Unit Commitment under Increased Wind

Kevin KimPENSA Summer 2011

Energy Markets: Overview

Energy Consumer

Demand

RTO

Power Generators

Supply Schedule

Unit Commitment ProblemHow 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?

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Challenge 1: Random DemandHow much demand do we have to satisfy tomorrow?How should we schedule our power generators

tomorrow to meet this demand?

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Challenge 2: Generator LimitationsMany 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/MWMaxed at ~500 MW

Natural Gas Plant~0.1 hours to turn on~$300/MWMaxed at ~20 MW

Now, the biggest challenge….

WIND ENERGYClean, renewable, and low cost/MW. However, wind is VOLATILE.

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Challenge 3: Random SupplyWith wind energy, part of our energy supply

is also random.

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Actual WindPredicted windActual DemandTotal Actual Power

Wind Energy: NewsDepartment of

EnergyTarget of 20% wind

penetration by 2030

Google$5 billion project to

build 350-mile cable on the east coast to power offshore wind farms.

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

Model: Basic Algorithm1. Predict demand and wind for tomorrow (t=1).2. Schedule generators based on these forecasts.3. Now, at tomorrow (t=1), change the outputs of

the faster generators to correct for errors in forecast

4. Run the following cases and compare costs:1. 5% wind penetration2. 20% wind penetration3. 40% wind penetration4. 60% wind penetration

Model: A Sneak Peek

…..…

Sample Output

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Actual WindPredicted windActual DemandTotal Actual Power

The cost of randomness

What if we could predict wind…

What if wind were constant…

The reality

Future WorkReduce shortages in stochastic wind casesReduce cost in stochastic wind cases.Analyze effects of offshore wind.