A Convexification Approach for Small-Signal Stability Constrained Optimal Power Flow

TL;DR

This paper introduces a convexification method for small-signal stability constrained optimal power flow that avoids eigenvalue analysis, utilizing SDP and BMI-based conditions for efficient large-scale system stability solutions.

Contribution

It presents a novel convexification approach based on BMI and SDP that improves computational efficiency for large power systems without relying on eigenvalue analysis.

Findings

Successfully applied to multiple test systems including WECC 9-bus and IEEE 118-bus.

Achieves stable equilibrium points with minimal additional cost.

Demonstrates computational efficiency and scalability for large systems.

Abstract

In this paper, a novel convexification approach for Small-Signal Stability Constraint Optimal Power Flow (SSSC-OPF) has been presented that does not rely on eigenvalue analysis. The proposed methodology is based on the sufficient condition for the small-signal stability, developed as a Bilinear Matrix Inequality (BMI), and uses network structure-preserving Differential Algebraic Equation (DAE) modeling of the power system. The proposed formulation is based on Semi-definite Programming (SDP) and objective penalization that has been proposed for feasible solution recovery, making the method computationally efficient for large-scale systems. A vector-norm based objective penalty function has also been proposed for feasible solution recovery while working over large and dense BMIs with matrix variables. An effectiveness study carried out on WECC 9-bus, New England 39-bus, and IEEE 118-bus test systems show that the proposed method is capable of achieving a stable equilibrium point without inflicting a high stability-induced additional cost.