Assignment 1: The Energy Grid

According to Barry K. Worthington, Executive Director of the United States Energy Association (USEA), and David Ropeik, Consultant in Risk Perception and Risk Communication at Ropeik and Associates, the energy grid is defined as the web of wires that connects people’s homes and offices to substations, which, through the grid, are also connected to sources of power. The black wires found at the tops of telephone poles are part of this grid; they connect to many different subsections, making it possible for power to be brought to people anywhere in the 9,000 square mile the grid covers. In an article from 2011 quoted at alternativeenergy.procon,org , Worthington and Ropeik take a moment to consider the larger picture of how this grid functions as a whole: “The wires are all interconnected. It is technically possible to light up a light bulb in Seattle with a watt that was generated in Tallahassee.” Although these wires are connected, the US Energy grid is divided into three parts: the Eastern Interconnection, which spans from the Rocky Mountains in the West to the Atlantic Ocean in the East; the Western Interconnection, which goes from the Rocky Mountains in the East to the Pacific Ocean in the West; and then the Texas Interconnection, which covers most of Texas.

(http://alternativeenergy.procon.org/files/usa_grid.gif)

 

This power grid looks, and is, complex. However it is not nearly so complex as most of the technology we have today; in fact there are many efforts to bring the technology involved in power grids up to date. The idea of modernizing the equipment to enable more transparency is referred to as the “smart grid,” which would include trackers and computer sensors, technology that could make it much easier for power companies to fix problems along the grid without having to search for the problem area first. These sensors would not only transmit data to the power companies; consumers would be able to monitor how much power they are using at any given time, something that is impossible to do now, when consumers only see the energy used in their end of the month statement. Users would also be able to see the price of power in real-time. Through a smart grid, it would also be possible to coordinate large-scale energy needs, for example if many people owned electric cars and needed to charge them at the same time. In addition to more informed consumers and more organized transfers of power, a smart grid would make it easier for people to use alternative energy on their homes, like solar or wind. So, not only are people more conscious of the power that they would take from the grid, but they might be inclined to use more clean energy. The pros seem endless, and the cons are few, but also large. The biggest obstacle in the way of a smart grid is cost, as it would cost a few hundred dollars to install just one smart grid, so giving every building a sensor would seriously add up. The other major thing preventing a complete smart grid is the attitude of power companies. Understandably, they are not thrilled that people would learn to consume less of their product with smart grids, so they are very hesitant to encourage their installation en masse. Hopefully soon enough their greed will peter out and the government will see the benefit of less power consumption and find money in their budget to install a complete and comprehensive smart grid across the whole country.

Sources:

http://alternativeenergy.procon.org/view.answers.php?questionID=001247

http://energy.gov/oe/services/technology-development/smart-grid

http://www.economist.com/node/13725843

Lego Mindstorm 1: Measuring Distance and Velocity

In this experiment, groups were asked to use the Lego cars to understand how to measure distance and velocity and also that through human error, these measurements may not always be perfectly accurate. The experiment was designed in a way such that the computer gave its measurement of distance traveled, which students could compare to the measurements they had taken themselves.

First, my partner and I measured the diameter of the wheel, which was necessary for us to find the circumference (pi*D) so that using the number of wheel turns, the computer could determine how far it traveled. We measured the wheel diameter at .055 m, so the circumference was .1728 m.

Then, we were asked to experiment with the power settings to see how far the car would travel. We experimented with three power settings, 75, 55, and 65, and performed three trials for each power setting to ensure that our data would be as accurate as possible. We kept our time at 1 second, both because our ruler was not very long and because it would make measuring velocity easy (velocity is measured in m/s, and if the distance is being measured over the time of 1 second, then the value for distance and velocity is the same).

 

This is the data we gathered on our very first trial:

Setting 1 (power=75) (time=1):

Trial 1, VI:  

    Rotation: 497 degrees

    Wheel Turns: 1.38056

    Distance: .238836 m

    Velocity: .238836 m

Trial 1, Student

    Distance .26 m

    Margin of Error: 8%

 

Through this data, some connections are clear. For example, the wheels rotated 497 degrees, which is just under 1.5 complete turns. The data shows that the wheels actually turned roughly 1.38 times. The wheel’s circumference was about .17 m, so it makes sense that the distance (according to the computer) was around 1.38 times that. All of this computer-generated data correlates. We were also asked to measure how far we thought the car traveled using a ruler, and each time we were slightly off. We used the difference between our measurement of distance and the computer’s measurement of distance to find the margin of error, which in this case was 8%. In some other cases the number was smaller (.614% which we were very proud of) but it was also larger in some trials (12.47% which we were less proud of). Our measurements were far from exact; we used a pen to try and align the ruler with the backs of the wheels, and our eyesight is not nearly as good at measuring as a computer is. We did constantly try to improve our exactness, but as is visible in the data below, this is a difficult thing to improve within such a short window of time. This experiment helped us to understand through hands-on learning how distance, wheel circumference, number of wheel turns, velocity, and timing were all related. Below you will find all of our data including the data I posted above:

 

Wheel Diameter= .055 m    Circumference= .1728 m

 

Setting 1 (power=75) (time=1):

Trial 1, VI:  

    Rotation: 497 degrees

    Wheel Turns: 1.38056

    Distance: .238836 m

    Velocity: .238836 m

Trial 1, Student

    Distance .26 m

    Margin of Error: 8%

 

Trial 2, VI:

    Rotation: 490 degrees

    Wheel Turns: 1.36111

    Distance: .2352 m

    Velocity: .2352 m/s

Trial 2, Student:

    Distance: .26m

        Margin of Error: .614%

 

    Trial 3, VI:

        Rotation: 488 degrees

        Wheel Turns: 1.35556

        Distance: .23424 m

        Velocity: .23424 m/s

    Trial 3, Student:

        Distance: .257 m

        Margin of Error: 9.266 %

 

Setting 2 (power=55) (time=1):

    Trial 1, VI:

        Rotation: 338 degrees

        Wheel Turns: .938889

        Distance: .16224 m

        Velocity: .16224 m/s

    Trial 1, Student:

        Distance: .184 m

        Margin of Error: .377%

   

   

 

Trial 2, VI:

        Rotation: 341 degrees

        Wheel Turns: .947222

        Distance: .16368 m

        Velocity: .16368 m/s

    Trial 2, Student:

        Distance: .182 m

        Margin of Error: 10.599%

 

    Trial 3, VI:

        Rotation: 342 degrees

        Wheel Turns: .95

        Distance: .16416 m

        Velocity: .16416 m/s

    Trial 3, Student:

        Distance: .186 m

        Margin of Error: 12.474%

 

Setting 3 (power=65) (time1):

    Trial 1, VI:

        Rotation: 419 degrees

        Wheel Turns: 1.16389

        Distance: .20112 m

        Velocity: .20112 m/s

    Trial 1, Student:

        Distance: .223 m

        Margin of Error: 10.318%

 

    Trial 2, VI:

        Rotation: 422 degrees

        Wheel Turns: 1.17222

        Distance: .20256 m

        Velocity: .20256 m/s

    Trial 2, Student:

        Distance: .22 m

        Margin of Error: 8.254%

 

    Trial 3, VI:

        Rotation: 427 degrees

        Wheel Turns: 1.18611

        Distance: .20496 m

        Velocity: .20496 m/s

    Trial 3, Student:

        Distance: .216 m

        Margin of Error: 5.245%