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Hydropower potential assessment using spatial technology and hydrological modelling in Nigeria river basin

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  • Fasipe, O.A.
  • Izinyon, O.C.
  • Ehiorobo, J.O.

Abstract

This paper describes the procedure for an extensive survey of hydropower potential locations to be employed at the originating hydropower development planning stage in a basin. The approach utilized spatial tools such as Arc-Hydro, Remote Sensing (RS), and GIS to characterize the Osse Sub-basin (OSB) and Natural Resources Conservation Service - Curve Number (NRCS-CN) hydrological model to compute the stream Peak discharge Qp. This method permits identification of Small Hydropower (SHP) potential locations by considering obtained catchment parameters along the drainage network in a run-of-river project that satisfies the requirement of establishing hydropower plants, which is difficult to achieve in traditional survey study. Results indicate 18 out of 148 points examined in OSB can produce between 677.485 kW and 7622.877 kW clean energy at 92% flow exceedance annually, preventing between 555.8 and 6543.0 tCO2/yr emission from polluting the environment if developed against GHG emission sources. The calculated project cost range is £ 937,167.48–4,048,733.99. This study establishes first clean development mechanism (CDM) procedure in Nigeria to help future activation of SHP resources by identifying SHP promising project sites that can earn saleable certified emission reduction (CER) credits in the optimization model named SHP Resource Assessment and Sustainability Analysis (SHPRASA).

Suggested Citation

  • Fasipe, O.A. & Izinyon, O.C. & Ehiorobo, J.O., 2021. "Hydropower potential assessment using spatial technology and hydrological modelling in Nigeria river basin," Renewable Energy, Elsevier, vol. 178(C), pages 960-976.
    • Handle: RePEc:eee:renene:v:178:y:2021:i:c:p:960-976
    DOI: 10.1016/j.renene.2021.06.133
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    References listed on IDEAS

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    1. MacMillan, Andrew & Schell, Kristen R. & Roughley, Colter, 2023. "A predictive model of velocity for local hydrokinetic power assessment based on remote sensing data," Renewable Energy, Elsevier, vol. 211(C), pages 285-295.

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