Optimal sizing and rule-based management of hybrid microgrids using SSA for rural electrification

Abstract

Microgrids play a crucial role in integrating renewable energy sources (RES) into hybrid renewable energy systems (HRES), enabling reliable and sustainable power supply for remote and rural areas. This study investigates the optimal sizing and energy management of an off-grid HRES consisting of photovoltaic (PV) panels, wind turbines (WT), diesel generators (DG), and battery storage systems (BSS), designed to meet the electricity demand of 100 residential homes in a rural area of Skikda, northern Algeria. A rule-based energy management strategy is applied to coordinate power distribution among the microgrid components (PV/WT/DG/BSS), ensuring real-time demand satisfaction. The analysis is based on hourly meteorological data (solar radiation, temperature, and wind speed) over a full year, combined with a hypothetical residential load profile. The Salp Swarm Algorithm (SSA) is employed as the primary optimization technique to minimize the cost of energy (COE) and loss of power supply probability (LPSP). MATLAB-based simulations yield optimal results with a COE of 0.24804 $/kWh, an LPSP of 0.2412% (specifically 0.002412), a total annual cost (TAC) of 245,230 $, and an annual dummy load of 230.57 kWh. To validate the effectiveness of SSA, its performance is compared with Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), and Artificial Rabbits Optimization (ARO). The results demonstrate that SSA achieves faster convergence and superior optimization of the objective function, ensuring efficient energy distribution and reduced operational costs. These findings provide valuable insights for researchers and energy system designers, contributing to the development of cost-effective and reliable off-grid hybrid microgrids for rural electrification.