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Information and transparency in wholesale electricity markets: evidence from Alberta

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Abstract

We examine the role of information transparency in Alberta’s wholesale electricity market. Using data on firms’ bidding behavior, we analyze whether firms utilize information revealed in near real-time through the Historical Trading Report (HTR), which is released 10 min after each hour and contains a complete (de-identified) list of every firms’ bids into the wholesale market from the previous hour. We demonstrate that firms are often able to identify the offers of specific rivals by offer patterns adopted by those firms. For one of these firms, these patterns are associated with higher offer prices. This is consistent with allegations by Alberta’s Market Surveillance Administrator that firms may be utilizing unique bidding patterns to reveal their identities to their rivals to elevate market prices. We show that certain firms respond to rival offer changes with a lag consistent with responding to information revealed through the HTR, and that they respond differently to different firms, suggesting that they are able to infer identification.

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Notes

  1. See Kuhn and Vives (1994), von der Fehr (2013), and Holmberg and Wolak (2016) for a related discussion.

  2. For additional details, see MSA (2011) and Brown and Olmstead (2017).

  3. While we investigate pricing behavior of firms, we do not test whether the information disclosed in the HTR reduces the degree of competition. As noted in Sect. 8, this question is the subject of future research.

  4. See also Kuhn and Vives (1994) and Vives (2011).

  5. Mansur and White (2012) provide evidence from the Eastern United States that the development of organized wholesale markets led to increased trading and reductions in production costs, due in part to increased information revelation. Further evidence of the efficiency benefits of market restructuring can be found in Cicala (2017).

  6. See Bolle (1992), Fabra (2006), and Crawford et al. (2007) for examples of electricity market models with multiple equilibria. The potential that communication could allow firms to coordinate on the most profitable equilibrium is discussed in the Alberta context in Baziliauskas et al. (2011).

  7. Benjamin (2016) extends this strand of literature by incorporating demand uncertainty in a setting with tacit collusion in the electricity market context.

  8. von der Fehr (2013) raises concerns that the European Commission’s regulations provide too much information. The author argues that this information may facilitate coordinated behavior or market power execution.

  9. In November 2016, the Alberta government announced that the market will be transitioning from an energy-only to a capacity market design. For additional details, see AESO (2016).

  10. Source: Alberta MSA.

  11. For further detail, see Olmstead and Ayres (2014) and Brown and Olmstead (2017).

  12. For additional discussion of the HTR, see AUC (2017b).

  13. The MWhs of an offer block, but not price, can also be adjusted after the 2 h limit if accompanied with an acceptable operating reason, such as physical constraints or safety. In our following discussion of adjustment timing, we will assume that no such acceptable operating reason is present.

  14. Other jurisdictions also allow firms to adjust their bids in near real-time. For example, Australia and New Zealand allow firms to adjust their bids up to 5 min before the market clears (Clements et al. 2016).

  15. We present an example illustrating this conduct in Brown et al. (2018).

  16. In addition, several coal assets have joint offer control, meaning that the asset’s capacity is offered into the wholesale market by two firms. This is an artifact of policies targeted to reduce market concentration during market restructuring (MSA 2010b). For all but two such assets, the vast majority of capacity is offered by one firm (87–99%). As a result, we treat all plant capacity as offered by that firm. There are two assets whose capacity is equally divided across TA and CP. Unfortunately, the HTR data does not indicate which firm makes a particular offer from these assets. In our primary analysis, we allocate these offers to TA to ensure that we do not mask the strategic behavior of CP because CP was a focus of the MSA’s concerns (MSA 2013). However, virtually all MWhs of these assets are offered at prices below $100/MWh (over 95%) implying that these units will have little effect on our analysis of high prices. Further, we re-estimated our model splitting all offers 50-50 across the two firms. Our results are robust to this alternative specification.

  17. In addition to ceasing continued publication of the HTR, in response to AUC (2017b), the AESO ended access to past HTR data. As a result, we are unable to extend our HTR dataset forward.

  18. While the HTR does not identify firms or assets in real-time, this information is made available at a sixty day delay. This delayed disclosure may allow firms to verify their beliefs about rivals’ offer price patterns ex-post.

  19. Regulatory authorities in Alberta were concerned with firms utilizing information from the HTR to coordinate on an outcome where certain firms “price out” their units at high price levels to create a high price offer ledge below which the market level offer curve is very steep in order to elevate the spot market price (MSA 2013).

  20. We focus on a single hour for illustrative purposes. The results are robust to the consideration of other hours.

  21. In general, Capital Power’s coal units continue to operate in the hours and days following the use of its pricing pattern, suggesting that the pattern is not part of a pricing strategy for temporarily taking the units off-line.

  22. Employing the longer time series available in the merit order data, it is observed that Capital Power’s use of this pattern is concentrated between July 2011 to July 2014. For details, see Figure A3 in Brown et al. (2018).

  23. This price effect is magnified when Capital Power’s pricing pattern involves large coal assets. Average peak prices are 44% higher in these hours. It is plausible that firms are able to both observe if certain price-quantity pairs in the HTR follow Capital Power’s pattern, and whether these units are large coal assets.

  24. As noted earlier, data of this nature was employed by the MSA and firms in the AUC proceeding (AUC 2017b).

  25. We run the econometric analyses presented below for both ENMAX and TransAlta and find limited evidence that these firms respond to information disclosed in the HTR. Detailed results are available upon request.

  26. This time period coincides with the time period when the MSA was concerned firms were utilizing information disclosed in the HTR to elevate market prices (AUC 2017b).

  27. Brown and Olmstead (2017) analyze Alberta’s wholesale electricity market for the period 2008 to 2014, and find that firms exercise a sizable amount of market power in peak hours, and limited market power in off-peak hours.

  28. We take the absolute value because existing theory does not make clear predictions regarding the direction of a firm’s response to rival changes (see our discussion in Sect. 2 above). It is possible that the pricing up of MWhs by one firm might lead others to do so as well. However, if the HTR is being used to coordinate among multiple asymmetric equilibria, one might expect responses in the opposite direction. Distinguishing whether the HTR is being used to coordinate on non-cooperative Nash Equilibria or to achieve higher profits than in Nash Equilibria is a subject for future research.

  29. We also included lagged rival offer behavior beyond six lags. These coefficients were statistically insignificant.

  30. See De Jong and Woutersen (2011) for a detailed proof of the validity of probit models with lagged dependent variables in the absence of unit roots. We reject the presence of unit roots in our analysis.

  31. See Gourieroux et al. (1985) and Gourieroux et al. (1987) for a detailed discussion of generalized residuals and their use in testing for serial correlation in a probit model.

  32. The results detailed below hold in other model specifications such as linear-linear or log-log. Box-Cox tests demonstrate that linear-log provides the best statistical fit.

  33. We also control for observed hourly demand and wind. The results are robust to this alternative specification.

  34. The observed multi-collinearity is driven by the limited number of offers above $100 that an individual firm makes in any given hour. Consequently, it is often the case that the 50th and 75th percentiles are equal.

  35. These calculations estimate the effect of one rival undertaking a sudden high priced restatement. We observe hours where both of the large rivals undertake a high priced restatement. We calculated the joint effect of this simultaneous change. This increases the magnitude of the coefficients and generates similar statistical significance.

  36. For CP, changes in \(|\Delta Demand_t|\) have a negative and significant impact. This could be capturing a dynamic response to changes in demand overtime. For example, when we include lagged measures of \(|\Delta Demand_t|\) this unexpected effect no longer holds for CP, while the positive and significance effect remains for ATCO and TC.

  37. The major critique of the LPM is that the model does not constrain predicted values to the set [0,1]. For ATCO and TransCanada, less than 2% predicted values fall outside the interval (0,1), while 17% and 19% of predicted values fall outside the interval (0,1) for Capital Power for the symmetric and asymmetric models, respectively.

  38. In addition to generating similar qualitative conclusions, the predicted values of the LPMs and probit models are highly correlated. For ATCO and TransCanada (Capital Power), the correlation between the fitted values of the LPM and probit models are approximately 0.97 (0.94) for the symmetric and asymmetric specifications.

  39. The lower statistical significance in the asymmetric model could be driven in part by the positive correlation in high priced restatements across firms. The asymmetric model identifies off the differential timing of sudden high price offer restatements across the firms ATCO, Capital Power, TransCanada, and Other.

  40. Table 10 excludes a small number (approximately 4%) of cases where a restated price exactly matches a price from 4 h ago. In defining the next highest or lowest price, Table 10 includes previous offers by the same firm. In general, the closest bids above and below belong to the same firm in 17% and 19% of cases, respectively; excluding cases where the nearest previous price is from the same firm yields only minor changes to the distributions.

  41. This pattern is also observed for the other two large firms (ENMAX and TransAlta). The exception is the fringe of small other firms, which tend to price the same distance on average from the next lowest and highest offer.

  42. We are unable to include multiple lagged values on the CP’s price pattern dummy (\(CPTag_{t-4}\)) because of the high correlation observed across hours. We included up to six lags on the interaction variable \(DCP_{t-j}^{100} \times DRival_{t-j}^{100} \times CPTag_{t-j}\) for \(j = 1, 2, 3, ..., 6.\) Joint test systematically find no statistical significance among all six lags.

  43. von der Fehr (2013) raises concerns regarding the degree of information disclosure in the recent policies implemented in the European electricity sector. Similar concerns that increased market transparency would facilitate coordination led regulators to reject a policy to increase information disclosure in the U.S. natural gas sector (United States Department of Justice, 2012; FERC 2012, 2015).

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Acknowledgements

Certain sections of this paper are based on parts of Chapter 3 of James Lin’s PhD Thesis. The authors thank the Editor, Menahem Spiegel, anonymous referees, Joel Bruneau, Shourjo Chakravorty, Andrew Leach, Michelle Phillips, Stephen Poletti, Mar Reguant, Jevgenijs Steinbuks, Xuejuan Su, and participants at the University of Auckland, 16th Annual International Industrial Organization Conference, and 52nd Annual Canadian Economic Association Conference for detailed comments and suggestions. The views expressed in this paper are those of the authors and do not reflect the policy or position of the Government of Alberta or the Alberta Department of Energy.

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  1. Department of Economics, University of Alberta, Edmonton, AB, Canada

    David P. Brown & Andrew Eckert

  2. Alberta Department of Energy, Government of Alberta, Edmonton, AB, Canada

    James Lin

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Correspondence to David P. Brown.

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Brown, D.P., Eckert, A. & Lin, J. Information and transparency in wholesale electricity markets: evidence from Alberta. J Regul Econ 54, 292–330 (2018). https://doi.org/10.1007/s11149-018-9372-z

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