Development of an energy-land-water-carbon nexus model for solar farm design and assessment

This study develops an Energy-Land-Water-Carbon nexus model to optimize solar farm design by minimizing the Levelized Cost of Energy (LCOE) while considering land, cleaning (water and labor), and carbon tax costs. The energy performance is calculated using the PVLIB-Python toolbox from hourly satellite-based SARAH-3 irradiance data for the Pishva case study, Tehran Province, Iran. The model optimizes azimuth direction, tilt angle, row spacing, and cleaning frequency of solar panels to find the design that balances economic and environmental objectives. The results show that the optimal LCOE of 0.0535 USD/kWh was achieved at an azimuth of 181°, a tilt of 23°, a row spacing of 1.2 m, and 19 cleaning cycles per year, corresponding to a 25-year lifetime energy production of 4,482 GWh, with 625 million liters of water consumption and 185,133 tons of CO 2 emissions. Sensitivity analysis reveals that LCOE is most sensitive to parameters that affect energy yield. These results demonstrate the importance of improving energy yield through solar farm design. A dedicated nexus trade‑off analysis that maintained constant energy output examined the consequences of reducing land, water, or carbon. Land reduction of up to 30 % is possible, resulting in tighter spacing and extreme tilting, raising water intensity from 139.4 to 311.6 L/MWh and increasing LCOE by 23 %. Water reduction of up to 90 % is possible, resulting in an 84.3 % increase in LCOE. Carbon emissions reductions from decreased panel use are limited to only 4.3 %. The model can be used as a decision-support tool for sustainable solar farm planning.