ExaPF

ExaPF.jl is a package to solve the power flow problem on upcoming exascale architectures. The code has been designed to be:

1. Portable: Targeting exascale architectures implies a focus on graphics processing units (GPUs) as these systems lack substantial computational performance through classical CPUs.
2. Differentiable: All the expressions implemented in ExaPF are fully compatible with ForwardDiff.jl, and routines are provided to extract first- and second-order derivatives to solve efficiently power flow and optimal power flow problems.

ExaPF implements a vectorized modeler for power systems, which allows to manipulate basic expressions. All expressions are fully differentiable : their first and second-order derivatives can be extracted efficiently using automatic differentiation. In addition, we provide extensions that leverage the packages CUDA.jl, [AMDGPU.jl]((https://github.com/JuliaGPU/AMDGPU.jl), and KernelAbstractions.jl to make ExaPF portable across GPU architectures.

Table of contents

• Quick Start

Manual

• Formulations
• PowerSystem
• AutoDiff
• Linear Solver
• Benchmark

Library

• Polar formulation
• Composition of expressions
• PowerSystem
• AutoDiff
• Linear solvers

Artifact

• ExaData Artifact

Funding

This research was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy’s Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation’s exascale computing imperative.