Numerical Performance of Different Formulations for Alternating Current Optimal Power Flow

TL;DR

This paper evaluates various ACOPF formulations and their computational performance using interior point methods across large-scale power systems, highlighting the most efficient approaches for real-time applications.

Contribution

It compares different ACOPF formulations with various solvers and constraints, identifying the most computationally efficient structures for large-scale power networks.

Findings

Least sparse formulations with polar voltages perform best.

Box constraints improve nodal and branch flow models.

Large test cases validate the results across diverse system sizes.

Abstract

Alternating current optimal power flow (ACOPF) problems are nonconvex and nonlinear optimization problems. Utilities and independent service operators (ISO) require ACOPF to be solved in almost real time. Interior point methods (IPMs) are one of the powerful methods for solving large-scale nonlinear optimization problems and are a suitable approach for solving ACOPF with large-scale real-world transmission networks. Moreover, the choice of the formulation is as important as choosing the algorithm for solving an ACOPF problem. In this paper, different ACOPF formulations with various linear solvers and the impact of employing box constraints are evaluated for computational viability and best performance when using IPMs. Different optimization structures are used in these formulations to model the ACOPF problem representing a range of sparsity. The numerical experiments suggest that the least sparse ACOPF formulations with polar voltages yield the best computational results. Additionally, nodal injected models and current-based branch flow models are improved by enforcing box constraints. A wide range of test cases, ranging from 500-bus systems to 9591-bus systems, are used to verify the test results.