Optimisation of Power Grid Stability Under Uncertainty

TL;DR

This paper addresses the challenge of tuning damping parameters in power grids under uncertainty, proposing robust optimization methods to enhance grid stability against worst-case scenarios.

Contribution

It introduces a quantile-based stability metric and compares optimization approaches, offering heuristics for robust damping parameter tuning in uncertain power systems.

Findings

Robust tuning improves grid stability under uncertainty.

Simpler methods can outperform complex ones in robustness.

Heuristics effectively identify stable damping parameters.

Abstract

The increased integration of intermittent and decentralised forms of power production has eroded the stability margins of power grids and made it more challenging to ensure reliable and secure power transmission. Reliable grid operation requires system-scale stability in response to perturbations in supply or load; previous studies have shown that this can be achieved by tuning the effective damping parameters of the generators in the grid. In this paper, we present and analyse the problem of tuning damping parameters when there is some uncertainty in the underlying system. We show that sophisticated methods that assume no uncertainty can yield results that are less robust than those produced by simpler methods. We define a quantile-based metric of stability that ensures that power grids remain stable even as worst-case scenarios are approached, and we develop optimisation methods for tuning damping parameters to achieve this stability. By comparing optimisation methods that rely on different assumptions, we suggest efficient heuristics for finding parameters that achieve highly stable and robust grids.