A quick post with commentary on a graphic.
I’ve been writing little but learning more recently. I’ve written multiple times on the inability of Ontario to fully utilize its water rights on the Niagara river, so that’s some data that I looked to learn some new data connections and summary techniques. Having advanced to where I can easily update to the latest available data I thought I’d share this view summarizing it – and offer some brief comments explaining the significance.
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Ontario’s Industrial Conservation Initiative program, which rewards large “Class A” consumers for lower consumption during periods of high demand from the system operator’s supplier, cost others $1.27 billion in past 12 months. I won’t review the history of the program today as I did 3 years ago in “Stakeholders” destroying the viability of Ontario’s electricity market, but I will note that since last March a Variance Account under the [un]Fair Hydro Plan – which shifts costs from ratepayers today to rubes sometime in the future – a debt of $1.2 billion accumulated with April’s total still to be posted.
Today the system operator (IESO) posted the top 5 peak hours for the adjustment period that ended April 30th, 2018 (it started May 1, 2017) – and Monday the IESO posted the final Global Adjustment figures for April. This post will contain:
- a quick demonstration of cost shift calculations,
- review of the ICI value proposition, and
- another jab at the province’s time-of-use (TOU) billing experiment performed on residential consumers.
For the 12 months of the ICI adjustment period the cost shift can be calculated as the difference between what Class A (larger consumers and ICI participants) did pay and what they would have paid were there not a separate class:
- The total global adjustment charge for the period was $11.821 billion dollars, and total consumption (both classes) 138.194 terawatt-hours (million MWh), so the average global adjustment rate was $85.54/MWh.
- Class A consumers were allocated a $1.8529 billion of the global adjustment total on 36.503 TWh of consumption which works out to an average global adjustment rate of $50.76/MWh
- The $35.78/MWh difference in that rate, on 36.503 million MWh, means $1.27 billion was avoided
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Electricity prices, and costs are aspects of a project I’m trudging through working with electricity data from the United States. I’ve developed a Power BI report which probably deserves a lot slicker interface, but time is limited. This post offers directions on controlling the reporting, and adds some Ontario context to the graphics.
My primary intent was to create imagery of average monthly electricity cost, by state, for residential consumers. Rates get a lot of discussion, even more so in recent weeks, but I’m not convinced an isolated rate analysis is useful.
A recent Scientific American article featured a smart BI report by Abhilash Kantamneni ( @akantamn on Twitter ).
Due to an exchange on Twitter I’d had with Abhilash a couple of weeks ago, I wanted to build a view that showed both rates, and average monthly consumer costs – because it turns out these are much different things.
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Yesterday Ontario Power Generation released their 2017 Financial Results:
Ontario Power Generation Inc. (OPG or Company) today reported net income attributable to the Shareholder of $860 million for 2017, compared to $436 million in 2016.
That must be considered a great number in the context of the income history at OPG as it’s the highest they’ve ever accomplished. The apparently excellent results may leave some wondering what critics commenting on the sector have been braying on about. I, a critic, have reviewed the results and found some things to bray about.
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A short post to debunk some belligerently dishonest claims regarding Ontario’s most inane electricity/social science project, Henvey Inlet Wind.
Background: Clement/Thibeault $billion negligence: Henvey Inlet Wind
The contract, according to the IESO’s contract list, was signed in June 2011 under the feed-in-tariff (FIT) program that paid $135/MWh, plus up to another $15/MWh as an “Aboriginal Price Adder.” While those contracts were expected to be operational 3-years after the project data, apparently this one is exceptional in ways other than costing $150/MWh (roughly 5 times what new 2019 wind in Alberta will cost).
Here are the claims I’ll rebuke (emphasis added):
Development of wind energy will help Ontario in meeting its goal of phasing out coal-fired power generation.
The windfarm is expected to displace 851,000t of carbon dioxide emissions a year, which is equivalent to the amount of carbon dioxide released by 200,000 cars. It will also offset 4,100t of sulphur dioxide, 1,200t of nitrogen oxide, and 13.4kg of Mercury emissions per year.
Unlike coal-fired power plants, the project will not use water leading to the conservation, which normally uses approximately two billion litres of water a year.
The project, of course, missed the coal era in Ontario’s electricity sector. The “goal of phasing out coal-fired power generation” is long since met.Read More »
Ontario’s electricity system operator (IESO) made a mistake in June.
In July they made it better for themselves in a way that specifically punished one set of consumers – Class B ones not covered by Regulated Price Plans (RPP). The OEB’s lacklustre oversight in recent years continues to harm this same set of consumers. This will be a wonkish post but if you’re connected with a company with a substantial electricity bill, it will alert you to probable overcharges.
This situation should be difficult to explain, because I’ve had friends questioning the IESO on it for half a year and I haven’t seen any evidence that a fulsome explanation is pending. That may be due to the beginning of the tale.
The beginning was the final global adjustment calculations the IESO made for June 2017. They erred in calculating total consumption. The inability of the organization to admit that, along with the regulator’s (OEB) disinterest in monitoring the IESO’s collection of global adjustment charges, is why confusion persists.
I noted on Twitter when the figures were released that they made no sense. To explain why requires getting into some obscure details.
- the IESO reports “Ontario Demand” hourly, but that figure is essentially demand for supply from their market participants,
- the IESO elsewhere reports a summary breakdown of this figure as “Monthly Energy Demand” which shows some of that figure is “Generator Consumption” and some is “Losses”,
- the IESO has not reported on generators embedded in the Local Distribution Companies (LDC’s) it services, but in the past couple of years it has noted monthly distribution-connection (Dx) figures in it’s 18-Month outlooks,
- the Global Adjustment process requires knowing the share of consumption for each consumer, so that reporting includes a “consumption” figure .
If you took the time to subtract out the generator consumption and losses from the IESO’s reporting of “Ontario Demand”, and added the embedded generation, you’d find you were usually about 1% short of the consumption as shown in the global adjustment reporting. In June 2017 the difference was far greater:
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My friend Parker Gallant has written on my updated estimates of annual curtailment in Wind waste should worry Ontario ratepayers. Producing the estimates doesn’t take me nearly the effort Parker puts into writing on them, so I felt compelled to add a new view of the data just to make our contributions a little more equitable.
The French language Radio-Canada has posted AU PAYS DE L’EAU NOIRE
Des résidents en Ontario vivent un cauchemar depuis l’installation d’éoliennes proches de leur domicile. I assume it’s best read in French, but the Google translation to English sufficed for me. As the journalism at Radio-Canada is more focused on the impacts to people of turbine construction of the North Kent wind farm, I decided today’s show of data will be on the performance of individual industrial wind turbine facilities.
Capacity Factor is the output of a generator divided by the theoretical maximum (full output in all hours). To estimate costs I need to estimate curtailment, but just viewing the history of capacity factors has the benefit of allowing the cynical reader (ie. the good ones) to verify my claims just by adding up columns from the IESO’s wind file. I won’t make it easy to do though, because for fairness I limit results to years where a facility was in commercial operation throughout, and to compare 2017 results I’ve made all years’ data the total as of the end of November.
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