Multi-operation management of a typical micro-grids using Particle Swarm Optimization: A comparative study

Nowadays, it becomes the head of concern for many modern power girds and energy management systems to derive an optimal operational planning with regard to energy costs minimization, pollutant emissions reduction and better utilization of renewable resources of energy such as wind and solar. Considering all the above objectives in a unified problem provides the desired optimal solution. In this paper, a Fuzzy Self Adaptive Particle Swarm Optimization (FSAPSO) algorithm is proposed and implemented to dispatch the generations in a typical micro-grid considering economy and emission as competitive objectives. The problem is formulated as a nonlinear constraint multi-objective optimization problem with different equality and inequality constraints to minimize the total operating cost of the micro-grid considering environmental issues at the same time. The superior performance of the proposed algorithm is shown in comparison with those of other evolutionary optimization methods such as conventional PSO and genetic algorithm (GA) and its efficiency is verified over the test cases consequently.