Benchmarking the performance of controllers for power grid transient stability

TL;DR

This paper develops an optimal control framework for power grid stability, providing a benchmark to evaluate distributed controllers and highlighting the importance of information sharing for cost-efficient and stable grid operation.

Contribution

It formulates a centralized optimal control problem for power grid stability and uses it to benchmark distributed controllers, revealing the benefits of information sharing.

Findings

Optimal control minimizes active power for stability within constraints.

Distributed controllers perform better when they share disturbance information.

Cost-efficient control involves distributing responses across all grid nodes.

Abstract

As the energy transition transforms power grids across the globe, it poses several challenges regarding grid design and control. In particular, high levels of intermittent renewable generation complicate the task of continuously balancing power supply and demand, requiring sufficient control actions. Although there exist several proposals to control the grid, most of them have not demonstrated to be cost efficient in terms of optimal control theory. Here, we mathematically formulate an optimal centralized (therefore non-local) control problem for stable operation of power grids and determine the minimal amount of active power necessary to guarantee a stable service within the operational constraints, minimizing a suitable cost function at the same time. This optimal control can be used to benchmark control proposals and we demonstrate this benchmarking process by investigating the performance of three distributed controllers, two of which are fully decentralized, that have been recently studied in the physics and power systems engineering literature. Our results show that cost efficient controllers distribute the controlled response amongst all nodes in the power grid. Additionally, superior performance can be achieved by incorporating sufficient information about the disturbance causing the instability. Overall, our results can help design and benchmark secure and cost-efficient controllers.