1. Energy procurement in the presence of intermittent sources Adam Wierman (Caltech) JK Nair (Caltech / CWI) Sachin Adlakha (Caltech)

2. Forget about energy for a second… This talk is really about the role of uncertainty in newsvendor problems

3. Forget about energy for a second… This talk is really about the role of uncertainty in newsvendor problems uncertainty “You have to decide today how many newspapers you want to sell tomorrow…”

4. Forget about energy for a second… This talk is really about the role of uncertainty in newsvendor problems “You have to decide today how many newspapers you want to sell tomorrow…” seasonal products perishable goods compute instances energy …

5. Key Constraint: Generation = Load (at all times) low uncertainty Generation Load Now, back to energy…

6. Generation Load Key Constraint: Generation = Load (at all times) low uncertainty controllable via markets Now, back to energy…

7. time int. /day ahead real time long term Utility buys power to meet demand Electricity markets markets

8. MW Worldwide Wind: MW Europe Americas China Solar PV: Renewable energy is coming!

9. …but incorporation into the grid isn’t easy They are typically  Uncontrollable (not available “on demand”)  Intermittent (large fluctuations)  Uncertain (difficult to forecast) Each line is wind generation over 1 day Renewable energy is coming!

10. Key Constraint: Generation = Load less controllable high uncertainty low uncertainty (at all times) Tomorrow’s grid

11. Key Constraint: Generation = Load less controllable high uncertainty low uncertainty (at all times) 1) Huge price variability, leading to generators opting out of markets! 2) More conventional reserves needed, countering sustainability gains!

12. “ON JUNE 16th something very peculiar happened in Germany’s electricity market. The wholesale price of electricity fell to minus €100 per megawatt hour (MWh). That is, generating companies were having to pay the managers of the grid to take their electricity.”

13. “Energiewende has so far increased, not decreased, emissions of greenhouse gases.”

14. What can be done? Reduce the uncertainty Design for the uncertainty Better prediction “Aggregation” … in time (storage) … in space (distributed generation) … in generation (heterogeneous mix) Redesign electricity markets Increase amount of demand response this session

15. time int. /day ahead real time long term markets PIRP

16. time int. /day ahead real time long term markets This talk: What is the impact of long term wind contracts? As renewable penetration increases: 1) Should markets be moved closer to real-time? 2) Should markets be added? 4 hr market

17. How should utilities procure electricity in the presence of renewable energy? First step: This talk: What is the impact of long term wind contracts? As renewable penetration increases: 1) Should markets be moved closer to real-time? 2) Should markets be added?

18. int. /day ahead real time long term price ↑

19. int. /day ahead real time long term price volatility ↑

20. int. /day ahead real time long term price ↑ (A generalization of the martingale model of forecast evolution) wind uncertainty ↓

21. Key Constraint: Generation = Load int. /day ahead real time long term price ↑ wind uncertainty ↓ (we ignore network constraints)

22. int. /day ahead real time long term price ↑ wind uncertainty ↓ Utility goal: Subject to causality constraints

23. int. /day ahead real time long term Utility goal: Subject to causality constraints Variant of the newsvendor problem [Arrow et. al. ’51], [Silver et. al. ’98], [Khouja ’99], [Porteus ’02], [Wang et. al. ’12].

25. int. /day ahead real time long term Scaling regime

26. Procurement with zero uncertainty Extra procurement due to uncertainty

27. Scaling regime Depends on markets & predictions - prices - forecasts

28. Scaling regime This form holds more generally than the model studied here: -- more than three markets: [Bitar et al., 2012] -- when prices are endogenous: [Cai & Wierman, 2014] -- when small-scale storage is included: [Hayden, Nair, & Wierman, Working paper]

29. time int. /day ahead real time long term markets Electricity markets This talk: What is the impact of long term wind contracts? As renewable penetration increases: 1) Should markets be moved closer to real-time? 2) Should markets be added? No! (See paper)

30. time int. /day ahead real time long term markets Electricity markets This talk: What is the impact of long term wind contracts? As renewable penetration increases: 1) Should markets be moved closer to real-time? 2) Should markets be added? 4 hr ahead market?

31. real time long term v/s int. real time long term

32. int. /day ahead real time long term 2 markets 3 markets 3 markets are always better! When does this happen?

33. Satisfied by the Gaussian distribution

34. int. /day ahead real time long term 2 markets 3 markets 3 markets can be worse! When does this happen?

35. Estimation errors are heavy-tailed (specifically, long-tailed)

36. time int. /day ahead real time long term markets This talk: What is the impact of long term wind contracts? As renewable penetration increases: 1) Should markets be moved closer to real-time? 2) Should markets be added? No! (See paper) It depends, Gaussian or heavy-tailed? 4 hr market

37. time int. /day ahead real time long term markets This talk: What is the impact of long term wind contracts? markets PIRP Big question: How should wind be incorporated into the markets?

38. Energy procurement in the presence of intermittent sources Adam Wierman (Caltech) JK Nair (Caltech / CWI) Sachin Adlakha (Caltech)