A Contextual Bandit Approach for Value-oriented Prediction Interval Forecasting

TL;DR

This paper introduces a value-oriented prediction interval forecasting method using a contextual bandit framework to optimize operational costs in power systems, especially with high wind power penetration.

Contribution

It presents a novel approach that guides prediction interval quantile selection based on operational costs, improving decision-making in power system operations.

Findings

Outperforms traditional PI methods in reducing operational costs

Effective in high wind power penetration scenarios

Demonstrates superiority in virtual power plant simulations

Abstract

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.