Multi-criteria selection of the mix of generating plants in local energy systems based on a modified analytic hierarchy process

Cover Page

Cite item

Full Text

Abstract

This paper presents a modification of the analytic hierarchy process in order to increase its efficiency for a multi-criteria comparison of mixes of generating plants in local energy areas during their development. The multi-criteria problem of selecting the most effective ratio of rated capacities is considered for power plants representing a single mix of generating plants during the development of a local energy area in the Khabarovsk Krai. The energy sources are represented by thermal, solar, wind and diesel power plants. The following estimation criteria for alternative solutions were accepted: levelized cost of electricity, ecological effeciency estimation; estimation of public opinion about the consequences involved with the establishment of power plants. In order to solve the multi-criteria problem, the analytic hierarchy process (AHP) was used. When using the original AHP for the set problem, a large quantity of alternatives at the stage of pairwise comparisons were found to represent a perceptible load on a decision maker. Thus, already during the estimation of 10 alternatives according to 5 criteria, decision makers should conduct 225 pairwise comparisons, which may eventually result in an unacceptable consistency of the results. In addition, this requires a procedure accounting for the uncertainty of the decision maker's preferences. The proposed solution represents a method of forming matrices of pairwise comparisons upon criteria. This method consists in generating an interval or fuzzy model of the decision maker's preferences for evaluating pairs of estimates according to the criterion. The proposed method was verified using a numerical example of solving the set problem. The obtained optimum mix of power plants consists of thermal, solar and diesel plants with a power of 30, 35 and 39 mW, respectively. The proposed method ensures a high consistency of the results obtained during alternative pairwise comparisons. In addition, the modified analytic hierarchy process takes into account the non-linear nature of the decision maker's preferences for estimating alternatives according to criteria.

About the authors

A. S. Nefedov

Bratsk State University

Email: domino1991@rambler.ru
ORCID iD: 0000-0001-5507-2798

V. A. Shakirov

Melentiev Energy Systems Institute SB RAS

Email: mynovember@mail.ru
ORCID iD: 0000-0001-8629-9549

S. M. Ignatieva

Bratsk State University

Email: Smignateva@yandex.ru
ORCID iD: 0000-0001-5179-1680

References

  1. Саати Т. Л., Керис К. П. Аналитическое планирование. Организация систем / пер. с англ. И. А. Ушакова. М.: Изд-во «Радио и связь», 1991. 244 с.
  2. Саати Т. Принятие решений. Метод анализа иерархий / пер. с англ. Р. Г. Вачнадзе. М.: Изд-во «Радио и связь», 1993. 278 с.
  3. Marinakis V., Doukas H., Xidonas P., Zopounidis C. Multicriteria decision support in local energy planning: an evaluation of alternative scenarios for the sustainable energy action plan // Omega. 2017. Vol. 69. Р. 1–16. https://doi.org/10.1016/j.omega.2016.07.005.
  4. Oree V., Sayed Hassen S. Z., Fleming P. J. Generation expansion planning optimisation with renewable energy integration: a review // Renewable and Sustainable Energy Reviews. 2017. Vol. 69. Р. 790–803. https://doi.org/10.1016/j.rser.2016.11.120.
  5. Malkawi S., Al-Nimr M., Azizi D. A multi-criteria optimization analysis for Jordan's energy mix // Energy. 2017. Vol. 127. Р. 680–696. https://doi.org/10.1016/j.energy.2017.04.015.
  6. Lombardi P., Sokolnikova T., Suslov K., Voropai N., Styczynski Z. A. Isolated power system in Russia: a chance for renewable energies? // Renewable Energy. 2016. Vol. 90. Р. 532–541. https://doi.org/10.1016/j.renene.2016.01.016.
  7. Ossadnik W., Kaspar R. Evaluation of AHP software from a management accounting perspective // Journal of Modelling in Management. 2013. Vol. 8. Iss. 3. P. 305–319. https://doi.org/10.1108/JM2-01-2011-0007.
  8. Сологубова Л. А., Кулаков А. А., Байбекова Ф. Н., Трунькина О. В., Беспалова Н. М. Программная реализация принятия решений с помощью метода анализа иерархий // iPolytech Journal. 2018. Т. 22. № 11. С. 145–158. https://doi.org/10.21285/1814-3520-2018-11-145-158.
  9. Ishizaka A., Labib A. Analytic hierarchy process and expert choice: benefits and limitations // OR Insight. 2009. Vol. 22. Iss. 4. P. 201–220. https://doi.org/10.1057/ori.2009.10.
  10. Подиновский В. В., Подиновская О. В. Ещѐ раз о некорректности метода анализа иерархий // Проблемы управления. 2012. № 4. С. 75–78.
  11. Dede G., Kamalakis T., Sphicopoulos T. Theoretical estimation of the probability of weight rank reversal in pairwise comparisons // European Journal of Operational Research. 2016. Vol. 252. Iss. 2. P. 587–600. https://doi.org/10.1016/j.ejor.2016.01.059.
  12. Majumdar A., Tiwari M. K., Agarwal A., Prajapat K. A new case of rank reversal in analytic hierarchy process due to aggregation of cost and benefit criteria // Operations Research Perspectives. 2021. Vol. 8. P. 100185. https://doi.org/10.1016/j.orp.2021.100185.
  13. Noghin V. D. A simplified variant of the analytic hierarchy process based on a nonlinear scalarizing function // Computational Mathematics and Mathematical Physics. 2004. Vol. 44. Iss. 7. Р. 1194–1202.
  14. Alam K. A., Ahmed R., Butt F. S., Kim Soon-Gohn, Ko Kwang-Man. An uncertainty-aware integrated fuzzy AHPWASPAS model to evaluate public cloud computing services // Procedia Computer Science. 2018. Vol. 130. P. 504–509. https://doi.org/10.1016/j.procs.2018.04.068.
  15. Zhu Guo-Niu, Hu Jie, Ren Hongliang. A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments // Applied Soft Computing. 2020. Vol. 91. Р. 106228. https://doi.org/10.1016/j.asoc.2020.106228.
  16. Dogan O. Process mining technology selection with spherical fuzzy AHP and sensitivity analysis // Expert Systems with Applications. 2021. Vol. 178. Р. 114999. https://doi.org/10.1016/j.eswa.2021.114999.
  17. Нефедов А. С., Шакиров В. А. Автоматизация процедуры заполнения матриц парных сравнений альтернатив по критериям при использовании метода анализа иерархий // Информационные технологии. 2019. Т. 25. № 6. С. 331–339. https://doi.org/10.17587/it.25.331-339.
  18. Khan I. Power generation expansion plan and sustainability in a developing country: a multi-criteria decision analysis // Journal of Cleaner Production. 2019. Vol. 220. P. 707–720. https://doi.org/10.1016/j.jclepro.2019.02.161.
  19. Ullah Z., Elkadeem M. R., Kotb K. M., Taha I. B. M., Wang Shaorong. Multi-criteria decision-making model for optimal planning of on/off grid hybrid solar, wind, hydro, biomass clean electricity supply // Renewable Energy. 2021. Vol. 179. P. 885–910. https://doi.org/10.1016/j.renene.2021.07.063.
  20. Нефедов А. С., Яковкина Т. Н. Многокритериальный выбор мощности электростанций на основе возобновляемых источников энергии и местных видов топлива в составе локальной энергосистемы // iPolytech Journal. 2020. Т. 24. № 6. С. 1255–1270. https://doi.org/10.21285/1814-3520-2020-6-1255-1270.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).