• Energy Research

In contrast to commercial photovoltaic (PV) power plants, PV systems at universities are not actively monitored for PV module failures, which can result in a loss of power generation. In this study, we used thermal imaging with drones to detect rooftop PV module failures at a university campus before comparing reductions in power generation according to the percentage of module failures in each building. Toward this aim, we adjusted the four factors affecting the power generation of the four buildings to have the same values (capacities, degradations due to aging, and the tilts and orientation angles of the PV systems) and calibrated the actual monthly power generation accordingly. Consequently, we detected three types of faults, namely open short-circuits, hot spots, and potential-induced degradation. Furthermore, we found that the higher the percentage of defective modules, the lower the power generation. In particular, the annual power generation of the building with the highest percentage of defective modules (12%) was reduced by approximately 25,042 kWh (32%) compared to the building with the lowest percentage of defective modules (4%). The results of this study can contribute to improving awareness of the importance of detecting and maintaining defective PV modules on university campuses and provide a useful basis for securing the sustainability of green campuses.

In contrast to commercial photovoltaic (PV) power plants, PV systems at universities are not actively monitored for PV module failures, which can result in a loss of power generation. In this study, we used thermal imaging with drones to detect rooftop PV module failures at a university campus before comparing reductions in power generation according to the percentage of module failures in each building. Toward this aim, we adjusted the four factors affecting the power generation of the four buildings to have the same values (capacities, degradations due to aging, and the tilts and orientation angles of the PV systems) and calibrated the actual monthly power generation accordingly. Consequently, we detected three types of faults, namely open short-circuits, hot spots, and potential-induced degradation. Furthermore, we found that the higher the percentage of defective modules, the lower the power generation. In particular, the annual power generation of the building with the highest percentage of defective modules (12%) was reduced by approximately 25,042 kWh (32%) compared to the building with the lowest percentage of defective modules (4%). The results of this study can contribute to improving awareness of the importance of detecting and maintaining defective PV modules on university campuses and provide a useful basis for securing the sustainability of green campuses.

Hourly irradiance values are essential data to reasonably estimate the electric power production (EPP) from a photovoltaic (PV) system. Worldwide monthly irradiance data are available from meteorological observation satellites; however, adequate hourly data are not widely available in developing countries or rural areas where PV systems are needed most. Aiming to supply such data, this study compared three different methods (i.e., sunshine hours mean, the SOLPOS algorithm, and the Duffie and Beckman algorithm) to convert the monthly accumulated irradiance data into hourly irradiance data. The monthly accumulated irradiance data at 11 sites in the United States and Korea, acquired from the National Renewable Energy Laboratory, were converted into hourly irradiance data by employing the three methods. The converted hourly data were entered into the System Advisor Model to estimate the monthly total EPP values (henceforth, EPPs) from the PV systems. Each estimated EPP value was compared with those analyzed from the measured hourly data (regarded as the reference values in this study). After considering the errors between the EPPs estimated from the converted hourly irradiance data and measured using the hourly irradiance data, the simulation results with identical PV capacities indicated that the SOLPOS algorithm was the most appropriate conversion method.

Hourly irradiance values are essential data to reasonably estimate the electric power production (EPP) from a photovoltaic (PV) system. Worldwide monthly irradiance data are available from meteorological observation satellites; however, adequate hourly data are not widely available in developing countries or rural areas where PV systems are needed most. Aiming to supply such data, this study compared three different methods (i.e., sunshine hours mean, the SOLPOS algorithm, and the Duffie and Beckman algorithm) to convert the monthly accumulated irradiance data into hourly irradiance data. The monthly accumulated irradiance data at 11 sites in the United States and Korea, acquired from the National Renewable Energy Laboratory, were converted into hourly irradiance data by employing the three methods. The converted hourly data were entered into the System Advisor Model to estimate the monthly total EPP values (henceforth, EPPs) from the PV systems. Each estimated EPP value was compared with those analyzed from the measured hourly data (regarded as the reference values in this study). After considering the errors between the EPPs estimated from the converted hourly irradiance data and measured using the hourly irradiance data, the simulation results with identical PV capacities indicated that the SOLPOS algorithm was the most appropriate conversion method.

An interest in floating photovoltaic (PV) is growing drastically worldwide. To evaluate the feasibility of floating PV projects, an accurate estimation of electric power output (EPO) is a crucial first step. This study estimates the EPO of a floating PV system and compares it with the actual EPO observed at the Hapcheon Dam, Korea. Typical meteorological year data and system design parameters were entered into System Advisor Model (SAM) software to estimate the hourly and monthly EPOs. The monthly estimated EPOs were lower than the monthly observed EPOs. This result is ascribed to the cooling effect of the water environment on the floating PV module, which makes the floating PV efficiency higher than overland PV efficiency. Unfortunately, most commercial PV software, including the SAM, was unable to consider this effect in estimating EPO. The error results showed it was possible to estimate the monthly EPOs with an error of less than 15% (simply by simulation) and 9% (when considering the cooling effect: 110% of the estimated monthly EPOs). This indicates that the approach of using empirical results can provide more reliable estimation of EPO in the feasibility assessment stage of floating PV projects. Furthermore, it is necessary to develop simulation software dedicated to the floating PV system.

An interest in floating photovoltaic (PV) is growing drastically worldwide. To evaluate the feasibility of floating PV projects, an accurate estimation of electric power output (EPO) is a crucial first step. This study estimates the EPO of a floating PV system and compares it with the actual EPO observed at the Hapcheon Dam, Korea. Typical meteorological year data and system design parameters were entered into System Advisor Model (SAM) software to estimate the hourly and monthly EPOs. The monthly estimated EPOs were lower than the monthly observed EPOs. This result is ascribed to the cooling effect of the water environment on the floating PV module, which makes the floating PV efficiency higher than overland PV efficiency. Unfortunately, most commercial PV software, including the SAM, was unable to consider this effect in estimating EPO. The error results showed it was possible to estimate the monthly EPOs with an error of less than 15% (simply by simulation) and 9% (when considering the cooling effect: 110% of the estimated monthly EPOs). This indicates that the approach of using empirical results can provide more reliable estimation of EPO in the feasibility assessment stage of floating PV projects. Furthermore, it is necessary to develop simulation software dedicated to the floating PV system.