Last week I wrote a post on wind farm capacity factors in America. Below is the key graph showing monthly variations in capacity factor:
This tells a simple story. The average capacity factor of America’s wind farms is very higher, much higher than most other countries. However, to imagine this is the same throughout America would be a mistake. The climate in California is rather different to that in North Dakota or Texas. And wind farm output follows a very different pattern.
We can find out how much wind farm output varies in California by looking at the hourly data provided by CAISO. Let’s look at it from a number of angles.
Highs versus lows
Put simply, California’s wind farm output essentially falls to zero on a relatively frequent basis.
In 2014, California’s wind farms had a mean output of 1191 MW. According to CAISO, the minimum output was actually 3 MW on 2nd January. This was 0.25% of mean output and 0.07% of maximum output in 2014.
Here is a graph showing daily lows in 2014:
As you can see the daily low frequently goes towards zero. Most problematically, it goes towards zero in December and January. This is when California’s solar output is also at its lowest point. Winter equals less sun and less wind in California.
Another way of looking at this is daily means.
These arguably provide a better picture of seasonal variation than daily lows. Lows may occur at night when demand is lowest, thought they mostly don’t. However, if there is no electricity from wind farms all day then we are in trouble.
Here are the daily means on every day in 2014.
Pronounced lulls are also striking. The first ten days of December and the middle of January saw week long lulls where wind farm output is consistently below 10% of the annual mean output.
The graphs above show large seasonal variation. But how much does this vary? Below is a graph of mean monthly output in 2014.
Monthly variations in output are clearly massive. The best month was June where the mean output was 2573 MW. The worst was January, which had a mean output of 733 MW. The best month therefore saw output 3.7 times greater than the worst month.
Wind farm output typically follows some kind of daily cycle (on average). And California’s is relatively pronounced.
Here is the mean output in each hour of 2014:
Ideally, the daily and seasonal cycle will follow the cycle of electricity demand as possible. But clearly California’s does not. Wind farm output peaks at midnight when demand is relatively low. On the other hand demand peaks in summer when wind farm output is also relatively high and relatively consistent.
However, these comments are getting rather close to hand waving. So if I find time I will try to write a blog post showing a simple model of curtailment of renewable electricity in California, and how this is related to the interaction between seasonal electricity demand and season renewables production.
Note on data
Wind farm data was downloaded from the CAISO website and processed in R and plotted using a custom theme in ggplot2. I will be posting the code used for these plots in a later blog post. Previously I included code below the posts, but this is probably a distraction for many. So, in a separate post code will now be shoved.
hypergeometric in the comments asked about the correlation between wind and solar output. Roughly speaking, wind and solar have similar(ish) seasonal cycles. Both see peaks in summer, and minimums in winter.
The year round average seasonal cycle for wind is also the opposite of that for solar. Solar peaks at mid-day however, while wind hits its minimum. However, the daily cycle for for wind is not consistent year round. And it flattens out a lot in winter.