Assessment of solar PV power potential over Asia Pacific region with remote sensing considering meteorological factors Available to Purchase

The intensity of solar radiation (SR) is one of the most important required inputs for the estimation of photovoltaic (PV) power station output. Meanwhile, the efficiency of solar PV systems is affected by meteorological factors such as temperature, dust, precipitation, and snow. Meteorological data from satellites provide a viable way for estimating PV potential due to its advantage in spatial coverage and temporal resolution. This paper presents a new approach to adjust SR data from satellites based on the cloud optical thickness (CLOT) before evaluating the solar PV power (P_PV) potential, with the effective efficiency of solar cells computed based on temperature, dust, precipitation, and snow. The objective of this study is to evaluate the over-all spatiotemporal solar PV potential in the Asia Pacific region which will holistically include limiting meteorological factors and identify which factor contributes most significantly to the decrease in solar PV potential in selected cities in the region. First, SR and CLOT data from Advanced Himawari Imager 8 and a SKYNET station were processed to derive the correction factor for solar radiation data. Second, satellite data for temperature (MOD11), precipitation (global satellite mapping of precipitation), dust (MOD04), and snow cover (MOD10) were processed to derive the effective solar PV efficiency. Finally, maps showing the seasonal PV power potential over the Asia Pacific region were generated, with selected cities zoomed in for detailed analysis using mean monthly values from March 2016 to February 2017. The results showed that the maximum theoretical P_PV in the region was estimated to be 1.9 GW per 17.5 km² effective pixel area. Moreover, P_PV decreased by maximum values of 180 MW, 550 MW, and 225 MW due to temperature, dust, and snow, respectively. For Beijing, Tokyo, and Jakarta, the major contributor to the decrease in P_PV is dust, while Khabarovsk is consistently affected by snow effects. Initial validation of the model shows over- and underestimation of solar PV output compared to the actual values by as high as 30%. However, very high values of coefficient of determination (>0.90) show promising results of the model. The contribution of this study is two-fold: regional-scale assessment of P_PV potential and investigation of the collective effect and individual contributions of dust, temperature, and snow to the decrease in P_PV potential.