Accepted papers
The following papers were accepted to the AI4PowerGrids workshop at NeurIPS 2026.
Oral presentations
Learning Adaptive Topology-Aware Line Margins for AC Optimal Power Flow under Forecast Uncertainty and Distribution Shift
FLARE: Physics-Grounded Evaluation of Language-Model Agents for Feeder Restoration with Degraded Observations
Toward Out-of-Distribution Generalization in Neural AC Power Flow
Benchmarking CPU, GPU, and ML Power-Flow Solvers from 14 to 70,000 Buses
Poster presentations
Closing the Accuracy Gap in Electrical Model-Free OPF with Concurrent Voltage Estimation
Spatio-Temporal GraphSAGE Framework for Generalizable Power Grid Representations
Extrapolating to Unseen Solar PV Levels: Physics-Informed Voltage Calculation in Low-Voltage Networks
Towards a Generalization Benchmark for GNN Power-Flow Models: An Out-of-Distribution Evaluation for Transmission Grids
Diffusion-based Random Attack Generators for Interdiction Analysis in Electric Power Systems
PowerFORM: A General-Purpose Foundation Model for Power Systems
DERBench: A Benchmark for Evaluating LLMs on Distributed Energy Resource Operations
Compositional Power System Dynamic Model Using Causality-Aware Physics-Informed DeepONet
Benchmarking LLM Bidding Agents in Electricity Markets: Hour-Specific Pricing and Experience Effects
Ground Flash Density as a Function of Climate: Scenario-Conditioned Lightning Hazard with Calibrated Uncertainty
The Cost of Coverage: Conformalising Native Forecast Uncertainty for GB Wind Power
Sensor-Aware Joint Data Fusion and State Estimation under Sparse Multi-rate Distribution Grid Sensing
Learning-Enhanced Column Generation for Large-Scale Bulk Dispatch Optimization
FP-QF: A Matrix-Inversion-Free AC Power Flow Solver for Differentiable ML Pipelines
Causal Models for Electricity Markets and Power Grid Congestion
Beyond Average Error: SpikeBench-PJM for Real-Time Electricity Price Forecasting
FALCON-UC: Feasibility-Aware Learning for Network-Constrained Unit Commitment
Cross-Market Meta-Learning for Electricity Price Forecasting
Reconciling European Electricity Data with Diffusion Priors Learned from Observations Alone
Deep Learning-Accelerated Shapley Value for Fair Allocation in Power Systems at Real-World Grid Scale
AutoB2G: Agentic Simulation and Reinforcement Learning for Spatio-Temporal Grid-Interactive Building Control
Metamorphic testing of learned AC-OPF evaluation reveals representation dependence
Decentralized Model-Free Voltage Calculation Using Neighboring Smart-Meter Data
Structured Equilibrium Learning for OPF Imitation
Beyond Price Signals: Coordinated Wholesale Market Participation for Flexible Data Centers
Structured Representation Learning for Behavior Cloning: How can we learn to safely control a nuclear power plant?
UNION: Topology-Conditioned AC-OPF under Structural Grid Shifts