1. Kevin Kim PENSA Summer 2011

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

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 1. 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 penetration 2. 20% wind penetration 3. 40% wind penetration 4. 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.