Multiobjective and distributed optimization algorithm for the economic dispatch problem

Currently, the growing demand for electrical energy is driving the integration of renewable energy sources into modern power systems. However, integrating renewable systems has increased uncertainty in the operating conditions of the electric system. To address these challenges, the development of algorithms to optimize energy management in power systems, satisfying electricity demand while respecting physical and operational requirements, has become necessary. This includes reducing pollutant emissions, resulting in what is known as the combined economic and environmental dispatch problem (CEEDP). Therefore, this project focuses on developing comprehensive global multi-objective optimization algorithms to holistically address economic and environmental dispatch in electrical networks. The approach aims to minimize costs and emissions by considering the integration of renewable energy sources. The project will contribute to improving the operation and planning of power distribution systems, facilitating the transition to a cleaner and more sustainable energy future, and reducing the environmental impact of the energy sector in the context of climate change.

Faculty Supervisor:

Maude Blondin

Student:

Partner:

Universidad Industrial de Santander

Discipline:

Engineering

Sector:

Education

University:

Université de Sherbrooke

Program: