Decision-Focused Learning for Power System Decision-Making Under Uncertainty

More accurate forecasts may not necessarily lead to better decision-making. To address this challenge, decision-focused learning (DFL) has been proposed as a new branch of machine learning that replaces traditional statistical loss with a decision loss to form an end-to-end paradigm. Applications of DFL in power systems have been developed in recent years. However, existing applications remain fragmented without systematic analysis of methodologies or comparative benchmarks. This review addresses this gap by performing a set of scenario analysis, taxonomy analysis, application analysis and comparative analysis. It first illustrates the inherent mismatch between statistical accuracy and operational decisions through power system example scenarios. It then establishes a structured taxonomy of DFL techniques, categorizing methods by model structure (direct/indirect) and gradient handling (gradient-based/free). An application-based analysis reviews existing DFL applications by forecasting targets and decision contexts. Furthermore, an open-source comparative benchmark is developed to assess different DFL models through power system-specific metrics like cost reduction, forecasting accuracy, decision speed, providing a baseline for future research. Finally, this paper identifies the challenges to adopting DFL in power systems and presents future research directions, offering researchers a roadmap to advance DFL beyond theoretical analysis into power grid-tailored models.