Artikel

Assessment power generation potential of small hydropower plants using GIS software

The hydropower stations are one of the most significant sources of renewable energy. In this research, the identification of suitable locations for hydroelectric power stations installation concerning electricity generation capacity has been investigated. Small-scale hydropower can potentially be quite important in the future of the renewable energy system that may have a limited regulatory capacity in energy storage and transmission capacity. The objective of this study, to develop methods that assess the power production potential associated with suitable location schemes for a system of small-scale hydropower stations. this manuscript that regards the use of Geographical Information Systems(GIS) to assess the power generation of alternative development plans for small-scale hydropower. In this study, four plans are proposed that examining each of the plans from the aspect of their electricity generation potential and cost. A plan is selected that gives better results in terms of cost and energy production. After selecting the best plan, locations that have the potential to installation hydroelectric power stations identified. the results are obtained from the GIS software and Digital Elevation Model (DEM) map showed that decreasing watershed elevation and going along the river and outlet of the watershed, the cumulative discharge increases, thus increasing hydroelectric power generation capacity.

Language
Englisch

Bibliographic citation
Journal: Energy Reports ; ISSN: 2352-4847 ; Volume: 6 ; Year: 2020 ; Pages: 1393-1404 ; Amsterdam: Elsevier

Classification
Wirtschaft
Subject
Renewable energy
Clean and reliable electricity
Geographic information system
Hydroelectric power
Remote sensing methods

Event
Geistige Schöpfung
(who)
Tian, Yizhi
Zhang, Feng
Yuan, Zhi
Che, Zihang
Zafetti, Nicholas
Event
Veröffentlichung
(who)
Elsevier
(where)
Amsterdam
(when)
2020

DOI
doi:10.1016/j.egyr.2020.05.023
Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Tian, Yizhi
  • Zhang, Feng
  • Yuan, Zhi
  • Che, Zihang
  • Zafetti, Nicholas
  • Elsevier

Time of origin

  • 2020

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