Adaptive dung beetle optimization-based agile perturb and observe technique for energy management system

Abstract

Energy storage system (ESS) plays a significant role in maximizing the use of renewable energies to ensure a balance between power generation and demand. ESS assists in maintaining grid stability by providing backup power during fluctuations or outages and smoothing out the variability of renewable energy source (RES). However, EMS fails to effectively balance dynamic interactions due to the unpredictable nature of renewable energy sources (RES) which results in a suboptimal performance. This research proposes an adaptive T-distribution dung beetle optimization-based agile perturb and observe technique (ADBO-APO) for EMS. Photovoltaic (PV) module, battery, and wind turbine are the three sources utilized to establish an effective EMS in a grid-connected system. The ADBO is applied to manage the switching between battery storage and wind turbines. The APO is utilized for triggering the bidirectional DC-DC switch to obtain stable power from wind, PV, and battery. APO enhances EMS by involving perturbation levels for optimal power extraction. It improves the stability and efficiency across variable energy sources. The proposed ADBO-APO achieves a superior average index of 1.2598×104 when compared to the existing method, levy flight quasi oppositional based learning smell agent optimization (LFQOBL-SAO).