Advanced Optimization Techniques for Renewable Energy Systems: A Comparative Review of Accuracy, Robustness, and Computational Performance

Abstract

The accelerating transition toward sustainable and low-carbon energy systems has significantly increased the global deployment of renewable energy technologies. Despite their environmental and economic advantages, renewable energy systems continue to face major technical challenges associated with intermittency, operational uncertainty, system complexity, and energy management. Consequently, optimization techniques have emerged as essential tools for improving the performance, reliability, and sustainability of renewable energy applications. This review investigates recent optimization approaches applied in renewable energy systems, with particular emphasis on deterministic optimization methods, metaheuristic algorithms, multi-objective optimization frameworks, and artificial intelligence-based techniques. The study examines optimization strategies employed during the planning, sizing, control, and operational stages of renewable energy technologies, including solar, wind, hydroelectric, thermal, and hybrid renewable energy systems. Particular attention is given to advanced optimization methods such as Mixed-Integer Linear Programming (MILP), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and machine learning-assisted optimization frameworks. Furthermore, the review presents a comparative technical evaluation of the reviewed optimization techniques in terms of accuracy, robustness, computational complexity, and runtime performance. The analysis demonstrates that deterministic methods provide high solution accuracy and reproducibility for structured optimization problems, whereas metaheuristic and AI-based approaches offer superior flexibility and adaptability for nonlinear and stochastic renewable energy applications. In addition, hybrid optimization frameworks integrating deterministic, evolutionary, swarm intelligence, and artificial intelligence techniques are identified as promising solutions for future smart energy systems. The findings emphasize the importance of balancing economic efficiency, environmental sustainability, and operational reliability while leveraging advanced optimization methodologies to support large-scale renewable energy integration and sustainable power generation.