Risk Warning of Power Grid Congestion Events Based on Probabilistic Power Flow

To address power grid congestion events stemming from the uncertainty of wind power output, this paper presents a risk early warning model for power grid congestion events based on probability power flow. Employing the deep mixture density network algorithm to compute probability power flow (PPF) enables accurate early warning of power system congestion events. Experimental results conducted on the IEEE 118 demonstrate that the proposed early warning method exhibits high prediction accuracy, requires minimal time consumption, and demonstrates strong generalization. The findings of this study contribute to enabling the power grid to make more proactive dispatching decisions.