Determination of the appropriate zone on dam surface for floating photovoltaic system installation using RS and GISc technologies

Yıl 2023, Cilt: 8 Sayı: 1, 63 - 75, 15.02.2023

Osman Salih Yılmaz , Fatih Gülgen , Ali Murat Ateş

https://doi.org/10.26833/ijeg.1052556

Cited By: 9

Öz

This study aims to reveal suitable places where floating photovoltaic-solar power plants (FPV-SPPs) can be installed on the dam surface using the possibilities of remote sensing (RS) and geographical information science (GISc) technologies. Past satellite images from Landsat and Sentinel platforms allow researchers to analyse shoreline changes in the dam surface. Shoreline extraction is a crucial process for the FPV-SPP to stay afloat despite external constraints. In this study, changes in dam water levels were determined by classifying 20-year satellite images and analysing a 32-year global surface water dynamics dataset. The water surface area was calculated as 1,562.40 ha using the random forest (RF) algorithm and the normalized differences water index (NDWI) on Google Earth Engine (GEE) cloud platform. In addition, solar analysis was carried out with GISc using annual solar radiation maps shuttle radar topography mission (SRTM) data, which directly affects the energy production of FPV-SPPs. It has been calculated that the solar radiation on the water surface varies between 1,554 kWh/m2-year and 1,875 kWh/m2-year. These calculated values were divided into five different classes, and it was observed that 88.5% of the dam surface had a very high level of solar radiation compared to other areas. Higher efficiency will be obtained from the FPV-SPP to be installed in this region compared to the systems to be installed in other regions. It has been observed that the radiation values in other parts of the water surface are lower due to topographic shading. These analyses revealed energy zones with high production potential, thereby easing the decision-making process for investors planning to establish FPV-SPPs.

Anahtar Kelimeler

Remote sensing, GIS, Google Earth Engine, Solar power plant, Solar analysis

Kaynakça

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