Identification of Module Replacements in US Utility-Scale Photovoltaic Installations

Including replaced modules is crucial to estimate PV waste generation. A new method is proposed to identify past module replacements, which could assist in estimating PV waste. The authors analyzed the variation in the capacity factor (CF) of the US solar plants from 2011 to 2020 to identify possible repowering. A sudden increase in CF is attributed to the possible replacement of old, less efficient modules with higher efficiency modules. The generation and construction data of major PV projects (=lMW) is collected from US Energy Information Administration and converted into a statistical model to evaluate the capacity factor performance. An algorithmic program is generated that analyses and identify the plants with repowering. Multiple methods, including satellite image, machine learning, and data comparison, are applied to validate and optimize the program. Results show that the method can overall evaluate and monitor the trend of module replacement, although the identification accuracy of a single plant needs further validation. The model will use more parameters, including temperature, location, and irradiance, to improve the success rate.