

# o

f LO

L Events

in 1

0 Y

ears



Reserve Margin

Average Loss of

Load Events in 10 Years

Average MWhs of ENS in 10

Years

Average Hours of ENS in 10

Years

Loss of Load

Probability (%)

10.00% 5.1 9,020 9 0.011%

12.00% 1.4 2,570 2.6 0.003%

14.00% 0.5 515 0.9 0.001%

16.00% 0 0 0 0.000%

18.00% 0 0 0 0.000%

20.00% 0 0 0 0.000%


Generic Coal Additions

• Due to the recent public announcements of new coal generation, the same analyses were conducted using additions of generic coal generation instead of generic gas combustion turbines.

• Result: Target reserve margin of one Loss of Load Event every 10 years was reached at a reserve margin of 13.6% using the addition of generic coal units.



0

1

2

3

4

5

6

10 11 12 13 14 15

Reserve Margin (%)

# o

f LO

L Events

in 1

0 Y

ears

Generic Gas Additions Generic Coal AdditionsSeries2


Why The Difference

• Generic coal units were larger than the generic gas combustion turbines (500 MW compared to 162 MW). One forced outage event resulted in the loss of more generation.

• Forced outage rates for the generic coal units were higher than for the generic combustion turbines (6% compared to 2%).

• Generic coal units require more maintenance days than generic combustion turbine units.


Next Steps

•Detailed report on study from GED will be distributed next week for feedback

•Study needs to be fully vetted, including consideration of specific questions:

• Should ERCOT adopt Effective Load-Carrying Capability as the capacity value of wind generation?

• Should incremental generation used to vary reserve margin be CTs or coal units?

•ROS, TAC and BOD endorsement of:– The one day (LOLE) in 10 years as criteria for assessing resource

adequacy– The probabilistic methodology, as contained in this report, to

calculate it– Updating the target reserve margin/LOLE study every two years

Questions or Comments?

Analysis of Target Reserve Margin for ERCOT

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