Stability And Uncertainty Propagation In Power Networks: A Lyapunov-based Approach With Applications To Renewable Resources Allocation

TL;DR

This paper introduces a Lyapunov-based method to assess the impact of renewable energy uncertainties on power grid stability, enabling efficient identification of stable nodes for renewable resource allocation.

Contribution

It presents a novel, scalable approach using Lyapunov spectrum optimization to evaluate stability in nonlinear power system models with renewable uncertainties.

Findings

The method accurately identifies stable nodes in standard power networks.

It is computationally efficient and scalable for large systems.

The approach improves renewable resource placement decisions.

Abstract

The rapid increase in the integration of intermittent and stochastic renewable energy resources (RER) introduces challenging issues related to power system stability. Interestingly, identifying grid nodes that can best support stochastic loads from RER, has gained recent interest. Methods based on Lyapunov stability are commonly exploited to assess the stability of power networks. These strategies approach quantifying system stability while considering: (i) simplified reduced order power system models that do not model power flow constraints, or (ii) data-driven methods that are prone to measurement noise and hence can inaccurately depict stochastic loads as system instability. In this paper, while considering a nonlinear differential algebraic equation (NL-DAE) model, we introduce a new method for assessing the impact of uncertain renewable power injections on the stability of power system nodes/buses. The identification of stable nodes informs the operator/utility on how renewables injections affect the stability of the grid. The proposed method is based on optimizing metrics equivalent to the Lyapunov spectrum of exponents; its underlying properties result in a computationally efficient and scalable stable node identification algorithm for renewable energy resources allocation. The developed framework is studied on various standard power networks.