IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i8p3396-d1121806.html
   My bibliography  Save this article

A Novel Methodology for Strengthening Stability in Electrical Power Systems by Considering Fast Voltage Stability Index under N − 1 Scenarios

Author

Listed:
  • Manuel Dario Jaramillo

    (Smart Grid Research Group—GIREI (Spanish Acronym), Electrical Engineering Deparment, Salesian Polytechnic University, Quito EC170702, Ecuador)

  • Diego Francisco Carrión

    (Smart Grid Research Group—GIREI (Spanish Acronym), Electrical Engineering Deparment, Salesian Polytechnic University, Quito EC170702, Ecuador)

  • Jorge Paul Muñoz

    (Smart Grid Research Group—GIREI (Spanish Acronym), Electrical Engineering Deparment, Salesian Polytechnic University, Quito EC170702, Ecuador)

Abstract

An electrical power system (EPS) is subject to unexpected events that might cause the outage of elements such as transformers, generators, and transmission lines. For this reason, the EPS should be able to withstand the failure of one of these elements without changing its operational characteristics; this operativity functionality is called N − 1 contingency. This paper proposes a methodology for the optimal location and sizing of a parallel static Var compensator (SVC) in an EPS to reestablish the stability conditions of the system before N − 1 contingencies take place. The system’s stability is analyzed using the fast voltage stability index (FVSI) criterion, and the optimal SVC is determined by also considering the lowest possible cost. This research considers N − 1 contingencies involving the disconnection of transmission lines. Then, the methodology analyzes every scenario in which a transmission line is disconnected. For every one of them, the algorithm finds the weakest transmission line by comparing FVSI values (the higher the FVSI, the closer the transmission line is to instability); afterward, when the weakest line is selected, by brute force, an SVC with values of 5 Mvar to 100 Mvars in steps of 5 Mvar is applied to the sending bus bar of this transmission line. Then, the SVC value capable of reestablishing each line’s FVSI to its pre-contingency value while also reestablishing each bus-bar’s voltage profile and having the lowest cost is selected as the optimal solution. The proposed methodology was tested on IEEE 14, 30, and 118 bus bars as case studies and was capable of reestablishing the FVSI in each contingency to its value prior to the outage, which indicates that the algorithm performs with 100% accuracy. Additionally, voltage profiles were also reestablished to their pre-contingency values, and in some cases, they were even higher than the original values. Finally, these results were achieved with a single solution for a unique SVC located in one bus bar that is capable of reestablishing operational conditions under all possible contingency scenarios.

Suggested Citation

  • Manuel Dario Jaramillo & Diego Francisco Carrión & Jorge Paul Muñoz, 2023. "A Novel Methodology for Strengthening Stability in Electrical Power Systems by Considering Fast Voltage Stability Index under N − 1 Scenarios," Energies, MDPI, vol. 16(8), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3396-:d:1121806
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/8/3396/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/8/3396/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Yang & Zhang, Meng & Chen, Chen, 2022. "A Deep-Learning intelligent system incorporating data augmentation for Short-Term voltage stability assessment of power systems," Applied Energy, Elsevier, vol. 308(C).
    2. Manuel Jaramillo & Diego Carrión & Jorge Muñoz, 2022. "A Deep Neural Network as a Strategy for Optimal Sizing and Location of Reactive Compensation Considering Power Consumption Uncertainties," Energies, MDPI, vol. 15(24), pages 1-21, December.
    3. Diego Carrión & Edwin García & Manuel Jaramillo & Jorge W. González, 2021. "A Novel Methodology for Optimal SVC Location Considering N-1 Contingencies and Reactive Power Flows Reconfiguration," Energies, MDPI, vol. 14(20), pages 1-17, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francisco Quinteros & Diego Carrión & Manuel Jaramillo, 2022. "Optimal Power Systems Restoration Based on Energy Quality and Stability Criteria," Energies, MDPI, vol. 15(6), pages 1-23, March.
    2. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    3. Jaime Pilatásig & Diego Carrión & Manuel Jaramillo, 2022. "Resilience Maximization in Electrical Power Systems through Switching of Power Transmission Lines," Energies, MDPI, vol. 15(21), pages 1-15, November.
    4. Manuel S. Alvarez-Alvarado & Johnny Rengifo & Rommel M. Gallegos-Núñez & José G. Rivera-Mora & Holguer H. Noriega & Washington Velasquez & Daniel L. Donaldson & Carlos D. Rodríguez-Gallegos, 2022. "Particle Swarm Optimization for Optimal Frequency Response with High Penetration of Photovoltaic and Wind Generation," Energies, MDPI, vol. 15(22), pages 1-12, November.
    5. Xie, Haonan & Jiang, Meihui & Zhang, Dongdong & Goh, Hui Hwang & Ahmad, Tanveer & Liu, Hui & Liu, Tianhao & Wang, Shuyao & Wu, Thomas, 2023. "IntelliSense technology in the new power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 177(C).
    6. Chao Yu & Yang Zhou & Xiaolong Cui, 2023. "A Client-Cloud-Chain Data Annotation System of Internet of Things for Semi-Supervised Missing Data," Mathematics, MDPI, vol. 11(21), pages 1-18, November.
    7. Oludamilare Bode Adewuyi & Komla A. Folly & David T. O. Oyedokun & Emmanuel Idowu Ogunwole, 2022. "Power System Voltage Stability Margin Estimation Using Adaptive Neuro-Fuzzy Inference System Enhanced with Particle Swarm Optimization," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    8. Shi, Zhongtuo & Yao, Wei & Zhao, Yifan & Ai, Xiaomeng & Wen, Jinyu & Cheng, Shijie, 2024. "Two-stage weakly supervised learning to mitigate label noise for intelligent identification of power system dominant instability mode," Applied Energy, Elsevier, vol. 359(C).
    9. Li, Yang & Wang, Ruinong & Li, Yuanzheng & Zhang, Meng & Long, Chao, 2023. "Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach," Applied Energy, Elsevier, vol. 329(C).
    10. Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).
    11. Zhan, Xianwen & Han, Song & Rong, Na & Cao, Yun, 2023. "A hybrid transfer learning method for transient stability prediction considering sample imbalance," Applied Energy, Elsevier, vol. 333(C).
    12. Xiaoying Ren & Yongqian Liu & Fei Zhang & Lingfeng Li, 2024. "A Deep Learning Quantile Regression Photovoltaic Power-Forecasting Method under a Priori Knowledge Injection," Energies, MDPI, vol. 17(16), pages 1-25, August.
    13. Li, Yang & Cao, Jiting & Xu, Yan & Zhu, Lipeng & Dong, Zhao Yang, 2024. "Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3396-:d:1121806. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.