Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Electrical Engineering and Systems Science > Systems and Control

arXiv:2211.15957 (eess)
[Submitted on 29 Nov 2022 (v1), last revised 12 Dec 2022 (this version, v2)]

Title:Advisory Tool for Managing Failure Cascades in Systems with Wind Power

Authors:Siyu Liu, Marija Ilic
View PDF
Abstract:This paper concerns the resilience of systems with wind power upon wind reduction by evaluating the potential of corrective actions, such as generation and load dispatch, on minimizing the effects of transmission line failures. Three functions (grid, consumer-centric loss, and resilience impact) are used to statistically evaluate the criticality of initial contingent failures and wind reductions. Our model is learned with Monte Carlo, convex optimization, and adaptive selection, illustrated on the IEEE-30 and IEEE-300 bus systems with both AC and DC models. We highlight the impact of wind reductions and propose physically implementable solutions.
Comments: Preprint. Submitted to the 2023 IEEE Power & Energy Society General Meeting (GM). arXiv admin note: substantial text overlap with arXiv:2211.01442
Subjects: Systems and Control (eess.SY)
Cite as: arXiv:2211.15957 [eess.SY]
  (or arXiv:2211.15957v2 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2211.15957

Submission history

From: Siyu Liu [view email]
[v1] Tue, 29 Nov 2022 06:33:11 UTC (1,540 KB)
[v2] Mon, 12 Dec 2022 04:35:15 UTC (1,533 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
license icon view license
Current browse context:
eess.SY
< prev   |   next >
new | recent | 2022-11
Change to browse by:
cs
cs.SY
eess

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)