Handling smart-grid with a multi-objective Particle Swarm Optimization algorithm

In this paper, an improved Multi-objective Particle Swarm Optimization (MOPSO) of a Smart-grid energy flow in order to achieve the lower cost of the consumed energy is presented. The Smart-Grid is provided with different energy sources: fossil energy sources, renewable energy sources (photovoltaic panels, wind turbines...) and consumers' network. The used algorithm is based on the approach of Multi-objective Particle Swarm Optimization algorithms. It is adapted to a generic Smart-Grid model in order to optimize the total cost of the consumers supporting constraints of Smart-Grids and integrating renewable energy sources. The algorithm is simulated on Matlab giving an optimal solution (called Pareto optimal) corresponding to an optimal cost. At the end of this paper, we will analyze the results of the simulation of this algorithm.