IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i10p2019-d234495.html
   My bibliography  Save this article

A Vine-Copula Based Voltage State Assessment with Wind Power Integration

Author

Listed:
  • Xiaolu Chen

    (State Grid East Inner Mongolia Electric Power Company Limited Electric Power Research Institute, Hohhot 010020, China)

  • Ji Han

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Tingting Zheng

    (State Grid East Inner Mongolia Electric Power Company Limited Electric Power Research Institute, Hohhot 010020, China)

  • Ping Zhang

    (State Grid East Inner Mongolia Electric Power Company Limited Electric Power Research Institute, Hohhot 010020, China)

  • Simo Duan

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Shihong Miao

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

With the increasing rate of wind power installed capacity, voltage state assessment with large-scale wind power integration is of great significance. In this paper, a vine-copula based voltage state assessment method with large-scale wind power integration is proposed. Firstly, the nonparametric kernel density estimation is used to fit the wind speed distribution, and vine-copula is used to construct the wind speed joint distribution model of multiple regions. In order to obtain voltage distribution characteristics, probabilistic load flow based on the semi-invariant method and wind speed independent transformation based on the Rosenblatt transformation are described. On this basis, a voltage state assessment index is established for the more comprehensive evaluation of voltage characteristics, and a voltage state assessment procedure is proposed. Taking actual wind speed as an example, the case study of the IEEE 24-node power system and the east Inner Mongolia power system for voltage state assessment with large-scale wind power integration are studied. The simulation results verify the effectiveness of the proposed voltage state assessment method.

Suggested Citation

  • Xiaolu Chen & Ji Han & Tingting Zheng & Ping Zhang & Simo Duan & Shihong Miao, 2019. "A Vine-Copula Based Voltage State Assessment with Wind Power Integration," Energies, MDPI, vol. 12(10), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:2019-:d:234495
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/10/2019/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/10/2019/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaojun Shen & Chongcheng Zhou & Xuejiao Fu, 2018. "Study of Time and Meteorological Characteristics of Wind Speed Correlation in Flat Terrains Based on Operation Data," Energies, MDPI, vol. 11(1), pages 1-16, January.
    2. Yan Li & Ming Zhou & Dawei Wang & Yuehui Huang & Zifen Han, 2017. "Universal Generating Function Based Probabilistic Production Simulation Approach Considering Wind Speed Correlation," Energies, MDPI, vol. 10(11), pages 1-15, November.
    3. Ping He & Seyed Ali Arefifar & Congshan Li & Fushuan Wen & Yuqi Ji & Yukun Tao, 2019. "Enhancing Oscillation Damping in an Interconnected Power System with Integrated Wind Farms Using Unified Power Flow Controller," Energies, MDPI, vol. 12(2), pages 1-16, January.
    4. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    5. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction," Energies, MDPI, vol. 12(2), pages 1-42, January.
    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. Johannes Kaufmann & Philipp Artur Kienscherf & Wolfgang Ketter, 2020. "Modeling and Managing Joint Price and Volumetric Risk for Volatile Electricity Portfolios," Energies, MDPI, vol. 13(14), pages 1-19, July.

    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. Mirosław Parol & Paweł Piotrowski & Piotr Kapler & Mariusz Piotrowski, 2021. "Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control," Energies, MDPI, vol. 14(5), pages 1-29, February.
    2. Paweł Piotrowski & Mirosław Parol & Piotr Kapler & Bartosz Fetliński, 2022. "Advanced Forecasting Methods of 5-Minute Power Generation in a PV System for Microgrid Operation Control," Energies, MDPI, vol. 15(7), pages 1-23, April.
    3. Yuzgec, Ugur & Dokur, Emrah & Balci, Mehmet, 2024. "A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting," Energy, Elsevier, vol. 300(C).
    4. Manisha Sawant & Rupali Patil & Tanmay Shikhare & Shreyas Nagle & Sakshi Chavan & Shivang Negi & Neeraj Dhanraj Bokde, 2022. "A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(21), pages 1-24, October.
    5. Zhiyan Zhang & Aobo Deng & Zhiwen Wang & Jianyong Li & Hailiang Zhao & Xiaoliang Yang, 2024. "Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model," Energies, MDPI, vol. 17(11), pages 1-15, May.
    6. Khatereh Ghasvarian Jahromi & Davood Gharavian & Hamid Reza Mahdiani, 2023. "Wind power prediction based on wind speed forecast using hidden Markov model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 101-123, January.
    7. Bokde, Neeraj Dhanraj & Tranberg, Bo & Andresen, Gorm Bruun, 2021. "Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling," Applied Energy, Elsevier, vol. 281(C).
    8. Manisha Sawant & Mayur Kishor Shende & Andrés E. Feijóo-Lorenzo & Neeraj Dhanraj Bokde, 2021. "The State-of-the-Art Progress in Cloud Detection, Identification, and Tracking Approaches: A Systematic Review," Energies, MDPI, vol. 14(23), pages 1-26, December.
    9. Luis Lopez & Ingrid Oliveros & Luis Torres & Lacides Ripoll & Jose Soto & Giovanny Salazar & Santiago Cantillo, 2020. "Prediction of Wind Speed Using Hybrid Techniques," Energies, MDPI, vol. 13(23), pages 1-13, November.
    10. Wu, Huijuan & Meng, Keqilao & Fan, Daoerji & Zhang, Zhanqiang & Liu, Qing, 2022. "Multistep short-term wind speed forecasting using transformer," Energy, Elsevier, vol. 261(PA).
    11. Hajirahimi, Zahra & Khashei, Mehdi & Etemadi, Sepideh, 2022. "A novel class of reliability-based parallel hybridization (RPH) models for time series forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    12. Zhengxuan Xiao & Fei Tang & Mengyuan Wang, 2023. "Wind Power Short-Term Forecasting Method Based on LSTM and Multiple Error Correction," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    13. Zhiyong Guo & Fangzheng Wei & Wenkai Qi & Qiaoli Han & Huiyuan Liu & Xiaomei Feng & Minghui Zhang, 2024. "A Time Series Prediction Model for Wind Power Based on the Empirical Mode Decomposition–Convolutional Neural Network–Three-Dimensional Gated Neural Network," Sustainability, MDPI, vol. 16(8), pages 1-20, April.
    14. Manisha Sawant & Sameer Thakare & A. Prabhakara Rao & Andrés E. Feijóo-Lorenzo & Neeraj Dhanraj Bokde, 2021. "A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics," Energies, MDPI, vol. 14(8), pages 1-30, April.
    15. Michele Lo Giudice & Edoardo Ferlazzo & Nadia Mammone & Sara Gasparini & Vittoria Cianci & Angelo Pascarella & Anna Mammì & Danilo Mandic & Francesco Carlo Morabito & Umberto Aguglia, 2022. "Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects," IJERPH, MDPI, vol. 19(23), pages 1-9, November.
    16. Mingcan Li & Hanbin Xiao & Lin Pan & Chengjun Xu, 2019. "Study of Generalized Interaction Wake Models Systems with ELM Variation for Off-Shore Wind Farms," Energies, MDPI, vol. 12(5), pages 1-32, March.
    17. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
    18. Qin Chen & Yan Chen & Xingzhi Bai, 2020. "Deterministic and Interval Wind Speed Prediction Method in Offshore Wind Farm Considering the Randomness of Wind," Energies, MDPI, vol. 13(21), pages 1-23, October.
    19. Rasool Bukhsh & Muhammad Umar Javed & Aisha Fatima & Nadeem Javaid & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Cost Efficient Real Time Electricity Management Services for Green Community Using Fog," Energies, MDPI, vol. 13(12), pages 1-23, June.
    20. Kai-Hung Lu & Chih-Ming Hong & Fu-Sheng Cheng, 2022. "Enhanced Dynamic Performance in Hybrid Power System Using a Designed ALTS-PFPNN Controller," Energies, MDPI, vol. 15(21), pages 1-22, November.

    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:12:y:2019:i:10:p:2019-:d:234495. 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.