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Unit Commitment under Increased Wind

Unit Commitment under Increased Wind. Kevin Kim PENSA Summer 2011. Energy Markets: Overview. Demand. RTO. Energy Consumer. Schedule. Supply. Power Generators. Unit Commitment Problem. How much demand do we need to meet tomorrow?

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Unit Commitment under Increased Wind

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  1. Unit Commitment under Increased Wind Kevin Kim PENSA Summer 2011

  2. Energy Markets: Overview Demand RTO Energy Consumer Schedule Supply Power Generators

  3. Unit Commitment Problem • How much demand do we need to meet tomorrow? • How should we schedule our generators to meet 100% of this demand? • How do we minimize overages/shortages in energy?

  4. Challenge 1: Random Demand • How much demand do we have to satisfy tomorrow? • How should we schedule our power generators tomorrow to meet this demand?

  5. Challenge 2: Generator Limitations • Many plants take several hours to warm up before they can be used. • Some plants turn on quickly, but they’re much more expensive and can’t generate as much power Coal Plant ~10 hours to turn on. ~$50/MW Maxed at ~500 MW Natural Gas Plant ~0.1 hours to turn on ~$300/MW Maxed at ~20 MW

  6. Now, the biggest challenge….

  7. WIND ENERGY • Clean, renewable, and low cost/MW. • However, wind is VOLATILE.

  8. Challenge 3: Random Supply • With wind energy, part of our energy supply is also random.

  9. Wind Energy: News • Department of Energy • Target of 20% wind penetration by 2030 • Google • $5 billion project to build 350-mile cable on the east coast to power offshore wind farms.

  10. We solve the unit commitment problem with a math model….

  11. Model: Basic Algorithm • Predict demand and wind for tomorrow (t=1). • Schedule generators based on these forecasts. • Now, at tomorrow (t=1), change the outputs of the faster generators to correct for errors in forecast • Run the following cases and compare costs: • 5% wind penetration • 20% wind penetration • 40% wind penetration • 60% wind penetration

  12. Model: A Sneak Peek …..…

  13. Sample Output

  14. The cost of randomness

  15. What if we could predict wind…

  16. What if wind were constant…

  17. The reality

  18. Future Work • Reduce shortages in stochastic wind cases • Reduce cost in stochastic wind cases. • Analyze effects of offshore wind.

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