Journals / Journal of Engineering and Technology / 2024 / Volume: 5 Issue: 2

Optimal Reconfiguration of Medium Voltage Distribution Networks: A MINLP Approach with Power Loss Minimization

Pages
—
Publication Date
2024-12-31
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Case studies on IEEE-standard networks validate the effectiveness of the methodology, demonstrating its potential to address the complex challenges of modern power distribution systems

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper presents a comprehensive framework for optimizing medium voltage distribution networks, addressing the challenges of energy loss reduction, voltage stability, and operational cost minimization.
📊
Key Findings & Data:The study combines methodologies from two complementary approaches: one focusing on the optimal reconfiguration of radial distribution networks using Mixed-Integer Nonlinear Programming (MINLP) models implemented in the General Algebraic Modeling System (GAMS), and the other highlighting advanced strategies for distributed generation (DG) integration and reactive power compensation.
💡
Conclusion & Impact:Case studies on IEEE-standard networks validate the effectiveness of the methodology, demonstrating its potential to address the complex challenges of modern power distribution systems
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Medium Voltage · Distribution Networks · MINLP

Abstract

This paper presents a comprehensive framework for optimizing medium voltage distribution networks, addressing the challenges of energy loss reduction, voltage stability, and operational cost minimization. The study combines methodologies from two complementary approaches: one focusing on the optimal reconfiguration of radial distribution networks using Mixed-Integer Nonlinear Programming (MINLP) models implemented in the General Algebraic Modeling System (GAMS), and the other highlighting advanced strategies for distributed generation (DG) integration and reactive power compensation. The proposed MINLP formulation employs branch-to-node incidence, enabling accurate representation of active and reactive power flows as functions of real and imaginary voltage and current components. By merging these approaches, the unified framework not only minimizes total power losses but also enhances voltage profiles and supports sustainable network operations. Case studies on IEEE-standard networks validate the effectiveness of the methodology, demonstrating its potential to address the complex challenges of modern power distribution systems

🔖

Cite This Article

Çelik, M. (2024). Optimal Reconfiguration of Medium Voltage Distribution Networks: A MINLP Approach with Power Loss Minimization. Journal of Engineering and Technology, Volume: 5 Issue: 2. https://ajindex.com/articles/1592167

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (33)

  1. Short, T. A. (2018). Electric power distribution equipment and systems. CRC press.
  2. Northcote-Green, J., & Wilson, R. G. (2017). Control and automation of electrical power distribution systems. CRC press.
  3. Lakervi, E., & Holmes, E. J. (1995). Electricity distribution network design (No. 212). IET.
  4. Wu, Y. K., Lin, J. H., & Lin, H. J. (2017). Standards and guidelines for grid-connected photovoltaic generation systems: A review and comparison. IEEE Tr. on Industry A., 53(4), 3205-3216.
  5. Wu, Y., Wang, H., Zhang, B., & Du, K. L. (2012). Using radial basis function networks for function approximation and classification. International Scholarly Research Notices, 2012(1), 324194.
  6. Gallegos, J., Arévalo, P., Montaleza, C., & Jurado, F. (2024). Sustainable electrification—advances and challenges in electrical-distribution networks: a review. Sustainability, 16(2), 698.
  7. A. Oymak, İ. H. Demirel, M. R. Tur, Conversion of reservoir dams to pumped storage dams: A case study Batman Dam, Journal of Energy Storage, Volume 95, 2024, 112417, ISSN 2352-152X, https://doi.org/10.1016/j.est.2024.112417.
  8. Haider, W., Hassan, S. J. U., Mehdi, A., Hussain, A., Adjayeng, G. O. M., & Kim, C. H. (2021). Voltage profile enhancement and loss minimization using optimal placement and sizing of distributed generation in reconfigured network. Machines, 9(1), 20.
  9. Bale, C. S., Varga, L., & Foxon, T. J. (2015). Energy and complexity: New ways forward. Applied Energy, 138, 150-159.
  10. Muruganantham, B., Gnanadass, R., & Padhy, N. P. (2017). Challenges with renewable energy sources and storage in practical distribution systems. Renewable and Sus. En. Rev., 73, 125-134.
  11. Tur, M. R. (2020). Reliability assessment of distribution power system when considering energy storage configuration technique. IEEE Access, 8, 77962-77971.
  12. Lesh, R. (1981). Applied mathematical problem solving. Educational studies in mathematics, 12(2), 235-264.
  13. Lotfi, H., Hajiabadi, M. E., & Parsadust, H. (2024). Power Distribution Network Reconfiguration Techniques: A Thorough Review. Sustainability, 16(23), 10307.
  14. Liu, Y., Li, J., & Wu, L. (2018). Coordinated optimal network reconfiguration and voltage regulator/DER control for unbalanced distribution sy. IEEE Tran. on Smart Grid, 10(3), 2912-2922.
  15. The, T. T., Quoc, S. N., & Ngoc, D. V. (2020). Symbiotic organism search algorithm for power loss minimization in radial dis. Mathematical Problems in Engineering, 2020(1), 1615792.
  16. Shobole, A., Baysal, M., Wadi, M., & Tur, M. R. (2020). An Adaptive Protection Technique for Smart Distribution Network. Elektronika Ir Elektrotechnika, 26(4), 46–56. https://doi.org/10.5755/j01.eie.26.4.25778
  17. Fadhel, M. A., Duhaim, A. M., Albahri, A. S., Al-Qaysi, Z. T., Aktham, M. A., Chyad, M. A.,. & Gu, Y. (2024). Navigating the metaverse: unraveling the impact of artificial intelligence—a comprehensive review and gap analysis. Artificial Intelligence Review, 57(10), 264.
  18. Shobol, A., Ali, M. H., Wadi, M., & TüR, M. R. (2019, November). Overview of big data in smart grid. In 2019 8th ICRERA (pp. 1022-1025). IEEE.
  19. Lee, J., & Leyffer, S. (Eds.). (2011). Mixed integer nonlinear programming (Vol. 154). Springer Science & Business Media.
  20. Belotti, P., Kirches, C., Leyffer, S., Linderoth, J., Luedtke, J., & Mahajan, A. (2013). Mixed-integer nonlinear optimization. Acta Numerica, 22, 1-131.
  21. Epelle, E. I., & Gerogiorgis, D. I. (2019). Mixed-Integer Nonlinear Programming (MINLP) for production optimisation of naturally flowing and artificial lift wells with routing constraints. Chemical Engineering Research and Design, 152, 134-148.
  22. Tur, M. R. (2022). Investigation of Optimum Reserve Capacity Requirement in Ancillary Services with Extreme Learning Machine. Electric Power Components and Systems, 49(20), 1555–1566. https://doi.org/10.1080/15325008.2022.2134515
  23. Montoya, O. D., Molina-Cabrera, A., & Hernández, J. C. (2021). A comparative study on power flow methods applied to AC distribution networks with single-phase. Electronics, 10(21), 2573.
  24. Tbaileh, A. A. (2017). Robust Non-Matrix Based Power Flow Algorithm for Solving Integrated Transmission and Distribution Systems.
  25. Babu, M. N., & Dhal, P. K. (2024). Impact of load flow and network reconfiguration for unbalanced distribution systems. Measurement: Sensors, 32, 101078.
  26. Pourakbari-Kasmaei, M., & Mantovani, J. R. S. (2018). Logically constrained optimal power flow: Solver-based mixed-integer nonlinear programming model. International Journal of Electrical Power & Energy Systems, 97, 240-249.
  27. Gil-González, W., Garces, A., Montoya, O. D., & Hernández, J. C. (2021). A mixed-integer convex model for the optimal placement and sizing of distributed generators in power distribution networks. Applied Sciences, 11(2), 627.
  28. Andrei, N., & Andrei, N. (2013). Nonlinear optimization applications using the GAMS technology (pp. 178-181). New York: Springer.
  29. Andrei, N. (2017). Continuous nonlinear optimization for engineering applications in GAMS technology (Vol. 121). Cham, Switzerland: Springer.
  30. MirHassani, S. A., & Hooshmand, F. (2019). Methods and models in mathematical programming. Springer International Publishing.
  31. The, T. T., Quoc, S. N., & Ngoc, D. V. (2020). Symbiotic organism search algorithm for power loss minimization in radial dis. Mathematical Problems in Engineering, 2020(1), 1615792.
  32. Shobole, A., Baysal, M., Wadi, M., & Tur, M. R. (2020). An Adaptive Protection Technique for Smart Distribution Network. Elektronika Ir Elektrotechnika, 26(4), 46–56. https://doi.org/10.5755/j01.eie.26.4.25778
  33. Fadhel, M. A., Duhaim, A. M., Albahri, A. S., Al-Qaysi, Z. T., Aktham, M. A., Chyad, M. A.,. & Gu, Y. (2024). Navigating the metaverse: unraveling the impact of artificial intelligence—a comprehensive review and gap analysis. Artificial Intelligence Review, 57(10), 264.