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:2212.12661 (eess)
[Submitted on 24 Dec 2022]

Title:Transmission Congestion Management with Generalized Generation Shift Distribution Factors

Authors:Shutong Pu, Guangchun Ruan, Xinfei Yan, Haiwang Zhong
View PDF
Abstract:A major concern in modern power systems is that the popularity and fluctuating characteristics of renewable energy may cause more and more transmission congestion events. Traditional congestion management modeling involves AC or DC power flow equations, while the former equation always accompanies great amount of computation, and the latter cannot consider voltage amplitude and reactive power. Therefore, this paper proposes a congestion management approach incorporating a specially-designed generalized generator shift distribution factor (GSDF) to derive a computationally-efficient and accurate management strategies. This congestion management strategy involves multiple balancing generators for generation shift operation. The proposed model is superior in a low computational complexity (linear equation) and versatile modeling representation with full consideration of voltage amplitude and reactive power.
Comments: 5 pages, 4 figures. Accepted by conference: ICPES 2022
Subjects: Systems and Control (eess.SY)
Cite as: arXiv:2212.12661 [eess.SY]
  (or arXiv:2212.12661v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2212.12661

Submission history

From: Shutong Pu [view email]
[v1] Sat, 24 Dec 2022 05:34:26 UTC (2,409 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 | 2022-12
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?)