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Physics-Aware Heterogeneous GNN Architecture for Real-Time BESS Optimization in Unbalanced Distribution Systems

Aoxiang Ma, Salah Ghamizi, Jun Cao, Pedro Rodriguez

TL;DR

This work tackles real-time, constraint-aware battery dispatch in three-phase unbalanced distribution grids by embedding explicit three-phase grid information into a heterogeneous graph neural network and training with a physics-informed loss that enforces SOC and C-rate constraints. The approach uses type-specific message passing and multi-task heads to jointly predict bus voltages, external grid power, and BESS dispatch, while ensuring feasibility. Validation on the CIGRE 18-bus system shows very low voltage prediction errors and dramatically reduced constraint violations, demonstrating practical viability for reliable, constraint-compliant dispatch. The results support the potential for real-time deployment and scalable extension to larger networks with multiple energy storage units and more advanced coordination mechanisms.

Abstract

Battery energy storage systems (BESS) have become increasingly vital in three-phase unbalanced distribution grids for maintaining voltage stability and enabling optimal dispatch. However, existing deep learning approaches often lack explicit three-phase representation, making it difficult to accurately model phase-specific dynamics and enforce operational constraints--leading to infeasible dispatch solutions. This paper demonstrates that by embedding detailed three-phase grid information--including phase voltages, unbalanced loads, and BESS states--into heterogeneous graph nodes, diverse GNN architectures (GCN, GAT, GraphSAGE, GPS) can jointly predict network state variables with high accuracy. Moreover, a physics-informed loss function incorporates critical battery constraints--SoC and C-rate limits--via soft penalties during training. Experimental validation on the CIGRE 18-bus distribution system shows that this embedding-loss approach achieves low prediction errors, with bus voltage MSEs of 6.92e-07 (GCN), 1.21e-06 (GAT), 3.29e-05 (GPS), and 9.04e-07 (SAGE). Importantly, the physics-informed method ensures nearly zero SoC and C-rate constraint violations, confirming its effectiveness for reliable, constraint-compliant dispatch.

Physics-Aware Heterogeneous GNN Architecture for Real-Time BESS Optimization in Unbalanced Distribution Systems

TL;DR

This work tackles real-time, constraint-aware battery dispatch in three-phase unbalanced distribution grids by embedding explicit three-phase grid information into a heterogeneous graph neural network and training with a physics-informed loss that enforces SOC and C-rate constraints. The approach uses type-specific message passing and multi-task heads to jointly predict bus voltages, external grid power, and BESS dispatch, while ensuring feasibility. Validation on the CIGRE 18-bus system shows very low voltage prediction errors and dramatically reduced constraint violations, demonstrating practical viability for reliable, constraint-compliant dispatch. The results support the potential for real-time deployment and scalable extension to larger networks with multiple energy storage units and more advanced coordination mechanisms.

Abstract

Battery energy storage systems (BESS) have become increasingly vital in three-phase unbalanced distribution grids for maintaining voltage stability and enabling optimal dispatch. However, existing deep learning approaches often lack explicit three-phase representation, making it difficult to accurately model phase-specific dynamics and enforce operational constraints--leading to infeasible dispatch solutions. This paper demonstrates that by embedding detailed three-phase grid information--including phase voltages, unbalanced loads, and BESS states--into heterogeneous graph nodes, diverse GNN architectures (GCN, GAT, GraphSAGE, GPS) can jointly predict network state variables with high accuracy. Moreover, a physics-informed loss function incorporates critical battery constraints--SoC and C-rate limits--via soft penalties during training. Experimental validation on the CIGRE 18-bus distribution system shows that this embedding-loss approach achieves low prediction errors, with bus voltage MSEs of 6.92e-07 (GCN), 1.21e-06 (GAT), 3.29e-05 (GPS), and 9.04e-07 (SAGE). Importantly, the physics-informed method ensures nearly zero SoC and C-rate constraint violations, confirming its effectiveness for reliable, constraint-compliant dispatch.

Paper Structure

This paper contains 20 sections, 22 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Physics-Aware Heterogeneous GNN Architecture
  3. Problem Formulation
  4. Unbalanced Grid Graph Construction
  5. Graph Embedding
  6. Prediction Targets
  7. Heterogeneous Graph Neural Network Architecture
  8. Type-Specific Message Passing
  9. Multi-Task Output Prediction
  10. Loss function with Physics-Aware Loss
  11. Constraint Formulation
  12. Penalty Function
  13. Empirical Study
  14. Use case
  15. Simulation tool
  16. ...and 5 more sections

Figures (2)

  • Figure 1: Graph embedding for CIGRE 18-bus grid
  • Figure 2: Validation losses for multi-task objectives across GCN, GAT, GPS, and SAGE architectures with and without physics-informed loss components