Many American Indians/Alaska Natives (AINA) support development and use of sustainable energy sources for a variety of reasons. We wanted to minimize reliance upon hydropower operations that have caused or contribute to the extinction of Salmon. We also wanted backup energy in the event of disruptions to the grid. Therefore, we decided to invest in a system that can power critical systems (e.g., well, refrigeration, etc.) and provide battery backup in case the grid goes down. The purpose of this blog is to provide information about how we designed our system and lessons learned. We also provide some programming code for analyzing data to evaluate energy development and system maintenance.
Step 1: Determine your energy needs
The first step of alternative energy development is to determine your energy need. This is done by taking an inventory of the amps each device, appliance, and light fixture (bulbs) that you are planning to hook up. In general, amps required are listed on sticker tags or stamped into bulbs. I put this information into a spread sheet and multiplied the amps by the total number of devices, bulbs, etc. For example, if you have two iphones you would multiply 0.0583 amps by 2 to get 0.116 amps. After we did this for the circuits and added it all up we came up to a total of 83.5 amps. You must remember this is the maximum amount of amps if everything came on at once, which will likely not happen. The total amps is an important number because it is used to size your system.
Step 2: Determine wind resource for your area
In this example, we used data from the Fort Hall Agrimet station. This is the closest station to my home. This is one way to gather data for determining wind resources In this example, I selected the following variables for the years of January 1, 2000 through May 8, 2020 which gave me 20 years of data.
- UA = Daily Average Wind Speed (mph)
- UD = Daily Average Wind Direction (deg az)
- WG = Daily Peak Wind Gust (mph)
- WR = Daily Wind Run (miles)
After you submit the online form you will get all the data that can be copied in the clip board and saved as txt file. This data can then be analyzed with R. Here is my code for reading the data and getting averages.
rm(list=ls())
#Read fort hall agrimet data
wind <- read.table("C:/Users/cleve/OneDrive/Code/Wind/fthallwind050821.txt",
header=T, sep=",")
#get rid of NO RECORD
wind_NA<- wind
wind_NA[wind_NA == "NO RECORD "] <- NA
wind_NA
#specify columns as numeric
i<- c("ABEI.UA","ABEI.UD","ABEI.WG","ABEI.WR")
wind_NA[ , i] <- apply(wind_NA[ , i], 2,
function(x) as.numeric(as.character(x)))
sapply(wind_NA, class)
#convert to meters per second
AvgWS_ms=wind_NA$ABEI.UA*.44704
WindGust_ms=wind_NA$ABEI.WG*.44704
#rename columns
colnames(wind_NA)= c("Date","AvgWS", "AvgWD", "WindGust","WindRun")
head(wind_NA)
#specify date as date
date=as.Date(wind_NA$Date, "%m/%d/%Y")
x=cbind(date,wind_NA[,2:5], AvgWS_ms, WindGust_ms)
head(x)
#Means of columns
colMeans(x[,2:7], na.rm = TRUE)
With this code I get the following averages.
| Average Wind Speed (mph) | Average Wind Direction (az) | Average Wind Gust (mph) | Average Wind Run (mi) | Average Wind Speed (m/sec) | Average Wind Gust (m/sec) |
| 6.54 | 194.68 | 21.41 | 156.86 | 2.92 | 9.57 |
Lets use the data and openair package to make a wind rose graph to get a visual.
library('openair')
windRose(x, ws = "AvgWS_ms",wd = "AvgWD", key.header="Average Wind Speed",
paddle=F, border = T, breaks = c(0, 0.5, 2.4, 3.6, 5.7, 8.8, 11.1),
key.position = 'right')