Algorithm of multiobjective operational control of microgrids

Authors

  • Владимир Андреевич Попов National Technical University of Ukraine «Kyiv Polytechnic Institute» Peremogy Avenue, 37, Kyiv, Ukraine, 03056, Ukraine https://orcid.org/0000-0003-3484-4597
  • Елена Сергеевна Ярмолюк National Technical University of Ukraine «Kyiv Polytechnic Institute» Peremogy Avenue, 37, Kyiv, Ukraine, 03056, Ukraine https://orcid.org/0000-0001-8571-2573
  • Петр Александрович Замковой National Technical University of Ukraine «Kyiv Polytechnic Institute» Peremogy Avenue, 37, Kyiv, Ukraine, 03056, Ukraine https://orcid.org/0000-0003-4600-8596

DOI:

https://doi.org/10.15587/1729-4061.2014.23158

Keywords:

microgrids, resource allocation, distributed generation, multicriteria decision-making, Bellman-Zadeh approach

Abstract

One of important energy development directions, as evidenced by the global practice, is the intellectualization of generation, transmission and distribution of energy, formation of the so-called microgrids. In this regard, the results of the research, related to developing the method for prompt operational control of the sources of distributed generation of autonomous microgrid are given in the paper. Solving such problems inUkraineis complicated by limited availability of material resources for the fundamental technical modernization of the industry, the lack of adequate information environment, the imperfection of legal and regulatory bases. These circumstances make the issues of forming adequate mathematical models, used to solve the problems of determining the optimal structure of microgrids and their operational control especially relevant. Given this, the necessity of comprehensive consideration of the uncertainty factor, including considering the uncertainty of source information, objectives and conditions, when analyzing the specified problem is substantiated in the paper. For this purpose, a series of objective functions, reflecting the economic, technical and environmental aspects of the microgrid operation, which were originally presented in a linguistic form that allows to take into account both the information uncertainty, and multicriterion nature of the problem is formed. Capacity allocation among individual sources is implemented based on a modified non-local search algorithm.

Thus, accounting multicriteriality is performed using the Bellman-Zadeh approach, which allows to obtain a solution, belonging to the region of compromise, where the optimality principle lies in the maximum satisfaction of all purposes. The proposed algorithm allows to flexibly and effectively consider both quantitative and qualitative characteristics, represented, in particular, by linguistic assessments, differentiate the importance degree of individual criteria, which allow to ensure the maximum adequacy and validity of the obtained solutions and, as a consequence, guarantee the actual efficiency of using generating equipment, installed in the microgrid

Author Biographies

Владимир Андреевич Попов, National Technical University of Ukraine «Kyiv Polytechnic Institute» Peremogy Avenue, 37, Kyiv, Ukraine, 03056

Candidate of technical science, Associate Professor

Department of electricity supply

Елена Сергеевна Ярмолюк, National Technical University of Ukraine «Kyiv Polytechnic Institute» Peremogy Avenue, 37, Kyiv, Ukraine, 03056

Assistant

Department of electricity supply

Петр Александрович Замковой, National Technical University of Ukraine «Kyiv Polytechnic Institute» Peremogy Avenue, 37, Kyiv, Ukraine, 03056

Undergraduates

Department of electricity supply

References

  1. Стогній, Б. С. Еволюція інтелектуальних електричних мереж та їхні перспективи в Україні [Текст] / Б. С. Стогній, О. В. Кириленко, А. В. Праховник, С. П. Денисюк // Технічна електродинаміка / Наук.-прикл. журнал. – 2012. – № 5. – С. 52–67.
  2. Hatziargyrion, N. Microgrids: An overview of ongoing research, development and demonstration projects [Text] / N. Hatziargyrion, H. Asano, R. Iravani, Ch. Marnay // IEEE power and energy magazine. – 2007. – Р. 78–94.
  3. Кобец, В. В. Инновационное развитие электроэнергетики на базе концепции Smart Grid [Текст] / В. В. Кобец, И. О. Волкова. – М. : ИАЦ Энергия, 2010. – 208 с.
  4. Qiao, L. A summary of optimal methods for the planning of state alone Microgrid System [Text] / L. Qiao // Energy and Power Engineering. – 2013. – № 5. – Р. 992–998.
  5. Hu, M. Operating Strategies and Management for Smart Microgrid Systems [Text] / M. Hu, Y. Chen, Y. Chang // Journal of Energy Engineering. – 2014. – Vol. 140, Іssue 1. – P. 356–364.
  6. Дилигенский, Н. В. Нечеткое моделирование и многокритериальная оптимизация производственных систем в условиях неопределенности: технология, экономика, экология [Текст] / Н. В. Дилигенский, Л. Г. Дымова, П. В. Севастьянов. – М. : Машиностроение, 2004. – 397 с.
  7. Asai, K. Applied Fuzzy Systems [Text] / К. Asai, М. Sugeno, Т. Terano. – New York : Academic Press, 1994. – 302 p.
  8. Hanss, M. Applied fuzzy arithmetic [Text] / M. Hanss // An introduction with engineering applications. – Berlin : Springer-Verlag, 2005. – 270 p.
  9. Жолен, Л. Прикладной интервальный анализ [Текст] / Л. Жолен, М. Кифер, О. Дидри, Э. Вальтер. – М., Ижевск : Институт компьютерных исследований, 2005. – 468 с.
  10. Дубов, Ю. А. Многокритериальные модели формирования и выбора вариантов систем [Teкст] / Ю. A. Дубов, С. И. Травкин, В. Н. Якимец. – М. : Наука, 1986. – 296 с.
  11. Hwang, C. L. Multiple Objective Decision Making: Methods and Applications [Text] / C. L. Hwang, A. S. Masud. – Berlin : Springer-Verlag, 1979. – Р. 366–375.
  12. Ehrgott, M. Multicriteria optimization [Text] / М. Ehrgott. – Berlin : Springer-Verlag, 2005. – 323 p.
  13. Ногин, В. Д. Принятие решений в многокритериальной среде: количественный подход [Текст] / В. Д. Ногин. – М. : Физматлит, 2004. – 176 с.
  14. Popov, V. A. Fuzzy logic in real time state estimation of distribution systems [Text] / V. A. Popov, P. Ya. Ekel, M. Fuchs // Methodologies for the Conception, Design, and Application of Intelligent Systems. – 1998. – Vol. 2. – Р. 136–139.
  15. Попов, В. А. Принципы учета неопределенности исходной информации при моделировании нагрузок в распределительных сетях [Текст] / В. А. Попов, Е. С. Ярмолюк, С. Банузаде Сахрагард, А. А. Журавлев // Енергетика: економіка, технології, екологія / Наук. журнал. – 2011. – № 1. – С. 61–66.
  16. Попов, В. А. Евристичний алгоритм моделювання режимів інтегрованих систем електропостачання з урахуванням невизначеності вихідної інформації [Текст] : зб. наук. пр. / В. А. Попов, О. С. Ярмолюк // Праці Інституту електродинаміки Національної академії наук України. Спецвипуск. – 2012. – С. 40–46.
  17. Pedrycz, W. Fuzzy Multicriteria Decision-Making: Models, Methods, and Applications [Text] / W. Pedrycz, P. Ekel, R. Parreiras. – New York : John Wiley & Sons, 2011. – 338 р.
  18. Bellman, R. E. Decision-making in a fuzzy environment [Text] / R. E. Bellman, L. A. Zadeh // Management Science. – 1970. – № 17. – P. 141–164.
  19. Zimmermann, H. J. Fussy set theory and its application [Text] / H. J. Zimmermann. – Boston : Kluwer Academic, 1990. – 400 p.
  20. Beliakov, G. Appropriate choise of aggregation operators in fuzzy decision support systems [Text] / G. Beliakov, J. Warren // IEEE Transactions on Fuzzy Systems. – 2001. – № 9. – P. 773–784.
  21. Bellman, R. On the analytic formalism of the theory of fuzzy sets [Text] / R. Bellman, V. Giertz // Information Science. – 1974. – № 5. – Р. 149–157.
  22. Жаркин, А. Ф. Функциональное эквивалентирование электрических сетей при оценке влияния источников распределенной генерации на их режимы [Текст] / А. Ф. Жаркин, В. А. Попов, В. В. Ткаченко, С. Банузаде Сахрагард // Электронное моделирование / Наук.-теор. журнал. – 2013. – Т. 35, № 3. – C. 99–111.
  23. Раскин, Л. Г. Анализ сложных систем и элементы теории оптимального управления [Текст] / Л. Г. Раскин. – М. : Советское радио, 1976. – 344 с.
  24. Гельфанд, І. М. О некоторых способах управления сложными системами [Текст] / І. М. Гельфанл, М. Л. Цетлин // Успехи математических наук. – 1962. – Т. 17, № 1 (103). – С. 3–25.
  25. Ekel, P. Ya. Fuzzy set based multiobjective allocation of resources and its application [Text] / P. Ya. Ekel, C. A. P. S. Martins, J. G. Pereira Jr. // Computers and Mathematics with Applications. – 2006. – № 52. – Р. 197–210
  26. Stohhnii, B. S., Kyrylenko, O. V., Prahovnyk, A. V., Denysiuk, S. P. (2012). Evolution of the smart electricity networks and their prospects in Ukraine. Technical Electrodynamics, Кyiv, IED NASU, 5, 52–67.
  27. Hatziargyrion, N. Asano, H., Iravani, R., Marnay, Ch. (2007). Microgrids: An overview of ongoing research, development and demonstration projects. IEEE power and energy magazine, 78–94.
  28. Коbеts, V. V., Volkova, I. O. (2010). Innovacionnoe razvitie jelektrojenergetiki na baze koncepcii Smart Grid. Мoskow, IATS Enеrgiia, 208.
  29. Qiao, L. (2013). A summary of optimal methods for the planning of state alone Microgrid System. Energy and Power Engineering, 5, 992–998.
  30. Hu, M., Chen, Y., Chang, Y. (2014). Operating Strategies and Management for Smart Microgrid Systems. Journal of Energy Engineering, 140, 1, 356–364.
  31. Dilihenskii, N. V., Dymova, L. H., Sevastianov, P. V. (2004). Fuzzy modeling and multi-criteria optimization of production systems under uncertainty: technology, economy, ecology. – Мoskow, Маshynоstroeniie, 397.
  32. Asai, K., Sugeno, М., Terano, Т. (1994). Applied Fuzzy Systems. New York, Academic Press, 302.
  33. Hanss, M. (2005). Applied fuzzy arithmetic. An introduction with engineering applications, Berlin, Springer-Verlag, 270.
  34. Zholen, L., Kifer, М., Didri, О., Valter, E. (2005). Applied interval analysis. Мoskow, Izhevsk, Institute of Computer Science, 468.
  35. Dubov, Yu. A., Travkin, C. I., Yakimetc, V. N. (1986). Multicriteria Models for Forming and Choosing System Alternatives. Moscow, Nauka, 296.
  36. Hwang, C. L., Masud, A. S. (1979). Multiple Objective Decision Making: Methods and Applications. Berlin, Springer-Verlag, 366–379.
  37. Ehrgott, M. (2005). Multicriteria optimization. Berlin, Springer-Verlag, 323.
  38. Nohin, V. D. (2004). Multicriteria decision making environment: a quantitative approach. Moscow, Fizmalit, 176.
  39. Popov, V. A., Ekel, P. Ya., Fuchs, M. (1998). Fuzzy logic in real time state estimation of distribution systems. Methodologies for the Conception, Design, and Application of Intelligent Systems, Singapore, New Jersey, London, World Scientific, 2, 136–139.
  40. Popov, V. А., Yarmoliuk, O. S., Bаnuzade Sаkhrаgаrd, S., Zhurрvlov, А. O. (2011). Principles of the consideration of the initial information uncertanly for loads modelling in distribution networks. Еnеrhеtykа: еkоnоmіkа, tеkhnоlоhіi, еkоlоhіia, Kyiv, NTUU «KPI», 1, 61–66.
  41. Popov, V. А., Yarmoliuk, O. S. (2012). Heuristic algorithm for modeling the integrated distribution system modes of operation considering initial information uncertainty. Works IED NASU, Кyiv, IED NASU, Special Issue, 40–46.
  42. Pedrycz, W., Ekel, P., Parreiras, R. (2011). Fuzzy Multicriteria Decision-Making: Models, Methods, and Applications. New York, John Wiley & Sons, 338.
  43. Bellman, R. E., Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17, 141–164.
  44. Zimmermann, H. J. (1990). Fussy set theory and its application. Boston, Kluwer Academic, 400.
  45. Beliakov, G., Warren, J. (2001). Appropriate choise of aggregation operato rs in fuzzy decision support systems. IEEE Transactions on Fuzzy Systems, 9, 773–784.
  46. Bellman, R., Giertz, V. (1974). On the analytic formalism of the theory of fuzzy sets. Information Science, 5, 149–157.
  47. Zharkin, A. F., Popov, V. А., Tkachenko, V. V., Bаnuzade Sаkhrаgаrd, S. (2013). Functional equivalenting electrical networks in assessing the impact of distributed generation sources in their modes. Electronic modeling, Кyiv, Institute of problem modelling in energy GE Pukhov University NAS of Ukraine, 35, 99–111.
  48. Rаskin, L. G. (1976). Analysis of complex systems and elements of the theory of optimal control. Мoskow, Soviet radio, 344.
  49. Gelfand, І. М., Tsetlin, M. L. (1962). Some methods of control for complex systems. Advances of Mathematical Sciences, 17 (103), 3–25.
  50. Ekel, P. Ya., Martins, C. A. P. S., Pereira, Jr. J. G. (2006). Fuzzy set based multiobjective allocation of resources and its application. Computers and Mathematics with Applications, 52, 197–210.

Published

2014-04-14

How to Cite

Попов, В. А., Ярмолюк, Е. С., & Замковой, П. А. (2014). Algorithm of multiobjective operational control of microgrids. Eastern-European Journal of Enterprise Technologies, 2(2(68), 61–68. https://doi.org/10.15587/1729-4061.2014.23158