Microgrid Pareto Dispatch Optimization with Multiobjective Particle Swarm Optimization

This has led to new problems of controlling the power network stability, as the use of renewables involves interconnecting of different power systems. The present paper deals with the adjustment of the MG functioning by means of utilizing the MOO technique with considerations with regard to economical cost, ecological influence, and dependability. For the optimization of dispatch for MG in PV-diesel-battery system, we have made use of an improved Multiobjective Particle Swarm Optimization (MOPSO) algorithm. Some modifications made to MOPSO are the management of archive and global best particle selection processes, and a linearly diminishing inertial weight to control exploration and exploitation. We then look at the results of the advancement of MOPSO against the NSGA-II algorithm real settings that contain both summer and winter weather with dissimilar solar radiation. The results prove that MOPSO is better than NSGA-II in yielding better distribution and convergence of Pareto-optimum solutions towards environmental and economical costs. Then, MOPSO is also approximately 30% faster than NSGA-II for solving day-ahead dispatch in the microgrids. This work proves that the introduced MOPSO method can become an effective instrument to improve the MG functioning, offering a sophisticated solution in terms of cost reduction, environmental impact reduction, and improved reliability of managing the modern power system.