Optimized Energy Management Strategy for Non-Autonomous Microgrids Using Crow Search Optimization

The need for renewable energy sources is growing because of climate change and rising fossil fuel prices. However, the variability of these sources necessitates an additional energy management unit to ensure smooth operation and a reliable power supply, prompting the development of an effective energy management strategy (EMS). This study introduces an innovative EMS for a grid-connected DC microgrid (MG) that utilizes the crow search optimization (CSO) algorithm. The hybrid energy system consists of a battery energy storage system (BESS), wind turbine (WT), and photovoltaic generator (GPV), with a focus on maximizing the use of renewables to enhance economic efficiency. A proportional-integral (PI) controller regulates source-side converters to optimize energy captured from the wind and PV sources while managing battery current to align with a reference generated by the CSO. The controller also oversees the grid-side inverter. In addition to minimizing various costs associated with energy consumption, battery degradation, and hourly grid energy usage, the CSO algorithm seeks to maintain the battery's state of charge (SOC) between 20% and 80%, with a target of 50%. It computes the overall energy costs by considering the battery's SOC, real-time renewable energy generation, and actual energy demand to determine instantly the optimal battery power. The performance of the CSO is assessed in the MATLAB/Simulink environment and compared to a rules-based EMS. The suggested CSO was highly promising for possible hybrid renewable energy management systems applications based on the obtained performances.