Scientists from Japan's Oita University investigated the impact of seasonal and meteorological factors on electricity generation efficiency in ground mounted solar plants.
For their modeling, the researchers employed meta-frontier data envelopment analysis (DEA), which assesses the relative efficiency of decision-making units (DMUs) – in this case, power stations – against its limits or the best achievable performance.
“The meta-frontier DEA framework is implemented to quantify the impact of seasonality and technical factors on generation efficiency at the monthly level,” they explained. “This study also combines the Monte Carlo method with DEA to identify the impact of variable input and output factors on the uncertainty of generation efficiency values and discuss the robustness of the results.”
The scientists considered six existing utility-scale PV power plants in Japan, two in Oita Prefecture, three in Kumamoto Prefecture, and one in Yamaguchi Prefecture. Data points were taken monthly from January 2016 to December 2020.
Except for one facility, all plants had a DC/AC ratio greater than 1, which indicates that the PV array-rated capacity surpasses the inverter-rated capacity. Therefore, electricity generation exceeding the inverter capacity is partially clipped when insolation reaches a certain level, and during periods of lower insolation, it enhances electricity generation, as there is no bottleneck at the inverter.
“Plants A, B, and D underwent an extension of the PV power systems, which increased the PV array rated capacity, DC/AC ratio, and the number of modules installed,” the academics highlighted. “Plant C comprises PV modules composed of monocrystalline solar cells, whereas the other plants feature PV modules composed of polycrystalline solar cells.”
To calculate the plants' inefficiency, the researchers took into account four parameters – solar irradiation, temperature, number of modules, and PV array-rated capacity – and compared them to the measured, real electricity generation output. DEA-based efficiency (DBE) of 0 to 1 was used to compare the different years and stations, where a greater value indicates higher performance.
“Looking at the average DBE over the entire analysis period, Plant D has the highest DBE value of 0.796, while Plants B and F have DBE averages of only 0.615 and 0.527, respectively, for the entire analysis,” noted the researchers. “The average inefficiency factors intra-temporal factor (ITF), seasonal factor (SSF), and technical factor (TCF) were 0.048 [proportion to DEA-based inefficiency (DBI): 10.7%], 0.216 (48.5%), and 0.182 (40.9 %), respectively.”
Per the results, the average power generation inefficiency during the study period was 0.445, primarily due to seasonal and technical factors.
“Employing PV modules with higher electricity output levels can boost the DC/AC ratio, thereby increasing power generation, enhancing efficiency, and contributing to a stable power supply, thus reducing daily and seasonal fluctuations in power generation. Since these factors are difficult to control once a power plant is in operation, it is important to select an optimal site for power plants by considering meteorological and geographical data,” the scientists concluded
Their results were presented in “How do seasonal and technical factors affect generation efficiency of photovoltaic power plants?,” published in Renewable and Sustainable Energy Reviews.
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