Computational tool for optimization of preventive maintenance interval of photovoltaic modules

Purpose Several studies have focused on maximizing the efficiency of solar energy conversion in photovoltaic (PV) systems. The accumulation of dirt on the surfaces of PV modules is a primary environmental factor that reduces performance. Periodic cleaning, implemented as preventive maintenance, mitigates these losses; however, it also incurs costs. Minimizing total system cost therefore requires determining the optimal cleaning interval based on techno–economic considerations. This study develops a computational optimization framework to determine the preventive maintenance interval for PV modules, balancing energy yields against operational costs. Design/methodology/approach The proposed computational tool was developed in a Python-based integrated development environment. It was designed to be generic and flexible, allowing users to input key parameters, such as the installed system capacity, cost of preventive maintenance, energy compensation tariff, average peak sun hours, and power-loss function due to soiling, allowing for both linear and exponential models. Based on these inputs, the tool calculates the optimal maintenance interval that minimizes the total system cost for various operating scenarios. Findings This study shows that using an optimized maintenance interval rather than relying on generic fixed schedules can lead to significant annual savings. The simulation results demonstrate the sensitivity of the optimal interval to variations in maintenance costs and soiling-related energy generation losses. Additionally, differences in installation type, local environmental conditions, and system scale affect the maintenance strategies. Originality/value The proposed tool advances the current state of practice by integrating both linear and asymptotic exponential models to represent the output power loss caused by soiling, thereby accommodating diverse operational conditions of PV modules, unlike most tools currently available. The optimization process employed analytical and numerical techniques to determine the optimal preventive maintenance interval. Using a linear formulation, the analytical approach ensured convergence to a global minimum. For an exponential formulation, the method combined analytical feasibility conditions with numerical procedures to identify optimal solutions efficiently. In addition, the study introduces a graphical diagram that illustrates the relationship between preventive maintenance cost and the optimal maintenance interval for different installed capacities. These diagrams function as practical decision-support instruments, enabling the rapid assessment of cost-effective maintenance strategies tailored to specific system configurations.