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This chapter we are going to take a detailed look at spikes. These are short periods when wind starts from a low or no output to a very high output within 10 to 20 hours, then drops right back down to a trickle within a few hours.
It is important to understand how this affects the entire production system because when the wind kicks up in a spike, and demand is low something else has to give way, or given away to the US. This means comparing these wind spikes with the output from other sources (next chapters). In particular coal since the aim of the current government is to eliminate coal.
Before we can compare the output from wind with coal we have to examine the anatomy of spikes of power that wind produces.
This is Amaranth in July of 2010:
You can see there are discrete tight spikes in the power output. The Y-Axis in this case is megaWatts not Hourly Capacity Factor. This will be necessary to plot when we look at coal and wind together.
This is what one of those spikes looks like close up:
The blue line shows the ramp up to the peak, in 14 hours, with the red line down back to near nothing, in 8 hours. The important point is the time spent at the peak is just an hour, likely less if we could look inside that one hour. The peak may have been just a few minutes. We will show, in subsequent chapters, that this is the major problem with wind.
In that same time frame what did the other projects look like? Did they also spike at the same time?
To compare them all together we have to return to having the Y-Axis as the Hourly Capacity Factor. You can see most of the projects peaked at near the same hour. This is the chart of those peaked times:
| Hour | AM | KNG | P. A. | P. B. | PNC | R. S. | UW | W. I. |
| 1 | 2.5% | 5.0% | 22.8% | 0.0% | 0.0% | 7.9% | 6.6% | 3.0% |
| 2 | 2.5% | 5.0% | 18.8% | 0.0% | 0.5% | 9.2% | 28.0% | 0.5% |
| 3 | 4.5% | 10.0% | 20.8% | 1.0% | 1.1% | 5.3% | 22.5% | 0.0% |
| 4 | 10.5% | 12.5% | 25.7% | 3.0% | 2.6% | 25.0% | 50.0% | 0.0% |
| 5 | 18.5% | 7.5% | 30.7% | 11.1% | 4.8% | 26.3% | 30.2% | 0.5% |
| 6 | 24.0% | 20.0% | 28.7% | 12.1% | 7.9% | 34.2% | 17.0% | 1.5% |
| 7 | 12.5% | 15.0% | 25.7% | 8.1% | 3.2% | 19.7% | 31.3% | 1.0% |
| 8 | 7.5% | 7.5% | 14.9% | 11.1% | 2.1% | 10.5% | 24.2% | 7.1% |
| 9 | 30.0% | 10.0% | 9.9% | 14.1% | 1.1% | 13.2% | 22.5% | 10.1% |
| 10 | 27.0% | 15.0% | 5.0% | 23.2% | 0.0% | 13.2% | 13.2% | 10.1% |
| 11 | 18.5% | 7.5% | 3.0% | 28.3% | 0.0% | 6.6% | 4.4% | 8.6% |
| 12 | 37.0% | 7.5% | 5.0% | 42.4% | 0.5% | 9.2% | 8.8% | 11.1% |
| 13 | 57.5% | 7.5% | 15.8% | 50.5% | 2.1% | 10.5% | 13.7% | 39.9% |
| 14 | 62.5% | 10.0% | 40.6% | 32.3% | 6.9% | 11.8% | 19.2% | 41.4% |
| 15 | 72.0% | 10.0% | 10.9% | 31.3% | 12.7% | 11.8% | 23.1% | 67.2% |
| 16 | 69.5% | 7.5% | 4.0% | 36.4% | 14.8% | 9.2% | 15.9% | 4.0% |
| 17 | 60.0% | 5.0% | 16.8% | 33.3% | 12.7% | 5.3% | 7.1% | 4.0% |
| 18 | 45.5% | 5.0% | 17.8% | 18.2% | 10.6% | 2.6% | 2.2% | 15.2% |
| 19 | 28.5% | 5.0% | 15.8% | 9.1% | 6.9% | 1.3% | 0.0% | 3.5% |
| 20 | 18.0% | 5.0% | 9.9% | 6.1% | 3.7% | 0.0% | 0.0% | 8.1% |
| 21 | 13.0% | 2.5% | 9.9% | 2.0% | 4.8% | 0.0% | 0.5% | 12.6% |
| 22 | 15.0% | 0.0% | 14.9% | 0.0% | 2.1% | 1.3% | 2.7% | 17.2% |
| 23 | 16.0% | 0.0% | 26.7% | 0.0% | 7.9% | 1.3% | 6.0% | 15.7% |
| 24 | 20.5% | 0.0% | 27.7% | 2.0% | 7.4% | 2.6% | 6.6% | 18.2% |
Blue numbers are those within the spike, yellowed back ground cells are the highest value reached. Notice they are all within 12 hours. Notice that Port Alma, Port Burwell and Wolfe Island peak within a 3 hour window. Thus confirming again that distance between sites does not distribute the output. Also notice Wolfe Island was at 67% at the top and in the next hour down to 4%. This must mean that that 67% HCF occurred for less than an hour.
If spikes are occurring for less than an hour, then the distribution count of each HCF we had seen in each of the project’s chapters are too heavily weighted to the high side. For example, that 67% for Wolfe Island is counted in that chapter as one complete hour in the stack, but since that output for that spike was less than an hour, then the count of how many hours at 67% is too high. This means that the skewness of the output is even further pushed to the low-end. This means the Summer’s Median Capacity Factor is too high. Maybe not by much, but the MCF values in those graphs are optimistically biased because of lumping output into units of hours.
This spiking of output has got to wreak havoc with the system fluctuating like this.
Putting them all together in megaWatts we get this profile for all the farm outputs that the grid would have to respond to:
On the up side the input into the system from all the farms goes from 100mW to 400mW in just four hours, and in the back side back down to 75mW in five hours. This has to be a major headache for the system operators to try to balance.
Though this is just one spike of many, it is clear from all seasons for all years, that wind output is a series of spikes, some nested within each other. This case above is typical of any of them. Some maybe wider, taking longer to get to the tip, and longer to get out, but nevertheless they are all spikes.
It’s important to remember this for the next chapters as we compare these spikes with coal output which is what wind is supposed to replace.
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Ontario's Wind Performance 
Thanks , I have recently been looking for info approximately this topic for a while and yours
is the greatest I’ve came upon so far. But, what about the conclusion? Are you certain about the supply?
Thanks. Glad this was useful for you. Future supply? Of course trying to predict the future is difficult. One can only really guess. Change of government policy can change things dramatically.