Abstract The addition of solar photovoltaic (PV) systems into modern energy infrastructure has gained prominence due to their sustainability and environmental benefits. However, optimal operation of PV systems remains a challenge due to intermittency, variable irradiance, and non-linear characteristics. This review critically examines various optimization techniques applied across three key areas of PV systems: Maximum Power Point Tracking (MPPT), system component sizing, and controller parameter tuning. The study categorizes optimization methods into classical, heuristic, metaheuristic, and hybrid approaches, offering detailed insights into their mechanisms, applications, and relative advantages. Bio-inspired metaheuristic algorithms demonstrate superior tracking accuracy and adaptability under rapidly changing environmental conditions, including partial shading. For optimal sizing, algorithms like FA, CS, and ABC minimize capital investment and energy losses while maximizing system efficiency. Controller tuning, especially for PI and FOPI controllers, benefits from metaheuristics by achieving better dynamic responses, minimizing steady-state errors, and reducing overshoot and settling time. This paper provides a structured, comparative framework that aids engineers and researchers in selecting suitable optimization techniques for enhancing PV system performance. The review contributes to a broader understanding of how algorithm-driven strategies can drive the next generation of resilient, cost-effective, and high-performance solar PV systems.
Jaiswal et al. (2025) studied this question.