Particle Swarm Optimization and Genetic Algorithms for Optimal Management of Network of Microgrids Based Renewable Energy and Diesel Generators

In this paper, particle swarm optimization and genetic algorithms are applied for the optimization of a network of microgrids based on renewable energy and diesel generators. The problem is formulated based on networked microgrids by developing the equalities, inequalities and bounds constraints using specific design variables and an objective function. These metaheuristic optimizations are applied based on the formulated problem to operate the network of microgrids during a day (24 hours). Simulations are conducted to operate a network of three microgrids by optimizing the diesel generators usage and the power exchange between the microgrid in the network. Results are provided to show optimization outcomes, of the two metaheuristic algorithms, using the formulated problem of the network of microgrids.