Tight LP Approximations for the Optimal Power Flow Problem

TL;DR

This paper introduces two LP approximation methods for the AC optimal power flow problem that balance accuracy and computational efficiency, outperforming traditional DC approximations and enabling better MILP extensions.

Contribution

The paper presents novel LP approximations based on tight polyhedral SOC constraints, improving accuracy while maintaining computational efficiency for large-scale power systems.

Findings

High accuracy validated on systems with up to 9241 buses

LP models outperform DC approximations in capturing physical aspects

Computational efficiency comparable or superior to SOCP models

Abstract

DC power flow approximations are ubiquitous in the electricity industry. However, these linear approximations fail to capture important physical aspects of power flow, such as the reactive power and voltage magnitude, which are crucial in many applications to ensure voltage stability and AC solution feasibility. This paper proposes two LP approximations of the AC optimal power flow problem, founded on tight polyhedral approximations of the SOC constraints, in the aim of retaining the good lower bounds of the SOCP relaxation and relishing the computational efficiency of LP solvers. The high accuracy of the two LP approximations is corroborated by rigorous computational evaluations on systems with up to 9241 buses and different operating conditions. The computational efficiency of the two proposed LP models is shown to be comparable to, if not better than, that of the SOCP models in most instances. This performance is ideal for MILP extensions of these LP models since MILP is computationally more efficient than MIQCP.