IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i12p4432-d841606.html
   My bibliography  Save this article

A Comprehensive Evaluation Method and Strengthening Measures for AC/DC Hybrid Power Grids

Author

Listed:
  • Junli Zhang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Guoteng Wang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Zheng Xu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Zheren Zhang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

Due to the complex operation characteristics of AC/DC hybrid power grids, it is a great challenge to comprehensively evaluate their stability and formulate appropriate strengthening schemes for them. To address this challenge, the following studies are carried out in this paper. First, an evaluation system including six indicators is established for AC/DC hybrid power grids. Next, aiming at the problems that may be revealed by the comprehensive evaluation, strengthening measures that can be utilized are introduced. Then, a comprehensive evaluation method for AC/DC hybrid power grids and their potential strengthening schemes is proposed. This method can deal with three issues, including normalization of the indicators, weighting of the indicators, and the trade-off of technology and cost. Finally, in the case study of the Qujing Power Grid, the main problems faced by regional power grids are pointed out, and four feasible strengthening schemes are formulated and evaluated.

Suggested Citation

  • Junli Zhang & Guoteng Wang & Zheng Xu & Zheren Zhang, 2022. "A Comprehensive Evaluation Method and Strengthening Measures for AC/DC Hybrid Power Grids," Energies, MDPI, vol. 15(12), pages 1-20, June.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4432-:d:841606
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/12/4432/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/12/4432/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dahai You, A. & QingQian Chen, B. & Xianggen Yin, C. & Bo Wang, D., 2011. "A study of Electrical Security Risk Assessment System based on Electricity Regulation," Energy Policy, Elsevier, vol. 39(4), pages 2062-2074, April.
    2. Rezaei, Jafar, 2016. "Best-worst multi-criteria decision-making method: Some properties and a linear model," Omega, Elsevier, vol. 64(C), pages 126-130.
    3. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    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. James J. H. Liou & Perry C. Y. Liu & Huai-Wei Lo, 2020. "A Failure Mode Assessment Model Based on Neutrosophic Logic for Switched-Mode Power Supply Risk Analysis," Mathematics, MDPI, vol. 8(12), pages 1-19, December.
    2. Liang, Fuqi & Brunelli, Matteo & Rezaei, Jafar, 2020. "Consistency issues in the best worst method: Measurements and thresholds," Omega, Elsevier, vol. 96(C).
    3. Salimi, Negin & Rezaei, Jafar, 2018. "Evaluating firms’ R&D performance using best worst method," Evaluation and Program Planning, Elsevier, vol. 66(C), pages 147-155.
    4. Ravindra Singh Saluja & Varinder Singh, 2023. "Attribute-based characterization, coding, and selection of joining processes using a novel MADM approach," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 616-655, June.
    5. Hamzeh Soltanali & Mehdi Khojastehpour & Siamak Kheybari, 2023. "Evaluating the critical success factors for maintenance management in agro-industries using multi-criteria decision-making techniques," Operations Management Research, Springer, vol. 16(2), pages 949-968, June.
    6. Yossi Hadad & Baruch Keren & Dima Alberg, 2023. "An Expert System for Ranking and Matching Electric Vehicles to Customer Specifications and Requirements," Energies, MDPI, vol. 16(11), pages 1-18, May.
    7. Vieira, Fabiana C. & Ferreira, Fernando A.F. & Govindan, Kannan & Ferreira, Neuza C.M.Q.F. & Banaitis, Audrius, 2022. "Measuring urban digitalization using cognitive mapping and the best worst method (BWM)," Technology in Society, Elsevier, vol. 71(C).
    8. Besharati Fard, Moein & Moradian, Parisa & Emarati, Mohammadreza & Ebadi, Mehdi & Gholamzadeh Chofreh, Abdoulmohammad & Klemeŝ, Jiří Jaromír, 2022. "Ground-mounted photovoltaic power station site selection and economic analysis based on a hybrid fuzzy best-worst method and geographic information system: A case study Guilan province," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    9. Negin Salimi & Jafar Rezaei, 2016. "Measuring efficiency of university-industry Ph.D. projects using best worst method," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1911-1938, December.
    10. Chun-Chieh Tseng & Jun-Yi Zeng & Min-Liang Hsieh & Chih-Hung Hsu, 2022. "Analysis of Innovation Drivers of New and Old Kinetic Energy Conversion Using a Hybrid Multiple-Criteria Decision-Making Model in the Post-COVID-19 Era: A Chinese Case," Mathematics, MDPI, vol. 10(20), pages 1-25, October.
    11. Tavana, Madjid & Khalili Nasr, Arash & Mina, Hassan & Michnik, Jerzy, 2022. "A private sustainable partner selection model for green public-private partnerships and regional economic development," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    12. Jairo Ortega & Sarbast Moslem & János Tóth & Tamás Péter & Juan Palaguachi & Mario Paguay, 2020. "Using Best Worst Method for Sustainable Park and Ride Facility Location," Sustainability, MDPI, vol. 12(23), pages 1-18, December.
    13. Vineet Kaushik & Ashwani Kumar & Himanshu Gupta & Gaurav Dixit, 2022. "Modelling and prioritizing the factors for online apparel return using BWM approach," Electronic Commerce Research, Springer, vol. 22(3), pages 843-873, September.
    14. Amelia Bilbao-Terol & Mar Arenas-Parra & Raquel Quiroga-García & Celia Bilbao-Terol, 2022. "An extended best–worst multiple reference point method: application in the assessment of non-life insurance companies," Operational Research, Springer, vol. 22(5), pages 5323-5362, November.
    15. Sarbast Moslem & Muhammet Gul & Danish Farooq & Erkan Celik & Omid Ghorbanzadeh & Thomas Blaschke, 2020. "An Integrated Approach of Best-Worst Method (BWM) and Triangular Fuzzy Sets for Evaluating Driver Behavior Factors Related to Road Safety," Mathematics, MDPI, vol. 8(3), pages 1-20, March.
    16. Sahar Moazzeni & Sobhan Mostafayi Darmian & Lars Magnus Hvattum, 2023. "Multiple criteria decision making and robust optimization to design a development plan for small and medium-sized enterprises in the east of Iran," Operational Research, Springer, vol. 23(1), pages 1-32, March.
    17. Xueliang Li & Bingkang Li & Long Zhao & Huiru Zhao & Wanlei Xue & Sen Guo, 2019. "Forecasting the Short-Term Electric Load Considering the Influence of Air Pollution Prevention and Control Policy via a Hybrid Model," Sustainability, MDPI, vol. 11(10), pages 1-21, May.
    18. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    19. van de Kaa, G. & Fens, T. & Rezaei, J. & Kaynak, D. & Hatun, Z. & Tsilimeni-Archangelidi, A., 2019. "Realizing smart meter connectivity: Analyzing the competing technologies Power line communication, mobile telephony, and radio frequency using the best worst method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 320-327.
    20. Lo, Huai-Wei & Liou, James J.H. & Huang, Chun-Nen & Chuang, Yen-Ching, 2019. "A novel failure mode and effect analysis model for machine tool risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 173-183.

    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:15:y:2022:i:12:p:4432-:d:841606. 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.