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Blackout prediction in interconnected electric energy systems considering generation re-dispatch and energy curtailment

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  • Kamali, Sadegh
  • Amraee, Turaj

Abstract

Blackouts or cascading outages are costly events that threaten the integrity of electric energy systems around the world. Controlled splitting is executed as the last countermeasure to reduce the undesired economic and social consequences of a blackout. In this paper, a new two-stage scheme is proposed to predict the risk of a blackout in electric energy systems. In the first stage, the boundaries of electric islands are determined using a Mixed Integer Non-Linear Programming model that minimizes the cost of generation re-dispatch and load curtailment. In the second step, a data-mining technique is perfected to predict the risk of electrical separation of an electric island from the rest of the network. Each predictor is trained based on the phasor-measurement data taken at the synchronous generator terminals. Using a wide-area measurement system, the required phasor measurements are gathered and processed in the Energy Management System. Various scenarios, including the island and non-island conditions, are generated and then utilized by the decision-tree classification technique to predict the risk of a blackout. The proposed algorithm is simulated over the IEEE 39-bus test system to demonstrate its performance in online applications.

Suggested Citation

  • Kamali, Sadegh & Amraee, Turaj, 2017. "Blackout prediction in interconnected electric energy systems considering generation re-dispatch and energy curtailment," Applied Energy, Elsevier, vol. 187(C), pages 50-61.
    • Handle: RePEc:eee:appene:v:187:y:2017:i:c:p:50-61
    DOI: 10.1016/j.apenergy.2016.11.040
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    4. Hassani, Hossein & Razavi-Far, Roozbeh & Saif, Mehrdad, 2022. "Real-time out-of-step prediction control to prevent emerging blackouts in power systems: A reinforcement learning approach," Applied Energy, Elsevier, vol. 314(C).
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    8. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
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    10. Farhan Hameed Malik & Muhammad Waleed Khan & Tauheed Ur Rahman & Muhammad Ehtisham & Muhammad Faheem & Zunaib Maqsood Haider & Matti Lehtonen, 2024. "A Comprehensive Review on Voltage Stability in Wind-Integrated Power Systems," Energies, MDPI, vol. 17(3), pages 1-36, January.
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    13. Tomáš Fröhlich & Zdeněk Hon & Martin Staněk & Jiří Slabý, 2023. "Method of Identification and Assessment of Security Needs of a Region against the Threat of a Large Power Outage," Energies, MDPI, vol. 16(11), pages 1-16, May.
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