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

Author:

Prasanna V.,Ravi G.

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.

Publisher

European Open Science Publishing

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3