Geographic information system-based investment system for photovoltaic power plants location analysis in Turkey


Tercan E., Saracoglu B. O., BİLGİLİOĞLU S. S., EYMEN A., Tapkin S.

ENVIRONMENTAL MONITORING AND ASSESSMENT, vol.192, no.5, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 192 Issue: 5
  • Publication Date: 2020
  • Doi Number: 10.1007/s10661-020-08267-5
  • Journal Name: ENVIRONMENTAL MONITORING AND ASSESSMENT
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Compendex, EMBASE, Environment Index, Food Science & Technology Abstracts, Geobase, Greenfile, MEDLINE, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Erciyes University Affiliated: Yes

Abstract

The motivation of this research, development, demonstration, deployment, and diffusion (RD3&D) study is to present the progress of designing the GIS-based location selection module of autonomous investment decision support system and its experimental application for photovoltaic power plants (PVPPs) in Antalya, Burdur, and Isparta planning region Turkey. The other motivation of this RD3&D study is to start investigating in combinations the applicability and usability of weighted linear combination with 4 subjective weighting approaches (rank sum weight method (RS), inverse or reciprocal weights method (RR), rank order centroid (ROC), point allocation (PA)) for 5 main criteria, 14 sub-criteria, and 79 value ranges. The results show that 38.48% of the planning region is unsuitable, 61.52% is suitable. Only 2.07% of this region is very highly suitable according to RS. 7.13%, 9.22%, and 5.58% are respectively very highly suitable according to RR, ROC, and PA. Similarities between RS, RR, ROC, and PA methods are presented such as RS-RR: 0.7834, RS-ROC: 0.8510, and RS-PA: 0.6384 with covariance and correlation analysis. A backward-looking performance verification and validation analysis is also performed with 7 PVPPs for only 4 decisive success factors (capacity factor, annual energy/land use, project cost/capacity, project cost/energy). This study is thus able to evaluate the optimal locations for future investments, as well as the suitability conditions of the available investments. This study will contribute to provide some useful recommendations for decision makers to identify and assess the hotspots which are suitable for PVPPs in the planning region.