Development of fuzzy statistical method of optimal resource allocation among technical departments of an electric utility company

Authors

DOI:

https://doi.org/10.15587/1729-4061.2016.70522

Keywords:

electric utility enterprise, allocation, risk, the Pareto set, weighting coefficients, Saaty method

Abstract

We formulated the approach to solving specific aspects of the task of strategic planning of sustainable development of an electric utility enterprise as a multi-criteria decision-making problem, which can be solved under conditions of fuzzy initial information. To solve this task, the method of constructing a set of feasible solutions was proposed, based on the specific functioning of an enterprise divisions. The set of feasible variants is compiled according to the Pareto method, when no element can be improved without worsening of at least one of the other elements, which makes it possible to define a compromise allocation of funding, at which the minimum possible value of the risk of an accident in the electric utility enterprise’s networks is provided. As an optimizationl criterion, we adopted the risk of an accident occurrence in an electric utility system.To solve the task of risk evaluation at solving a linear programming problem, we applied the method, which allows evaluating the risk by analytical way without carrying out probabilistic-statistical modeling.

As a result, the developed method is efficient for practical application during an express evaluation of the risk at different variants of the allocation  of funds among the units of electric utility enterprise without carrying out of cumbersome probabilistic-statistical modeling.

Author Biographies

Mykola Kosterev, National technical university of Ukraine “Kyiv polytechnic institute” Peremohy ave., 37, Kyiv, Ukraine, 03056

Doctor of Science, Professor

Department of Central Power Plants 

Volodymyr Litvinov, Zaporizhia State Engineering Academy Soborny ave., 226, Zaporizhia, Ukraine, 69006

PhD, Associate Professor

Department of Hydro Power 

References

  1. Handschin, E., Jurgens, I., Neumann, C. (2008). Long term optimization for risk-oriented asset management. 16th Power Systems Computation Conference, Glasgow.
  2. Manousakis, N. M., Korres, G. N., Georgilakis, P. S. (2011). Optimal placement of phasor measurement units: A literature review. 16th International Conference on Intelligent System Applications to Power Systems. doi: 10.1109/isap.2011.6082183
  3. Soudi, S. (2013). Distribution System Planning With Distributed Generations Considering Benefits and Costs. International Journal of Modern Education and Computer Science, 5 (10), 45–52. doi: 10.5815/ijmecs.2013.09.07
  4. Bae, I. S., Kim, J. O., Kim, J. C., Singh, C. (2004). Optimal Operating Strategy for Distributed Generation Considering Hourly Reliability Worth. IEEE Transactions on Power Systems, 19 (1), 287–292. doi: 10.1109/tpwrs.2003.818738
  5. Gambarov, L. A., Shevchenko, S. V., Chernyshova, N. P. (2009). Optimizatsia proizvodstva, raspredelenia i postavok elektroenergii v usloviah energorynka Ukrainy. Biznesinform, 9, 68–72.
  6. Fathi, B. (2011). Investigation the Interest Rate of Each Energy Source on Optimal Sizing of Distributed Energy Resources. Research Journal of Applied Sciences, Engineering and Technology, 3 (8), 812–817.
  7. Amin, H., Golkar, M. A. (2009). Intelligent power management strategy of hybrid distributed generation system. International Journal of Electrical Power Engineering Systems, 2 (9), 783–795.
  8. Kosterev, M. V., Bardyk, E. I., Litvinov, V. V. (2015). Preventive Risk-Management of Power System for Its Reliability Increasing. WSEAS Transactions on Power Systems, 10, 251–258.
  9. Orlovskiy, S. A. (1981). Problemy priniatia resheniy pri nechetkoy ishodnoy informatsii. Moscow: Nauka, 208.
  10. Nogin, V. D. (2002). Priniatie resheniy v mnogokriterialnoi srede. Moscow: Fizmatlit, 144.
  11. Ketabi, A., Nosratabadi, S. M., Sheibani, M. R. (2012). Optimal PMU Placement with Uncertainty Using Pareto Method. Mathematical Problems in Engineering, 2012, 1–14. doi: 10.1155/2012/501893
  12. Terano, T., Asai, K., Sugeno, M. (1992). Fuzzy systems theory and its application. Tokyo: Academic press, 268.
  13. Saaty, T. L. (1990). Eigenvector and logarithmic least squares. European Journal of Operational Research, 48 (1), 156–160. doi: 10.1016/0377-2217(90)90073-k
  14. Kosterev, M. V., Bardyk, E. I., Litvinov, V. V. (2013). Nechitko-statystychnyi pidhid do otsiniuvannia ekspluatatsiynoi ta regimnoi nadiynosti obyektiv pidsystem elektroenergetychnoi systemy. Naukovi pratsi DonNTU, Elektrotehnika i energetika, 1 (14), 122–128.
  15. Litvinov, V. V. (2012). Otsinka ryzyku porushennia stiikosti dvygunovogo navantazhennia pry vidmovah elektroobladnannia v pidsystemi EES. Kyiv, 20.
  16. Kosterev, M. V., Litvinov, V. V. (2015). Development of an analytical estimation method of the fault risk in the power system. Eastern-European Journal of Enterprise Technologies, 4 (2 (76)), 44–50. doi: 10.15587/1729-4061.2015.47290

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Published

2016-06-21

How to Cite

Kosterev, M., & Litvinov, V. (2016). Development of fuzzy statistical method of optimal resource allocation among technical departments of an electric utility company. Eastern-European Journal of Enterprise Technologies, 3(4(81), 20–27. https://doi.org/10.15587/1729-4061.2016.70522

Issue

Vol. 3 No. 4(81) (2016): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2016 Mykola Kosterev, Volodymyr Litvinov

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