6 papers
UNION: A Unified AC-OPF Framework for Topology-Varying Real-Time Grid Operation
Kyungnam Park, Keunju Song, Yeji Lim +3
Secure real-time grid operation requires fast AC optimal power flow (AC-OPF) tools that stay accurate and feasible as operating conditions and topology change. Learning-based metho…
SABLE: GPU-Based Power Flow Accelerator for Sparsity-Aware Batched Learning
Suho Park, Keunju Song, Hongseok Kim
Recent studies have developed GPU-based approaches for solving AC power flow and successfully applied them to standalone power flow problems. However, integrating these approaches…
Physics-Informed Graph Learning Acceleration for Large-Scale AC-OPF with Topology Changes
Keunju Song, Kyungnam Park, Sua Choi +5
In power systems, alternating current optimal power flow (AC-OPF) has been a challenging problem for decades due to its nonconvexity, but fast and efficient solutions are even more…
LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
Hongwei Jin, Keunju Song, Zeeshan Memon +5
AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations, driving learning-based surrogates for large-scale grid analysis. These surroga…
LUMINA: Foundation Models for Topology Transferable ACOPF
Yijiang Li, Zeeshan Memon, Hongwei Jin +7
Foundation models in general promise to accelerate scientific computation by learning reusable representations across problem instances, yet constrained scientific systems, where p…
Locational Scenario-based Pricing in a Bilateral Distribution Energy Market under Uncertainty
Hien Thanh Doan, Minsoo Kim, Keunju Song +1
In recent years, there has been a significant focus on advancing the next generation of power systems. Despite these efforts, persistent challenges revolve around addressing the op…