  Driven by ambitious renewable portfolio standards, large-scale inclusion of
variable energy resources (such as wind and solar) are expected to introduce
unprecedented levels of uncertainty into power system operations. The current
practice of operations planning with deterministic optimization models may be
ill-suited for a future with abundant uncertainty. To overcome the potential
reliability and economic challenges, we present a stochastic hierarchical
planning (SHP) framework. This framework captures operations at day-ahead,
short-term and hour-ahead timescales, along with the interactions between the
stochastic processes and decisions. In contrast to earlier studies where
stochastic optimization of individual problems (e.g., unit commitment, economic
dispatch) have been studied, this paper studies an integrated framework of
\emph{planning under uncertainty}, where stochastic optimization models are
stitched together in a hierarchical setting which parallels the deterministic
hierarchical planning approach that is widely adopted in the power industry.
Our experiments, based on the NREL-118 dataset, reveal that under high
renewable penetration, significant operational improvements can be expected by
transitioning to the SHP paradigm. In particular, the computational results
show that significant improvements can be achieved in several metrics,
including system reliability, environmental sustainability, and system
economics, solely by making a strategic choice to adopt the new SHP paradigm.
