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Electricity Trade Patterns in a Network

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Abstract

Using high-frequency trade volume and price data in a transmission network we investigate patterns of trade and its impacts in the market price formation process. In particular, we study the Ontario wholesale electricity market and its trade with multiple interconnected markets, including New York, Michigan, and Minnesota, through 13 interconnections. This research has regulatory implications on integration of electricity markets, and possible investments in transmission and production capacity. The main findings are in order: (a) imports are unambiguously related to prices (significant Granger causality), while exports are not; (b) trade mainly occurs due to the market price differentials between the markets and traders can use past price observation to take trade positions before the markets clear; (c) there is a high degree of integration across the markets in the network, where the speed of convergence of cross prices is almost instantaneous.

Keywords

  • Electricity trade
  • Transmission network
  • Electricity prices
  • Event study
  • Non-linear Granger causality
  • Ontario, New York, Michigan, Manitoba, Quebec wholesale electricity markets

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Fig. 47.1
Fig. 47.2
Fig. 47.3

Notes

  1. 1.

    According to IESO 2010 market’s programme (www.ieso.ca), there are 13 facilities operating as “dispatchable load” in the market, offering 700 MW of potential demand response.

  2. 2.

    In 2008 the IESO Board approved the implementation of an Enhanced Day-Ahead Commitment Process (EDAC) to deliver some minor changes to the existing Day-Ahead Commitment Process.

  3. 3.

    The pre-dispatch prices and quantities are posted at www.theimo.com/imoweb/marketdata/marketToday.asp.

  4. 4.

    See http://www.ieso.ca/imoweb/pubs/consult/mep/MP_WG_2004Aug20_ISS01_PreDispPrice.pdf and the Issue 30 on forecasting real-time prices.

  5. 5.

    See http://www.ieso.ca/imoweb/pubs/consult/mep/MP_WG-20060303-Issue7-Imports-Exports-Setting-Price.pdf. The day-ahead market has not been implemented by IESO yet.

  6. 6.

    The data is available at www.ieso.ca. We also analyzed this data set in subcategories of peak and off-peak hours and obtained qualitatively similar results. Peak time was defined as hours between 08:00 and 22:00 (including 8.00 and 22:00) during week days and excluding whole weekends (27,825 data points for each variable). Off-peak time data include week day hours between 23:00 and 07:00 and whole weekends (34,503 data points for each variable). These results are available upon request.

  7. 7.

    The pre-dispatch prices were not published by the IESO for August 14–23, 2003 (the period following the Northeast Blackout of 2003).

  8. 8.

    We measure the mean and standard deviation by using the data up to point t. We also applied the same event rule by eliminating all prices below the mean; hence we imposed another condition in addition to the above one. This leads to a significant decrease in the number of events; however the results remained qualitatively the same.

  9. 9.

    Hence we have 30 data points before and after events to carry out the t-test. We have tried different event windows and obtained qualitatively similar results. They are available upon request. Note also that we assume unequal variances in the t-test.

  10. 10.

    As is well known, in stationary systems the distribution of Wald test is asymptotically chi-squared. However, the asymptotic theory of Wald tests is typically much more complex in systems that involve variables with stochastic trends and the distribution depends on the number of unit roots and cointegration relations in the system (see Toda and Phillips [17]). The augmented Wald test is indifferent whether the series in VAR are cointegrated or not, or whether they are I(0) or I(1), or mixed. To avoid pretesting biases (either in unit root or cointegration tests) one can directly use this procedure without embarking on problematic unit root or cointegration tests. Also note that we tested time series properties of the above variables with different unit root tests and found that they appear to be stationary.

  11. 11.

    We also included average temperature in Ontario as an additional variable in the system in (47.1). We find that the results outlined here are not sensitive to the presence of average temperature in the system. The results with temperature data are available upon request:

  12. 12.

    To determine the lag length chosen by AIC we initially set the maximum p to be equal to 720 (1 month). To compute the test statistics we run Matlab codes using Matlab 7.9 64-bit version in an Intel(R) Xeon(R) CPU X5570 at 2.93 GHz and 3.14 GHz, 16 GB of RAM machine. Given the huge matrix operations, the codes would not be able to be run in a less qualified machine due to the large-size data set.

  13. 13.

    The C code for computations has been provided by Diks and Panchenko [20].

  14. 14.

    We only report the results of 1-hour-ahead pre-dispatch prices. The results with 2- and 3-hour-ahead pre-dispatch prices are qualitatively similar to the results of 1-hour-ahead pre-dispatch prices.

  15. 15.

    As is well known, the speed of price convergence is rather low when measured across aggregate price indices or the same goods of different countries or regions.

  16. 16.

    We do not report these results here to save some space. They are available upon request.

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Acknowledgements

The first author is corresponding author who acknowledges financial support from the Social Sciences and Humanities Research Council of Canada, and can be reached at <tgenc@uoguelph.ca>.

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Correspondence to Talat S. Genc .

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Genc, T.S., Yazgan, E., Pineau, PO. (2015). Electricity Trade Patterns in a Network. In: Dincer, I., Colpan, C., Kizilkan, O., Ezan, M. (eds) Progress in Clean Energy, Volume 2. Springer, Cham. https://doi.org/10.1007/978-3-319-17031-2_47

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  • DOI: https://doi.org/10.1007/978-3-319-17031-2_47

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