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A robust decision-support method based on optimization and simulation for wildfire resilience in highly renewable power systems

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  • Tapia, Tomás
  • Lorca, Álvaro
  • Olivares, Daniel
  • Negrete-Pincetic, Matías
  • Lamadrid L, Alberto J.

Abstract

Wildfires can pose a major threat to the secure operation of power networks. Chile, California, and Australia have suffered from recent wildfires that have induced considerable power supply cuts. Further, as power systems move to a significant integration of variable renewable energy sources, successfully managing the impact of wildfires on the power supply can become even more challenging due to the joint uncertainty in wildfire trajectories and the power injections from wind and solar farms. Motivated by this, this paper develops a practical decision-support approach that concatenates a stochastic wildfire simulation method with an attacker-defender model that aims to find a worst-case realization for (i) transmission line and generator contingencies, out of those that can potentially be affected by a given wildfire scenario, and for (ii) wind and solar power trajectories, based on a max-min structure where the inner min problem represents a best adaptive response on generator dispatch actions. Further, this paper proposes an evaluation framework to assess the power supply security of various power system topology configurations, under the assumption of limited transmission switching capabilities, and based on the simulation of several wildfire evolution scenarios. Extensive computational experiments are carried out on two representations of the Chilean power network with up to 278 buses, showing the practical effectiveness of the proposed approach for enhancing wildfire resilience in highly renewable power systems.

Suggested Citation

  • Tapia, Tomás & Lorca, Álvaro & Olivares, Daniel & Negrete-Pincetic, Matías & Lamadrid L, Alberto J., 2021. "A robust decision-support method based on optimization and simulation for wildfire resilience in highly renewable power systems," European Journal of Operational Research, Elsevier, vol. 294(2), pages 723-733.
    • Handle: RePEc:eee:ejores:v:294:y:2021:i:2:p:723-733
    DOI: 10.1016/j.ejor.2021.02.008
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    References listed on IDEAS

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    1. Li, M.S. & Lin, Z.J. & Ji, T.Y. & Wu, Q.H., 2018. "Risk constrained stochastic economic dispatch considering dependence of multiple wind farms using pair-copula," Applied Energy, Elsevier, vol. 226(C), pages 967-978.
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    3. Postek, Krzysztof & den Hertog, Dick & Kind, Jarl & Pustjens, Chris, 2019. "Adjustable robust strategies for flood protection," Omega, Elsevier, vol. 82(C), pages 142-154.
    4. Erick Delage & Yinyu Ye, 2010. "Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems," Operations Research, INFORMS, vol. 58(3), pages 595-612, June.
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    Cited by:

    1. Jesus Beyza & Jose M. Yusta, 2021. "Integrated Risk Assessment for Robustness Evaluation and Resilience Optimisation of Power Systems after Cascading Failures," Energies, MDPI, vol. 14(7), pages 1-18, April.
    2. Akdemir, Kerem Ziya & Robertson, Bryson & Oikonomou, Konstantinos & Kern, Jordan & Voisin, Nathalie & Hanif, Sarmad & Bhattacharya, Saptarshi, 2023. "Opportunities for wave energy in bulk power system operations," Applied Energy, Elsevier, vol. 352(C).
    3. Zhang, Tao & Li, Hong-Zhou & Xie, Bai-Chen, 2022. "Have renewables and market-oriented reforms constrained the technical efficiency improvement of China's electric grid utilities?," Energy Economics, Elsevier, vol. 114(C).

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