An Effective Control Approach of Hybrid Energy Storage System Based on FLC/Grey Wolf Optimisation

Abstract

In the modern era, the integration of renewable energy sources (RES) has bolstered the autonomy of urban energy infrastructures, reducing reliance on distant sources and grids. Batteries serve as a vital bridge between power supply and fluctuating load demands within RES systems. However, the unpredictable nature of RES behavior and varying load requirements often subject batteries to repeated deep cycles and irregular charging patterns. These cycles diminish the battery’s lifespan and escalate replacement costs. This study presents an innovative control strategy for a Solar-Wind model featuring a Battery-Supercapacitor Hybrid Energy Storage System. The objective is to prolong the battery’s operational lifespan by mitigating intermittent strain and high current demands. In contrast to conventional methods, the proposed control approach incorporates a Low-Pass Filter (LPF) and a Fuzzy Logic Controller (FLC). Firstly, the LPF minimizes the oscillations in battery consumption. Simultaneously, the FLC optimizes the high current demand on the battery while vigilantly monitoring the supercapacitor’s charge levels. Moreover, Grey Wolf Optimization (GWO) is employed to fine-tune the FLC’s membership functions, ensuring optimal peak current attenuation in batteries. The effectiveness of the proposed model is benchmarked against standard control techniques, namely Rule- Based Controller and Filtration-Based Controller. Comparative analysis reveals that the proposed method substantially reduces peak current and high power requirements of the battery. Consequently, this enhances the utilization of the supercapacitor, significantly augmenting the battery’s operational life. The results demonstrate a remarkable improvement over conventional systems, emphasizing the potential of this approach in optimizing energy storage systems for sustainable, long-term performance.