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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM52826
Volume:
9 |
9 |
Sep 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
. The integration of renewable energy sources into distributed generation systems has accelerated interest in hybrid micro-grids that combine solar photovoltaic (PV), wind, and battery storage technologies. Among these, small-scale hybrid wind–solar–battery micro-grids represent a promising solution for rural electrification, off-grid applications, and enhancement of local energy resilience. However, the intermittency of renewable resources, variability of load demand, and limitations of battery storage necessitate an effective Energy Management System (EMS) to ensure reliable, efficient, and economical operation. This review paper provides a comprehensive overview of EMS strategies designed for small-scale hybrid micro-grids. It discusses fundamental system components, operational modes, and control architectures, with emphasis on rule-based, optimization-based, and intelligent approaches for power flow management. Recent developments in forecasting, converter control, and supervisory algorithms are summarized, highlighting their strengths and limitations in practical deployment. The review also identifies key challenges such as forecasting uncertainty, battery degradation, computational complexity, and transition between grid-connected and islanded modes. Finally, future research directions are outlined, including the integration of advanced storage technologies, adaptive and learning-based EMS frameworks, and real-time hardware validation. The insights from this study aim to guide researchers and practitioners in developing efficient EMS frameworks for reliable and sustainable operation of small-scale hybrid wind–solar–battery micro-grid.
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Publication Date
1/9/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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