IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i4p727-d137704.html
   My bibliography  Save this article

A Load-Shedding Model Based on Sensitivity Analysis in on-Line Power System Operation Risk Assessment

Author

Listed:
  • Zhe Zhang

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Hang Yang

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Xianggen Yin

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Jiexiang Han

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Yong Wang

    (Guangzhou Power Supply Company, Ltd., Guangzhou 510000, China)

  • Guoyan Chen

    (Guangzhou Power Supply Company, Ltd., Guangzhou 510000, China)

Abstract

The traditional load-shedding models usually use global optimization to get the load-shedding region, which will cause multiple variables, huge computing scale and other problems. This makes it hard to meet the requirements of timeliness in on-line power system operation risk assessment. In order to solve the problems of the present load-shedding models, a load-shedding model based on sensitivity analysis is proposed in this manuscript. By calculating the sensitivity of each branch on each bus, the collection of buses which have remarkable influence on reducing the power flow on over-load branches is obtained. In this way, global optimization is turned to local optimization, which can narrow the solution range. By comprehensively considering the importance of load bus and adjacency principle regarding the electrical coupling relationship, a load-shedding model is established to get the minimum value of the load reduction from different kinds of load buses, which is solved by the primal dual interior point algorithm. In the end, different cases on the IEEE 24-bus, IEEE 300-bus and other multi-node systems are simulated. The correctness and effectiveness of the proposed load-shedding model are demonstrated by the simulation results.

Suggested Citation

  • Zhe Zhang & Hang Yang & Xianggen Yin & Jiexiang Han & Yong Wang & Guoyan Chen, 2018. "A Load-Shedding Model Based on Sensitivity Analysis in on-Line Power System Operation Risk Assessment," Energies, MDPI, vol. 11(4), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:727-:d:137704
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/4/727/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/4/727/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Anna Rita Di Fazio & Mario Russo & Sara Valeri & Michele De Santis, 2016. "Sensitivity-Based Model of Low Voltage Distribution Systems with Distributed Energy Resources," Energies, MDPI, vol. 9(10), pages 1-16, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Biyun Chen & Haoying Chen & Yiyi Zhang & Junhui Zhao & Emad Manla, 2019. "Risk Assessment for the Power Grid Dispatching Process Considering the Impact of Cyber Systems," Energies, MDPI, vol. 12(6), pages 1-18, March.
    2. Amir Abdel Menaem & Rustam Valiev & Vladislav Oboskalov & Taher S. Hassan & Hegazy Rezk & Mohamed N. Ibrahim, 2020. "An Efficient Framework for Adequacy Evaluation through Extraction of Rare Load Curtailment Events in Composite Power Systems," Mathematics, MDPI, vol. 8(11), pages 1-21, November.
    3. Pau Casals-Torrens & Juan A. Martinez-Velasco & Alexandre Serrano-Fontova & Ricard Bosch, 2020. "Assessment of Unintentional Islanding Operations in Distribution Networks with Large Induction Motors," Energies, MDPI, vol. 13(2), pages 1-25, January.
    4. Michael Felix Pacevicius & Marilia Ramos & Davide Roverso & Christian Thun Eriksen & Nicola Paltrinieri, 2022. "Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures," Energies, MDPI, vol. 15(9), pages 1-40, April.

    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. Hamed Moazami Goodarzi & Mohammad Hosein Kazemi, 2017. "A Novel Optimal Control Method for Islanded Microgrids Based on Droop Control Using the ICA-GA Algorithm," Energies, MDPI, vol. 10(4), pages 1-17, April.
    2. Yunhwan Lee & Hwachang Song, 2019. "A Reactive Power Compensation Strategy for Voltage Stability Challenges in the Korean Power System with Dynamic Loads," Sustainability, MDPI, vol. 11(2), pages 1-19, January.
    3. Chong Cao & Zhouquan Wu & Bo Chen, 2020. "Electric Vehicle–Grid Integration with Voltage Regulation in Radial Distribution Networks," Energies, MDPI, vol. 13(7), pages 1-18, April.
    4. Hongmei Li & Hantao Cui & Chunjie Li, 2019. "Distribution Network Power Loss Analysis Considering Uncertainties in Distributed Generations," Sustainability, MDPI, vol. 11(5), pages 1-17, March.
    5. Giuseppe Fusco & Mario Russo & Michele De Santis, 2021. "Decentralized Voltage Control in Active Distribution Systems: Features and Open Issues," Energies, MDPI, vol. 14(9), pages 1-31, April.
    6. Andrés Felipe Pérez Posada & Juan G. Villegas & Jesús M. López-Lezama, 2017. "A Scatter Search Heuristic for the Optimal Location, Sizing and Contract Pricing of Distributed Generation in Electric Distribution Systems," Energies, MDPI, vol. 10(10), pages 1-16, September.
    7. Anna Rita Di Fazio & Mario Russo & Michele De Santis, 2019. "Zoning Evaluation for Voltage Optimization in Distribution Networks with Distributed Energy Resources," Energies, MDPI, vol. 12(3), pages 1-28, January.
    8. Min-Rong Chen & Huan Wang & Guo-Qiang Zeng & Yu-Xing Dai & Da-Qiang Bi, 2018. "Optimal P-Q Control of Grid-Connected Inverters in a Microgrid Based on Adaptive Population Extremal Optimization," Energies, MDPI, vol. 11(8), pages 1-19, August.
    9. Mu-Gu Jeong & Young-Jin Kim & Seung-Il Moon & Pyeong-Ik Hwang, 2017. "Optimal Voltage Control Using an Equivalent Model of a Low-Voltage Network Accommodating Inverter-Interfaced Distributed Generators," Energies, MDPI, vol. 10(8), pages 1-19, August.
    10. Jiawei Chen & Shuaicheng Hou & Xiang Li, 2018. "Decentralized Circulating Currents Suppression for Paralleled Inverters in Microgrids Using Adaptive Virtual Inductances," Energies, MDPI, vol. 11(7), pages 1-16, July.

    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:11:y:2018:i:4:p:727-:d:137704. 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.