arXiv subjects

Keunju Song

Publications and source records attributed to Keunju Song.

6 recordsLinked to original sources

arXiv2026

UNION: A Unified AC-OPF Framework for Topology-Varying Real-Time Grid Operation

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 methods have advanced, but most are trained per system or per topology, and delivering an operating point that satisfies every operational limit remains challenging. This paper proposes UNION, a unified graph-based AC-OPF framework for heterogeneous systems and topology-varying operation. UNION proposes a shared graph encoder, a scalar-gated aggregation with explicit consensus correction, and a sparse-aware differentiable implicit layer embedding the AC power-flow equations. The remaining inequalities are handled by primal-dual training and the deterministic restoration layer. A single model trained jointly across seven systems, including a real-world 4,492-bus Korean transmission grid, attains a 1.23% mean objective gap and satisfies every operational limit on 99.56% of test instances. It sustains this under zero-shot $N-1$ contingencies, i.e., line and generator outages, and over five days of time-varying Korean topologies; it retains full snapshot coverage at a 2.51% gap under lightweight online fine-tuning. UNION pre-restoration inference takes 55$-$58 ms per instance on the three largest systems, and 108$-$114 ms including restoration. These results indicate that one jointly trained, physics-consistent model can support real-time AC-OPF across heterogeneous systems and evolving topologies.

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arXiv2026

SABLE: GPU-Based Power Flow Accelerator for Sparsity-Aware Batched Learning

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 into modern differentiable learning frameworks while preserving sparsity remains challenging. To this end, we present SABLE, a GPU-based sparse batched power flow accelerator for differentiable learning via an implicit power flow layer. SABLE leverages a block-diagonal embedding that reformulates batched three-dimensional Jacobians as a fixed-pattern two-dimensional sparse template that is shared across PyTorch, CuPy, and cuDSS. This common template enables zero-copy interoperability and memory-efficient sparse reuse across the software stack. On top of this representation, SABLE accelerates repeated power flow computations through reusable sparse templates, custom GPU kernels, a cuDSS-based sparse-direct LU solver, and mixed-precision techniques. Extensive experiments show that SABLE improves standalone power flow solving throughput by up to 253.4$\times$ over pandapower and 5.7$\times$ over ExaPF. In end-to-end training, evaluated on AC optimal power flow learning models based on DC3 and DeepLDE, SABLE expands the feasible training batch range by up to 64$\times$ and improves training throughput by up to 206.7$\times$ over the corresponding baseline.

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arXiv2026

Physics-Informed Graph Learning Acceleration for Large-Scale AC-OPF with Topology Changes

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 needed because of high penetration of large scale renewable generation and load growth. Recently, neural networks (NN) have gained attention in solving AC-OPF, but it is still in an early stage to be applicable for real and large-scale power system operation with topology-changing characteristics. To end this, we propose a novel framework called GraphOPF that considers topology-adaptability, scalability, NN training time, self-supervision, and feasibility altogether. Extensive experiments show that the proposed framework against the baselines is up to 200 times faster in NN training and up to 66 times faster in solving AC-OPF for large-scale power systems including the real Korean power system, while achieving more than 99% feasibility.

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arXiv2026

LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning

AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations, driving learning-based surrogates for large-scale grid analysis. These surrogates, however, often fail to generalize across network topologies, a critical gap for deployment on grids not seen during training and for routine operational what-if studies. We introduce LUMINA-Bench, a comprehensive benchmark suite for ACOPF surrogate learning covering multi-topology pretraining, transfer, and adaptation. The benchmark evaluates homogeneous and heterogeneous architectures under single- and multi-topology learning settings using unified metrics that capture both predictive accuracy and physics-informed constraint violations. We additionally compare constraint-aware training objectives, including MSE, augmented Lagrangian, and violation-based Lagrangian losses, to characterize accuracy-robustness trade-offs across settings. Data processing, training, and evaluation frameworks are open-sourced as the LUMINA suite to support reproducibility and accelerate future research on feasibility-aware OPF surrogates.

cs.LG↗

arXiv2026

LUMINA: Foundation Models for Topology Transferable ACOPF

Foundation models in general promise to accelerate scientific computation by learning reusable representations across problem instances, yet constrained scientific systems, where predictions must satisfy physical laws and safety limits, pose unique challenges that stress conventional training paradigms. We derive design principles for constrained scientific foundation models through systematic investigation of AC optimal power flow (ACOPF), a representative optimization problem in power grid operations where power balance equations and operational constraints are non-negotiable. Through controlled experiments spanning architectures, training objectives, and system diversity, we extract three empirically grounded principles governing scientific foundation model design. These principles characterize three design trade-offs: learning physics-invariant representations while respecting system-specific constraints, optimizing accuracy while ensuring constraint satisfaction, and ensuring reliability in high-impact operating regimes. We present the LUMINA framework, including data processing and training pipelines to support reproducible research on physics-informed, feasibility-aware foundation models across scientific applications.

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arXiv2024

Locational Scenario-based Pricing in a Bilateral Distribution Energy Market under Uncertainty

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 operational impact of uncertainty on predicted data, especially concerning economic dispatch and optimal power flow. To tackle these challenges, we introduce a stochastic day-ahead scheduling approach for a community. This method involves iterative improvements in economic dispatch and optimal power flow, aiming to minimize operational costs by incorporating quantile forecasting. Then, we present a real-time market and payment problem to handle optimization in real-time decision-making and payment calculation. We assess the effectiveness of our proposed method against benchmark results and conduct a test using data from 50 real households to demonstrate its practicality. Furthermore, we compare our method with existing studies in the field across two different seasons of the year. In the summer season, our method decreases optimality gap by 60% compared to the baseline, and in the winter season, it reduces optimality gap by 67%. Moreover, our proposed method mitigates the congestion of distribution network by 16.7\% within a day caused by uncertain energy, which is a crucial aspect for implementing energy markets in the real world.

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