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A Comprehensive Review on Performance Analysis, Sizing Optimisation, and Energy Management Strategies for Solar-Wind Hybrid Renewable Energy Systems for Off-Grid and Rural Electrification Applications

Authors: S. J. Mulani;

A Comprehensive Review on Performance Analysis, Sizing Optimisation, and Energy Management Strategies for Solar-Wind Hybrid Renewable Energy Systems for Off-Grid and Rural Electrification Applications

Abstract

The global imperative to decarbonise electricity generation, combined with the challenge of electrifying approximately 733 million people without reliable grid access, has positioned solar-wind hybrid renewable energy systems (HWRES) as a central strategy in sustainable energy planning. This comprehensive and updated review critically analyses the state of the art in solar photovoltaic and wind turbine technologies, battery and alternative energy storage integration, techno-economic optimisation methodologies, and intelligent energy management strategies for standalone off-grid hybrid systems, with a systematic focus on literature published between 2020 and 2025. A total of 82 research papers is reviewed 37 published in the last five years encompassing simulation studies, experimental field deployments, optimisation algorithm comparisons, and case studies from diverse geographical contexts. Key performance indicators including Loss of Power Supply Probability (LPSP), Levelised Cost of Energy (LCOE), CO₂ emissions reduction, and system reliability are compared across 55 case studies. Optimally designed solar-wind hybrid systems with battery storage consistently achieve LCOE of 0.062–0.094 USD/kWh with LPSP below 2% 60–80% more economical than diesel-only off-grid generation. Deep reinforcement learning-based energy management systems (emerging strongly in 2022–2025 literature) demonstrate 8–18% improvement in LPSP compared to conventional rule-based dispatch. Research gaps including agrivoltaics integration, green hydrogen seasonal storage, and battery degradation modelling for tropical climates are identified and prioritised.

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