Adaptive Optimization Framework for On-Grid Photovoltaic Systems under Fluctuating Solar Irradiance and Temperature Conditions

Abstract

The increasing integration of photovoltaic (PV) systems into modern electrical grids has highlighted critical challenges related to environmental variability, operational efficiency, and grid stability. Solar irradiance and temperature fluctuations introduce significant stochasticity in PV output, leading to voltage deviations, power losses, and potential safety concerns in on-grid networks. This paper presents a novel adaptive optimization framework for on-grid PV systems designed to address these challenges by combining real-time environmental monitoring, predictive modeling, and dynamic inverter control. The framework incorporates four interlinked modules: an Environmental Monitoring Module (EMM) that captures high-resolution irradiance and temperature data; a Predictive Modeling Module (PMM) employing temperature-corrected PV performance models and Kalman filter-based estimation; a Control and Optimization Module (COM) implementing dynamic reactive power management, maximum power point tracking (MPPT), and voltage set-point adjustments; and a Grid Interaction and Validation Module (GIVM) that evaluates system impact on network voltage profiles, short-circuit contributions, and fault response. Multi-objective optimization, utilizing a Grey Wolf Optimization algorithm, simultaneously maximizes energy yield and minimizes voltage deviations while maintaining safe short-circuit contributions from distributed PV generation. Scenario-based simulations were conducted under stable, moderate, and high variability conditions to evaluate the framework’s effectiveness. Results indicate a 5–18% increase in energy yield compared to static inverter strategies, substantial reduction in voltage deviations (up to 50% under moderate variability), and controlled short-circuit contributions within safe operational thresholds. The framework also demonstrates resilience in hybrid networks with wind and other distributed generation sources, mitigating adverse interactions and stabilizing transient voltage fluctuations. Critical analysis highlights the framework’s theoretical contribution to adaptive distributed generation modeling and practical significance for utility operators in enhancing grid reliability and operational efficiency. Limitations include dependency on sensor accuracy, computational complexity in large-scale networks, and the need for coordination in hybrid systems. This research provides a comprehensive solution for optimizing PV system performance under dynamic environmental conditions, offering significant insights for both the planning and real-time operation of renewable-rich electrical grids. The study contributes to advancing adaptive PV control strategies, enabling higher penetration of solar energy without compromising grid stability, and establishes a foundation for future research into hierarchical control, machine learning-based prediction, and large-scal 
deployment in complex distribution networks (Yunjun, 2008; Al-Omari, 2014; Abdel-Salam et al., 2013).