  Prediction interval (PI) is an effective tool to quantify uncertainty and
usually serves as an input to downstream robust optimization. Traditional
approaches focus on improving the quality of PI in the view of statistical
scores and assume the improvement in quality will lead to a higher value in the
power systems operation. However, such an assumption cannot always hold in
practice. In this paper, we propose a value-oriented PI forecasting approach,
which aims at reducing operational costs in downstream operations. For that, it
is required to issue PIs with the guidance of operational costs in robust
optimization, which is addressed within the contextual bandit framework here.
Concretely, the agent is used to select the optimal quantile proportion, while
the environment reveals the costs in operations as rewards to the agent. As
such, the agent can learn the policy of quantile proportion selection for
minimizing the operational cost. The numerical study regarding a two-timescale
operation of a virtual power plant verifies the superiority of the proposed
approach in terms of operational value. And it is especially evident in the
context of extensive penetration of wind power.
