Performance Analysis of Photovoltaic System using Proposed Assessment Method: An Inquiry on Output Energy Analysis Depending on Distance
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The losses in grid-connected photovoltaic (PV) power generation systems were calculated using the monitoring data that were collected in 2018-2019 from the path between the input and output energy. Thus, the various loss factors were identified. The performances of seven PV power plants in a specific area of Korea were evaluated by developing a quantitative performance evaluation model based on the loss factors. The meteorological environment data were considered vital for the accurate quantification of the input energy in this model. We performed quantitative analyses of the performance deviations and the errors, based on the distances between the weather-monitoring locations and the PV power plants. The proposed analytical method employed two sets of meteorological data that were obtained from the monitoring stations at different locations. Furthermore, one set of interpolated data was employed to quantify the deviations between the expected power and the actual power. The expected power was estimated using the proposed assessment method, while the actual power was measured at the seven PV power plants that were 1.2-17.1 [km] away from the location of the meteorological data measurement. When the distance between the input energy and the plant was minimum (1.2 [km]), the root mean square error (RMSE) and the mean absolute error (MAE) of the output energy were 0.0633 [kWh/kWP/h] and 0.0396 [kWh/kWP/h], respectively. When the distance between the input energy and the plant was maximum (17.1 [km]), the RMSE and MAE of the output energy were 0.1202 [kWh/kWP/h] and 0.0854 [kWh/kWP/h], respectively. Therefore, the error increased with the increase in the distance between the input energy and the PV power plants. It was interesting to note that the errors remained high for two of the seven sites, irrespective of the distance from the input energy. These sites were investigated via site inspections; thus, the errors were ascribed to factors such as module shading and incorrect system installation.
Abstract
The losses in grid-connected photovoltaic (PV) power generation systems were calculated using the monitoring data that were collected in 2018-2019 from the path between the input and output energy. Thus, the various loss factors were identified. The performances of seven PV power plants in a specific area of Korea were evaluated by developing a quantitative performance evaluation model based on the loss factors. The meteorological environment data were considered vital for the accurate quantification of the input energy in this model. We performed quantitative analyses of the performance deviations and the errors, based on the distances between the weather-monitoring locations and the PV power plants. The proposed analytical method employed two sets of meteorological data that were obtained from the monitoring stations at different locations. Furthermore, one set of interpolated data was employed to quantify the deviations between the expected power and the actual power. The expected power was estimated using the proposed assessment method, while the actual power was measured at the seven PV power plants that were 1.2-17.1 [km] away from the location of the meteorological data measurement. When the distance between the input energy and the plant was minimum (1.2 [km]), the root mean square error (RMSE) and the mean absolute error (MAE) of the output energy were 0.0633 [kWh/kWP/h] and 0.0396 [kWh/kWP/h], respectively. When the distance between the input energy and the plant was maximum (17.1 [km]), the RMSE and MAE of the output energy were 0.1202 [kWh/kWP/h] and 0.0854 [kWh/kWP/h], respectively. Therefore, the error increased with the increase in the distance between the input energy and the PV power plants. It was interesting to note that the errors remained high for two of the seven sites, irrespective of the distance from the input energy. These sites were investigated via site inspections; thus, the errors were ascribed to factors such as module shading and incorrect system installation.
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