A review of optimization strategies for hybrid renewable energy systems toward sustainable clean energy

• Metaheuristic algorithms reduce LCOE to $0.06192/kWh in off-grid HRES. • NSGA-II cuts system cost by up to 56.7 % in grid-connected hybrid setups. • PV-Hydro-storage combos reduce curtailment by 60 % with LCOE below $0.10/kWh. • AI and GIS tools enhance forecasting, planning, and energy dispatch accuracy. • HRES faces high CAPEX, regulatory gaps, and needs circular economy models. The global transition toward decarbonization has accelerated the development of HRES as resilient, cost-effective, and sustainable energy solutions. This review synthesises findings from >30 recent high-impact studies (2024–2025) on optimisation strategies integrating solar, wind, hydro, and storage technologies. Metaheuristic algorithms, including DE, PSO, and NSGA-II, have demonstrated significant effectiveness in minimising system costs and improving reliability. In off-grid rural electrification applications, DE achieved the lowest Levelized Cost of Energy (LCOE) of $0.062/kWh (USD2024) with an LPSP of 0.05, highlighting its superior cost-efficiency and stability. In grid-connected systems, NSGA-II enabled multi-objective optimisation, reducing total system costs by up to 56.7 % through coordinated use of hybrid battery and pumped-hydro storage. In Turkey, optimised PV–hydro–battery systems maintained a continuous power balance while reducing curtailed energy by 60 %, achieving an LCOE below $0.10/kWh. Moreover, incorporating frequency-constrained UC models with virtual inertia improved grid stability, though at the cost of approximately 12 % lower renewable penetration and 35 % higher operating costs. Emerging research trends emphasise AI-based forecasting, GIS-assisted spatial optimisation, blockchain-enabled peer-to-peer energy trading, and lifecycle sustainability assessments. Persistent challenges include high capital expenditure, technical complexity, and inadequate regulatory and sectoral integration. The review concludes with recommendations for AI-integrated real-time control, modular and scalable HRES design, policy-algorithm co-development, and circular economy frameworks to support the global deployment of intelligent, adaptive, and sustainable hybrid energy systems.