GPU-Native Multi-Area State Estimation via SIMD Abstraction and Boundary Condensation
For power system operators needing real-time grid monitoring, this work provides a scalable GPU-based solution to multi-area state estimation, though it is an incremental improvement over existing hierarchical methods.
This paper presents a GPU-native framework for hierarchical multi-area state estimation that uses SIMD abstraction and sparse Schur local condensation to improve computational efficiency. On benchmark systems up to 10,000 buses, the method achieves high GPU throughput and device residency, addressing scalability bottlenecks in centralized solvers.
Power system state estimation (SE) is foundational for grid monitoring, yet conventional centralized solvers face increasing computational pressure as the system scale and real-time requirements grow. This paper presents a GPU-native framework for hierarchical multi-area state estimation (MASE) that addresses these bottlenecks through a single-instruction, multiple-data (SIMD) abstraction and sparse Schur local condensation. We partition the network into areas, evaluate measurement residuals and derivatives using fixed-sparsity templates, and directly assemble local normal-equation blocks through a fused GPU accumulation kernel without materializing explicit Jacobians. Each area is then factorized on the GPU in Schur mode to export a dense local boundary block and condensed right-hand side, after which a reduced global boundary system is assembled and solved on device. This design preserves device residency across measurement evaluation, local condensation, and boundary coordination while exposing parallelism across areas. Numerical experiments on partitioned PEGASE 2869-bus, PEGASE 9241-bus, and ACTIVSg10k benchmark systems demonstrate that the proposed approach effectively leverages GPU throughput by maintaining full device residency and high arithmetic intensity.