r.sun

Abstract: The results of the application and evaluation of the r.sun model for calculation of the total solar radiation for the Wedel Jarlsberg Land (SW Spitsbergen) are presented. Linke Turbidity Factor (LTF), which is the obligatory parameter for direct and diffused radiation calculations with the r.sun model, is derived here with the empirical formula and meteoro− logical measurements. Few different approaches for calculation of LTF are presented and tested. The r.sun model results, calculated with these various LTF, are evaluated through comparison with total solar radiation measurements gathered at Polish Polar Station. The r.sun model is found to be in good agreement with the measurements for clear sky condi− tions, with the explained variance (R 2 ) close to 0.9. Overall, the model slightly underesti− mates the measured total radiation. Reasonable results were calculated for the cloudiness condition up to 2 octas, and for these r.sun model can be considered as a reliable and flexible tool providing spatial data on solar radiation for the study area.

Abstract: In the study, the solar energy resource in the Central Luzon Region (Region 3), Philippines was determined using r.sun – a topography-based solar radiation model implemented in GRASS GIS – and suitable sites for the installation of ground-mounted solar photovoltaic (PV) farms were identified using the Analytic Hierarchy Process (AHP) to determine the weights of different physical, environmental, socio-economic, risk, and constraint criteria. For the resource assessment, the inputs to r.sun used in the study consisted of freely available data that include: an SRTM (90m resolution) Digital Elevation Model (DEM) and monthly average Linke turbidity coefficients available from the SoDA (Solar Radiation Database) webservice (www.soda-is.com). Daily solar radiation data from eight (8) measuring stations throughout the region were gathered. Readings from six (6) stations were used to interpolate monthly clear-sky index rasters while the readings from the remaining two (2) stations were used to validate the modelled monthly average Global Horizontal Irradiation (GHI) computed by r.sun. For the site suitability analysis, different criteria rasters were created and combined using weighted overlay to generate a suitability map for ground-mounted solar PV farms in the region. From the results, the monthly average GHI in the region computed by r.sun ranged from 3706.8 Wh/m day in December to 6021.0 Wh/m-day in May with an annual average GHI of 4727.12 Wh/m-day indicating a good amount of resource potential. High GHI values were observed for the summer months of March to May (Mean: 5640.26 Wh/m-day) while the cold and rainy season ranging from July to December showed relatively lower values (Mean: 4298.98 Wh/m-day). The Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) between the measured and modelled GHI were 352.88 Wh/m-day and 8.53%, respectively, with the lowest error in March (73.94 Wh/m-day, 1.44%) and the highest in August (844.01 Wh/m-day, 21.65%). In fact, the model performed well for the months of January to June (MAE: 192.18 Wh/m-day, MAPE: 3.83%) and slightly poorer for July to December (MAE: 512.824 Wh/m-day, MAPE: 13.22%). For further study, other data sources and inputs can be looked into to improve the accuracy of the resource assessment and site suitability analysis. Aside from this, the use of more solar radiation recording stations for validation is preferred in order to better validate the results of r.sun and its applicability for solar energy resource assessment in the Philippines.

Abstract: Solar3D is an open-source software application designed to interactively calculate solar irradiation at three-dimensional (3D) surfaces in a virtual environment constructed with combinations of 3D city models, digital elevation models (DEMs), digital surface models (DSMs) and feature layers. The GRASS GIS r.sun solar radiation model computes solar irradiation based on two-dimensional (2D) raster maps for given day, latitude, surface and atmospheric conditions. With the increasing availability of 3D city models and demand for solar energy, there is an urgent need for better tools to computes solar radiation directly with 3D city models. Solar3D extends GRASS GIS r.sun from 2D to 3D by feeding the model with input, including surface slope, aspect and time-resolved shading, that is derived directly from the 3D scene using computer graphics techniques. To summarize, Solar3D offers several new features which, as a whole, distinguish itself from existing 3D solar irradiation tools: (1) the ability to consume massive heterogeneous 3D city models, including massive 3D city models such as oblique airborne photogrammetry-based 3D city models (OAP3Ds or integrated meshes); (2) the ability to perform near real-time pointwise calculation for duration from daily to annual; (3) the ability to integrate and interactively explore large-scale heterogeneous geospatial data. (4) the ability to calculate solar irradiation at arbitrary surface positions including at rooftops, facades and the ground. Solar3D is publicly available at https://github.com/jian9695/Solar3D.

Abstract: The package r.sun within the open source Geographical Resources Analysis Support System (GRASS) can be used to compute insolation including temporal and spatial variation of albedo and solar photovoltaic yield. A complete algorithm is presented covering the steps of data acquisition and preprocessing to post simulation whereby candidate lands for incoming solar farms projects are identified. The optimal resolution to acquire reliable solar energy outputs to be integrated into PV system design software was determined to be 1 square km. A case study using the algorithm developed here was performed on a North American region encompassing fourteen counties in Southeastern Ontario. It was confirmed for the case study that Ontario has a large potential for solar electricity. This region is found to possess over 935,000 acres appropriate for solar farm development, which could provide 90 GW of PV. This is nearly 60% of Ontario’s projected peak electricity demand in 2025. The algorithm developed and tested in this paper can be generalized to any region in the world in order to foster the most environmentally-responsible development of large-scale solar farms.