Due to the many benefits that come along with using photovoltaic (PV) systems, they are now in the driver's seat when it comes to using solar power as a renewable energy source (RES). This trend is becoming more prevalent, particularly in grid-connected applications, as a direct result of the many advantages brought about by the use of RES inside distributed generation (DG) systems. This new scenario makes it necessary to have an efficient tool for evaluating photovoltaic (PV) systems that are connected to the grid. This thesis focuses on addressing load fluctuation challenges in residential environments by developing an optimization framework for scheduling elastic residential appliances and integrating solar PV systems. The objective is to flatten the load profile and enable effective demand response implementation in smart grid systems. The proposed methodology utilizes the Particle Swarm Optimization (PSO) algorithm to optimize the scheduling of appliances and the utilization of solar PV energy. The thesis provides an introduction to the problem, outlines the methodology, presents the obtained results, and evaluates the effectiveness of the scheduling approach in load curve flattening.

Keywords : PSO Particle Swarm Optimization, DG Distributed Generation, PV Photo Voltaic