This study provides an integrated and quantitative assessment that jointly examines energy storage technologies, control architectures, optimization algorithms, and capacity-demand matching in renewable-based microgrids. A distinctive contribution of this review is the comparative evaluation of optimization techniques in terms of convergence speed, accuracy, and real-time applicability, along with a unified analysis of how different renewable sources and storage systems collectively influence demand matching, system reliability, and cost-effectiveness. The study systematically examines centralized, decentralized, and distributed control frameworks, emphasizing their roles in maintaining system stability, enhancing energy efficiency, and ensuring resilient microgrid operation. Various optimization algorithms are critically compared in terms of convergence rate, computational efficiency, and accuracy to evaluate their suitability for real-time control applications. The review further analyzes key performance metrics - such as efficiency, capacity factor, and cost-effectiveness - across different renewable energy technologies, including photovoltaic, wind, biomass, and energy storage systems. Also, the study discusses potential solutions such as hybrid energy storage systems, multi-agent and hierarchical control, and AI-driven predictive energy management. Moreover, an in-depth analysis of capacity-demand matching is presented to assess how effectively renewable generation profiles align with fluctuating load demands over time.
Srivastava et al. (Wed,) studied this question.
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