Accelerating Optimal Power Flow With Structure-Aware Automatic Differentiation and Code Generation

doi:10.1109/TPWRS.2024.3483489

Accelerating Optimal Power Flow With Structure-Aware Automatic Differentiation and Code Generation

2025-01-01

0

PRE

AI

DOI：10.1109/TPWRS.2024.3483489

Original

Original request for help

Save

Abstract

En 中文

This letter proposes a structure-aware automatic differentiation method to accelerate the solution of alternating current optimal power flow (ACOPF) with nonlinear programming (NLP) solvers. By exploiting the isomorphic structure of nonlinear power flow constraints in ACOPF, specialized binary code is generated to efficiently compute the Jacobian and Hessian matrix. Numerical tests show that our implementation achieves over 18% speedup in the total solution process and 40% speedup in automatic differentiation for large-scale ACOPF problems compared to state-of-the-art algebraic modeling languages of NLP.

Keywords:

Jacobian matrices

Load flow

Codes

Computational modeling

Programming

Numerical models

Generators

Computational efficiency

Mathematical models

Laboratories

Automatic differentiation

nonlinear programming

optimal power flow

Accelerating Optimal Power Flow With Structure-Aware Automatic Differentiation and Code Generation

Abstract

Journal

Organization