A Comprehensive Review of Techniques for Optimal Power Extraction in Solar Photovoltaic Systems

The rapid growth in global energy demand, coupled with environmental concerns and the depletion of fossil fuel resources, has accelerated the adoption of renewable energy technologies, particularly solar photovoltaic (PV) systems. Although solar PV offers clean and sustainable energy generation, its relatively low conversion efficiency and strong dependence on environmental conditions pose significant challenges to optimal power extraction. Variations in solar irradiance, temperature, partial shading, dust accumulation, and load conditions prevent PV systems from consistently operating at their maximum power point (MPP). To address these issues, extensive research has been conducted on power optimization strategies, including photovoltaic modeling, maximum power point tracking (MPPT) algorithms, power electronic interfaces, intelligent control techniques, and system-level optimization methods. This paper presents a comprehensive and systematic review of techniques used to harness solar PV output power in an optimum manner. The review covers the fundamentals of PV systems, factors affecting output power, PV modeling approaches, conventional and intelligent MPPT methods, DC–DC converters, inverter technologies, grid integration challenges, energy storage integration, and thermal management strategies. Recent advancements in artificial intelligence, machine learning, and IoT-based optimization are also discussed. Furthermore, a comparative analysis of conventional and advanced optimization techniques is provided, highlighting their advantages, limitations, and practical implementation challenges. The paper aims to serve as a consolidated reference for researchers, engineers, and students, while identifying research gaps and future directions for improving the efficiency, reliability, and sustainability of solar PV energy systems.