Optimizing battery energy storage sizing in microgrids using manta ray foraging optimization algorithm

Abstract

The integration of renewable energy sources (RES) into microgrids (MGs) is becoming increasingly important as the world strives to transition towards more sustainable and eco-friendly energy systems. Unfortunately, integrating RES such as solar and wind power into MGs poses challenges due to their intermittent nature. The batteries need to be integrated into the MG system to overcome these challenges and ensure a stable and reliable power supply. However, the size of the battery presents another challenge as it affects the total operation cost of the MG system. Manta ray foraging optimization (MRFO) is used as an optimization technique to minimize the total operation cost of the MG system while ensuring optimum battery capacity. This algorithm is compared with the particle swarm optimization (PSO), differential evolution (DE), and the sine cosine algorithm (SCA). As a result, the proposed technique achieved a better solution than the existing algorithms.