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Exploiting Coarse-Grained Parallelism Using Cloud Computing in Massive Power Flow Computation

Author

Listed:
  • Dong-Hee Yoon

    (Department of New and Renewable Energy, Kyungil University, Gyeongsan 38428, Korea)

  • Sang-Kyun Kang

    (School of Electrical Engineering, Kyungil University, Gyeongsan 38428, Korea)

  • Minseong Kim

    (Department of Electrical and Computer Engineering, Korea University, Seoul 02841, Korea)

  • Youngsun Han

    (School of Electronic Engineering, Kyungil University, Gyeongsan 38428, Korea)

Abstract

We present a novel architecture of parallel contingency analysis that accelerates massive power flow computation using cloud computing. It leverages cloud computing to investigate huge power systems of various and potential contingencies. Contingency analysis is undertaken to assess the impact of failure of power system components; thus, extensive contingency analysis is required to ensure that power systems operate safely and reliably. Since many calculations are required to analyze possible contingencies under various conditions, the computation time of contingency analysis increases tremendously if either the power system is large or cascading outage analysis is needed. We also introduce a task management optimization to minimize load imbalances between computing resources while reducing communication and synchronization overheads. Our experiment shows that the proposed architecture exhibits a performance improvement of up to 35.32× on 256 cores in the contingency analysis of a real power system, i.e., KEPCO2015 (the Korean power system), by using a cloud computing system. According to our analysis of the task execution behaviors, we confirmed that the performance can be enhanced further by employing additional computing resources.

Suggested Citation

  • Dong-Hee Yoon & Sang-Kyun Kang & Minseong Kim & Youngsun Han, 2018. "Exploiting Coarse-Grained Parallelism Using Cloud Computing in Massive Power Flow Computation," Energies, MDPI, vol. 11(9), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2268-:d:166372
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    References listed on IDEAS

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    1. Alizadeh, M.I. & Parsa Moghaddam, M. & Amjady, N. & Siano, P. & Sheikh-El-Eslami, M.K., 2016. "Flexibility in future power systems with high renewable penetration: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1186-1193.
    2. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    3. Tuballa, Maria Lorena & Abundo, Michael Lochinvar, 2016. "A review of the development of Smart Grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 710-725.
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    Cited by:

    1. Ahmed Al-Shafei & Hamidreza Zareipour & Yankai Cao, 2022. "High-Performance and Parallel Computing Techniques Review: Applications, Challenges and Potentials to Support Net-Zero Transition of Future Grids," Energies, MDPI, vol. 15(22), pages 1-58, November.
    2. Dong-Hee Yoon & Youngsun Han, 2020. "Parallel Power Flow Computation Trends and Applications: A Review Focusing on GPU," Energies, MDPI, vol. 13(9), pages 1-18, May.
    3. Giuseppe Barone & Giovanni Brusco & Daniele Menniti & Anna Pinnarelli & Gaetano Polizzi & Nicola Sorrentino & Pasquale Vizza & Alessandro Burgio, 2020. "How Smart Metering and Smart Charging may Help a Local Energy Community in Collective Self-Consumption in Presence of Electric Vehicles," Energies, MDPI, vol. 13(16), pages 1-18, August.

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