Designing and implementing a location-based model to identify areas suitable for multi-renewable energy development for supplying electricity to agricultural wells

Hossein Azizi Moghaddam and Saman Nadizadeh Shorabeh

Renewable Energy, 2022, vol. 200, issue C, 1251-1264

Abstract: The purpose of this study is to design and implement a spatial model to identify optimal areas for energy sources (solar power plant (SPS), wind turbine generator (WTG), Distributed Gas Turbine-Generators (DGTG), and direct connection to power distribution system (PDS) to supply electricity to agricultural wells using a combination of data-driven methods and geographic information system (GIS). This study used 13 spatial criteria to identify suitable areas for SPS, 11 for WTG, 11 for DGTG, and 3 for PDS. A combination of data-driven decision-making (DDDM) and GIS was used for data analysis. The results of the present study showed that in the pilot study area, solar energy has a higher potential than other energies. Also, the results of the sensitivity analysis showed that changing the weight of the criteria has no significant effect on the model outputs and clearly proves the stability of the proposed model. The hybrid power plant scenarios showed that had the highest number of pixels suitable for development were associated with double and triple hybrid power solutions generator, respectively. These were hybrid SPS and DGTG and hybrid SPS, PDS, and DGTG systems.

Keywords: Multi-renewable energy; Agricultural wells; Entropy-TOPSIS; GIS; Jiroft (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148122015166
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:200:y:2022:i:c:p:1251-1264

DOI: 10.1016/j.renene.2022.10.023

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().