Optimal parameter identification of fractional-order proportional integral controller to improve DC voltage stability of photovoltaic/battery system

Abstract

This study addresses the critical challenges of voltage stabilization in DC microgrids, where the inherent variability of renewable energy sources significantly complicates reliable operation. The focus is on optimizing the fractional-order proportional-integral (FO-PI) controller using four advanced techniques a whale optimization algorithm (WOA), grey wolf optimizer (GWO), genetic algorithm (GA), and sine cosine algorithm (SCA). Voltage instability poses substantial risks to the reliability and efficiency of DC microgrids, making the optimization of the FO-PI controller an essential task. Through comparative analysis, the study demonstrates that WOA outperforms the other methods, achieving superior voltage stability, resilience, and overall system performance. Notably, WOA achieves the lowest average cost function at 0.0004, compared to 0.892 for GWO, 0.659 for GA, and 0.096 for SCA, showcasing its effectiveness in fine-tuning the controller’s parameters. These findings highlight WOA robustness as a powerful tool for enhancing microgrid performance, especially in voltage regulation. The study underscores WOA potential in ensuring the reliable and efficient integration of renewable energy systems into DC microgrids and lays the groundwork for further research into its application in more complex and dynamic grid scenarios. By optimizing the FO-PI controller, WOA significantly contributes to the long-term stability and efficiency of DC microgrids.