Enhancing Power Prediction of Photovoltaic Systems: Leveraging Dynamic Physical Model for Irradiance-to-Power Conversion

Abstract

Power prediction is crucial to the efficiency and reliability of Photovoltaic (PV) systems. For the model-chain-based (also named indirect or physical) power prediction, the conversion of ground environmental data (plane-of-array irradiance and module temperature) to the output power is a fundamental step, commonly accomplished through physical modeling. The core of the physical model lies in the parameters. However, traditional parameter estimation either relies on datasheet information that cannot reflect the system's current health status or necessitates additional I-V characterization of the entire array, which is not commonly available. To address this, our paper introduces PVPro, a dynamic physical modeling method for irradiance-to-power conversion. It extracts model parameters from the recent production data without requiring I-V curve measurements. This dynamic model, periodically-updated (as short as daily), can closely capture the actual health status, enabling precise power estimation. To evaluate the performance, PVPro is compared with the smart persistence, nominal physical, and various machine learning models for day-ahead power prediction. The results indicate that PVPro achieves an outstanding power estimation performance with the average nMAE =1.4% across four field PV systems, reducing the error by 17.6% compared to the best of other techniques. Furthermore, PVPro demonstrates robustness across different seasons and weather conditions. More importantly, PVPro can also perform well with a limited amount of historical production data (3 days), rendering it applicable for new PV systems. The tool is available as a Python package at: https://github.com/DuraMAT/pvpro.