Maximum efficiency of distributed generation resources in a microgrid using genetic algorithm and its comparison with particle swarm optimization algorithm in two scenarios with and without assuming fossil unit pollution

Distributed power plants has a production capacity of a few kW to ۱۰ MW, which are used to produce electricity in areas close to consumers, including solar cells, fuel cells, micro turbines, wind power plants, etc. If these power plants connect to the microgrid, they will have different effects on microgrid, such as microgrid loss reduction, improved voltage profile and increased microgrid reliability. Failure to locate distributed power plants appropriately in the microgrid leads to increasing losses and rising costs of production and transmission of energy, thus it is necessary to locate these power plants using optimization methods; therefore the number of distributed power plants, installation site and their capacity is determined so that maximum loss reduction occurred according to constraints of problem. Therefore, minimizing the cost of generation, minimizing the loss in whole power system and minimizing the effect of greenhouse effects are considered as objective function while each power plant limits, limits of reactive power generation and voltage profile losses are considered as constraints. In this paper, Genetic optimization algorithm was used to solve the multi objective function. After extracting the flowchart of this method in locating distributed power plants optimally, to have a better view of performance of proposed objective function, the mentioned method applied on standard IEEE ۳۳-bus radial microgrid, and the results obtained from program implementation for several time, are compared and their advantages and disadvantages are discussed. Obtained results indicate improved voltage profile and reduction of losses in the microgrid.