A two-stage stochastic programming model for the electricity distribution network design problem

Yıl 2025, Cilt: 40 Sayı: 2, 1027 - 1038

Tuğba Özdamar , Mehmet Alegöz

https://doi.org/10.17341/gazimmfd.1366691

Öz

The network that ensures the delivery of electricity to end customers, and consists of actors such as transformer centers, distribution centers, distribution transformers and field distribution boxes is called electricity distribution network. Effective design of electricity distribution networks plays an important role in terms of ensuring the continuous supply of electricity and decreasing the costs of electricity distribution companies. Motivated by this fact, in this study, we focus on an electricity distribution network consisting of transformer centers, distribution centers, distribution transformers and field distribution boxes, and propose a two-stage stochastic programing model for the location, cable and flow decisions under demand uncertainty. The proposed model is tested on a real-life case study regarding Eskişehir, which on one hand shows the applicability of the proposed model on real-life instances, and on the other hand brings important managerial insights. As an example, computational results reveal that ignoring the uncertainties in the electricity distribution networks may bring substantial additional costs.

Anahtar Kelimeler

Supply chain network design, Electricity distribution network, Stochastic programming

Elektrik dağıtım ağı tasarımı problemi için iki aşamalı bir stokastik programlama modeli

Öz

Elektrik enerjisinin son kullanıcılara ulaştırılmasını sağlayan ve trafo merkezleri, dağıtım merkezleri, dağıtım transformatörleri ve saha dağıtım kutuları gibi elemanlardan oluşan ağa elektrik dağıtım ağı adı verilmektedir. Elektrik dağıtım ağlarının etkin bir şekilde tasarlanması, elektrik enerjisi arzının kesintisiz devam etmesi ve elektrik dağıtım firmalarının maliyetlerinin azaltılması açısından büyük öneme sahiptir. Bu motivasyonla, bu çalışmada, trafo merkezleri, dağıtım merkezleri, dağıtım transformatörleri ve saha dağıtım kutularından oluşan bir elektrik dağıtım ağına odaklanılmış, talep belirsizliği altında lokasyon, kablo ve akış kararları için iki aşamalı bir stokastik programlama modeli öne sürülmüştür. Öne sürülen model Eskişehir iline yönelik bir gerçek hayat uygulaması üzerinde test edilmiş, bir yandan modelin gerçek hayat problemlerinde uygulanabilirliği kanıtlanırken, bir yandan da önemli yönetimsel çıkarımlar elde edilmiştir. Örnek olarak, sayısal sonuçlar, elektrik dağıtım ağlarında belirsizliği göz ardı etmenin ciddi ek maliyetleri beraberinde getirebileceğini göstermiştir.

Anahtar Kelimeler

Tedarik zinciri ağı tasarımı, Elektrik dağıtım ağı, Stokastik programlama

Kaynakça

• 1. Ganguly, S., Sahoo, N. C., Das, D., Mono-and multi-objective planning of electrical distribution networks using particle swarm optimization, Applied Soft Computing, 11 (2), 2391-2405, 2011.
• 2. Rafique, R., Mun, K. G., Zhao, Y., Designing energy supply chains: Dynamic models for energy security and economic prosperity, Production and Operations Management, 26 (6), 1120-1141, 2017.
• 3. Murele, O. C., Zulkafli, N. I., Kopanos, G., Hart, P., Hanak, D. P., Integrating biomass into energy supply chain networks, Journal of Cleaner Production, 248, 119246, 2020.
• 4. Zhong, H., Zhang, G., Tan, Z., Ruan, G., Wang, X., Hierarchical collaborative expansion planning for transmission and distribution networks considering transmission cost allocation, Applied Energy, 307, 118147, 2022.
• 5. Rafique, R., Jat, M., Chudhery, M. A. Z., Bioenergy supply chain optimization for addressing energy deficiency: A dynamic model for large-scale network designs, Journal of Cleaner Production, 318, 128495, 2021.
• 6. Santoso, T., Ahmed, S., Goetschalckx, M., Shapiro, A., A stochastic programming approach for supply chain network design under uncertainty, European Journal of Operational Research, 167 (1), 96-115, 2005.
• 7. Bidhandi, H. M., Yusuff, R. M., Ahmad, M. M. H. M., Bakar, M. R. A., Development of a new approach for deterministic supply chain network design, European Journal of Operational Research, 198 (1), 121-128, 2009.
• 8. Alumur, S. A., Nickel, S., Saldanha-da-Gama, F., Verter, V., Multi-period reverse logistics network design. European Journal of Operational Research, 220 (1), 67-78, 2012.
• 9. Correia, I., Melo, T., Saldanha-da-Gama, F., Comparing classical performance measures for a multi-period, two-echelon supply chain network design problem with sizing decisions, Computers & Industrial Engineering, 64 (1), 366-380, 2013.
• 10. Yildiz, H., Yoon, J., Talluri, S., Ho, W., Reliable supply chain network design, Decision Sciences, 47 (4), 661-698, 2016.
• 11. Savadkoohi, E., Mousazadeh, M., Torabi, S. A., A possibilistic location-inventory model for multi-period perishable pharmaceutical supply chain network design, Chemical Engineering Research and Design, 138, 490-505, 2018.
• 12. Wang, J., Wang, X., Yu, M., Multi-period multi-product supply chain network design in the competitive environment. Mathematical Problems in Engineering, 2020, 1-15, 2020.
• 13. Govindan, K., Mina, H., Esmaeili, A., Gholami-Zanjani, S. M., An integrated hybrid approach for circular supplier selection and closed loop supply chain network design under uncertainty, Journal of Cleaner Production, 242, 118317, 2020.
• 14. Hasani, A., Mokhtari, H., Fattahi, M., A multi-objective optimization approach for green and resilient supply chain network design: a real-life case study, Journal of Cleaner Production, 278, 123199, 2021.
• 15. Kazancoglu, Y., Yuksel, D., Sezer, M. D., Mangla, S. K., Hua, L., A green dual-channel closed-loop supply chain network design model, Journal of Cleaner Production, 332, 130062, 2022.
• 16. Alegoz, M., Kaya, O., Bayindir, Z. P., Closing the loop in supply chains: Economic and environmental effects, Computers & Industrial Engineering, 142, 106366, 2020.
• 17. Abbasi, S., Daneshmand-Mehr, M., Ghane Kanafi, A., Green closed-loop supply chain network design during the coronavirus (COVID-19) pandemic: A case study in the Iranian automotive industry, Environmental Modeling & Assessment, 28 (1), 69-103, 2023.
• 18. Kumar, A., Kumar, K., An uncertain sustainable supply chain network design for regulating greenhouse gas emission and supply chain cost, Cleaner Logistics and Supply Chain, 10, 100142, 2024.
• 19. Tirkolaee, E. B., Golpîra, H., Javanmardan, A., Maihami, R., A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study, Socio-Economic Planning Sciences, 85, 101439, 2023.
• 20. Hu, H., Guo, S., Zhen, L., Wang, S., Bian, Y., A multi-product and multi-period supply chain network design problem with price-sensitive demand and incremental quantity discount, Expert Systems with Applications, 238, 122005, 2024.
• 21. Farzan, N., Mahmoodirad, A., Niroomand, S., Molla-Alizadeh-Zavardehi, S., A sustainable uncertain integrated supply chain network design and assembly line balancing problem with U-shaped assembly lines and multi-mode demand, Soft Computing, 28 (4), 2967-2986, 2024.
• 22. Dündar A.O., Tekin M., Peker K., Şahman M.A, Karaoğlan İ., A mathematical model for multi-period multi-stage multi-mode multi-product capacitated wheat supply network design problem and a case study, Journal of the Faculty of Engineering and Architecture of Gazi University, 37 (1), 265-282, 2022.
• 23. Hamzaçebi C., Kutay F., Electric consumption forecasting of Turkey using artificial neural networks up to year 2010, Journal of the Faculty of Engineering and Architecture of Gazi University, 19 (3), 227-233, 2004.
• 24. Bilici Z., Özdemir D., Comparative analysis of metaheuristic optimization algorithms for natural gas demand forecast with meteorological parameters, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (2), 1153-1167, 2023.
• 25. Balıkçı V., Gemici Z., Taner T., Dalkılıç A.S., Forecasting natural gas demand in Istanbul by artificial neural networks method and planning of city gate stations, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (2), 1017-1027, 2024.