Dynamic Connectivity and Local Frequency Strength under Stochastic Variations

TL;DR

This paper presents the Generalized Fiedler Vector (GFV), a new metric combining network topology and inertia to assess dynamic connectivity in power systems, aiding optimal placement of stochastic generation.

Contribution

It introduces the GFV metric that integrates system Laplacian and inertia distribution to evaluate dynamic connectivity under stochastic variations.

Findings

GFV effectively captures the impact of topology and inertia on frequency variability.

Monte Carlo simulations validate GFV's utility in power system analysis.

Optimal siting strategies improve system stability under stochastic conditions.

Abstract

This paper introduces a novel metric, termed the Generalized Fiedler Vector (GFV), to evaluate the \textit{dynamic connectivity} in power systems. The proposed metric leverages the network connectivity, represented by the system Laplacian matrix, together with the nodal inertia distribution, following a formulation previously developed by the first author. By capturing the interplay between system topology and dynamic properties, the GFV provides valuable insights for the optimal siting of stochastic generation to mitigate its impact on local and system-wide frequency variability. The effectiveness of the proposed approach is demonstrated through Monte Carlo simulations performed on the IEEE 68-bus test system.