OPG's nuclear facilities are now the major cause of increasing electricity costs

Ontario rates are on the rise again, after going largely unchanged for the final two years of the previous, Liberal, government. Many consumers won't realize the 7% increase during the first half of 2019 as the impact is hidden by subsidies, which have grown to about $4 billion a year in the recent provincial budget. In stark contrast to past years, this year it is publicly owned Ontario Power Generation (OPG) nuclear units driving the increase. I estimate all supply costs up a little over $400 million (nominal) during the first half of 2019, while the cost of OPG's nuclear output is up a little under $500 million.

This article is going to be about electricity rates - promised and realized. It will touch on too many complex areas I've spent too little time on to understand fully, but enough to understand Ontario’s rate-setting process cannot deliver reliable pricing on regulated nuclear supply.

Ontario's government announced it was "moving forward with nuclear refurbishment at Darlington Generating Station," in January 2016. That station has 4 reactors - the last 4 new-builds in the province, with the last of those entering commercial operation 25 years ago.

The average cost of power from Darlington nuclear units post-refurbishment is estimated to range between $72/MWh and $81 MWh, or 7 and 8 cents per kilowatt hour.

The low end of the estimate, $72/MWh, is what Ontario's consumers were paying for supply from OPG's nuclear power plants at the time of the announcement in 2016. Today we are paying $89.70, which could generously be considered as just above the high end of the estimate (adjusted to real 2015 dollars). This is somewhat explained by the inability of the regulator, the Ontario Energy Board to set a rate in 2017, but another 6% rate hike is already baked in for 2020, so we will be back at the high end of the estimated range in 2020 regardless.

It is not, however, accurate to blame the recent rate escalation on the refurbishment project.

Baseload's threatened ability to contribute to lower emissions

“Baseload” is a contentious term in energy discourse. In analysing electricity data in Ontario it occurred to me there’s a simple way to demonstrate the potential value of supply that delivers a consistent output all of the time - one that ignores all generation technology, using only hourly demand data. In this post I’ll demonstrate this methodology before discussing implications for supply mixes.

“Base” and “Load” are two fairly well-defined terms - neither of which are strictly adhered to in my methodology.

“Load” I treat as whatever data I have. I’ve collected available hourly, or half-hourly, data for 3 Canadian provinces, 5 Australian states, and 5 US systems. The data is unlikely to be equal: one example is the figure used for Alberta is “Alberta Internal Load” which includes “behind-the-fence” self-generation unlike the Ontario system operator’s “Ontario Demand”, which only reflects supply from their grid. I am not aware of what supply is included, or excluded, in data I’ve collected from the U.S. Energy Information Administration (EIA) or the Australian National Energy Market. Until the case study section of this analysis the differences can be ignored.

“Base” could be called minimum, but I think it’s helpful to eliminate outliers. The most extreme example is the great blackout in August 2003 that impacted most of Ontario, but more generally there will be some ideal nights on holiday weekends where demand is below its normal lows. In this analysis I define “Baseload” in relation to the statistical mean, which is better known as the Average (A) by those of us who determine it using the available spreadsheet, or other database, function.

While I am well-acquainted with data, I’ve only met statistics. Wikipedia explains the standard deviation, represented by the symbol “σ” (sigma), “is a measure that is used to quantify the amount of variation or dispersion of a set of data values,” and provides a very helpful graphic displaying 1st, 2nd and 3rd standard deviations on a plot of a normal distribution.

(By M. W. Toews - Own work, based (in concept) on figure by Jeremy Kemp, on 2005-02-09, CC BY 2.5, https://commons.wikimedia.org/w/index.php?curid=1903871)

People who are well-acquainted with statistics might be able to anticipate the results of much of my analysis, and probably could use it to determine how the distribution of electricity demands differs from a standard distribution. For instance, the average percentage of hours where I find demand is below A - σ (the statistical mean less one standard deviation), in 13 electricity system hourly data sets, is 15.45%: in the diagram above of a standard distribution it’s 15.8%. That result should not surprise a statistician, but perhaps some other metrics I’ve collected will be - and if not I will attempt to present the analysis for those that those unfamiliar with statistics.