Solar farm model

Ground-mounted solar arrays, or solar farms, are being implemented for renewable energy generation. This impervious cover elevated over pervious surface has the potential to alter hydrologic processes. To fully understand these changes and inform selection of optimal sites or management practices for solar farms, it is imperative to model hydrological changes accurately. Here, a modeling framework is designed for simulating hydrologic dynamics within solar farms leveraging OpenHydroQual software, an open-source, component-based model capable of representing unsaturated zone hydrology. This study focuses on a solar farm with a high (20-30%) slope within central Pennsylvania, USA. A model was created to represent the current condition in the solar farm, where each row of the solar farm is divided into four zones, the impervious solar panel, a dripline zone that captures runoff from the panel, an interspace zone between the panel rows, and a zone under the solar panel. The model was calibrated and validated using one year of soil moisture data collected from three of the zones; Nash-Sutcliffe Efficiencies varied from 0.74 to 0.79 for the three zones. Comparison of the solar farm model with a model representing pre-development conditions indicates redistribution of soil moisture and increased runoff. When comparing the runoff from solar farms in current condition to the predevelopment condition for one year, there is a change in annual runoff generation from 2 cm to 3.5 cm. Additional scenarios performed to explore implications of different management options indicated that increasing the interspace width from 3 m to 4 m led to a decrease in runoff depth by 28.6 %. Reduction of Manning’s n from 0.06 to 0.03 leads to a 2.86% increase in runoff depth. The comparison of different scenarios for 2- and 100-year return period storm events also indicated that solar panel implementation led to an increase in runoff volume and peak flow rate. Over the entire solar farm area, the implementation of solar panels resulted in a 21.5% decrease in annual AET. Though this work demonstrated increases in runoff due to solar panel implementation, these changes can be managed by implementation of stormwater management practices; such information on anticipated runoff changes is critical for appropriate selection and sizing of these practices. Thus, this model offers a valuable tool for examining the hydrological impacts associated with solar farms, facilitating informed decisions regarding mitigation strategies and sustainable implementation of renewable energy.