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

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

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish
Pages (from-to)723-733
Number of pages11
JournalEuropean Journal of Operational Research
Volume294
Issue number2
DOIs
StatePublished - 16 Oct 2021
Externally publishedYes

Keywords

  • Decision-support systems
  • Mixed-integer optimization
  • OR in energy
  • Power system resilience
  • Wildfires

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