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A novel congestion management method through power system partitioning

Dehnavi, E ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1016/j.epsr.2022.108672
  3. Publisher: Elsevier Ltd , 2022
  4. Abstract:
  5. Congestion management (CM) is inevitable in today's competitive power markets. A CM method should be fast, fair, effective, and motivational. Moreover, in critical congestions, the system simplification and congestion clearing time are also of considerable importance. All the mentioned features can be found in an intelligent zonal CM. In this paper, using sensitivity analysis and power tracing techniques, a new CM model is developed based on power system partitioning. By the proposed model, a congestion index (CI) is introduced by which a candidate zone(s) is specified including some elements (generators and loads) with the highest CIs which means that they have high participation in congestion creation and simultaneously high effectiveness in congestion alleviation. Using the proposed method, power system operators can put their focus on the candidate zone(s) and alleviate the congestion effectively by some remedial measures such as generation rescheduling and load shedding. The proposed methodology is applied on IEEE 39-bus New England test system including 46 transmission lines. Results show the effectiveness and practicality of the proposed model, so that the congestion at the critical lines is alleviated well and their available transfer capabilities (ATCs) are increased to the reliable amounts. © 2022 Elsevier B.V
  6. Keywords:
  7. Congestion index ; Congestion management ; Power tracing techniques ; Electric load shedding ; Energy transfer ; Congestion index ; Congestions managements ; Management IS ; Management method ; Power tracing technique ; Power tracings ; Power transfer distribution factors ; System partitioning ; Through power ; Tracing technique ; Sensitivity analysis
  8. Source: Electric Power Systems Research ; Volume 213 , 2022 ; 03787796 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0378779622007416