Ensuring Transient Stability with Guaranteed Region of Attraction in DC Microgrids

TL;DR

This paper introduces a control synthesis method for DC microgrids that guarantees a specific region of attraction, ensuring transient stability despite the destabilizing effects of constant power loads.

Contribution

It develops a novel control synthesis algorithm that guarantees a desired region of attraction in general DC microgrids, addressing limitations of existing numerical approaches.

Findings

The proposed method guarantees a specified ROA for DC microgrids.

The control synthesis problem is formulated as a linear constraint optimization.

Simulation results confirm the effectiveness of the guaranteed stability approach.

Abstract

DC microgrids have promising applications in renewable integration due to their better energy efficiency when connecting DC components. However, they might be unstable since many loads in a DC microgrid are regulated as constant power loads (CPLs) that have a destabilizing negative impedance effect. As a result, the state trajectory displacement caused by abrupt load changes or contingencies can easily lead to instability. Many existing works have been devoted to studying the region of attraction (ROA) of a DC microgrid, in which the system is guaranteed to be asymptotically stable. Nevertheless, existing work either focuses on using numerical methods for ROA approximations that generally have no performance guarantees or cannot ensure a desired ROA for a general DC microgrid. To close this gap, this paper develops an innovative control synthesis algorithm to make a general DC microgrid have a theoretically guaranteed ROA, for example, to cover the entirety of its operating range regarding state trajectories. We first study the nonlinear dynamics of a DC microgrid to derive a novel transient stability condition to rigorously certify whether a given operating range is a subset of the ROA; then, we formulate a control synthesis optimization problem to guarantee the condition's satisfaction. This condition is a linear constraint, and the optimization problem resembles an optimal power flow problem and has a good computational behavior. Simulation case studies verify the validity of the proposed work.