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Determining criteria for optimal site selection for solar power plants

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Site selection is one of the basic vital decisions in the start-up process, expansion or relocation of businesses of all kinds. Construction of a new industrial system in the form of solar photovoltaic power plant is a major long-term investment, and in this sense determining the location is critical point on the road to success or failure of industrial system. One of the main objectives in industrial site selection is finding the most appropriate site with desired conditions defined by the selection criteria. This work suggests how to define and classify particular criteria considered for solar PV farm siting. Multi-criteria decision analysis (MCDA) is proposed as a method to process available technical information to support decisions in many fields, especially in environmental decision making. In some cases, due to the lack of reliable information on the impact of various natural factors on the economic activity related to the use of land resources, the method of expert assessments can be used. The peculiarity of this method is the lack of empirical evidence of the influence of a given factor on the final result of decision-making. In this case the value of separate factor is judged by experts. When applying this method, expert groups are formed of leading specialists, the main influence factors is established, a questionnaire and a scale of criteria are compiled. Level of reliability of the results is assessed by the coefficient of concordance.
Rocznik
Tom
Strony
39--54
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
autor
  • Lviv Polytechnic National University Department of Cadastre of Territories 79013 Lviv, 6 Karpinskyi Str.
autor
  • Lviv Polytechnic National University Department of Photogrammetry and Geoinformatics 79013 Lviv, 6 Karpinskyi Str.
Bibliografia
  • Arán-Carrión J., Espin Estrella A., Aznar Dols F., Zamorano Toro M.,Rodriguez M., Ramos Ridao A. 2008. Environmental decision-support system for evaluating the carrying capacity of land areas: Optimal site selection for grid-connected photovoltaic power plants. Renew. Sust. Energy Rev., 12, 2358–2380.
  • Arnette A.N., Zobel C.W. 2011. Spatial analysis of renewable energy potential in the greater southern Appalachian mountains. Renew. Energy, 36(11), 2785–2798.
  • Borgogno Mondino E., Fabrizio E., Chiabrando R. 2015. Site selection of large groundmounted photovoltaic plants: A GIS decision support system and an application to Italy. Int. J. Green Energy, 12(5), 515–525.
  • Brewer J., Ames D.P., Solan D., Lee R., Carlisle J. 2015. Using GIS analytics and social preference data to evaluate utility-scale solar power site suitability. Renew. Energy, 81, 825–836.
  • Charabi Y., Gastli A. 2011. PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation, Renew. Energy, 36, 9, 2554–2561.
  • Dominguez Bravo J., Garcia Casals X., Pinedo Pascua I. 2007. GIS approach to the definition of capacity and generation ceilings of renewable energy technologies. Energy Policy, 35, 4879–92.
  • Eastman J.R., Jin W., Keym P., Toledano J. 1995. Raster procedures for Multi-Criteria/Multi-Objective Decisions. Photogram. Engineer. Remote Sens., 61(5), 539–547.
  • Georgiou A., Skarlatos D. 2016. Optimal site selection for sitting a solar park using multicriteria decision analysis and geographical information systems. Geosci. Instrum. Methods Data Syst., 5, 321–332.
  • Janke J.R. 2010. Multicriteria GIS modeling of wind and solar farms in Colorado, Renew. Energy, 35(10), 2228–2234.
  • Legendre P. 2010. Coefficient of concordance. [In:] N.J. Salkind (ed.), Encyclopedia of Research Design, SAGE Publications, Los Angeles, 164–169.
  • Liu Xinyang. 2013. GIS-based local ordered weighted averaging: A case study in London, Ontario. Electronic Thesis and Dissertation Repository, 1227, available at http://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=2599&context=etd. (accesse: 19.01.2018)
  • Perovych I., Vynarchyk L. 2013. Economic and mathematical approach to estimate the land of settlements based on their functional-planning structure. Interdepartmental scientific and technical review. Geod. Cartogr. Aerial Photogr., 78, 241–247.
  • Saaty T.A. 1992. Decision-Making for Leaders. 2nd Ed. RWS Publications, Pittsburgh, USA.
  • Saaty T.A. 1997. A scaling method for priorities in hierarchical structures. J. Mathem. Psychol., 234–281.
  • Sanchez-Lozano M.J., Teruel-Solano J., Soto-Elvira L.P., Garcia-Cascales S.M. 2013. Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in southeastern Spain. Renew. Sust. Energ. Rev., 24, 544–556.
  • Shepherd J.D., Dymond J.R. 2003. Correcting satellite imagery for the variance of reflectance and illumination with topography. Intern. J. Remote Sens., 24, 3503–3514.
  • Stoms D.M., Dashiell S.L., Davis F.W. 2013. Siting solar energy development to minimize biological impacts, Renew. Energy, 57, 289–298.
  • Tahri M., Hakdaoui M., Maanan M. 2015. The evaluation of solar farm locations applying Geographic Information System and Multi-Criteria Decision-Making methods: Case study in southern Morocco, Renew. Sustain. Energy Rev., 51, 1354–1362.
  • United States Geological Survey (USGS), Shuttle Radar Topography Mission.
  • Uyan M. 2013. GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya, Turkey, Renew. Sustain. Energy Rev., 28, 11–17.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d29af8c5-baec-4076-bf6a-5cbaf45c397a
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