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Hydropower plant site spotting using geographic information system and a MATLAB based algorithm

Serpoush, B ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1016/j.jclepro.2017.03.095
  3. Abstract:
  4. Meanwhile with increasing demand for energy sources in the world, hydropower plants can be considered as clean energy sources for sustainable development. Hydropower plant environmental impact is almost none. They are easy to construct and operate with much lower cost in comparison with other types of power plants. Currently in Iran, Conventional methods for determining hydropower plant potent location are very complicated and do not always eventuated to the best result. This study focuses on run of river projects and proposes a new methodology to spot hydropower plan best location according to specific engineering criteria, using ArcGIS and an algorithm developed in MATLAB. An economic analysis is then run upon engineering selected alternatives to assess their economic feasibility. For a case study, this methodology is applied for Sefidbarg basin in Nokhan area nearby the Kermanshah province. Nokhan area is a mountainous district with steep and durable rivers. Finally, four alternatives are suggested for hydropower plant location on Sefidbarg basin considering engineering criteria. Economic parameters are then calculated for these suggested hydropower plants to assess their economic feasibility and rank them according to their economy. This procedure is able to quickly survey vast areas for spotting best location for hydropower plant. © 2017 Elsevier Ltd
  5. Keywords:
  6. Geographic information system ; Hydropower plant ; Economic analysis ; Environmental impact ; Geographic information systems ; Hydroelectric power ; Information systems ; Location ; Planning ; Sustainable development ; Clean energy sources ; Conventional methods ; Economic feasibilities ; Economic parameters ; Engineering criterion ; Hydropower plants ; Mountainous district ; Site spotting ; Hydroelectric power plants
  7. Source: Journal of Cleaner Production ; Volume 152 , 2017 , Pages 7-16 ; 09596526 (ISSN)
  8. URL: https://www.sciencedirect.com/science/article/pii/S0959652617305371