English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Declines in cost and concerns about the environmental impact of traditionalgeneration have boosted the penetration of renewables and non-conventionaldistributed energy resources into the power grid. The intermittent availabilityof these resources causes the inertia of the power system to vary over time. Asa result, there is a need to go beyond traditional controllers designed toregulate frequency under the assumption of invariant dynamics. This paperpresents a learning-based framework for the design of stable controllers basedon imitating datasets obtained from linear-quadratic regulator (LQR)formulations for different switching sequences of inertia modes. The proposedcontroller is linear and invariant, thereby interpretable, does not require theknowledge of the current operating mode, and is guaranteed to stabilize theswitching power dynamics. We also show that it is always possible to stabilizethe switched system using a communication-free local controller, whoseimplementation only requires each node to use its own state. Simulations on a12-bus 3-region network illustrate our results.

Learning Invariant Stabilizing Controllers for Frequency Regulation under Variable Inertia