Effective sizing and optimization of hybrid renewable energy sources for micro distributed generation system

Abstract

Renewable energy sources (RES) are vital for addressing fossil fuel challenges and promoting environmental sustainability by reducing air pollution. Hybrid RES (HRES) in microgrids (MGs) enhance energy efficiency and reliability but face issues like energy management, load demand, and efficiency. Existing research on HRES in MGs often lacks efficiency, reliability, and accuracy. This model proposes a solution using ant lion colony optimization with particle swarm optimization (ALCO-PSO) for Maximum Power Point Tracking (MPPT) to improve power efficiency. The ant colony optimization (ACO) algorithm offers higher efficiency and better global search, but suffers from limitations such as computational complexity and premature convergence. The lion optimization algorithm (LOA) addresses these issues, enhancing the algorithm's robustness. However, ALCO faces challenges like limited scalability and global search ability, which are overcome by integrating particle swarm optimization (PSO). Additionally, direct current (DC) fault detection is enhanced using an artificial neural network (ANN) with solar data. The model's performance is evaluated using power, voltage, and power quality metrics, achieving 99.56% accuracy, faster convergence (0.11 s), an oscillation around 4.25 W, a tracking time of 0.2 s, an interruptible load of 0.009%, cost of energy (COE) of 0.0413%, and a penalty of 0.94 $/kWh.