
Model reduction for a power grid model
Author(s) -
Jing Li,
Panos Stinis
Publication year - 2022
Publication title -
journal of computational dynamics
Language(s) - English
Resource type - Journals
eISSN - 2158-2505
pISSN - 2158-2491
DOI - 10.3934/jcd.2021019
Subject(s) - reduction (mathematics) , grid , computer science , power (physics) , power grid , order (exchange) , state (computer science) , algorithm , theoretical computer science , mathematics , physics , geometry , quantum mechanics , finance , economics
We examine the complexity of constructing reduced order models for subsets of the variables needed to represent the state of the power grid. In particular, we apply model reduction techniques to the DeMarco-Zheng power grid model. 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 because one has to account for long memory effects. In addition, we show that a reduced model that includes even a short memory is drastically better than a memoryless model.