TY - CPAPER AU - Tung Bui AU - Wanshi Hong AU - Bin Wang AU - Mengqi Yao AU - Duncan S Callaway AU - Larry L Dale AU - Can Huang AB -
 
Wildfire activities are increasing in the western United States in recent years, causing escalating threats to power systems. This paper developed an optimal and data-driven decision-making framework that improves power system resilience under wildfire risks. An optimal load shedding plan is formulated based on optimal power flow analysis. To avoid power system cascading failure caused by wildfire, we added additional transmission line flow constraints based on the identification of power lines with high ignition risk. Finally, a data-driven method is developed, leveraging multiple machine learning techniques, to model the complex correlations between input wildfire scenarios and the output power management strategy with significantly reduced computational complexities. The proposed data-driven decision-making framework can reduce the safety impacts on the electricity consumers, improve power system resilience under wildfire events.
BT - Hawaii International Conference on System Sciences - Proceedings of the 55th Hawaii International Conference on System Sciences DA - 01/2022 DO - 10.24251/HICSS.2022.00010.24251/HICSS.2022.436 LA - eng N2 -
 
Wildfire activities are increasing in the western United States in recent years, causing escalating threats to power systems. This paper developed an optimal and data-driven decision-making framework that improves power system resilience under wildfire risks. An optimal load shedding plan is formulated based on optimal power flow analysis. To avoid power system cascading failure caused by wildfire, we added additional transmission line flow constraints based on the identification of power lines with high ignition risk. Finally, a data-driven method is developed, leveraging multiple machine learning techniques, to model the complex correlations between input wildfire scenarios and the output power management strategy with significantly reduced computational complexities. The proposed data-driven decision-making framework can reduce the safety impacts on the electricity consumers, improve power system resilience under wildfire events.
PB - Hawaii International Conference on System Sciences PY - 2022 T2 - Hawaii International Conference on System Sciences - Proceedings of the 55th Hawaii International Conference on System Sciences T3 - Hawaii International Conference on System Sciences - - 55th Hawaii International Conference on System Sciences TI - Proceedings of the Annual Hawaii International Conference on System Sciences: Data-Driven Power System Optimal Decision Making Strategy under Wildfire Events UR - http://hdl.handle.net/10125/79139 ER -