A novel framework for optimization of a grid independent hybrid renewable energy system: A case study of Iran

• An optimization model is introduced for sizing hybrid systems.
• The system is optimized for being cost-effective and reliable.
• Particle swarm optimization variants are proposed for sizing.
• The performance of PSO with adaptive inertia weight is superior.

In this paper, an optimization model is developed to determine the best size of a stand-alone hybrid renewable energy system (HRES) for electrification to a remote area located in Kerman, Iran. The model is defined based on three decision variables related to the system components, namely, total area occupied by the set of PV panels (a continuous variable), total swept area by the rotating turbines’ blades (a continuous variable) and the number of batteries (an integer variable). In order to find the optimal values of the variables, particle swarm optimization (PSO) and some of its variants are proposed. Due to the non-linearity and non-convexity of the sizing problem, PSO which is an efficient population-based heuristic technique can be a good candidate. Particles of PSO probe the search space to minimize the life cycle cost (LCC), ensuring at the same time certain level of system reliability. Simulation results reveal that the PV/WT/battery system is the most cost-effective one and adaptive inertia weight-based PSO algorithm yields more promising results than the other PSO variants.