An emerging leader in alternative energy sources, Texas is moving toward a smart grid delivery system—and companies providing energy services are rushing to determine the best ways to balance supply and demand. Shouyi Wang, associate professor of industrial engineering, is developing ways to help them.
Using a three-year, $466,068 grant from the National Science Foundation, Dr. Wang is researching how to meet the demands of an extremely dynamic and uncertain energy system. Electrical engineering Professor Wei-Jen Lee and industrial engineering Professors Victoria Chen and Jay Rosenberger are co-principal investigators on the project.
Wang and his team will develop machine-learning models that predict real-time market prices and manage large-scale participation of residential demand-response programs. The goal is to create a dynamic decision analytics and optimization framework that enables a highly efficient, real-time energy management system for future smart energy markets.
“If we can determine how best to predict energy demand and react quickly to fluctuations in demand and market prices, then we can use that information to make a much more efficient smart energy supply system with reduced operational costs and increased system reliability,” Wang says. “Greater efficiency on the part of the energy markets translates to greater savings on energy costs for everyone.”
