  This paper addresses a central challenge of jointly considering shorter-term
(e.g. hourly) and longer-term (e.g. yearly) uncertainties in power system
planning with increasing penetration of renewable and storage resources. In
conventional planning decision making, shorter-term (e.g., hourly) variations
are not explicitly accounted for. However, given the deepening penetration of
variable resources, it is becoming imperative to consider such shorter-term
variation in the longer-term planning exercise. By leveraging the abundant
amount of operational observation data, we propose a scenario-based robust
planning framework that provides rigorous guarantees on the future operation
risk of planning decisions considering a broad range of operational conditions,
such as renewable generation fluctuations and load variations. By connecting
two-stage robust optimization with the scenario approach theory, we show that
with a carefully chosen number of scenarios, the operational risk level of the
robust solution can be adaptive to the risk preference set by planners. The
theoretical guarantees hold true for any distributions, and the proposed
approach is scalable towards real-world power grids. Furthermore, the
column-and-constraint generation algorithm is used to solve the two-stage
robust planning problem and tighten theoretical guarantees. We substantiate
this framework through a planning problem of energy storage in a power grid
with deep renewable penetration. Case studies are performed on large-scale test
systems (modified IEEE 118-bus system) to illustrate the theoretical bounds as
well as the scalability of proposed algorithm.
