Model reduction for a power grid model

TL;DR

This paper explores the challenges of creating reduced order models for the DeMarco power grid model, highlighting the importance of memory effects for accuracy due to oscillatory dynamics and lack of timescale separation.

Contribution

It demonstrates that incorporating short memory effects significantly improves the accuracy of reduced models for the DeMarco power grid.

Findings

Memory-inclusive models outperform memoryless ones

Oscillatory dynamics complicate model reduction

Long memory effects are crucial for accuracy

Abstract

We apply model reduction techniques to the DeMarco power grid model. The DeMarco model, when augmented by an appropriate line failure mechanism, can be used to study cascade failures. Here we examine the DeMarco model without the line failure mechanism and we investigate how to construct reduced order models for subsets of the state variables. We show that due to the oscillating nature of the solutions and the absence of timescale separation between resolved and unresolved variables, the construction of accurate reduced models becomes highly non-trivial since one has to account for long memory effects. In addition, we show that a reduced model which includes even a short memory is drastically better than a memoryless model.