ASSESSMENT OF SOLAR PV POWER POTENTIAL IN THE ASIA PACIFIC REGION WITH REMOTE SENSING CONSIDERING THE EFFECTS OF HIGH TEMPERATURE, DUST AND SNOW
Keywords: Solar PV power, AHI8, MODIS, GSMaP, AMSR-E, dust, temperature, snow
Abstract. The last half century has witnessed the increasing trend of renewable energy utilization with solar photovoltaic (PV) systems as one of the most popular option. Solar PV continues to supplement the main grid in powering both commercial establishments (mainly for reduced electricity expense) as well as residential houses in isolated areas (for basic energy requirement such as for lighting purposes). The objective of this study is to assess the available solar PV power (PPV) potential considering the effects of high temperature, dust and snow in the Asia Pacific region. The PPV potential was estimated considering the effects of the said meteorological parameters using several satellite data including shortwave radiation from Advanced Himawari Imager 8 (AHI8), MOD04 aerosol data from Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation rate from Global Satellite Mapping of Precipitation (GSMaP), air temperature from NCEP/DOE AMIP-II Reanalysis-2 data, and snow water equivalent (SWE) from Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). The model is validated by comparing its outputs with the measured PV power from two solar PV installations in Bangkok, Thailand and Perth, Australia. Results show that maximum PPV is estimated at 2.5 GW (cell efficiency of 17.47%) for the region with the maximum decrease in PPV estimated to be about < 2%, 22% and 100% due to high temperature (temperature coefficient of power = 0.47%/K), dust and snow, respectively. Moreover, areas in India and Northern China were observed to experience the effects of both dust and temperature during March-April-May (MAM) season. Meanwhile, countries located in the higher latitudes were severely affected by snow while Australia by high temperature during Dec-Jan-Feb (DJF) season. The model has a mean percentage prediction error (PPE) range of 5% to18% and 7% to 23% in seasonal and monthly estimations, respectively. Outputs from this study can be used by stakeholders of solar PV in planning for small-scale or large-scale solar PV projects in the solar rich region of Asia Pacific.