Today the government of Canada approved a pipeline, which some see as contrasting with it declaring a climate emergency yesterday. To assuage the concern the government has promised to do blindingly good things with profits from the pipeline, including promising “every dollar the federal government earns from this project will be invested in Canada’s clean energy transition,” and launching, “the next phase of engagement with Indigenous groups on ways they could share in the benefits of the expansion, including through equity ownership or revenue sharing.”
When the Prime Minister was elected he brought two veterans of Ontario’s Liberal government to Ottawa as his top advisors, so this seems an opportune time to examine one “clean energy” initiative geared to invest in Aboriginal communities.
The $2.6 billion expansion on hydroelectric generating stations on the Lower Mattagami river.
The $2.6 billion Lower Mattagami Project has allowed Ontario Power Generation [OPG] to produce more clean, renewable electricity from new generating units.
I checked – all the way back to the construction of the first of 3 generating stations OPG built on the Lower Mattagami. A fourth site, Smoky, already existed but it was a private generator until 1991, so I had to estimate that data. If the completed project has allowed OPG to to more, OPG has found other reasons not to do so.
I saw an opportunity for presenting Ontario electricity data in trying out the free public version of the Tab|eau business intelligence tool.
I’m not sure I have the patience, or the smarts, to learn how to do all I’ve learned with Microsoft’s Power BI, but maybe I should. I’m astonished with the power of this map in filtering the data table!
(afraid the attempt at embedding on wordpress was a failure – but the link works!)
I was alerted that the MNR’s GIS tool also has an “Out” option, and that reveals a fire identified as Parry Sound 7, started May 17, 2018. As I post this, that fire remains identified as having a “HUMAN” cause.
Parry Sound 7 is listed as only 0.2 hectares in size (Parry Sound 33 is nearing 9000 ha), but it was reported and that begs the questions about the Ministry’s, and the wind farm developer’s, investigation and response. This is particularly true as fire intensity codes for industrial operations exist.
Were fire intensity codes applicable to Pattern’s wind project, and were the “several small fires” the CBC was told, by “a number of workers”, preceded Parry Sound 33 reported to the appropriate authority – presumably the MNR?
1 last question: will the MNR scrub the “HUMAN” from the Cause field of Parry Sound 7 too?Read More »
TORONTO — Ontario ratepayers will benefit from $790 million in savings thanks to the Government of Ontario’s decision to cancel and wind down 758 renewable energy contracts, Minister of Energy, Northern Development and Mines Greg Rickford announced today…
All of the cancelled projects have not reached project development milestones. Terminating the projects at this early stage will maximize benefits for ratepayers.
Rickford also confirmed that the government intends to introduce a legislative amendment that, if passed, will protect hydro consumers from any costs incurred from the cancellation. Even after all costs are accounted for, ratepayers can expect to benefit from $790 million in savings from this one decision.
I thought a short post is in order as the incoming mainstream media reports are not informative or in any way helpful.
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.
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
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.