In the retail energy space, customers often have the option to fix their future energy costs at current market prices, or fix their energy costs over time at varying prices. Sometimes it can be difficult to communicate / visualize the difference between these two options. This post will show one to do this and also provide an excuse to play with the awesome animation package in R!
The chart below is an example of a completely naive buying strategy – buy 1/12 of your power needs on the 15th of each month. The data below is the 12 month average ATC (around the clock) PJM Western Hub price for Jul13 – Jun14 and Jul14 – Jun15. This would result in the 2013-2014 price being fixed by July 2013, and 2014-2015 price being fixed by July 2014. This would produce a fixed power price per megawatt by the start of the fiscal year, and help avoid budget uncertainty due to fluctuations in energy prices.
The large dots represent buys and the horizontal line is the final average price per megawatt for each year. Now, this chart shows a lot of information, but to really drive home how this strategy will work over time I’ve used the animation package in R to ‘grow’ the chart. The final result is the video below:
As you can see, there is additional information conveyed when you see the above chart evolve over time that you don’t see in the final slide. Particularly, you get to see the horizontal lines move over the course of the year – visualizing the ‘path’ to the final fixed price or, depending on the strategy, the amount of volatility that should be expected.
The main package used to create the above video is the animation package in R. The resulting .mp4 video can be uploaded to YouTube as I have done, or if needed for a presentation, can be easily embedded into a slide deck.
library(dplyr) library(lubridate) library(ggplot2) library(animation) library(tidyr) library(RCurl)
First we need to get a set of dates on which we will buy, for this example we are picking the closet weekday to the 15th of each month. This will obviously change depending on the strategy being employed.
buys<-c('7/13/2012','8/15/2012','9/14/2012',
'10/15/2012','11/15/2012','12/14/2012',
'1/15/2013','2/15/2013','3/15/2013',
'4/15/2013','5/15/2013','6/14/2013',
'7/15/2013','8/15/2013','9/13/2013',
'10/15/2013','11/15/2013','12/13/2013',
'1/15/2014','2/14/2014','3/14/2014',
'4/15/2014','5/15/2014','6/13/2014'
)
Download the data from GitHub and do some data munging.
url <- "https://raw.githubusercontent.com/cam333/blog_posts/master/blogCode_animationChart/13_14.csv"
x <- getURL(url)
rawdata <- read.csv(text = x) %>%
select(Date,July_13_June_14_fw,July_14_June_15_fw) %>%
mutate(Date = mdy(Date)) %>%
filter(Date > ymd('2012-06-29')) %>%
mutate(buy = as.character(ifelse(Date %in% mdy(buys),1,0)),
July_13_June_14_fw = July_13_June_14_fw,
July_14_June_15_fw = July_14_June_15_fw) %>%
gather(variable,value,-Date,-buy) %>%
na.omit()
Next we need to write a function that creates our chart. Notice how the labels, point formatting, and hline geom are all dependent on attributes of the data being charted. This is important since the animation will be created by generating many charts, each with a slightly larger data set – allowing the labels to adjust as we move through time.
chart<-function(cutoff){
a <-ggplot(filter(rawdata,Date <= cutoff),aes(x=Date, y=value, group = variable)) +
geom_line() +
geom_hline(yintercept = filter(rawdata,Date <= cutoff &
buy == 1 &
variable == 'July_13_June_14_fw') %>%
summarize(mean = mean(value, na.rm=TRUE)) %>% use_series(.,mean),
colour = "red", geom_text(aes(label = "2013 Avg."))
) +
geom_hline(yintercept = filter(rawdata,Date <= cutoff &
buy == 1 &
variable == 'July_14_June_15_fw') %>%
summarize(mean = mean(value, na.rm=TRUE)) %>% use_series(.,mean),
colour = "blue", geom_text(aes(label = "2013 Avg."))
) +
geom_point(aes(size=buy,alpha = buy, color = variable, fill = buy)) +
scale_colour_manual(values = c("red","blue")) +
ylim(35,50) +
xlim( ymd('2012-06-29'),ymd('2014-06-30')) +
annotate("text", x =ymd('2013-12-01'), y = 50,
label = paste("Average Price 2013-2014: $", as.character(round(filter(rawdata,Date <= cutoff &
buy == 1 &
variable == 'July_13_June_14_fw') %>%
summarize(mean(value, na.rm=TRUE)),
digits=2)),sep =" "),colour = 'red',hjust =0) +
annotate("text", x =ymd('2013-12-01'), y = 49,
label = paste("Average Price 2014-2015: $", as.character(round(filter(rawdata,Date <= cutoff &
buy == 1 &
variable == 'July_14_June_15_fw') %>%
summarize(mean(value, na.rm=TRUE)),
digits=2)),sep =" "), colour = 'blue', fontface = 'bold',hjust =0)+
annotate("text", x =ymd('2013-12-01'), y = 48,
label = paste("Date: ",filter(rawdata,Date <= cutoff) %>% summarize(date = last(Date))%>%
extract2(.,1),sep=" " ),hjust =0, fontface = 'bold') +
theme_bw(base_size = 15) +
theme(legend.position="none") +
ggtitle("Simulation of Monthly Buying") +
ylab("$/MWh") +
geom_point(data = data.frame(x=ymd('2012-07-29 UTC'),y=50,variable = 'July_13_June_14_fw'),aes(x,y),color = 'red',size=7) +
annotate("text", x =ymd('2012-11-01 UTC'), y = 50,label = "Buy Points 2013-2014") +
geom_point(data = data.frame(x=ymd('2012-07-29 UTC'),y=48,variable = 'July_14_June_15_fw'),aes(x,y),color = 'blue',size=7) +
annotate("text", x =ymd('2012-11-01 UTC'), y = 48,label = "Buy Points 2014-2015")
print(a)
}
Now we need a function that loops over a sequence of dates and produces a chart each time. This is the function we will call with the ‘saveVideo’ function in the animation package.
animate <- function() {
lapply(seq(ymd('2012-06-29 UTC'),ymd('2014-06-30'),by = '2 day'), function(i) {
chart(ymd(i))
})
}
Lastly we need to run the animate function and save the video. This can take some time depending on how complicated your chart is and how many times you are updating it. We will be producing 366 charts to create our video.
saveVideo(animate(),interval=.05, video.name="C:\\Users\\cmohan\\Desktop\\buypoint2.mp4", ani.width = 800, ani.height = 372)
You should now have a video clip on your desktop.
Charles Mohan 