Prioritizing power outages causes in different scenarios of the global business network matrix by using BWM and TOPSIS

Authors

DOI:

https://doi.org/10.31181/dmame0301072022m

Keywords:

Power distribution networks, Power outage, Scenario planning, TOPSIS, BWM

Abstract

Power outage is one of the significant problems for electricity distribution companies. Power outages cause customer dissatisfaction and reduce distribution companies' profits and revenues. Therefore, the electricity distribution companies are trying to moderate the leading causes of the outage. However, the dynamics of environmental conditions create uncertainties that require prioritizing the solutions of outages causes in different situations. Therefore, this study presents a scenario-based approach to prioritize power outage causes. Four case studies have been conducted in four cities of Kerman province in Iran. First, the prioritization criteria and causes of the outage were identified using literature and interviews with experts in this field. Then, the Global Business Network matrix was used to create four possible scenarios. Then, the Best-Worst method and TOPSIS method were applied to weight the prioritizing criteria and prioritize the causes of the outages in different scenarios. The results showed that working in the power network limit zone, as one of the causes of outage in Sirjan and Jiroft cities, has the most priority. Also, the collision of external objects, birds, and annoying trees should be considered by managers as the leading causes of outages in Bam and Kahnuj cities.

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References

Al-Shaalan, A. M. (2017). Investigating Practical Measures to Reduce Power Outages and Energy Curtailments. Journal of Power and Energy Engineering, 05(11), 21–36.

Azimifard, A., Moosavirad, S. H., & Ariafar, S. (2018). Selecting sustainable supplier countries for Iran’s steel industry at three levels by using AHP and TOPSIS methods. Resources Policy, 57(June 2017), 30–44.

Carlsson, F., Kataria, M., Lampi, E., & Martinsson, P. (2021). Past and present outage costs – A follow-up study of households’ willingness to pay to avoid power outages. Resource and Energy Economics, 64, 101216.

Castillo, A. (2014). Risk analysis and management in power outage and restoration: A literature survey. Electric Power Systems Research, 107, 9–15.

Cerrai, D., Koukoula, M., Watson, P., & Anagnostou, E. N. (2020). Outage prediction models for snow and ice storms. Sustainable Energy, Grids and Networks, 21, 100294.

Dongli, J., Yinglong, D., Cunping, W., & Renle, H. (2018). Research on fault risk ranking and screening of distribution network based on uncertainty theory. China International Conference on Electricity Distribution, CICED, 1561–1565.

Ghasemian Fard, E., & Mousavirad, S. H. (2017). Undelivered Electricity in North-Kerman Electricity Distribution Company: System Dynamics Analysis. Journal of Energy Planning and Policy Research, 3(8 #F00305), 119–145.

Gjorgiev, B., & Sansavini, G. (2022). Identifying and assessing power system vulnerabilities to transmission asset outages via cascading failure analysis. Reliability Engineering & System Safety, 217, 108085.

Hafezi, M., & Eslami, M. (2016). Self-repair in intelligent power distribution networks to reduce customer’s outage time. 2nd International Conference on New Research Findings in Electrical Engineering and Computer Science.

Hajduk, S., & Jelonek, D. (2021). A Decision-Making Approach Based on TOPSIS Method for Ranking Smart Cities in the Context of Urban Energy. Energies 2021, Vol. 14, Page 2691, 14(9), 2691.

He, J., & Cheng, M. X. (2021). Machine learning methods for power line outage identification. The Electricity Journal, 34(1), 106885.

Hosein Abadi, M., Allahdad, M., & Keyvanpour, H. (2016). Automation of Distribution Networks with Optimal locating of Remote Control Power Switches by AHP - Case Study. International Conference on Electrical Engineering.

Iešmantas, T., & Alzbutas, R. (2014). Bayesian assessment of electrical power transmission grid outage risk. International Journal of Electrical Power and Energy Systems, 58, 85–90.

Jha, D. K., Sinha, S. K., Garg, A., & Vijay, A. (2012). Estimating electricity supply outage cost for residential and commercial customers. 2012 North American Power Symposium (NAPS), 1–6.

Lakrevi, E., & Holmes, E. J. (2014). Electricity Distribution Network Design Translated by Jamali and Shakeri. Iran University of Science and Technology.

Landegren, F. E., Johansson, J., & Samuelsson, O. (2016). A Method for Assessing Margin and Sensitivity of Electricity Networks with Respect to Repair System Resources. IEEE Transactions on Smart Grid, 7(6), 2880–2889.

Nalini Ramakrishna, S. K., Koziel, S., Karlsson, D., & Hilber, P. (2021). Component ranking and importance indices in the distribution system. 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings.

Rahimkhani, M., & Jafartabar, A. (2012). Study of effective factors on the rate of outage in the distribution network of Khuzestan province and their prioritization using the AHP method. 17th Conference on Power Distribution Networks.

Rahman, S., Pipattanasomporn, M., & Teklu, Y. (2007). Intelligent Distributed Autonomous Power Systems (IDAPS). In 2007 IEEE Power Engineering Society General Meeting, PES.

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.

Schwartz, P. (1991). Art of the long view: Doubleday Currency.

Sepúlveda Mora, S. B., & Hegedus, S. (2021). Design of a Resilient and Eco-friendly Microgrid for a Commercial Building. Aibi Revista de Investigación, Administración e Ingeniería, 9(1), 8–18.

Shield, S. A., Quiring, S. M., Pino, J. V., & Buckstaff, K. (2021). Major impacts of weather events on the electrical power delivery system in the United States. Energy, 218, 119434.

Tavakoli, M., & Nafar, M. (2021). Estimating and ranking the impact of human error roots on power grid maintenance group based on a combination of mathematical expectation, Shannon entropy, and TOPSIS. Quality and Reliability Engineering International, 37(8), 3673–3692.

Wethal, U. (2020). Practices, provision and protest: Power outages in rural Norwegian households. Energy Research & Social Science, 62(November 2019), 101388.

Yari, A., Shakarami, M., & Beygi, M. C. H. (2017). Using an efficient method to locate remote control switches in air distribution networks and performing simulations on a real network in Tehran. 22nd Electrical Power Distribution Conference.

Yuan, S., Quiring, S. M., Zhu, L., Huang, Y., & Wang, J. (2020). Development of a Typhoon Power Outage Model in Guangdong, China. International Journal of Electrical Power and Energy Systems, 117, 105711.

Zavadskas, E. K., Mardani, A., Turskis, Z., Jusoh, A., & Nor, K. M. (2016). Development of TOPSIS Method to Solve Complicated Decision-Making Problems - An Overview on Developments from 2000 to 2015. International Journal of Information Technology and Decision Making, 15(3), 645–682.

Zhai, C., Chen, T. Y. jeh, White, A. G., & Guikema, S. D. (2021). Power outage prediction for natural hazards using synthetic power distribution systems. Reliability Engineering and System Safety, 208, 107348.

Published

2023-04-08

How to Cite

Shahi Moridi, S., Moosavirad, S. H., Mirhosseini, M., Nikpour, H., & Mokhtari, A. (2023). Prioritizing power outages causes in different scenarios of the global business network matrix by using BWM and TOPSIS. Decision Making: Applications in Management and Engineering, 6(1), 321–340. https://doi.org/10.31181/dmame0301072022m