A New Decision Framework for Hybrid Solar and Wind Power Plant Site Selection Using Linear Regression Modeling Based on GIS-AHP

Meysam Asadi, Kazem Pourhossein, Younes Noorollahi, Mousa Marzband*, Gregorio Iglesias

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
13 Downloads (Pure)

Abstract

Currently, worldwide attention to clean energy and sustainable energy has been expedited because of its many environmental benefits. In fact, wind and solar energies play a prime role in decarbonizing the energy market. However, finding the most suitable locations for wind/solar power plants is difficult because of the non-homogeneous distribution of these sources. This paper presents a novel method for selecting the optimal locations for wind and solar farms by mapping the space of the decision criteria to the site score. In addition, the multiple linear regression model was used, with the help of the combination of GIS and AHP methods, to model the siting of wind and solar power plants. The site scoring method used in this study is reliable and globally evaluated; therefore, the scores are accurate and effective. To reveal the ability of the proposed method, two study areas were investigated and researched. The results achieved based on the introduced method showed that, in case study 1, areas with an area of about 9, 4 and 7 km2 are suitable for the construction of wind, solar and wind/solar power plants, respectively. This paper also used fourteen existing wind/solar, wind and solar farms from five continents around the world. The results showed that the suggested model acts the same as the real data. In addition to the interest these results hold for the development of renewable energy in the study area, this novel approach may be applied elsewhere to select optimum sites for wind, solar, and combined wind and solar farms.
Original languageEnglish
Article number8359
Number of pages24
JournalSustainability
Volume15
Issue number10
DOIs
Publication statusPublished - 21 May 2023

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