Estimating potential photovoltaic yield with r.sun and the open source Geographical Resources Analysis Support System

• Published in: Solar energy Vol. 84; no. 5; pp. 831 - 843
• Main Authors: Nguyen, H.T., Pearce, J.M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-05-2010; Elsevier
• Subjects: ALBEDO; ALGORITHMS; COMPUTER CODES; DATA ACQUISITION; DATA PROCESSING; DESIGN; Electric power; ENERGY PARKS; Engineering Sciences; Environment and Society; Environmental Sciences; GRASS GIS; INSOLATION; ONTARIO; Photovoltaic; PHOTOVOLTAIC CELLS; POWER DEMAND; Renewable energy; RESOLUTION; SIMULATION; SITE SELECTION; SOLAR CELL ARRAYS; SOLAR ENERGY; Solar irradiation modeling; VARIATIONS; YIELDS; Solar irradiation modeling; Photovoltaic; GRASS GIS; Renewable energy; Solar energy; renewable energy; solar energy; solar irradiation modeling; photovoltaic
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2010.02.009

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 South-eastern 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.