An MDP framework with approximate dynamic programming optimizes power line switching during wildfires to minimize costs under decision-dependent uncertainty, tested on 54-bus and 138-bus systems.
Population exposure to pre-emptive de-energization aimed at averting wildfires in northern california
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Markov Decision Process Framework for Enhancing Power System Resilience during Wildfires under Decision-Dependent Uncertainty
An MDP framework with approximate dynamic programming optimizes power line switching during wildfires to minimize costs under decision-dependent uncertainty, tested on 54-bus and 138-bus systems.