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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 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? • How should we schedule our generators to meet 100% of this demand? • How do we minimize overages/shortages in energy?
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?
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
WIND ENERGY • Clean, renewable, and low cost/MW. • However, wind is VOLATILE.
Challenge 3: Random Supply • With wind energy, part of our energy supply is also random.
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.
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
Model: A Sneak Peek …..…
Future Work • Reduce shortages in stochastic wind cases • Reduce cost in stochastic wind cases. • Analyze effects of offshore wind.