A comprehensive framework based on GIS-AHP for the installation of solar PV farms in Kahramanmaraş, Turkey

Günen M. A.

Renewable Energy, vol.178, pp.212-225, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 178
  • Publication Date: 2021
  • Doi Number: 10.1016/j.renene.2021.06.078
  • Journal Name: Renewable Energy
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, Greenfile, Index Islamicus, INSPEC, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Page Numbers: pp.212-225
  • Keywords: Solar photovoltaics farms, Site selection, Geographic information system (GIS), Analytic hierarchy process (AHP), Multi-criteria decision making (MCDM), OPTIMAL SITE SELECTION, DECISION-ANALYSIS, MODELING TECHNIQUE, POWER-PLANTS, LOCATIONS, SYSTEMS, ENERGY, WIND, MCDM
  • Erciyes University Affiliated: Yes


© 2021Solar photovoltaic (PV) technologies receive investment, support, and incentives despite their apparent high costs. In terms of renewable energy policies and spatial planning, site selection for solar PV farms is a critical issue. A novel comprehensive framework for assessing the site suitability of solar PV farms is proposed, which considers the preservation of natural, ecological conservation, and cultural areas. The framework combines a Geographical Information System (GIS) with layers of satellite-derived data for energy resources as well as locally collected data, and the Multi Criteria Decision Making (MCDM) method based on the Analytic Hierarchy Process (AHP). In the GIS environment, maps belonging to fourteen sub-criteria of the three main criteria (Geography, Climate and Location) for Kahramanmaraş, Turkey were obtained, as well as suitability map derived from their weighted sum. The AHP method generates the appropriate weight for each input criteria using a pairwise comparison matrix. GHI, aspect, distance from power line network, land use/cover, annual average temperature, and others were found as the most necessary sub-criteria, respectively. According to the findings, 9.62% of study area very low, 20.15% is low, 22.51% is moderate, 23.98% is high and 23.74% is very high, and 26% is unsuitable for solar PV farms. Based on the results, the northern districts of the study region were determined to be the most suitable for the construction of solar PV farms. When existing five PV farms' location selection decisions were examined, it was observed that the investment outcomes were consistent with the results of the study. The proposed framework is projected to minimize the cost, time, and resources used on the construction of solar PV farms.