Towards Systematic Generalization for Power Grid Optimization Problems

• URL: http://arxiv.org/abs/2605.02026v1
• Date: Sun, 03 May 2026 19:23:37 GMT
• Title: Towards Systematic Generalization for Power Grid Optimization Problems
• Abstract summary: AC Optimal Power Flow (ACOPF) and Security-Constrained Unit Commitment (SCUC) are fundamental optimization problems in power system operations.<n>We propose a learning framework that jointly models ACOPF and SCUC through a shared graph-based backbone.
• Score: 13.619002117559958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AC Optimal Power Flow (ACOPF) and Security-Constrained Unit Commitment (SCUC) are fundamental optimization problems in power system operations. ACOPF serves as the physical backbone of grid simulation and real-time operation, enforcing nonlinear power flow feasibility and network limits, while SCUC represents a core market-level decision process that schedules generation under operational and security constraints. Although these problems share the same underlying transmission network and physical laws, they differ in decision variables and temporal coupling, and prior learning-based approaches address them in isolation, resulting in disjoint models and representations.We propose a learning framework that jointly models ACOPF and SCUC through a shared graph-based backbone that captures grid topology and physical interactions, coupled with task-specific decoders for static and temporal decision-making. Training includes solver supervision with physics-informed objectives to enforce AC feasibility and inter-temporal operational constraints. To evaluate generalization, we assess cross-case transfer on unseen grid topologies for ACOPF and SCUC without retraining, and systematic generalization on the UC-ACOPF problem using unsupervised, physics-based objectives and a power-dispatch consensus mechanism. Experiments across multiple grid scales demonstrate improved performance and transferability relative to existing learning-based baselines, indicating that the model can support learning across heterogeneous power system optimization problems.

Related papers

• Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling [50.176885352114674]
  Heterogeneous agile Earth observation satellite (AEOS) scheduling requires task selection, satellite assignment, and observation sequencing.<n>This paper proposes an evolutionary policy optimization framework for heterogeneous AEOS scheduling with preference-adjustable weighted objectives.
  arXiv  Detail & Related papers  (2026-08-25T12:16:28Z)
• LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning [13.448601803960136]
  AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations.<n>We introduce LUMINA-Bench, a benchmark suite for ACOPF surrogate learning covering multi-topology pretraining, transfer, and adaptation.
  arXiv  Detail & Related papers  (2026-05-04T01:25:16Z)
• QoS-Aware Hierarchical Reinforcement Learning for Joint Link Selection and Trajectory Optimization in SAGIN-Supported UAV Mobility Management [52.15690855486153]
  A space-air-ground integrated network (SAGIN) has emerged as an essential architecture for enabling ubiquitous UAV connectivity.<n>This paper formulates UAV mobility management in SAGIN as a constrained multiobjective joint optimization problem.
  arXiv  Detail & Related papers  (2025-12-17T06:22:46Z)
• Power Grid Control with Graph-Based Distributed Reinforcement Learning [60.49805771047161]
  This work advances a graph-based distributed reinforcement learning framework for real-time, scalable grid management.<n>A Graph Neural Network (GNN) is employed to encode the network's topological information within the single low-level agent's observation.<n>Experiments on the Grid2Op simulation environment show the effectiveness of the approach.
  arXiv  Detail & Related papers  (2025-09-02T22:17:25Z)
• PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis [75.14189839277928]
  We present PowerGrow, a co-generative framework that significantly reduces computational overhead while maintaining operational validity.<n> Experiments across benchmark settings show that PowerGrow outperforms prior diffusion models in fidelity and diversity.<n>This demonstrates its ability to generate operationally valid and realistic power grid scenarios.
  arXiv  Detail & Related papers  (2025-08-29T01:47:27Z)
• Backscatter Device-aided Integrated Sensing and Communication: A Pareto Optimization Framework [59.30060797118097]
  Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban non-line-of-sight scenarios.<n>This paper proposes a backscatter approximation (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in environments of enhancement.
  arXiv  Detail & Related papers  (2025-07-12T17:11:06Z)
• Dynamic Domain Adaptation-Driven Physics-Informed Graph Representation Learning for AC-OPF [15.100794164684956]
  Alternating Current Optimal Power Flow (AC-OPF) aims to optimize generator power outputs by utilizing the non-formed relationships between voltage magnitudes and phase angles in a power system.<n>Current AC-OPF solvers struggle to effectively represent the complex relationship between variable distributions in the constraint space and their corresponding optimal solutions.<n>We propose DDA-PIGCN, a new method designed to address constraint-related issues and build a graph-based learning framework.
  arXiv  Detail & Related papers  (2025-05-31T09:07:19Z)
• Transformer-Empowered Actor-Critic Reinforcement Learning for Sequence-Aware Service Function Chain Partitioning [1.9120720496423733]
  We introduce a Transformer-empowered actor-critic framework specifically designed for sequence-aware SFC partitioning.<n>Our approach effectively models complex inter-dependencies among VNFs, facilitating coordinated and parallelized decision-making processes.
  arXiv  Detail & Related papers  (2025-04-26T12:18:57Z)
• Robo-taxi Fleet Coordination at Scale via Reinforcement Learning [21.266509380044912]
  This work introduces a novel decision-making framework that unites mathematical modeling with data-driven techniques.<n>We present the AMoD coordination problem through the lens of reinforcement learning and propose a graph network-based framework.<n>In particular, we present the AMoD coordination problem through the lens of reinforcement learning and propose a graph network-based framework.
  arXiv  Detail & Related papers  (2025-04-08T15:19:41Z)
• Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach [51.63921041249406]
  Non-orthogonal multiple access (NOMA) enables multiple users to share the same frequency band, and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) deploying STAR-RIS indoors presents challenges in interference mitigation, power consumption, and real-time configuration. A novel network architecture utilizing multiple access points (APs), STAR-RISs, and NOMA is proposed for indoor communication.
  arXiv  Detail & Related papers  (2024-06-19T07:17:04Z)
• QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints [4.1920378271058425]
  We propose an innovated computational learning ACOPF, where the input is mapped to the ACOPF network in a computationally efficient manner. We show through simulations that our proposed method achieves superior feasibility rate and cost in situations where the existing-based approaches fail.
  arXiv  Detail & Related papers  (2024-01-11T20:17:44Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.