Operational Optimization of PV-B (Photovoltaic-Battery) renewable energy communities with the objectives of minimizing OPEX and peak load

This project focuses on creating a smart, efficient energy management system for local communities powered by solar panels and batteries. By using advanced reinforcement learning (RL) techniques, the system will automatically control energy use, storing excess solar power in batteries to reduce costs and keep the grid stable. This approach helps communities rely more on renewable energy and less on traditional grid power, making them more independent and environmentally friendly. Ultimately, this project offers a flexible, future-ready solution to help communities lower energy costs, extend battery life, and actively contribute to a greener, more resilient energy system.

Faculty Supervisor:

Ursula Eicker

Student:

Partner:

University of Naples

Discipline:

Engineering

Sector:

Education

University:

Concordia University

Program: