Modelling of Risk Indicators in a Smart Grid Newtork By Fuzzy Analytic Hierarchy Process

Öz

In smart grid systems, a comprehensive risk analysis is required to be able to transfer supply of energy continuous and secure. In this study, an advanced, multiple and detailed Risk Assessment Index Framework has been established for a smart grid system. Risks are constituted as Financial, Security, Environmental, Technological and Management Risks. The significance of the risks are determined by using Chang's Fuzzy Analytic Hierarchy Process (BAHP) Method, Enhanced Integral Value and Quadratic Mean Method.

Anahtar Kelimeler

• Smart grid,fuzzy AHP,risk assessment,fuzzy numbers,fuzzy rankings

Akıllı Bir Şebekedeki Risk İndikatörlerinin Bulanık Analitik Hiyerarşi Prosesi ile Modellenmesi

Öz

Akıllı şebeke sistemlerinde, enerji arzının kesintisiz ve güvenli bir şekilde yapılması için kapsamlı bir risk analizi gerektirmektedir. Bu çalışmada, akıllı şebeke sistemi için, gelişmiş, çoklu ve detaylı bir Risk Değerlendirme Endeks çerçevesi oluşturulmuş, riskler; Finansal, Güvenlik, Çevresel, Teknolojik ve Yönetimsel Riskler olmak üzere beş ayrı ana başlık altında toplanmıştır. Çalışmada, Chang’ın Bulanık Analitik Hiyerarşi Prosesi (BAHP) Yöntemi, Geliştirilmiş Entegral Değer ve Kuadratik Ortalama Metot yöntemleri kullanılarak risklerin önem dereceleri belirlenmiştir.

Anahtar Kelimeler

• Akıllı Şebekeler,bulanık AHP,risk analizi,bulanık sayılar,bulanık sıralamalar

Kaynakça

1. [1] ABB, “Toward a Smarter Grid ABB’ s Vision for the Power System of the Future,” 2010.
2. [2] A. Janjic, S. Savic, G. Janackovic, M. Stankovic, and L. Velimirovic, “Multi-criteria assessment of the smart grid efficiency using the fuzzy analytic hierarchy process,” Facta Univ. - Ser. Electron. Energ., vol. 29, no. 4, pp. 631–646, 2016.
3. [3] A. Janjıc, S. Savic, L. Velimirovic, and V. Nikolic, “Renewable energy integration in smart grids-multicriteria assessment using the fuzzy analytical hierarchy process.,” Turkish J. Electr. Eng. Comput. Sci., vol. 23, no. 6, pp. 1896–1912, 2015.
4. [4] Y. Li, X. Guo, H. A. O. Tang, and D. Li, “Construction of Hazard Source Evaluation Index System of Smart Grid,” no. Aiea, pp. 487–491, 2017.
5. [5] W. Xiaojing, C. Xingyin, Y. Kun, and S. Haojie, “Construction of Smart Distribution Grid Efficiency Evaluation Index System,” IEEE Conf. Energy Internet Energy Syst., no. 2, pp. 1–4, 2017.
6. [6] W. Xu, “Research on Risk Assessment of Smart Grid Project,” 2015.
7. [7] R. Liu, “Preliminary Analysis of Smart Grid Risk Index System and Evaluation Methods,” Energy Power Eng., vol. 5, no. 4, pp. 807–810, 2013.
8. [8] D. B. Rawat and C. Bajracharya, “Cyber security for smart grid systems: Status, challenges and perspectives,” Conf. Proc. - IEEE SOUTHEASTCON, vol. 2015–June, no. June, pp. 1–6, 2015.

9. [9] T. Hecht, L. Langer, and P. Smith, “Cybersecurity Risk Assessment in Smart Grids,” 5th Symp. Commun. Energy Syst. (ComForEn 2014), 2014.
10. [10] T. L. Saaty, “Decision making with the analytic hierarchy process,” Int. J. Serv. Sci., vol. 1, no. 1, p. 83, 2008.
11. [11] M. Daǧdeviren, D. Akay, and M. Kurt, “Iş deǧerlendirme sürecinde analitik hiyerarşi prosesi ve uygulamasi,” J. Fac. Eng. Archit. Gazi Univ., vol. 19, no. 2, pp. 131–138, 2004.
12. [12] T. L. Saaty, The Analytic Hierarchy Process. McGraw-Hill International Book, 1980.
13. [13] A. Emrouznejad and W. Ho, Fuzzy Analytic Hierarchy Process. CRC Press, 2012.
14. [14] H.-J. Zimmermann, Fuzzy set theory and its applications, vol. 47, no. 3. 2001.
15. [15] L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3. pp. 338–353, 1965.
16. [16] J. A. Goguen, “L-fuzzy sets,” J. Math. Anal. Appl., vol. 18, no. 1, pp. 145–174, 1967.
17. [17] G. Büyüközkan, C. Kahraman, and D. Ruan, “A fuzzy multi-criteria decision approach for software development strategy selection,” Int. J. Gen. Syst., vol. 33, no. 2–3, pp. 259–280, 2004.
18. [18] M. S. Toshiro Terano, Kiyoji Asai, Fuzzy systems theory and its applications. Academic Press, 1992.
19. [19] G. Chen and T. T. Pham, Introduction to Fuzzy Sets, Fuzzy Logic and Fuzzy Control Systems. CRC Press, 2001.
20. [20] H. J. Zimmerman, Fuzzy Set Theory and Its Applications. Springer, 1992.
21. [21] M. S. Kuo, G. S. Liang, and W. C. Huang, “Extensions of the multicriteria analysis with pairwise comparison under a fuzzy environment,” Int. J. Approx. Reason., vol. 43, no. 3, pp. 268–285, 2006.
22. [22] J. J. Buckley, “Fuzzy Hierarchical Analysis,” Fuzzy Sets Syst., vol. 17, pp. 233–247, 1985.
23. [23] P. J. M. P. W. van Laarhoven, “A Fuzzy Extension of Saaty’s Priority Theory,” vol. 11, pp. 229–241, 1983.
24. [24] T. L. Saaty, The analytic hierarchy process. 1980.
25. [25] F. T. S. Chan and N. Kumar, “Global supplier development considering risk factors using fuzzy extended AHP-based approach,” Omega, vol. 35, no. 4, pp. 417–431, 2007.
26. [26] G. Bortolan and R. Degani, “A review of some methods for ranking fuzzy subsets,” Fuzzy Sets Syst., vol. 15, no. 1, pp. 1–19, 1985.
27. [27] L. Mikhailov, “Deriving priorities from fuzzy pairwise comparison judgements,” Fuzzy Sets Syst., vol. 134, no. 3, pp. 365–385, 2003.
28. [28] D. Chang, “Applications of the extent analysis method on fuzzy AHP,” vol. 2217, no. 95, 1996.
29. [29] Y. Deng, Z. Zhenfu, and L. Qi, “Ranking fuzzy numbers with an area method using radius of gyration,” Comput. Math. with Appl., vol. 51, no. 6–7, pp. 1127–1136, 2006.
30. [30] S.-J. J. Chen and C.-L. Hwang, Fuzzy Multiple Attribute Decision Making Methods and Applications, vol. 375. 1992.
31. [31] R. Jain, “Decision making in the presence of fuzzy variables,” IEEE Trans. Syst. Man Cybern., vol. 6, no. 10, pp. 698–703, 1976.
32. [32] S. M. Baas and H. Kwakernaak, “Rating and ranking of multiple-aspect alternatives using fuzzy sets,” Automatica, vol. 13, no. 1, pp. 47–58, 1977.
33. [33] J. F. Baldwin and N. C. F. Guild, “Comparision of Fuzzy Set On the Same Decision Space,” Fuzzy Sets Syst., vol. 2, pp. 213–231, 1979.
34. [34] S. Chen, “Ranking Fuzzy Numbers with Maximizing Set and Minimizing Set,” Fuzzy Sets Syst., vol. 17, pp. 113–129, 1985.
35. [35] C. H. Cheng, “A new approach for ranking fuzzy numbers by distance method,” Fuzzy Sets Syst., vol. 95, no. 3, pp. 307–317, 1998.
36. [36] T.-C. Chu and C.-T. Tsao, “Ranking Fuzzy Numbers with an Area between the Centroid Point and Original Point,” Comput. Math. with Appl., vol. 43, pp. 111–117, 2002.
37. [37] S. Abbasbandy and B. Asady, “Ranking of fuzzy numbers by sign distance,” Inf. Sci. (Ny)., vol. 176, pp. 2405–2416, 2006.
38. [38] T. S. Liou and M. J. J. Wang, “Ranking fuzzy numbers with integral value,” Fuzzy Sets Syst., vol. 50, no. 3, pp. 247–255, 1992.