  Inspired by the challenges of running the Israel's power system -- with its
increasing integration of renewables, significant load uncertainty, and primary
reliance on natural gas -- we investigate an emergency scenario where there's a
need to transition temporarily to a pricier secondary fuel until the emergency
resolves. Our objective is to devise tools that can assist power system
operators in making decisions during such critical periods. We frame this
challenge as a Markov Decision Process (MDP) optimization, considering
uncertainties like potential failures of dual-fuel generators during the
transition, operator attentiveness under stress, available but finite amount of
primary fuel (linepack available in the natural gas part of the system), power
forecast (net demand after renewable production), and the cost implications of
unavoidable load shedding. By solving the MDP in a simplified context, we
identify viable policies through simulations of multiple parametrized Markov
Processes (MPs). We verify our methodology using a realistic open-source model
replicating Israel's power-gas infrastructure and outline next steps for
refining and adapting this approach.
