2 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
Event-Driven Deep RL Dispatcher for Post-Storm Distribution System Restoration
Farshad Amani, Faezeh Ardali, Amin Kargarian
Natural hazards such as hurricanes and floods damage power grid equipment, forcing operators to replan restoration repeatedly as new information becomes available. This paper devel…
Learning Optimal Crew Dispatch for Grid Restoration Following an Earthquake
Farshad Amani, Faezeh Ardali, Amin Kargarian
Post-disaster crew dispatch is a critical but computationally intensive task. Traditional mixed-integer linear programming methods often require minutes to several hours to compute…
Learning Interior Point Method Central Path Projection for Optimal Power Flow
Farshad Amani, Amin Kargarian, Ramachandran Vaidyanathan
This paper proposes a learning-based approach to accelerate the interior-point method (IPM) for solving optimal power flow (OPF) problems by learning the structure of the IPM centr…
Optimal Power Flow Solutions via Noise-Resilient Quantum-Inspired Interior-Point Methods
Farshad Amani, Amin Kargarian
This paper presents three quantum interior-point methods (QIPMs) tailored to tackle the DC optimal power flow (DCOPF) problem using noisy intermediate-scale quantum devices. The op…