Self-learning Microgrids for Decarbonization of Industrial Applications

Industrial applications are among the heaviest producers of greenhouse gas emissions (GHGs) and require a large amount of energy to operate. Industrial applications have now begun to invest in “microgrids”, which are small-scale power systems that utilize clean energy sources, such as solar, batteries, and electric vehicles, to satisfy energy demand. This project will develop an artificially intelligent, learning based method to learn the individual patterns of the clean energy sources, and schedule them in a way to maximize the reduction of GHGs and energy cost. In addition, a special feature of electric vehicles will be involved, named “vehicle to grid”, where energy from the vehicle battery can be fed back to the grid as a clean energy source. The project will be demonstrated at a real-world demonstration site, and track the number of GHGs reduced over a minimum of 6 months. The outputs of this project will be used to publish and showcase innovative methods to reduce the devastating impacts of climate change by intelligently leveraging clean energy sources to lower energy costs and reduce GHG emissions.

Faculty Supervisor:

Hany Farag

Student:

Partner:

TROES

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

York University

Program: