  The rapid increase in the integration of intermittent and stochastic
renewable energy resources (RER) introduces challenging issues related to power
system stability. Interestingly, identifying grid nodes that can best support
stochastic loads from RER, has gained recent interest. Methods based on
Lyapunov stability are commonly exploited to assess the stability of power
networks. These strategies approach quantifying system stability while
considering: (i) simplified reduced order power system models that do not model
power flow constraints, or (ii) datadriven methods that are prone to
measurement noise and hence can inaccurately depict stochastic loads as system
instability. In this paper, while considering a nonlinear differential
algebraic equation (NL-DAE) model, we introduce a new method for assessing the
impact of uncertain renewable power injections on the stability of power system
nodes/buses. The identification of stable nodes informs the operator/utility on
how renewables injections affect the stability of the grid. The proposed method
is based on optimizing metrics equivalent to the Lyapunov spectrum of
exponents; its underlying properties result in a computationally efficient and
scalable stable node identification algorithm for renewable energy resources
allocation. The proposed method is validated on the IEEE 9-bus and 200-bus
networks
