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.

翻译：预测区间（PI）是量化不确定性的有效工具，通常作为下游鲁棒优化的输入。传统方法从统计指标角度提升PI质量，并假设质量提升会带来电力系统运行价值的提高。然而，这种假设在实践中并非始终成立。本文提出一种价值导向的PI预测方法，旨在降低下游运行环节的运营成本。为此，需要在鲁棒优化中以运营成本为指导生成PI，这一问题在此通过上下文赌博机框架解决。具体而言，智能体用于选择最优分位数比例，而环境将运行中的成本作为奖励反馈给智能体。通过这种方式，智能体能够学习最小化运营成本的分位数比例选取策略。针对虚拟电厂两时间尺度运行的数值研究验证了所提方法在运行价值方面的优越性，尤其在风电大规模渗透背景下效果显著。