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Energy procurement in the presence of intermittent sources

Energy procurement in the presence of intermittent sources. Adam Wierman (Caltech ) JK Nair (Caltech / CWI) Sachin Adlakha (Caltech). Forget about energy for a second…. This talk is really about the role of uncertainty in newsvendor problems. Forget about energy for a second….

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Energy procurement in the presence of intermittent sources

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  1. Energy procurement in the presence of intermittent sources Adam Wierman (Caltech) JK Nair (Caltech / CWI)SachinAdlakha (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 “You have to decide today how many newspapers you want to sell tomorrow…” uncertainty Estimate demand, Demand is realized lost revenue wasted inventory Purchase,

  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 perishablegoods compute instances energy …

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

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

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

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

  9. 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)

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

  11. 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

  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 • Better prediction • “Aggregation” … in time (storage) • … in space (distributed generation) • … in generation (heterogeneous mix) Design for the uncertainty this session • Redesign electricity markets • Increase amount of demand response

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

  16. markets real time long term int. /day ahead time 4 hrmarket This talk: 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?

  17. First step: How should utilities procure electricity in the presence of renewable energy? This talk: 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. real time long term int. /day ahead price↑

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

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

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

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

  23. 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

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

  25. 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

  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 Theorem: Procurement with zero uncertainty Extra procurementdue to uncertainty

  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: Depends on wind aggregation - =1/2 (independent) - =1 (correlated) Depends on markets & predictions - prices - forecasts

  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: 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. Electricity markets markets real time long term int. /day ahead time This talk: 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! (See paper)

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

  31. 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?

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

  33. 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

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

  35. 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.

  36. markets real time long term int. /day ahead time 4 hrmarket This talk: 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! (See paper) It depends, Gaussian or heavy-tailed?

  37. PIRP markets markets real time long term int. /day ahead time This talk: What is the impact of long term wind contracts? 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)SachinAdlakha (Caltech)

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