Renewable energy & Electricity markets

1. Renewable energy & Electricity markets Be careful what you wish for Adam Wierman, Caltech Joint work with SachinAdlakha, Subhonmesh Bose, Desmond Cai, John Ledyard, Steven Low, and Jayakrishnan Nair.

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

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

4. Today’s grid Load Generation Key Constraint: Generation = Load (at all times) low uncertainty

5. Today’s grid Load Generation Key Constraint: Generation = Load (at all times) controllable(via markets) low uncertainty

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

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

8. “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.”

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

10. What can be done? Reduce the uncertainty • Better prediction • “Aggregation” … in time (storage) • … in space (distributed generation) • … in generation (heterogeneous mix) Design for the uncertainty our focus at Caltech • Redesign electricity markets • Increase amount of demand response

11. This talk: Two electricity market design challenges 1) How many markets should there be? and when should they occur? 2) The nasty economic consequences of Kirchhoff's laws stochastic The newsvendor problem Networked Cournot competition networks

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

13. Forget about energy for a second… This section is really about the role of uncertainty in newsvendor problems “You have to decide today how many newspapers you want to sell tomorrow…” uncertainty Estimate demand, Demand is realized lost revenue wasted inventory Purchase,

14. Forget about energy for a second… This section 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 perishablegoods compute instances energy …

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

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

17. markets real time long term int. /day ahead time 4 hrmarket What is the impact of long term wind contracts? • As renewable penetration increases: • Should markets be moved closer to real-time? • Should markets be added?

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

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

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

21. wind uncertainty ↓ real time long term int. /day ahead price↑ Assumption: and are independent (A generalization of the martingale model of forecast evolution)

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

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

24. 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]. real time long term int. /day ahead

25. Theorem: The optimal procurement strategy is characterized by reserve levels and such that where and uniquely solves

26. Scaling regime baseline, e.g., average output of a wind farm scale, e.g., number of wind farms aggregation, e.g., degree of correlation between wind farms real time long term int. /day ahead

27. Scaling regime baseline, e.g., average output of a wind farm scale, e.g., number of wind farms aggregation, e.g., degree of correlation between wind farms Theorem: Procurement with zero uncertainty Extra procurementdue to uncertainty

28. Scaling regime baseline, e.g., average output of a wind farm scale, e.g., number of wind farms aggregation, e.g., degree of correlation between wind farms Theorem: Depends on wind aggregation - =1/2 (independent) - =1 (correlated) Depends on markets & predictions - prices - forecasts

29. Scaling regime baseline, e.g., average output of a wind farm scale, e.g., number of wind farms aggregation, e.g., degree of correlation between wind farms Theorem: 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]

30. Electricity markets markets real time long term int. /day ahead time What is the impact of long term wind contracts? • As renewable penetration increases: • Should markets be moved closer to real-time? • Should markets be added? No!

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

32. long term real time long term real time v/s int. What happens to if a market is added? What happens to if a market is added?

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

34. Theorem: If is increasing for , decreasing for , and satisfies: is decreasing for is decreasing for then the expected procurement is lower with 3 markets than with 2 markets. Satisfied by the Gaussian distribution

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

36. Estimation errors are heavy-tailed(specifically, long-tailed) Theorem: If satisfies the condition: =0 , then there exist prices such that the expected procurement is higher with 3 markets than with 2 markets.

37. markets real time long term int. /day ahead time 4 hrmarket What is the impact of long term wind contracts? • As renewable penetration increases: • Should markets be moved closer to real-time? • Should markets be added? No! It depends, Gaussian or heavy-tailed?

38. PIRP markets markets real time long term int. /day ahead time What is the impact of long term wind contracts? How should wind be incorporated into the markets?

39. This talk: Two electricity market design challenges 1) How many markets should there be? and when should they occur? 2) The nasty economic consequences of Kirchhoff's laws The newsvendor problem Networked Cournot competition

40. Forget about energy for a second… This section is really about intermediaries & competition in networked markets

41. Forget about energy for a second… This section is really about intermediaries & competition in networked markets Rarely is competition in a single, well defined market… firms typically compete across a variety of markets Firms Markets

42. Forget about energy for a second… This section is really about intermediaries & competition in networked markets Rarely is competition in a single, well defined market… firms typically compete across a variety of markets Examples: gas, airlines, construction, … , energy Gas pipelines in the US

43. Key Constraint: Generation = Load (at all times)

44. L L L G G G Key Constraint: Generation = Load (at all times) G controllable(via markets) G

45. L L L cost G G quantity G Market run by the Independent System Operator (ISO) Determines the quantity to procure and price to charge each generator in order to meet the load s.t. network constraints. G G

46. A toy example cost cost capacity = 1 quantity quantity Load = 6

47. 3 2 capacity = 1 1 Load = 6

48. 3 2 1 capacity = 1 1 Load = 6 2 3

49. But what if is strategic? 3 2 cost capacity = 1 quantity 1 Load = 6 Kirchhoff's laws create a hidden monopoly!

50. Kirchoff’s laws can have nasty market consequences… “…supply-demand imbalance, flawed market design and inconsistent rules made possible significant market manipulation” -- FERC