Category Archives: Economics

Update on Electric Prices

It’s been a while since I’ve updated my Ohio vs Ontario electric price charts.  I own properties in both and I have my electric bills starting in 2000.  In both locations the prices can vary with locality, but at least the general trends are clear.  Below is a graph showing the prices in their respective local currencies, for 2000 up to October 2015.  There’s no question whose rates are going up the fastest.  The trendline equations need some explanation.  As an example, the “0.064x” means that the price in Ontario is going up at a rate of 0.064 cents/kw-h each month.  So in the 193 months covered by the chart the price has gone up by 12.35 cents.

ont-ohio-elect-151112-1 Regardless of how anyone might spin it, the price increases in Ontario far exceed those in Ohio, by almost a factor of 4.

Next I show the comparison in Canadian dollars.  The Ontario line is the same as the first chart but now the Ohio line is drawn as though a Canadian person or company were purchasing it.  Note that the trendline is DOWN!


Finally I show the comparison in US Dollars.  The Ohio line is the same as the first chart but now the Ontario line is drawn as though a US person or company were purchasing it.  Note the now even-steeper climb of Ontario’s rates.


MPAC and Wolfe Island, again


Several months ago Stewart Fast, a new professor at Queens University in Kingston, Ontario, undertook a study of why southern Ontario was such a hotbed of anti wind energy sentiments.  His conclusions were interesting, and I’ll be having more to say about them in a future posting.  As part of his study he looked at property values and in particular he looked at MPAC (the Ontario real estate assessors), Wolfe Island and the property assessment reductions thereon.

As it happens, I had also looked at MPAC and Wolfe Island and posted on it about 18 months ago.   It seems that Fast and I used the same FOIA-obtained spreadsheet.  My main conclusion was that there seemed to be a large number of large reductions on Wolfe Island, but there wasn’t enough of a pattern to convincingly tie the reductions to the 86 wind turbines on Wolfe’s west end.

I’ve also posted on MPAC and property assessments in a 4-part series.  My main conclusion, contained in part 1’s section, was that MPAC seemed to be hiding the reductions by lowering the values in neighborhoods that just coincidentally happened to be around wind turbines, but not formally incorporating distance to a wind turbine into their regressions.

What Dr. Fast’s work added to mine was that (1) he was able to group MPAC’s reductions on Wolfe Island by their distance to the nearest wind turbine, and (2) he reminded me of how to use chi-square to test the differences between the bands for statistical significance.  The quick summary is that MPAC has been providing reductions to properties close to wind turbines significantly more often that those further away.  And I’m not using the word “significantly” in some fuzzy qualitative manner – I mean “significantly” in the hard statistical quantitative manner.  In other words, the odds of the getting a wind-turbine-centered pattern just randomly are vanishingly small.  Wolfe Island provides a good hard-to-refute example of how MPAC is finessing the numbers to deny the obvious.


The raw data (i.e. the spreadsheet) is quite detailed, so to save space here’s the summary of it.  There are 4 major areas in the municipality of Frontenac Islands:  Wolfe West (where the turbines are), Wolfe East, Howe Island and Simcoe Island.  The number of properties and total reductions are in the following table.


As both Fast and I have written, these numbers aren’t really indicative of anything having to do with wind turbine proximity. About the only thing that stands out is that Simcoe Island had a far higher rate than the other areas, which was at least partially due to reasons other than wind turbines.

The next step was what Fast added: he was able to use GIS software to group the reductions into buffers based on the distance to the nearest wind turbine.  He had 5 buffers: < 1km, 1 – 2 km, 2 – 5 km, 5 – 10 km and > 10 km.  He used chi-square to see if there were significant differences between the buffers and found that the 1 – 2 km and 2 – 5 km buffers were significantly more likely to have reductions than the other buffers.  As he said, this may be suggestive but is not quite conclusive.

Dr. Fast graciously provided me the data that went into his buffers and I re-ran his chi-square calculations to make sure I could replicate his results.  Initially I thought the non-significance of the < 1 km buffer (you’d expect the buffer closest to the turbines to show the most significant effect) was due to the income-producing nature of any land close to a wind turbine, plus the setback that rendered about 25% of that buffer unoccupied.  While those could be important, I also noticed that by chance there were a lot of reductions just outside of the 1 km border.  As an example I show the following picture of the reductions around Wolfe’s main city, Marysville:


The 1 km buffer ends about where Highway 95 T’s: to the left is inside that buffer while to the right is in the 1 – 2 km buffer.  Since all the buffer borders are fairly arbitrary anyway, I decided to proceed with 4 buffers, with my results below.


As the buffers get closer to the wind turbines you can see that the ratios of reductions to properties generally gets higher.  The chi-square is a test to see if these ratios could simply be due to chance.  The “Chi-2 p” column provides the p (probability) that this buffer varies from the total Frontenac Islands ratio by random chance.  The two buffers closest to the wind turbines have less than a 1% chance of having values that high by chance, while the buffer farthest from the wind turbines has a much less than 1% chance of having a ratio that low by chance.  Note that BOTH close-in and far-away reductions are significantly different from the mean, and in directions that are BOTH consistent with the hypothesis that wind turbines are associated with property assessment reductions.


In my earlier posting on the MPAC 2012 study I predicted that they would lower assessments close to wind turbines while never explicitly recognizing wind turbines as the cause.  I offered up Wolfe Island as an example of how this might proceed.  Thanks to Dr. Fast and a fair amount of serendipity we now have a solid indication that MPAC is proceeding as predicted.  While some aberrations in assessments and reductions would be expected (some chi-squares show significance where none really exists), the pattern shown above is just too consistent to be cast aside as coincidental or anecdotal.  We proposed a hypothesis that there would be more reductions closer to the wind turbines and the data clearly support that hypothesis.

Dr. Fast does have a point that this is indirect evidence of lower values.  After all, we don’t have the municipality’s “bufferized” assessed values, not to mention bufferized sales data.  In the 2012 study, MPAC did provide bufferized assessments for the entire province and they show a 25% decrease within 5 km of the turbines, a result that somehow got lost in their summary.  As for actual sales, there have been so few, especially on Wolfe’s west end, that any sort of statistically-valid testing would be difficult.  In the meantime, reductions will have to serve.  That MPAC seems to be going out of its way to hide this trend indicates that MPAC is being used to implement a political agenda – a problem greater than just wind turbine assessments.

Overall, some 22% of the properties in the Frontenac Islands were granted reductions by MPAC.  I’d love to know if that is typical – it would be interesting to study the assessments and reductions in the municipalities with and without wind turbine projects.  Unfortunately, as even Dr. Fast commented, MPAC has made getting their data just about impossible.

In his report Fast says that the evidence of reductions due to wind turbine proximity is “suggestive but not conclusive”.  Given the numbers above you are of course free to come to your own conclusion, but to me they are more than “suggestive”.  Perhaps there are confounding effects from the 2008 melt-down and following economic malaise, but I’d think they would affect the area as a whole.  If it isn’t the wind turbines that produce this rather clear pattern, then what is?

Heath Gets It

The town of Heath, Massachusetts, like many other town faced with wind turbine projects, formed a committee to study the issue.  Their final report is a very accurate  and very readable compilation of the issues surrounding wind energy.  Hats off to the members who took the time to do the research and had the strength to do it honestly.

Heath, Final Report, text

Heath, Final Report, full

Unfortunately, the Town of Heath hasn’t posted the report on its web site, so there’s no link to the original.  In the meantime, the town has proposed a bylaw that outright bans wind turbines within the town.  UPDATE, February 27, 2013 – Heath voters unanimously passed the ban.

Palmer – the Real Coal Story

On January 10, 2012, the Ontario premier’s office was crowing about shutting down the remaining Ontario coal plants.  That story was picked up by sympathetic outlets.  It is expected that a politician will play loose with the facts when it suits them, but unfortunately many of the media outlets seem to not have the skills or interest to find out the real story.  Bill Palmer is in a position to know and relates it below, in more details than he did on this site earlier.  Note that Bill is politically even-handed in his criticism.


I’ve tried to respond to a number of newspaper articles and technical publications that ran this story … to date none have chosen to print my comment. However, you deserve the chance to know the truth.

Renewable generation, and in particular wind had very little to do with the reduction in the output of coal fired generators in Ontario. Here are the facts. You can refer to the attached figure if you like pictures better than words.  [The little purple line in the lower right corner is the wind production.]

In 1994, nuclear generation supplied over 90 TWh of Ontario’s electrical supply, coal supplied about 15, and hydro about 35. Performance of the nuclear plants was deteriorating in part because of political decisions made by the Bob Rae (NDP) government to minimize maintenance and give early retirement to senior staff at Ontario Hydro in the early 1990’s due to the increase in costs brought about by putting Darlington into service after the start up was delayed by a David Peterson (Liberal) government decision to hold construction and start up in the late 80’s even though most of the costs had been spent, and high interest rates continued to rack up cost of the borrowed money. The rules were (and still are) that new “hydro” construction costs are not put on the consumer bills until a new station are put into service. The Darlington “cost over-runs” were mostly due to the political decision to hold startup at a time interest rates on borrowed money were double digit.

The Mike Harris (PC) government that followed Bob Rae decided that the nuclear units at Bruce A and Pickering A would be shut down to focus improvements on the newer B stations at Pickering B, Bruce B, and Darlington. The output of the nuclear generators dropped to about 60 TWh. Coal picked up the slack, increasing in output to about 40 TWh in the early 2000’s. 2003, the year the Dalton McGuinty (Liberal) government was formed, coal supplied about 40 TWh, nuclear about 62 TWh, hydro about 35 TWh, and the Ontario demand was about 155 TWh. However, the improvements in the nuclear plants, which had been started some years before resulted in the return to service of Bruce A units 3 and 4, and Pickering A Units 1 and 4 by 2004. The nuclear output started to rise and the coal output started to fall. Then after 2004, a further surprise occurred … the Ontario demand started to fall as mines, mills, pulp and paper, and other users closed up shop, or moved out of Ontario. From 2005 to 2012, the Ontario demand dropped by about 15 TWh, nearly 10% … not from conservation, but from loss of industrial output. As nuclear output continued to increase, including in 2012 the return to service of Bruce A units 1 and 2, the nuclear output rose to over 80 TWh again. The 20 TWh increase in nuclear output, the 15 TWh loss of Ontario demand, and the start up of a number of natural gas generators, bringing their output up to 20 TWh meant there was no need to run coal …

And the 4 TWh of output from the wind generators really had just about nothing to do with the reduction in the coal generation, as the wind production is mostly when coal generators are not needed – at night, and in the spring and fall. In the hot summer, and even in the cold winter days, when wind output is low, the coal plants continue to run. They can now be shut down now as there are enough gas generators to fill in … mind you at considerably greater cost.

And that friends, is the true story … the reason coal generation could drop 40 TWh was that the nuclear units picked up over 20 TWh, the system demand dropped 15 TWh, and natural gas generators picked up about 10 TWh. The 4 TWh of wind had very little impact on shutting down coal … no matter what you read elsewhere.

Feel free to share the truth, as it needs to be known. I’ve even shared it with some of the Liberal candidates … but it does not seem to be popular to say as it runs against the spin that “coal was shut down by bringing in clean renewables.”

Bill Palmer

Hughes on Degradation

In December 2012 the Renewable Energy Foundation published a study by Dr. Gordon Hughes [backup link] that detailed the degradation of capacity factors in Denmark and the UK over time.  As it happens, I’ve been posting on this issue ever since John Harrison first brought it to my attention over a year ago.  John and I and others have run our own numbers, obtaining results that are fairly consistent with Hughes’ results.  First I’ll summarize his report and then discuss the similarities. Continue reading Hughes on Degradation

A Tale of Two Homes

I am fortunate (I think) to be able to own two homes: my main residence in Ohio and a secondary home on Amherst Island, Ontario.  One of my great joys (not!) is paying monthly electric bills at both places.  Call me A-R, but I’ve still got all those bills, starting with January 2000.  Finally, my rat-pack tendencies pay off – I can compare Ontario’s electric rates with Ohio’s, and see how they have changed over the 12+ years.  I really feel sorry for ordinary Ontarians. Continue reading A Tale of Two Homes

Degradation Update, part 2

John Harrison was the first person (at least in public) to notice the year-to-year degradation of the output of Ontario wind turbines.  Then I followed up with Denmark and Mars Hill, and Paul-Frederick Back chimed in with more Denmark.  While our numbers vary, and none of us is completely happy with the accuracy of our results, there’s little doubt remaining that the efficiency of a wind turbine decreases, sometimes fairly dramatically, over time. Continue reading Degradation Update, part 2

Degradation Update, part 1

I’m always reviewing what I’ve written and if new evidence comes to light I’ll update accordingly.  A group of us have been discussing my results about how wind turbines lose efficiency due to wear and tear.  Several of the group had some reservations about my numbers being so large, on the order of 1.5 – 2% per year decline in Capacity Factors.  They surprisingly large to me as well, but I will generally go wherever the numbers take me.

One of the group, Paul-Frederik Bach, ran a lengthier analysis of Denmark’s wind turbines, running 10 “cohorts” through the years since their installation, with each cohort made up of the turbines placed in service during a particular year.  He didn’t correct for the wind speed during each year, but over that long of a period (18 years total) you’d think the wind speeds would average out enough so that his analysis would be reasonably accurate.  In place of my 1.5%, he averaged 0.38%.

None of us doubt there’s a decline, but how big is it?  And how could our numbers, both figured using reasonable (although different) techniques, come to such different results?  The answer, at least for Denmark, may lie in the period I had to use to do my (well, originally, John Harrison’s) technique.  One of my requirements is that the total number of turbines doesn’t change appreciably during the period I’m analyzing.  In Denmark’s case, only the 5 years 2004 through 2008 qualified.  I went back to 2000, calculating the average wind speed through 2011, using Copenhagen’s daily average data.  This is the result:

Note that the years I considered show up as 5 through 9 above and note their trend – sharply increasing.  You’d expect the Capacity Factors to increase along with the cube of the increasing wind speed, but they didn’t.  And so when I normalized the CF’s they showed a decline which I consequently attributed to wear and tear.  Bach’s numbers are over a longer horizon, when the wind, if anything, was in a downward trend.

So why might my numbers be too large?   Well, in a windier year there may be a significantly larger number of hours when the wind turbines are maxed out, and their output is no longer increasing with increasing wind speed.  Typically turbines are in this condition about 10-15% of the time, but in a higher average wind speed year that percentage may become enough larger that my technique starts attributing a turbine’s declines due to normal operating limitations to declines due to wear and tear.

So we know for sure that my numbers are off?  No, we don’t.  And that they’ve held up in widely different environments, with widely different average Capacity Factors, would be a tick in their favor.  How could we figure this out for sure?  The only way I see would be to work on much finer time increments, like hourly.  Mars Hill and Ontario were monthly, Denmark was annually.  Unfortunately, that kind of data is quite difficult to obtain, except maybe in Ontario.  And even there it would take quite a bit of effort, plus Environment Canada tends to shut down overnight in remote locations (Ontario considers  “remote” pretty much anywhere but Toronto!).

So this story isn’t finished yet.  After all, this is how science works.  I’ll continue analyzing projects as I can, and as always go where the numbers take me.

Mars Hill and Degrading Performance

Some History/Background

About 6 months ago John Harrison started calculating how the performance of wind turbines degrades over time.  He noted that as projects are in place over a number of years that their Capacity Factor (the percentage of their nameplate capacity that they actually generate) annually decreased somewhere in the 1-2% range.  For example, a project that operated at a CF of 30% initially could be expected to be operating in the 15% CF range after 10 years of a 1.5% annual decline.

There are 3 main reasons the CF might decline: (1) a change in wind speed or direction, (2) dispatching (being told to turn them off) and (3) wear and tear.  In Ontario and most other areas #2 is never done, per political fiat.  #1 is calculated by taking the historical wind records from a nearby weather station and adjusting  the actual output for the change in wind speed.  And that leaves #3, which is what is left over after the changes in wind speed have been factored out.

I took his method, applied it to Denmark and found a similar decline.  And as you can see from the comments, others are reporting similar observances.  But it takes a stable environment (with the same turbines in place and aging together, with no more being added) to be able to observe the decline, at least from public information.

Mars Hill

A few days ago I received the monthly output numbers from Mars Hill, a project in Maine that just celebrated its 5th year in operation.  I’ve written a lot about Mars Hill’s health and noise issues, but I had never come across how much electricity it actually generates.  It turns out that it has quite good numbers, averaging almost 39% CF.  Here is the graph of the 5 years (minus the first month, when their status wasn’t consistent).  I did include July 2009, when they were down for maintenance.

The trendline is unmistakeably down.  As far as I know Maine has never dispatched any wind project.  That leaves declining wind speed as the main alternative to wear and tear.  I got the 5 year’s worth of wind speeds from nearby Caribou, Maine via WeatherUnderground.  I used daily averages, then averaged them into each month, then cubed them to reflect the possible energy that can be extracted.  I normalized these 59 months’ averages and obtained the following chart.

You can certainly tell when Winter is.  The trendline is pretty much flat, indicating that a decline in winds is probably not the cause of the declining CF’s.  Finally I divide the raw CF numbers by the normalized wind and get the following chart.

The trend here is also unmistakeable, and agrees quite well with our previous observations in Ontario and Denmark.  The monthly CF decline calculates out to 0.216% per month, for an initial annual rate of ~2.6%.  In other words, what started with a 52% CF in month 1 has become a 40% rate.

I say “initial” because the halfway point in this chart should be the same as the overall average from the first chart, and 46% is quite a bit above 39%.  After puzzling over this for a while (and checking my numbers) I figured that the averages could be different if the turbines performed relatively better (or worse) at certain speeds that were more (or less) common.  In this case, the higher wind speed averages are certainly less frequent than lower speeds (see the second chart above) and the turbines perform less well vis-a-vis their potential at these higher speeds.

To follow up on this insight I sorted the data by the normalized wind potential power from low to high.  This technique makes any trendline slopes arbitrary, but at least a general trend can appear.  The first one graphs the raw CF vs. the potential.   We’d expect a reasonably straight ascending line and that is pretty much what we get.

The second chart graphs the normalized CF vs. the potential.  It shows that the turbines are less effective at getting energy out of the wind at higher wind speeds than they are at lower speeds.

Then I remembered an earlier posting where I took a turbine’s rated output at different wind speeds  and compared it to the potential energy of the wind at those speeds.  So I guess I shouldn’t have been surprised.

Does this nuance affect the results I presented above?  Most importantly, this nuance should affect all years equally, so the overall trendline remains down – but at what rate?  The normalized average is about 17% above the initial average, and I would think we should lower the decline by that 17%.  So instead of 2.6% per year maybe a figure of 2.1% is more appropriate.  That is quite close to Harrison’s Ontario work and slightly above my Denmark figures.  We are now 3 for 3, with maybe more in the offing.   If anybody out there has any better techniques or insights I’d certainly appreciate them.

Applying this to Mars Hill, we would have a month zero normalized CF of ~44%, declining to a CF of ~34%, which gets us back to the very first chart, except that now we’ve factored out any wind speed change.  That leaves us with wear and tear.  Along with that, of course, is a declining economic and environmental value.  Wind turbines don’t make any sense in the best of conditions, and those conditions just got a lot worse.

Wolfe on the Ontario Grid

Denise Wolfe lives on Amherst Island (along with John Harrison and me, part time) and really knows how to do research and even better how to summarize it.  As part of her efforts to convince the powers-that-be to stop the project on Amherst, she prepared a summary of what is currently known about the effects of wind turbines on Ontario’s grid.  It is brief (6 pages) and to the point, and is totally accurate.  If you need a good summary of what a mess wind energy has made of the Ontario grid this is excellent.


Denise Wolfe, Ontario Fact Sheet

The OSPE Submission

The Palmer Submission


In many pro-and-anti-wind energy discussions the topic of subsidies to the wind energy industry comes up.  Just for the record, those subsidies are now larger even in absolute terms (not to mention relative terms) than subsidies for the fossil fuel industries.  The usual response is that all new industries need subsidies.  The underlying theme is that wind energy is in its infancy and has lots of room to mature.

This infancy theme was thrown a monkey wrench when DOE secretary Chu mentioned that onshore wind is already mature.  So is wind infantile or mature?  One indication of which it is might be to look at the efficiency of today’s wind turbines at converting the dynamic energy of the wind into electricity.

It is surprisingly easy to calculate the amount of energy available from a given amount of wind moving through the diameter of a turbines blades.  You multiply the density of the air, it’s speed (cubed) and the swept area, divide by two and voila you’ve got the watts available.  One problem is that it is impossible to extract all the energy from the wind – you’d have to stop the wind to do so, but this wouldn’t allow any new wind to pass through the swept area.  A clever man named Betz (among others) figured out the maximum amount you could extract (now known as the Betz limit), which turns out to be about 59%.

So how close do modern turbines get to the Betz limit?  If they are pretty close then I think it is safe to call the industry mature.  The only way to extract more energy is to go higher and larger, and at some point this becomes self-defeating from both economic and engineering perspectives.  I cranked up a spreadsheet where I calculated the potential energy vs. what turbines are rated at and it produced the following chart (which is clickable to enlarge, thank goodness).The turbine I used for this chart is the Vestas low-wind-speed 1.8MW turbine that is proposed for Amherst Island, but the spreadsheet is set up to allow other turbines to be modeled.  The spreadsheet itself (an xls) is available for the asking.  The most important column is labeled “%age of Betz” which shows how close that turbine comes to perfection.  The subsequent column shows more realistic numbers that take into account the necessary generation/rectification/inversion/sychronization that must take place in a real turbine trying to join a real grid – I used a fixed 5% loss, which I think is probably low.  Over a large percentage of the time (as shown by the far right column) the efficiency of the turbine is quite close to the Betz limit.  I’m not sure how much more maturity we can expect out of turbine design.

Over all wind speeds the efficiency of this model is about 50% of the Betz limit, so perhaps there is some potential design that could capture all the energy at all wind speeds.  Such a design would likely require a shape-changing blade, made of materials that do not currently exist and controlled by a means that doesn’t exist either.  If wind turbines, at 50% of their potential efficiency, are not mature then I think it is hard to claim that coal plants, at 45% of their potential efficiency, are.

The influential 20% by 2030 report partly justifies the extraordinary investments in wind energy by estimating that wind turbines over time will increase their capacity factors from a current 30% to an anticipated 45%.  That would correspond to an increase in efficiency from about 75% of what is possible to 112%.  Too bad all those PhD’s at the DOE didn’t have access to my spreadsheet, or to an earlier posting of mine.

Wind energy supporters have every right to be nervous about all the subsidies flowing their way, so they’ll say whatever they can to keep them coming.  But the infancy theme is, like so much of what they say, simply not defensible.

Harrison on Viability

John Harrison continues to produce high-quality papers on a variety of wind-turbine-related topics. Amherst Island, where John lives, is slated for a 75-mw project that is now in the planning stages. In an effort to give the financial backers of such a project something to think about, he looked at the economics of the Amherst project and their underlying assumptions – many of which are unrealistic and uncertain. In summary, there are any number of very plausible (even expected) things that can cause the returns from such a project to go negative.

Financial Viability of the Ontario Wind Energy Generating System

Continue reading Harrison on Viability

U.S. Subsidies, 2010

When discussing the economic merits of wind energy and the large subsidies wind energy receives, the rejoinder from a typical proponent is usually something about how much more subsidies the gas and oil companies receive, and if we’d just level the playing field, wind would be competitive.  This is, of course, pure nonsense.  The actual numbers, which proponents seem universally unaware of (don’t confuse us with the facts!), tell a different story. Continue reading U.S. Subsidies, 2010

Another Look at 20% by 2030

One of the major weapons in the wind energy proponents’ quiver is a report titled 20% Wind Energy by 2030.  It was published by NREL, the National Renewable Energy Labs, which is part of the U.S. Dept. of Energy, in May 2008.  It lays out a blueprint on how the U.S. could attain 20% of its electricity production by 2030.  It has been widely used as an authoritative source by just about every industry body and many green-leaning politicians as well, all the way up to President Obama.  Since the DOE is headed by a Nobel-prize-winning physicist (Dr. Steven Chu), and they’ve got lots of money and PhD-level people, you’d think such an important report would be unassailable, especially by a mere mortal from a small town in Ohio, working out of his garage (literally).  You might be thinking that I’m just another anti-wind agitator who would always find something to quibble about in any otherwise solid piece of work.  I hope that after reading this posting you’ll have some appreciation of just non-quibbly the problems are, and how truly stupid we are for using it to justify all the financial, environment and social costs of wind energy. Continue reading Another Look at 20% by 2030

More on Ontario’s Exports

After my first posting on Ontario’s Exports, where I asked why Ontario was still burning coal when every bit of it was being exported, I received a note from Donald Jones, who has done a lot of digging into the details of Ontario’s operation.  Here’s his letter, which gives you an idea of just how screwed up Ontario’s electricity system is, and why renewable energy (and wind turbines specifically) are making the problems worse. Continue reading More on Ontario’s Exports

Wolfe Island Shoals Economic Impact

The Wolfe Island Shoals project was the largest contract awarded in the last round of wind projects, with a capacity of 300MW.  It is located at the eastern end of Lake Ontario and is slated to be the first “off-shore” wind project in at least Canada, and may end up beating out Cape Wind for that dubious honour in North America.

The project developer (Windstream) thought it would be a good idea to remind everybody how many jobs are at stake and what the economic impact of the project’s construction and operation would be.  So their project manager (Ortech) hired a consultant (Aecom) to write a paper entitled “Potential Employment and Income Impacts in Ontario from the WI Shoals Project“.  The executive summary sounds impressive.  It’s a $1.36B (yes, that’s a “B”) construction project, 1900 jobs and $89M in labour income  for 5 years.  Then 175 jobs and $9M in labour income for 20 years.  No mention of labour income to dismantle the project – they must have just forgotten to mention it.  I am (of course) more interested how they got those numbers and what other details they forgot to mention. Continue reading Wolfe Island Shoals Economic Impact

Wind Power is Gas Power

Energy analyst Steve Aplin wrote an article for that explains in rather simple and explicit terms why wind + gas (actually, gas + wind) is a really terrible way to reduce our emissions, and that nuclear + coal would be far cheaper and (surprise!) cleaner.  Perhaps if my musings on this topic aren’t convincing, maybe Steve’s are.  And don’t forget masterresource and bravenewclimate.

Original Link.

Backup Link.

Ontario’s Record Day Examined

For the 48 hours on Tuesday and Wednesday, October 26-27, 2010 Ontario’s wind projects generated a record amount of energy.  A massive record-setting storm system moved mostly to the south, providing Ontario with just about the best two days of production that could feasibly be expected.  Not surprisingly CanWEA crowed about it [backup link], and even the wire services [backup link] picked up on it.  As always, me being me, I took a closer look to see just how wonderful these days were for Ontario’s electric users.  And after looking at it, I’m not so sure I’d like to have many more record days. Continue reading Ontario’s Record Day Examined