  This is the second part of a two-part paper on data-based distributionally
robust stochastic optimal power flow (OPF). The general problem formulation and
methodology have been presented in Part I [1]. Here, we present extensive
numerical experiments in both distribution and transmission networks to
illustrate the effectiveness and flexibility of the proposed methodology for
balancing efficiency, constraint violation risk, and out-of-sample performance.
On the distribution side, the method mitigates overvoltages due to high
photovoltaic penetration using local energy storage devices. On the
transmission side, the method reduces N-1 security line flow constraint risks
due to high wind penetration using reserve policies for controllable
generators. In both cases, the data-based distributionally robust model
predictive control (MPC) algorithm explicitly utilizes forecast error training
datasets, which can be updated online. The numerical results illustrate
inherent tradeoffs between the operational costs, risks of constraints
violations, and out-of-sample performance, offering systematic techniques for
system operators to balance these objectives.
