Learning Optimal Power Flow with Infeasibility Awareness (Papers Track)

Gang Huang (Zhejiang Lab); Longfei Liao (Zhejiang Lab); Lechao Cheng (Zhejiang Lab); Wei Hua (Zhejiang Lab)

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Optimal power flow provides an energy-efficient operating point for power grids and therefore supports climate change mitigation. This function has to be run every few minutes day and night, thus a reliable and computationally efficient solution method is of vital importance. Deep learning seems a promising direction, and related works have emerged recently. However, considering feasible scenarios only during the learning process, existing works will mislead system operators in infeasible scenarios and pose a new threat to system resilience. Paying attention to infeasibility in the decision making process, this paper tackles this emerging threat with multi-task learning. Case studies on the IEEE test system validate the effectiveness of the proposed method.

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