Designing of an optimal standalone hybrid renewable energy micro-grid model through different algorithms

Electricity is regarded as a basic human requirement. Electric demand is met using either the grid (online) or the off-grid (standalone) method. The electrification of loads in remote areas requires high investment costs for extending the transmission system. A standalone Microgrid (MG) system is a low-cost method of supplying electricity to remote areas where a grid connection is not possible. This study concentrates on designing an optimal MG model for rural electrification with different renewable energy resources. The performance of the system is evaluated by considering power system reliability, economic costs, and greenhouse gas emission ef-fects. To obtain the optimal design parameters (i.e., component sizing), different optimization techniques like Particle Swarm Optimization (PSO), Differential Evolution (DE), Manta Ray Foraging Optimization (MRFO), Shuffled Frog-Leaping Algorithm (SFLA), Reptile Search Algorithm (RSA) and RUNge Kutta Optimizer (RUN) are implemented and compared. The goal of these optimization methods is to find the most reliable and cost-effective model.