With reports of power engineers soon retiring in record numbers, many IEEE student members seem ready to stand in their places and tackle the building of the smart grid. When The Institute asked to be contacted by students working on smart-grid projects, we got dozens of responses from around the world. Here are three representative projects.
Craig Carlson was intrigued when he first learned of the smart grid. A graduate student in the School of Electrical and Information Engineering at the University of the Witwatersrand, in Johannesburg, Carlson was inspired by the need to improve South Africa's electric grid because of the increasing number of regional blackouts.
"Researching the smart grid was like delving into the depths of the unknown because it's so different from our current grid structure, which is more than 100 years old," Carlson says. "I started off with grand ideas of designing an entire system by myself—which proved to be quite absurd!"
For his dissertation, Carlson decided to focus on increasing the efficiency of power delivery to mitigate the effects of peak demand. He realized this could be achieved with load forecasting, which predicts how much power will be used in a specific area over a certain time. "If you know how much power an area needs, you can plan to get that power there most efficiently," he says.
To predict the load, Carlson developed a fuzzy logic algorithm using the MATLAB language. The algorithm depends on analyzing the power used over the same period the week before. For example, if the algorithm is to predict the amount of power that will be used on Monday, Carlson needs to find out how much was used the previous Monday. Next, he checks the weather because, naturally, it can have a great effect. He incorporates temperature into the algorithm, particularly the maximum temperature ever recorded for the upcoming date and the high temperature being predicted by weather forecasters. The algorithm then takes the data and computes what the load is likely to be.
Carlson is now working to fine-tune the algorithm, and he plans to enhance it by factoring in the effects of renewable energy sources.
Anthony Schoofs, a Ph.D. candidate at the Clarity Centre for Sensor Web Technologies—a partnership of University College, Dublin; Dublin City University; and Tyndall National Institute, Cork—is developing a pattern-recognition system that uses data from smart meters to determine how much power household appliances use. The local utility could use the information to provide its consumers with, for example, the cost of running a home's five most power-hungry machines.
"Such a system could pinpoint when the appliances use the most energy and help detect aging appliances that should be replaced with more efficient ones," Schoofs says. Consumers could also decide to reduce their electricity bills by using the appliances only during off-peak hours.
Schoofs has tested his prototype at the Clarity Centre and in several Dublin houses. He has come across one big obstacle: Most buildings don't have smart meters.
"We have been using other methods to measure a building's power load, such as clamping electricity monitors to a meter's wires to create makeshift smart meters," he says. "Electricity monitors typically embed current transformer sensors that are clamped around live wires to measure current, power, and phase difference between them so that the electrical parameters of a given load can be measured and sent to a PC-class controller for further use. Unfortunately, these monitors don't come cheap. It will be much easier to test our system once smart meters are rolled out."
Ph.D. candidate Renke Huang led a team of classmates at the Georgia Tech School of Electrical and Computer Engineering in a General Electric Co.–sponsored competition designed to generate more research on the smart grid. Teams of students were asked to develop solutions to one of three smart-grid challenges: reducing peak load, improving the reliability of power-distribution systems, or minimizing power losses. "GE decided to continue to sponsor the project for the next year, since all the teams performed quite well," Huang says.
His team tackled the peak-load challenge. The goal was to reduce the power distribution system's peak use by 30 percent between 4 p.m. and 9 p.m. without affecting customers. This is the period when people tend to use the most electricity at home. Huang and his team designed a monitoring system to analyze peak use. It employs load-forecasting techniques to predict when peak load will occur and optimization algorithms that can then be applied to flatten, or reduce, the peak load. The algorithms determine the best times, with respect to the utility bill, for customers to use certain appliances, or when to connect energy-storage devices such as high-capacity batteries and plug-in hybrid electric vehicles—which can put energy back into the grid—to reduce peak energy usage.
Although his work was part of a competition, Huang says it's not about winning but about the thrill of discovery. "If our work can help get full control of such a complex system as the grid, I will feel like a treasure seeker who has just found something invaluable."