Effective End-to-End Learning Framework for Economic Dispatch
This addresses economic dispatch in power systems, likely incremental as it builds on end-to-end learning concepts.
The paper tackles the problem of economic dispatch by proposing an end-to-end machine learning framework with task-specific learning criteria, which theoretically and empirically demonstrates effectiveness and efficiency.
Conventional wisdom to improve the effectiveness of economic dispatch is to design the load forecasting method as accurately as possible. However, this approach can be problematic due to the temporal and spatial correlations between system cost and load prediction errors. This motivates us to adopt the notion of end-to-end machine learning and to propose a task-specific learning criteria to conduct economic dispatch. Specifically, to maximize the data utilization, we design an efficient optimization kernel for the learning process. We provide both theoretical analysis and empirical insights to highlight the effectiveness and efficiency of the proposed learning framework.