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Efficient Economic and Resilience-Based Optimization for Disaster Recovery Management of Critical Infrastructures

Author

Listed:
  • Eng Tseng Lau

    (School of Electronic Engineering and Computer Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, UK)

  • Kok Keong Chai

    (School of Electronic Engineering and Computer Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, UK)

  • Yue Chen

    (School of Electronic Engineering and Computer Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, UK)

  • Jonathan Loo

    (School of Computing and Engineering, University of West London, St Mary’s Road, London W5 5RF, UK)

Abstract

The traditional grid operation is unfortunately lacking the resilience and responsiveness in reacting to contingency events due to the poor utilization of available resources in mitigating the shortfalls. Such an unaddressed issue may affect the grid stability and the ultimate grid blackout. Therefore, this paper models a grid optimization module consisting of a mid and low (microgrid) voltage level grid component of an urban grid network for a disaster recovery. The model minimizes the cost of generation required to meet the demand through the economic dispatch in combination with the unit commitment. Two optimization problems are formulated that resemble the grid operation: normal (grid-connected) and islanded. A constrained-based linear programming optimization problem is used to solve the formulated problems, where the dual-simplex algorithm is used as the linear solver. The model ensures sufficient demand to be met during the outages through the N -1 contingency criterion for critical infrastructures. The simulation length is limited to 24 h and is solved using the MATLAB ® R2017b software. Three different cases are established to evaluated the modelled grid resilience during the grid-connected or the islanding of operations subject to adversed events. The simulated results provide the economical outage recovery that will maintain the grid resilience across the grid.

Suggested Citation

  • Eng Tseng Lau & Kok Keong Chai & Yue Chen & Jonathan Loo, 2018. "Efficient Economic and Resilience-Based Optimization for Disaster Recovery Management of Critical Infrastructures," Energies, MDPI, vol. 11(12), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3418-:d:188421
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    References listed on IDEAS

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    1. Parisio, Alessandra & Rikos, Evangelos & Tzamalis, George & Glielmo, Luigi, 2014. "Use of model predictive control for experimental microgrid optimization," Applied Energy, Elsevier, vol. 115(C), pages 37-46.
    2. Ting Yang & Yajian Zhang & Zhaoxia Wang & Haibo Pen, 2018. "Secondary Frequency Stochastic Optimal Control in Independent Microgrids with Virtual Synchronous Generator-Controlled Energy Storage Systems," Energies, MDPI, vol. 11(9), pages 1-14, September.
    3. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    4. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
    5. Winkler, James & Dueñas-Osorio, Leonardo & Stein, Robert & Subramanian, Devika, 2010. "Performance assessment of topologically diverse power systems subjected to hurricane events," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 323-336.
    6. Howlader, Harun Or Rashid & Matayoshi, Hidehito & Senjyu, Tomonobu, 2016. "Distributed generation integrated with thermal unit commitment considering demand response for energy storage optimization of smart grid," Renewable Energy, Elsevier, vol. 99(C), pages 107-117.
    7. Alam, Sheraz & Sohail, M. Farhan & Ghauri, Sajjad A. & Qureshi, I.M. & Aqdas, Naveed, 2017. "Cognitive radio based Smart Grid Communication Network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 535-548.
    8. Detroja, Ketan P., 2016. "Optimal autonomous microgrid operation: A holistic view," Applied Energy, Elsevier, vol. 173(C), pages 320-330.
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    Cited by:

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