1. A Comparison of Price Patterns in Deregulated Markets Katherine Ying Li and Peter Flynn Dept. of Mechanical Eng. University of Alberta

2. 9 th Power Conference, UCEI2 Alberta’s Daily High/Low Price

3. 9 th Power Conference, UCEI3 The Research Focus Deregulated markets send clear signals to generators. Do they send intelligible signals to consumers? Can consumers meaningfully respond (demand side management): Unplanned vs. planned demand side management.

4. 9 th Power Conference, UCEI4 Overview Are there differences in diurnal patterns? How erratic is price in the short term? Is there a reliable relationship between price and load? Are price patterns consistent over the long term? What might we do differently?

5. 9 th Power Conference, UCEI5 The Data Markets with a correlation of greater than 0.8 are not independent. Balance of markets: strongest correlation <0.6. Some correlation arises from common human patterns. Data cleaning is negligible source of error.

6. 9 th Power Conference, UCEI6 Diurnal Patterns Differ The gap will likely drive industrial patterns.

7. 9 th Power Conference, UCEI7 Price Ratios WDR: ratio of average maximum to minimum daily price on weekday WD/WEAP:ratio of weekday to weekend average price WDR: ratio of average maximum to minimum daily price on weekday WD/WEAP:ratio of weekday to weekend average price

8. 9 th Power Conference, UCEI8 Implications Work scheduling to shift power consumption is far more rewarded in some markets than in others. Will different patterns of work emerge in otherwise comparable cultures? European / NZ load and price patterns show a double peak, North American a single peak. Is space heating a factor?

9. 9 th Power Conference, UCEI9 Is the pattern created by outliers? South Australia California

10. 9 th Power Conference, UCEI10 Implications In Britain and Spain, price and pattern are consistent over time; planning has a reasonable prospect of success. In California, pattern is consistent, and hence scheduling is rewarded. Neither price nor pattern are consistent in Australia; planning would be undone by infrequent events.

11. 9 th Power Conference, UCEI11 A 2 nd Look at Price Volatility Price “velocity”: how much, on average, does the price change per hour over the course of a day? For each day, average the absolute values of each period’s change in price divided by the period’s duration. Normalize to the average price (daily or long term): the unit is then hr -1.

12. 9 th Power Conference, UCEI12 But Some Velocity is Expected

13. 9 th Power Conference, UCEI13 Calculating Unexpected Velocity Start with each day’s price velocity. Subtract the expected velocity that arises from the average diurnal pattern. The residual is the velocity of power price that is not arising from the expected change in price over the day.

14. 9 th Power Conference, UCEI14 Velocity Distribution Each deregulated market has a unique distribution of velocity that characterizes that market.

15. 9 th Power Conference, UCEI15 Unexpected Price Volatility Varies California Britain Spain Scandinavia Alberta Australia

16. 9 th Power Conference, UCEI16 Price and Volatility Patterns Are price excursions or periods of high volatility “bursty”, e.g., are they caused by a system upset such as a unit going down, or a weather extreme? If random, the implicit message to consumers is “don’t face the market”.

17. 9 th Power Conference, UCEI17 Daily Velocity and Average Price Alberta Britain

18. 9 th Power Conference, UCEI18 Can load (weather) predict price? The primary determinant of load is weather. Can the consumer relate weather forecasts to expected price?

19. 9 th Power Conference, UCEI19 Average Price vs. Average Load

20. 9 th Power Conference, UCEI20 Hourly Correlation: Price and Load

21. 9 th Power Conference, UCEI21 Price History Look at price by season and year. Three patterns emerge: Consistency and relative stability: Spain, Britain, Scandinavia. “One bad period” or season: California, PJM, Alberta, New Zealand. Chaos: Australia. Can “one bad period” be avoided?

22. 9 th Power Conference, UCEI22 Price History: Stability vs. “One Bad Period” California Britain Winter 2001 Fall 2001 Summer 2001

23. 9 th Power Conference, UCEI23 Price History: “One Bad Period” With a Comprehensible Cause California New Zealand Winter 2001 Fall 2001 Summer 2001 Spring 2000 Winter 2001

24. 9 th Power Conference, UCEI24 Chaotic Markets Weekday Weekend Unexpected Velocity UVDA 8PM7,8PM 5,6PM 2AM 3PM 1PM 6PM 2PM 7AM

25. 9 th Power Conference, UCEI25 Persistent Public Backlash Combine: A very high price excursion (greater than 2.5 times normal) over a season, and No readily identifiable event, such as:  Drought  Catastrophic outage of a generation plant This occurred in California, Alberta and Ontario. Results can be fatal to deregulation.

26. 9 th Power Conference, UCEI26 Consumer (Voter) Revolts Make someone pay: California: the heretofore innocent shareholder. Alberta: the consumer, in the future. Ontario: the taxpayer. “Do Something About This” California: rate caps and rollbacks. Alberta: rate caps and deferral. Ontario: first rate caps, then cancel deregulation.

27. 9 th Power Conference, UCEI27 Possible Policy Issues for Future Deregulating Markets Shorten the time to implementation. Hit deregulation “d” day with a surplus of generation capacity. Use wholesale price caps to “save the patient”; is any signal greater than 30 times average price meaningful? Avoid retail caps, but if needed to “save the patient”, tie to wholesale caps.

28. 9 th Power Conference, UCEI28 Conclusions Some markets have enough order to be comprehensible to the consumer, who has a chance of planning behaviors. Some markets appear to be more random and unpredictable, and hedging is more favored. For the consumer, deregulation works very well in some markets and less well in others. We don’t know why.