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GIS augmented computational intelligence technique for rural cluster electrification through prioritized site selection of micro-hydro power generation system

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  • Choudhury, Shibabrata
  • Parida, Adikanda
  • Pant, Rajive Mohan
  • Chatterjee, Saibal

Abstract

Energy policymakers address rural energy poverty with locally available renewable sources. Further, an acquaintance in the adoption of similar renewable based rural electrification technology (RBRET) would enhance the community acceptance in the neighboring villages. As a result of which, the pace of RBRET dissemination can be heightened in the corresponding geographic cluster. Amongst the varied RBRET projects, Micro Hydro Power (MHP) is relatively economic, efficient, reliable, and ease to operate. However, the sustainability of MHP cluster relies upon the appropriate prioritization process of site selection. In this regard, a novel computational intelligence technique has been proposed through a case study in the eastern Himalayan region of India. The proposed model encompasses four phases. First, refinement of the initial technical feasibility conformations through geographical information system (GIS) and past hydrological data. Second, the establishment of interdependency influencing relationship using interpretive ranking process (IRP). Third, the computation of Pareto based prioritization of entropy weights derived through IRP. Fourth, the prioritization of potential sites for MHP in the sequential manner adopting fuzzy similarity analysis measure, and Ardalan heuristic method. The proposed hybrid model surpasses the existing application of standalone methods such as multi-criterion analysis tools, geographic information system, and econometric analysis method.

Suggested Citation

  • Choudhury, Shibabrata & Parida, Adikanda & Pant, Rajive Mohan & Chatterjee, Saibal, 2019. "GIS augmented computational intelligence technique for rural cluster electrification through prioritized site selection of micro-hydro power generation system," Renewable Energy, Elsevier, vol. 142(C), pages 487-496.
    • Handle: RePEc:eee:renene:v:142:y:2019:i:c:p:487-496
    DOI: 10.1016/j.renene.2019.04.125
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    2. Sánchez-Aparicio, M. & Martín-Jiménez, J. & Del Pozo, S. & González-González, E. & Lagüela, S., 2021. "Ener3DMap-SolarWeb roofs: A geospatial web-based platform to compute photovoltaic potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).

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