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Siting hydropower plant by rough set and combinative distance-based assessment

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Warianty tytułu
PL
Planowanie usytuowania elektrowni wodnej metodą wstępną i kombinowaną ocena na podstawie odległości
Języki publikacji
EN
Abstrakty
EN
Each power plant (PP) is solo entity whose construction site is determined by different criteria in accordance with some physical rules. Latterly, great importance is provided to siting PP in inexact surroundings. Multiple-criteria decision-making for the proper location of the PP construction is relevant. The objective of this research is to create a model for decision-makers to rank available sites for installing hydropower plant (HPP) in accordance with multiple-criteria attributes e.g. accessibility to electrical grid, power potential, economical respects, environmental influence, topography, and natural hazards. In this research, a novel application of a hybrid approach that employs rough set theory (RST) and combinative distance-based assessment (CODAS) method is proposed to prioritize available locations for installing HPP. Firstly, the strength of RST is adopted to get minimal attributes reduction set. Secondly, the relative weights of minimal attributes are determined using RST. Finally, CODAS technique is utilized to calculate the rank of alternatives. The comparison between the proposed method-based results and the results without attributes reduct, proves that the proposed method saves the time and energy.
PL
Zaproponowano nowatorskie zastosowanie podejścia hybrydowego, które wykorzystuje teorię zbiorów przybliżonych (RST) i metodę oceny kombinowanej opartej na odległości (CODAS) w celu ustalenia priorytetów dostępnych lokalizacji do zainstalowania elektrowni wodnej (HPP) zgodnie z atrybutami wielokryterialnymi, np. dostępność do sieci elektrycznej, potencjał energetyczny, aspekty ekonomiczne, wpływ środowiska, topografia i zagrożenia naturalne.
Rocznik
Strony
15--20
Opis fizyczny
Bibliogr. 33 poz., tab.
Twórcy
  • Northern Border University, Arar 1321, Saudi Arabia & Electrical Engineering Department, Faculty of Engineering
  • Al-Azhar University, Cairo 11651, Egypt
  • Northern Border University, Arar 1321, Saudi Arabia & Department of Engineering Basic Science
  • Menoufia University, Shebin El-Kom 32511, Egypt
Bibliografia
  • [1] Sowinski J., Green Paper – challenges to RES development in Poland, Przegląd Elektrotechniczny, 90 (2014), No. 4, 145-148.
  • [2] Paska J., Pawlak K., Ronkiewicz P., Terlikowski P., Wojciechowski J., Polish hydropower resources and example of their utilization, Przegląd Elektrotechniczny, 96 (2020), No. 1, 1-5.
  • [3] Zaidi A.Z., Khan M., Identifying high potential locations for runof- the-river hydroelectric power plants using GIS and digital elevation models, Renewable and Sustainable Energy Reviews, 89 (2018), 106-116.
  • [4] Moiz A., Kawasaki A., Koike T., Shrestha M., A systematic decision support tool for robust hydropower site selection in poorly gauged basins, Applied Energy, 224 (2018), 309-321.
  • [5] Romanelli J.P., Silva L.G.M., Horta A., Picoli R.A., Site selection for hydropower development: a GIS-based framework to improve planning in Brazil, Journal of Environmental Engineering, 144 (2018), No. 7, 1-10.
  • [6] Sanchez-Lozano J.M., García-Cascales M.S., Lamata M.T., Identification and selection of potential sites for onshore wind farms development in Region of Murcia, Spain, Energy, 73 (2014), 311-324.
  • [7] Larentis D.G., Collischonn W., Olivera F., Tucci C.E.M., Gisbased procedures for hydropower potential spotting, Energy, 35 (2010), 4237-4243.
  • [8] Lakshmi S.V., Sarvani G.R., Selection of suitable sites for small hydropower plants using Geo-Spatial technology, International Journal of Pure and Applied Mathematics, 119 (2018), No. 17, 217-240.
  • [9] Kaliraj S., Malar V.K., Geospatial analysis to assess the potential site for coal based thermal power station in Gujarat, India, Advances in Applied Science Research, 3 (2012), No. 3, 1554-1562.
  • [10] Temel P., Evaluation of potential run-of river hydropower plant sites using multi-criteria decision making in terms of environmental and social aspects, MSc thesis, Middle East Technical University, (2015).
  • [11] Shimray B.A., Singh K.M., Khelchandra T., Mehta R.K., Ranking of sites for installation of hydropower plant using MLP neural network trained with GA: a MADM approach, Computational Intelligence and Neuroscience, 2017 (2017), 1-8.
  • [12] Shimray B.A., Singh K.M., Khelchandra T., Mehta R.K., A new MLP–GA–Fuzzy decision support system for hydro power plant site selection, Arabian Journal for Science and Engineering, 43 (2018), 6823-6835.
  • [13] Adhikary P., Roy P.K., Mazumdar A., Selection of small hydropower project site: a multi-criteria optimization technique approach, ARPN Journal of Engineering and Applied Sciences, 10 (2015), No. 8, 3280-3285.
  • [14] Silva H., Blengini A., Mota L., Pezzuto C., Lavorato M., Carvalho M., Multi-criteria analysis of Brazilian wind farms, International Journal of Renewable Energy Research, 10 (2020), No. 2, 1042-1053.
  • [15] Erol İ., Sencer S., Özmen A., Searcy C., Fuzzy MCDM framework for locating a nuclear power plant in Turkey, Energy Policy. 67 (2014), 186-197.
  • [16] Deveci M., Cali U., Kucuksari S., Erdogan N., Interval type-2 fuzzy sets based multi-criteria decision-making model for offshore wind farm development in Ireland, Energy, 198 (2020), 117317.
  • [17] Wang C.N., Su C.C., Nguyen V.T., Nuclear power plant location selection in Vietnam under fuzzy environment conditions, Symmetry, 10 (2018), 548.
  • [18] Kurt Ü., The fuzzy TOPSIS and generalized Choquet fuzzy integral algorithm for nuclear power plant site selection – a case study from Turkey, Journal of Nuclear Science and Technology, 51 (2014), No. 10, 1241-1255.
  • [19] Erdebilli B., Yildizbasi A., Arikan Ü.Z.B., Using intuitionistic fuzzy TOPSIS in site selection of wind power plants in Turkey, Advances in Fuzzy Systems, 2018 (2018), 6703798.
  • [20] Locatelli G., Mancini M., A framework for the selection of the right nuclear power plant, International Journal of Production Research, 50 (2012), no. 17, 4753-4766.
  • [21] Erdogan M., Kaya I., A combined fuzzy approach to determine the best region for a nuclear power plant in Turkey, Applied Soft Computing, 39 (2016), 84-93.
  • [22] Sambasivarao K., Raj D.K., Dua D., An expert system for site selection of thermal power plants, Journal of Basic and Applied Engineering Research, 1 (2014), No. 8, 36-40.
  • [23] Cho S., Kim J., Multi-site and multi-period optimization model for strategic planning of a renewable hydrogen energy network from biomass waste and energy crops, Energy, 185 (2019), 527-540.
  • [24] Dev N., Attri R., Site selection for a power plant using graph theory and matrix method, Twelfth AIMS International Conference on Management, (2015), 1328-1335.
  • [25] Cebi S., Kahraman C., Using multi attribute Choquet integral in site selection of wind energy plants: the case of Turkey, Journal of Multiple-Valued Logic and Soft Computing, 20 (2013), 423-443.
  • [26] Lingga M.M., Developing a hierarchical decision model to evaluate nuclear power plant alternative siting technologies, PhD thesis, Portland State University, (2016).
  • [27] Jurasz J., Mikulik J., Site selection for wind and solar parks based on resources temporal and spatial complementarity – mathematical modelling approach, Przegląd Elektrotechniczny, 93 (2017), No. 7, 86-91.
  • [28] Feng R., Optimal site selection for thermal power plant based on rough sets and multi-objective programming, International Conference on E-Product E-Service and E-Entertainment (ICEEE), (2010), 1-5.
  • [29] Ghorabaee M.K., A new combinative distance-based assessment (CODAS) method for multi-criteria decisionmaking, Economic Computation and Economic Cybernetics Studies and Research, 50 (2016), No. 3, 25-44.
  • [30] Yu J., Li Y., Chen M., Zhang B., Xu W., Decision-theoretic rough set in lattice-valued decision information system,” Journal of Intelligent & Fuzzy Systems, 36 (2019), 3289-3301.
  • [31] Tiwari A.K., Shreevastava S., Subbiah K., Som T., An intuitionistic fuzzy-rough set model and its application to feature selection, Journal of Intelligent & Fuzzy Systems, 36 (2019), 4979-4969.
  • [32] Badi I.A., A. Abdulshahed M., Shetwan A.G., Supplier selection using combinative distance-based assessment (CODAS) method for multi-criteria decision-making, The 1st International Conference on Management, Engineering and Environment (ICMNEE), (2017), 395-407.
  • [33] Dahooei J.H., Zavadskas E.K., Vanaki A.S., Firoozfar H.R., Keshavarz-Ghorabaee M., An evaluation model of business intelligence for enterprise systems with new extension of CODAS (CODAS-IVIF), Ekonomie a Management, 21 (2018), No. 3, 171-187.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4f56ed7a-d3b6-4139-93cd-462b6af7ea1a
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