Secure System Scheduling with High Wind Penetrations

1
Secure System Scheduling with High Wind
Penetrations

• J. Kennedy
• March 2009

2
Contents

• Background to the Irish Power system
• Problems scheduling the System
• Spinning and standing reserve on the system
• Wilmar scheduling model
• Adjustment of reserve requirements
• Fast start balancing plant
• Conclusions
• Future Research

3
Background (Growth of Wind Generation)

• Currently (2008 data)
• 10 wind energy penetration in Ireland
• 2 wind energy penetration in the UK
• Projections for 2020 indicate
• 42 wind energy penetration likely in Ireland
• 13 wind energy penetration likely in the UK 1
• Instantaneous power penetration could be much
  greater
• Achieving projected growth figures will
• Significantly reduced imported natural gas in the
  UK Ireland
• Reduce Carbon emissions, inline with government
  targets

4
Background (Challenges of Large Scale Wind)

• Balancing problems due to wind
• Increased uncertainty (limitations to wind
  forecasts accuracy)
• Variability, increased cycling of thermal plant
  2
• Increased part loading of thermal plant
• Balancing options

5
High wind penetrations
30 wind penetration
Light load on Sunday morning
Strong wind
6
Wilmar Scheduling Model

• Developed by Risø for Denmark, Finland, Germany,
  Norway and Sweden
• Realistic forecasting mode
• Modified to include mixed Integer variables for
  use in Irish system
• Used extensively in the All Island Grid Study
  report
• Deterministic mode - assumes a perfect forecast
• Stochastic mode assumes a realistic forecast
  error
• Incorporates Day ahead, and intraday
  scheduling Unit ramp rates
• Min/Max unit generation Min unit up and down
  times
• Temperature dependent start-up costs
  Realistic unit reserves
• Time variable fuel costs Carbon tax
  Stochastic optimisation
• Validated against Plexos in the All Island Grid
  Study

7
Reserve classification
Spinning
Non-spinning?
Non-spinning
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Assumptions for 2020

• As per AIGS
• 1000 MW of asynchronous interconnection
• 100 MW interconnection available for tertiary
  reserve
• 6000 MW of installed wind (portfolio 5)
• Tertiary reserve (90 seconds to 5 minutes)
  covers
• 100 of largest scheduled unit additional
  tertiary reserve due to wind
• Replacement reserve (5 minutes onwards) covers
• 90th percentile of total forecast error

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Assumptions for 2020

• Tertiary reserve (90 seconds to 5 minutes)
  covers
• 100 of largest scheduled unit additional
  tertiary reserve due to wind

Provide from off-line plant?
10
Operational costs and reserve
Deterministic mode
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Fast start off-line plant for TR1

• Additional tertiary reserve due to wind supplied
  from off-line plant
• For 6000 MW wind, 131 MW of extraTR1 required
  easily achieved
• Tertiary reserve cost reduced by 22,or 3.6 M
• 146 less start-ups per year with modified TR1
• 213 less start-ups per year on plant larger than
  110 MW

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Conclusions

• Allow offline plant to participate in Tertiary
  reserve band one
• Sufficient fast start generation already
  available for high wind penetration scenarios
• Reducing TR1 from online plant, reduces cycling
  of thermal plant, a major worry of new AIGS
• Existing peaking plant more profitable for
  owners

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What if simulations

• With and without additional tertiary reserve due
  to wind
• Examine distribution of
• Power changes on each unit
• Number of problem periods (low reserve) per year

14
What if simulations

• With and without interconnection to GB
• With and without replacement reserve
• With and without nominal tertiary spinning
  reserve
• With and without additional tertiary reserve due
  to wind
• With and without Turlough Hill pumped storage
  plant
• Examine periods of peak wind penetration
• with and without reduced tertiary reserve
• Examine distribution of
• Power changes on each unit
• Number of problem periods (low reserve) per year

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Forecast vs. Fuel saver (summary)

• Forecast mode with diesel delivers significant
  savings over fuel saver
• 150 MW max gen-set capacity required for up to
  600 MW wind (25 of wind)
• Even with perfect forecasting net demand is more
  variable more flexible plant required
• Capacity factor of plant increased in forecast
  mode

With diesel
Fig. 4.31