V.A. Popov, Dr Sc. (Eng.), О.S. Yarmolіuk, Cand. Sc. (Eng.), F.V. Tkachenko, D.V. Yatsenko, post-graduate students,
National Technical Institute of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”
22, Bldg, 37 Pobeda St, Kyiv, 03056, Ukraine, e-mail:
Èlektron. model. 2018, 40(2):105-118
https://doi.org/10.15407/emodel.40.02.105
ABSTRACT
A method of the complex comparative estimation of alternative options of integrating sources of distributed generation taking into account their influence on the main parameters of the electrical networks modus of operation is proposed. Solving this problem on the basis of modified algorithms of multicriteria decision making VIKOR and TOPSIS assumes considering the uncertainty of the initial information given in the form of fuzzy sets. A procedure of calculation of entropy and corresponding coefficients of criteria importance with allowance for uncertainty of initial information presented in the form of fuzzy sets is considered.
KEYWORDS
uncertainty of information, multicriteria decision-making, distributed generation, modes of electrical networks.
REFERENCES
1. Zharkin, A.F., Popov, V.A., Banuzade, S.S., Zamkovyi, P.A. and Spodinskaya, A.V. (2016), “Multicriteria evaluation of alternative options for the distributed generation sources integration into the distribution networks”, Elektronnoe modelirovanie, Vol. 38, no. 1, pp. 99-112.
2. Opricovic, S. and Tzeng, G.H. (2004), Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS, European Journal of Operational Research, no. 156, pp. 445-455.
https://doi.org/10.1016/S0377-2217(03)00020-1
3. Kauffmann, A. and Gupta, M.M. (1991), Introduction to Fuzzy Arithmetic: Theory and Applications, Van Nostrand Reinhold, New York, USA.
4. Zimmerman, H.J. (1991), Fuzzy set theory and its application, Kluwer Academic Publishers, Boston, Dordrecht, London.
https://doi.org/10.1007/978-94-015-7949-0
5. Chen, C.T. (2000), Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets and Systems, Vol. 14, pp. 1-9.
https://doi.org/10.1016/S0165-0114(97)00377-1
6. Tran L., Duckstain, L. (2002), “Comparison of fuzzy numbers using a fuzzy distance measure”, Fuzzy Sets and Systems, Vol. 130, pp. 331-341.
https://doi.org/10.1016/S0165-0114(01)00195-6
7. Ebrahimnejada, S., Mousavib, S.M., Tavakkoli-Moghaddamc, R. and Heydard, M. (2012), Evaluating high risks in large-scale projects using an extended VIKOR method under a fuzzy environment, International Journal of Industrial EngineeringComputations, no. 3, pp. 463-476.
8. Chen, T.Y. and Li, C.H. (2010), Determining objective weights with intuitionistic fuzzy entropy measures: a comparative analysis, Information Sciences, Vol. 180, no. 21, pp. 4207-4222.
https://doi.org/10.1016/j.ins.2010.07.009
9. Yarmolyuk, Î.S. (2012), “Modeling parameters of distributed generation sources in integrated distribution system with uncertainty information”, Tekhnichna elektrodynamika, no. 3, pp. 57-58.