The urgent need for sustainable energy solutions in developing economies has intensified interest in Hybrid Renewable Energy Systems (HRES), which combine multiple sources to improve stability and reduce fossil fuel dependence. However, optimizing HRES for efficiency and reliability remains challenging due to resource intermittency, economic constraints, and infrastructural limitations. This scoping review systematically examines optimization methods for HRES in developing economies, analyzing academic and grey literature (2013–2024) to evaluate techniques ranging from traditional heuristics (Genetic Algorithms, Particle Swarm Optimization) to emerging AI-driven approaches (e.g., hybrid fuzzy-logic models). Evidence suggests optimized HRES configurations can reduce energy costs by 30–40% while enhancing reliability in case studies. Dominant challenges include computational complexity, data scarcity, and the lack of standardized frameworks for resource-constrained contexts. The review highlights the growing role of AI and hybrid models (e.g., GDPNFC, EMMBO) in addressing these gaps, outperforming conventional methods in real-world applications. We recommend prioritized research into adaptive AI-driven optimization, decentralized energy frameworks, and policy-stakeholder collaboration to accelerate sustainable energy transitions. These insights aim to guide researchers, policymakers, and practitioners in designing context-sensitive HRES solutions.

Keywords: Hybrid Renewable; Energy Systems; Optimization Methods; Developing Economies; Sustainable Energy.