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[email protected] +91 9840521421 Scholarly Open Access, Peer-reviewed, Refereed Journal E-ISSN 3139-8138 Since 2024
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Review Article

Hybrid Energy Storage Management in Off-Grid Solar Microgrids

  • Paper ID 26
  • Volume 3, Issue 1
  • Pages: 09-13
  • Published Online: January-April 2026
  • 262 Views
Open Access Peer Reviewed

Authors

  • Badramraju Laxmi Narayana1
  • Rathod Venkatesh2
  • Burri Dinesh3
  • Megavath Supriya4
  • Anguluri Jyothirmayi5

1 2 3 4 UG Scholar, Department of Electrical and Electronics Engineering, Holy Mary Institute of Technology and Science, Hyderabad, Telangana, India. 5 Assistant Professor, Department of Electrical and Electronics Engineering, Holy Mary Institute of Technology and Science, Hyderabad, Telangana, India

Article Information

DOI
No Doi
Published Online
January-April 2026
Pages
09-13
Article Type
Review Article
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Abstract

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Off-grid solar microgrids represent a critical solution for rural electrification and energy access in remote regions, yet their reliability depends significantly on effective energy storage management. This literature review examines hybrid energy storage management systems that combine multiple storage technologies (lithium-ion batteries, supercapacitors, and thermal storage) to address the intermittency challenges inherent in solar generation. The review synthesizes current research on optimal sizing algorithms, battery management systems (BMS), state-of-charge (SOC) estimation techniques, and advanced control strategies for microgrid integration. Special emphasis is placed on experimental validation methodologies and comparative analysis with baseline storage approaches. Current findings indicate that hybrid battery-supercapacitor systems demonstrate 15-20% improved round-trip efficiency and faster dynamic response compared to single- technology solutions. However, significant gaps remain in real-world implementation data, long-term operational cost analysis, and standardized performance metrics across diverse climatic conditions. This review identifies critical research opportunities in AI-driven energy management, predictive maintenance algorithms, and cost-optimization frameworks for hybrid systems in off-grid applications.

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  • ISSN 3139-8138
  • Open Access
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