Optimal Energy Management in Urban Microgrids using Reinforcement Learning

This research project addresses the critical challenge of managing energy in residential urban areas, where the rapid adoption of rooftop solar panels and electric vehicles is placing unprecedented pressure on local power grids. Instead of relying on traditional forecasting methods that struggle with the unpredictable nature of household energy usage, this project develops an intelligent Energy Management System using Reinforcement Learning. This AI agent learns to coordinate residential devices in real-time, optimizing when to store excess solar energy in batteries and proactively managing EV charging to support the grid rather than strain it. The project benefits the participating institutions by combining Canadian expertise in advanced urban building modeling with Vietnam’s practical insights into volatile grid operations, resulting in a novel, transferable control methodology that strengthens international research collaboration and future smart city innovation.

Faculty Supervisor:

Liangzhu Wang

Student:

Partner:

Hanoi University of Science and Technology

Discipline:

Engineering

Sector:

Education

University:

Concordia University

Program: