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This assignment will also focus on the use of graphics to aid in examining data and making extrapolations to the future.

Questions:

1. The build out of wind energy in the US has proceeded in a somewhat stochastic manner. The table below contains the raw data on installations and cumulative capacity per year since 1999 until July 1 2013 (coded as year 2012.5):
   
   
   
   First thing to do is clear out all the default data in the data table so that you have no chart. The first row of the chart needs to be a string, not a number. So type in Year and Capacity in the first row of Column A and B Populate column A with the years (e.g. 1999-2012.5). In column B enter in the cumulative installed capacity (from column C in table above) for the years 1998 through 2005. You have now established a baseline. The value of this chart tool hopefully will now become apparent as we are going to use it to compare data against a model. Do all parts if this question in the exact order in which they appear.
   
   
   a) Average the values in column B from 2001 through 2005 to determine the average intall capacity. Divide that number by 1000 and then use that value to fill out the years 2006 to 2012.5.
   
   
   Ex: If the average turned out to be 2200 MW (it isn't) then your model number is 2200/1000 = 2.2. Hence the value for 2006 in column B would be 7.6 +2.2 = 9.8, for 2007 it would 9.8+2.2, etc - this is your model.
   
   
   Report your 2012.5 number that you get based on this model
   
   b) In column C we will make a new model. Label column C (in the first row) EXP. This model assumes 20% annual growth starting in 2006. So in column C for 2006 you would enter a number that is 20% higher than the 2005 value of 7.6 and keep doing this. (Yes I want you to build up this chart manually because you will pay more attention to it this way). Report your 2012.5 number that you get based on this model
   
   c) In column D label it as DATA and now enter the actual data from 2006 to 2012.5
   
   d) There is a national aspirational goal that 20% of our nameplate capacity should be in the form of Wind energy by the year 2030. Consider our nameplate capacity to be 1.3 TerraWatts and that our current wind energy nameplate is 60,000 MW. In column A after 2012 then enter 2030. In column E make the label GOAL and enter 2012.5 values of 80 (Gigawatts) and 2030 value that corresponds to 20% of 1.3 TW (make sure you get the units correct) and attach a screen shot of your graph.
   
   e) From the form of your graph comment on the feasibility of reaching the GOAL from our present state
   
   f) Calculate (you don't have to graph) how much installed capacity you would have to add per year to reach this target (in 17 more years) and compare that to the actual install data (column B in the above table) and comment on whether or not this goal is possible to achieve.
   
   
   
2. The scalability of cellulosic ethanol (switchgrass, etc) as a good alternative to gasoline is currently a much debated topic. Consult various resources on this subject an report on the range of views about the total yield that is potentially available in this resource. Here is one article to start with
   
   
3. During the entire month of June 2008, most consumers consistently paid $4.00+ for a gallon for gas. Currently gasoline prices are again at these levels. Most all popular media and bar stool wisdom claims that consumption of gas "dramatically" goes down during when the price reaches these levels. Well, that's a nice story, but what story do you get from the actual analysis of the real data?

   The master data site is located at the link below:
   
   Go get the real weekly gas consumption data .

   If you know how to use Excel, you can import the data directly. Also note, that although the graphs are useful, there seems to be now way to adjust the Y-axis to graphically see some important details. In general, the Y-axis doesn't need to start at 0 so that all the data is compressed.

   From that data source answer the following questions and make sure you show your process:

   a) We always hear about fuel efficiency getting better and that we are all becoming "greener" as we drive more fuel efficient vehicles. Under that premise we should be using less fuel on an annual basis, if our driving patterns remain the same.

   Calculate the weekly average gasoline consumption for the calendar year 1992. Do the same for the calendar year 2007:

   You should have discovered that consumption has increased. Calculate the percentage increase from 1992 to 2007 and comment on why you think gasoline consumption increased in spite of increased fuel efficiency and higher prices and whether or not this increased consumption percentage is significant.
   
   

   b) Summer time driving patterns are usually the most intensive and usually the time when the consumer bitches about the price of gasoline:

   Using a resource like Gasbuddy.com determine the average price of gasoline in the summers (June, July, August) of 2005,2006 and 2007. Now compute the average weekly consumption of gas for the same time period.

   Determine the price of gas in the summer of 2008 and the average weekly consumption of gas. Compare the 2008 data with the 2005--2007 data and comment on whether or not gasoline consumption dramatically lowers with increasing price.

   
   
   

   c) Below is some data from 1990 to 2011 on a) the median household income and b) the average price of gasoline (per gallon).

   Assume the average household uses 600 gallons per year

   Make a graph (using the chart resource that you used for question 1) of annual gasoline costs divided by median income (Y-axis) as a function of year (X-axis). Attach a screen shot of that graph.

   From that graph assess whether or not the american consumer is paying "too much" for gas over this time period: