Multiobjective and distributed optimization algorithm for the economic dispatch problem

The project concerns the development and validation of a distributed multi-objective optimization algorithm for the economic dispatch problem (EPD), guaranteeing an individual optimization that leads to the global optimization of the system. The algorithms to be developed is based on the multiagent framework.
Many distributed optimization techniques had solved the original EDP, i.e., minimizing the sum of the energy fuel cost generated by each agent. But this problem is incomplete from an environmental standpoint. Minimizing energy emissions is essential to the environmentalists as minimizing the cost is to the users and stakeholders. Multi-objective strategies have been proposed to minimize the emissions and but few- almost none of them, from a purely distributed optimization standpoint. Thus, the important need for distributed multi-objective optimization algorithms for minimizing the energy cost and energy emission cost is still unmet. The proposed project aims at developing a decentralized optimization strategy that deals with time-varying objectives that measure the economic and environmental costs while guaranteeing the global optimization of the entire system. The algorithm will explore the Pareto set and select the best solution for the current state of the system.

Faculty Supervisor:

Maude Josée Blondin

Student:

Partner:

Universidad Industrial de Santander

Discipline:

Engineering

Sector:

Education

University:

Université de Sherbrooke

Program: