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Assessment of Transmission Reliability Margin: Existing Methods and Challenges and Future Prospects

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  • Uchenna Emmanuel Edeh

    (Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand)

  • Tek Tjing Lie

    (Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand)

  • Md Apel Mahmud

    (College of Science and Engineering, Flinders University, Adelaide SA 5001, Australia)

Abstract

The integration of renewable energy sources (RESs), such as wind and solar, introduces significant uncertainties into power system operations, complicating Available Transfer Capability (ATC) assessment. A key factor in ATC determination, the Transmission Reliability Margin (TRM), accounts for uncertainties like load variations, generation fluctuations, and network dynamics. The traditional deterministic TRM methods often fail to capture the stochastic nature of modern grids, leading to inaccurate estimations. This paper reviews the TRM assessment methodologies, emphasizing probabilistic approaches that enhance accuracy in high-RES environments. It explores adaptive statistical techniques, such as rolling window analysis, for dynamic TRM computation. Key challenges, emerging trends, and potential solutions are discussed to support the development of robust ATC modeling frameworks for secure and efficient renewable energy integration.

Suggested Citation

  • Uchenna Emmanuel Edeh & Tek Tjing Lie & Md Apel Mahmud, 2025. "Assessment of Transmission Reliability Margin: Existing Methods and Challenges and Future Prospects," Energies, MDPI, vol. 18(9), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2267-:d:1645645
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    References listed on IDEAS

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    1. Batalla-Bejerano, Joan & Trujillo-Baute, Elisa, 2016. "Impacts of intermittent renewable generation on electricity system costs," Energy Policy, Elsevier, vol. 94(C), pages 411-420.
    2. Chib, Siddhartha, 2001. "Markov chain Monte Carlo methods: computation and inference," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 57, pages 3569-3649, Elsevier.
    3. Anurag Gautam & Ibraheem & Gulshan Sharma & Pitshou N. Bokoro & Mohammad F. Ahmer, 2022. "Available Transfer Capability Enhancement in Deregulated Power System through TLBO Optimised TCSC," Energies, MDPI, vol. 15(12), pages 1-16, June.
    4. Jiang, Tao & Li, Xue & Kou, Xiao & Zhang, Rufeng & Tian, Guoda & Li, Fangxing, 2022. "Available transfer capability evaluation in electricity-dominated integrated hybrid energy systems with uncertain wind power: An interval optimization solution," Applied Energy, Elsevier, vol. 314(C).
    5. Siavash Asiaban & Nezmin Kayedpour & Arash E. Samani & Dimitar Bozalakov & Jeroen D. M. De Kooning & Guillaume Crevecoeur & Lieven Vandevelde, 2021. "Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System," Energies, MDPI, vol. 14(9), pages 1-41, May.
    6. Rongquan Fan & Wenhui Zeng & Ziqiang Ming & Wentao Zhang & Ruirui Huang & Junyong Liu, 2023. "Risk Reliability Assessment of Transmission Lines under Multiple Natural Disasters in Modern Power Systems," Energies, MDPI, vol. 16(18), pages 1-14, September.
    7. Changgi Min, 2020. "Impact Analysis of Transmission Congestion on Power System Flexibility in Korea," Energies, MDPI, vol. 13(9), pages 1-11, May.
    8. Nielsen, Steffen & Østergaard, Poul Alberg & Sperling, Karl, 2023. "Renewable energy transition, transmission system impacts and regional development – a mismatch between national planning and local development," Energy, Elsevier, vol. 278(PA).
    9. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    10. Hou, Lingxi & Li, Weiqi & Zhou, Kui & Jiang, Qirong, 2019. "Integrating flexible demand response toward available transfer capability enhancement," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    11. Navon, Aviad & Kulbekov, Pavel & Dolev, Shahar & Yehuda, Gil & Levron, Yoash, 2020. "Integration of distributed renewable energy sources in Israel: Transmission congestion challenges and policy recommendations," Energy Policy, Elsevier, vol. 140(C).
    12. Adam B. Birchfield & Eran Schweitzer & Mir Hadi Athari & Ti Xu & Thomas J. Overbye & Anna Scaglione & Zhifang Wang, 2017. "A Metric-Based Validation Process to Assess the Realism of Synthetic Power Grids," Energies, MDPI, vol. 10(8), pages 1-14, August.
    13. Pupo-Roncallo, Oscar & Campillo, Javier & Ingham, Derek & Hughes, Kevin & Pourkashanian, Mohammed, 2019. "Large scale integration of renewable energy sources (RES) in the future Colombian energy system," Energy, Elsevier, vol. 186(C).
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