Darrell Ross

Given specific power supply regions, this map is a gradient of distance to the nearest power supply.

I am chugging right along on my MS Thesis and I am happy to say that I am nearing the results. Above is an image of a plot produced in the program. The shape in the middle is Humboldt County. I have specified power supply sources in the general vicinity of three already existent biomass (woodchip) power plants near Samoa (Fairhaven Power Company), Scotia (formerly PALCO — now called Humboldt Redwood Company), and Blue Lake (Ultrapower 3) as well as a wind power plant (by Shell on Bear RIver Ridge), ocean wave power plant (by PG&E off the coast and north of Humboldt Bay), and solar power plant (placed in Blue Lake because I know it’s sunny there). The gradient represents distance to the nearest power supply where white is closest and dark-blue furthest. I was able to get a one-year load shape for Humboldt County recorded every 15 minutes. I also used local resources for hourly wind, wave, and solar data. This gives the model a more resonant and realistic fluctuation and makes it unnecessary for me to implement stochastic fluctuations of my own.

I am doing a 10 minute presentation about my thesis at the 2009 North Coast Student Research Conference March 26-28, 2009. For the conference, I hope to have three scenarios running –

1. current Humboldt County Power system – this will include the Humboldt Bay PG&E Natural Gas Fired plant as well as the three transmission lines we import much of our power from
2. infinite intermittent supply – this will show the patterns of the sun rising and winds and waves changing
3. realistic (relatively) intermittent supply – an attempt to meet 100% of demand using only the sources listed in the above plot.

Drop me a line sometime with comments or questions.

I’m testing out flot, a pure Javascript plotting library for jQuery. It produces graphical plots of arbitrary datasets on-the-fly client-side. The bit below allows you to input a gas mileage for two cars and the amount of money you would like to save. Then click the “Flot It!” button and the result is a plot of cost per gallon of gas vs miles needed to drive to reach the “Savings Goal”.

Currently, this is compatible with Google Chrome and Internet Explorer. I have had trouble getting it to work in Firefox. I have not tried loading it in Safari yet.

[UPDATE] December 9, 2008
Learned a little more JavaScript but at this point am unable to call the function specifically. I'm not sure Flot is actually designed to be called from form data. I have not found a record of it being used this way yet. If you find one, please forward me a link.

[UPDATE] December 9, 2008
It now works in Google Chrome web browser but not yet in Firefox or Internet Explorer. Working on that now...

[UPDATE] December 9, 2008
Now it works in IE (there turned out to be an IE specific .js library called excanvas that I had to include). Still no luck in FireFox. ><

So I recently replaced my aging 91 Isuzu Rodeo with a 91 Honda Civic. Although they were the same model year, the Isuzu had far more mileage (250k) than it was really built fore. So now Sharyn and I have two vehicles: 94 Jeep Cherokee and 91 Honda Civic LX. The Jeep gets around 18 mpg and we recently clocked the Honda on a long trip at 35 mpg. I bought the Honda for $2500 and then spent around $500 on maintenance and taxes, etc. The Isuzu was getting old so we didn’t trust it for long trips and kept driving the Jeep. But now we will be using the Honda. We have already noticed huge savings.

The Question
If the Honda gets 35 mpg and the Jeep gets 18 mpg and I had to spend the extra $3000 on the Honda, how many miles will we have to drive to save $3000 in gas?

The Answer
First off, this is a fun problem for anyone to do if they want to come up with the payback time for upgrading to a more fuel-efficient vehicle. That being said, let’s solve this with a little algebra. The price of gas is going to very and it would be nice to know the distance needed under several different prices so we will set our independent variable, x, to gas price in dollars per gallon and our dependent variable, y, to distance traveled in miles.

I like visually pleasing answers so I chose to create a graph of the curve where the savings is equal to $3000. To do this, we first invert the miles per gallon values to find the gallons per mile.

The gallons per mile multiplied by cost per gallon give us the cost per mile for each vehicle. Unit math is shown on the right in brackets. You can see how the gallons cancel each other out.

The above equations are generalized for mileage, m, and cost, x. The difference between the cost per mile for the Jeep and the Honda can be calculated and then simplified with some factoring giving us the savings per mile. Multiplying this by the number of miles traveled, y, gives us the total savings for y miles.

So back to the original question of saving $3000. If we flip the equation around and replace the “savings for y miles” with the $3000 and then take a few steps to make the formula easier to read, we can plot and interpret the results.

Now substituting the numbers in for the mileage of the Jeep and the Honda and plotting the result, we get a hyperbola. Below, you can see the data point near $3.00 per gallon for x which reads out at approximately 37,000 miles for y.

Now what is fun is that we can see the relationship between how many miles it would take to save $3000 in gas at different prices. Here are a few examples for $2.00 per gallon on the left and $5.00 per gallon on the right.

Taking my equation, you can put in numbers for your cars and compare savings. Here’s an equation to work with:

The output, y, will be the number of miles you need to travel in the vehicle with better mileage to save your desired input at the designated price per gallon.