Towards Systematic Generalization for Power Grid Optimization Problems

TL;DR

This paper introduces a unified learning framework for power grid optimization problems, specifically ACOPF and SCUC, enhancing their generalization and transferability across different grid scenarios.

Contribution

A shared graph-based model with physics-informed training that jointly addresses ACOPF and SCUC, improving cross-case transfer and systematic generalization.

Findings

Enhanced transferability on unseen grid topologies.

Improved performance over existing learning baselines.

Supports learning across heterogeneous power system problems.

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.