As Texas moves toward a “smart grid” energy delivery system, companies are rushing to find the best ways to balance supply and demand.
Enter Shouyi Wang. The associate professor of industrial engineering is using a National Science Foundation grant to do just that.
Electricity is a commodity for which generation and load must be constantly balanced in real time for system reliability and power quality. In addition to traditional generation-side management, load participation is critical for future sustainable smart grid development.
Dr. Wang and his team—which includes Professors Wei-Jen Lee, Victoria Chen, and Jay Rosenberger—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.
“From a consumer standpoint, greater efficiency for the energy markets translates to greater savings on energy costs for everyone,” Wang says.