Data Purification for Improved Power Dispatch Against Renewable Uncertainty

Advancements in information technology and the exponential growth of data in energy systems present significant potential for intelligent and secure grid operations. However, variability in data quality remains a critical constraint. To address the lack of focus on the role of high-quality data in improving decision-making, this paper proposes a two-stage data purification framework. The first stage employs reinforcement learning-based valuation with refined policy strategies to quantify data quality, providing ranked references for decision-focused filtering in the second stage. Applied to stochastic optimization in wind-integrated unit commitment, the proposed method demonstrates its effectiveness on IEEE 30 and IEEE 118-bus systems by accurately identifying high-quality data and achieving economic benefits under varying uncertainty levels. This work aims to introduce a conceptual framework for data-centric decision-making improvement and provide methodological guidance for both academia and industry.