| Work Detail |
Scientists in Bangladesh have developed a precursor formulation for various photovoltaic simulation tools that can help calculate the expected performance of solar farms deployed on mountain slopes. Validation tests using an experimental setup showed an error of less than 3%. Researchers at East West University in Bangladesh have developed a novel method for analyzing bifacial photovoltaic systems on sloping terrain. The novel technique consists of a precursor formulation that is then integrated into existing photovoltaic simulation models that do not account for rugged solar farms. The proposed method recalculates the solar path and irradiance (direct and diffuse) to that of a tilted surface view and inputs these values ??into a conventional photovoltaic model. The updated inputs ensure that the overall results are those of a photovoltaic array on a tilted surface, the researchers explain. Therefore, we can design and analyze photovoltaic parks for any slope and orientation without redesigning any other part of existing photovoltaic models. The technique consists of three parts: modeling the suns path on a given hill; correcting the sunlight values; and integrating them into existing models. In the first step, the global coordinate is rotated along with the slope orientation and angle. This process produces a modified sun path from the perspective of the inclined surface. In the second step, the diffuse horizontal irradiance (DHI) is reduced by the slopes view factor to the sky, while the direct normal irradiance (DNI) remains unchanged. The new DHI value accounts for the limited sky exposure of the mountainous surface. In the final step, the corrected incidence and sun angles are entered into the available photovoltaic models, which treat them as flat terrain. The team validated their model in several ways. A self-validation detected an error of up to 2% in most cases, and comparison with PVsyst software, which can account for leaning trees, yielded similar results. Finally, the team also built a small-scale experimental setup that included a monofacial array of panels placed on an east-facing hill with a 20° slope. The panels were placed flat against the slope, at a height of 51.5 cm and a width of 28.5 cm. “This experiment was conducted on the rooftop of East West University, Dhaka (23.8°N, 90.4°E) for 10 days from February to March 2022. Output was measured every 2 minutes from 6:30 a.m. to 6:00 p.m. and integrated over the day to obtain the daily energy output,” the team explains. “We used the measured global horizontal irradiance (GHI) as input to the coupled model to simulate the output of the same setup and location. The comparison between the energy output of our coupled model and that of the experiment showed a daily error of less than 3%.” Finally, the team demonstrated their model, using their precursor formulation with the PV-MAPS simulation software. They tested a tilt angle of 20° on a north-, south-, east-, or west-facing hill. The panel, whether monofacial or bifacial, was mounted parallel to the slope. In the bifacial simulation, they assumed a bifaciality of 1 (unity) and an albedo of 0.2. All panels had an efficiency of 16.8%, with a panel and mounting height of one meter. The analysis showed that the bifacial PV system produces more, as expected, and that the annual energy of the bifacial PV systems on several hills ranges from 211.33 to 290.45 kWh/m2/year, while that of the monofacial PV system ranges from 187.18 to 259.72 kWh/m2/year. Overall, the developed model simplifies the implementation of numerical models for bifacial panel arrays on sloping terrain. Models for mono/bifacial farms, mono/bifacial tracking farms (single and tandem junction), and agrivoltaic systems can now be applied to corresponding systems on sloping surfaces for prediction and analysis, the team concludes. The novel approach was presented in “ Modeling any bifacial solar panel array configuration on sloped terrain: Generalization using a precursor formulation,” published in Energy Conversion and Management: X. |
