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Identifying high potential locations for run-of-the-river hydroelectric power plants using GIS and digital elevation models

Arjumand Z. Zaidi and Majid Khan

Renewable and Sustainable Energy Reviews, 2018, vol. 89, issue C, 106-116

Abstract: The recent global energy crisis has provoked a need to explore alternate energy sources including run-of-the-river hydropower projects. To derive maximum payback for a given investment, finding the most advantageous siting of power plants is imperative. If a selection of potential sites misses some of the apparently indistinct sites with significant power potential, there is a chance of acquiring only partial benefits out of these investments. A review of the existing methods for evaluating power potential of a river is discussed in this paper with their limitations along with a new proposed approach. The new approach can be used to evaluate different installation schemes along a river to assess run-of-the-river hydropower potentials using geospatial data techniques to select sites exhibiting higher total hydropower potential. The case study of Kunhar River, located in the northern part of Pakistan, presents the applicability of the approach. Open source Advanced Spaceborne Thermal Emission (ASTER)’s digital elevation model (DEM) and regional hydrologic gauged data are used for identifying the best locations for hydropower plants, demonstrating this approach is substantially more cost effective and robust compared to other field based assessment. Replicating the proposed approach for other locations is easy following the step-by-step method presented in this paper and giving consideration to the limitations described. This study may provide guidelines for the development of cost-effective and energy efficient hydropower projects. The use of this approach is most advantageous in the preliminary assessment phase of a project to narrow the scope of the detailed study focusing only on the higher potential sites.

Keywords: Energy; Geospatial; GIS; Hydropower potential; Remote sensing; Run-of-the-river (search for similar items in EconPapers)
Date: 2018
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