IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i21p2771-d669981.html
   My bibliography  Save this article

A Mathematical Approach to Simultaneously Plan Generation and Transmission Expansion Based on Fault Current Limiters and Reliability Constraints

Author

Listed:
  • Mohamed M. Refaat

    (Photovoltaic Cells Department, Electronics Research Institute, Cairo 11843, Egypt
    Electrical Power Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt)

  • Shady H. E. Abdel Aleem

    (Electrical Engineering Department, Valley High Institute of Engineering and Technology, Science Valley Academy, Qalubia 44971, Egypt)

  • Yousry Atia

    (Photovoltaic Cells Department, Electronics Research Institute, Cairo 11843, Egypt)

  • Ziad M. Ali

    (Electrical Engineering Department, College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia
    Electrical Engineering Department, Aswan Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Adel El-Shahat

    (Energy Technology Program, School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA)

  • Mahmoud M. Sayed

    (Electrical Power Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt)

Abstract

Today, generation and transmission expansion planning (G&TEP) to meet potential load growth is restricted by reliability constraints and the presence of uncertainties. This study proposes the reliability constrained planning method for integrated renewable energy sources and transmission expansion considering fault current limiter (FCL) placement and sizing and N-1 security. Moreover, an approach for dealing with uncertain events is adopted. The proposed planning model translates into a mixed-integer non-linear programming model, which is complex and not easy to solve. The problem was formulated as a tri-level problem, and a hybridization framework between meta-heuristic and mathematical optimization algorithms was introduced to avoid linearization errors and simplify the solution. For this reason, three meta-heuristic techniques were tested. The proposed methodology was conducted on the Egyptian West Delta system. The numerical results demonstrated the efficiency of integrating G&TEP and FCL allocation issues in improving power system reliability. Furthermore, the effectiveness of the hybridization algorithm in solving the suggested problem was validated by comparison with other optimization algorithms.

Suggested Citation

  • Mohamed M. Refaat & Shady H. E. Abdel Aleem & Yousry Atia & Ziad M. Ali & Adel El-Shahat & Mahmoud M. Sayed, 2021. "A Mathematical Approach to Simultaneously Plan Generation and Transmission Expansion Based on Fault Current Limiters and Reliability Constraints," Mathematics, MDPI, vol. 9(21), pages 1-21, November.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2771-:d:669981
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/21/2771/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/21/2771/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zolfaghari, Saeed & Akbari, Tohid, 2018. "Bilevel transmission expansion planning using second-order cone programming considering wind investment," Energy, Elsevier, vol. 154(C), pages 455-465.
    2. Yamchi, Hamid Bakhshi & Safari, Amin & Guerrero, Josep M., 2021. "A multi-objective mixed integer linear programming model for integrated electricity-gas network expansion planning considering the impact of photovoltaic generation," Energy, Elsevier, vol. 222(C).
    3. Miguel Cañas-Carretón & Miguel Carrión & Florin Iov, 2021. "Towards Renewable-Dominated Power Systems Considering Long-Term Uncertainties: Case Study of Las Palmas," Energies, MDPI, vol. 14(11), pages 1-38, June.
    4. Yuhong Wang & Xu Zhou & Yunxiang Shi & Zongsheng Zheng & Qi Zeng & Lei Chen & Bo Xiang & Rui Huang, 2021. "Transmission Network Expansion Planning Considering Wind Power and Load Uncertainties Based on Multi-Agent DDQN," Energies, MDPI, vol. 14(19), pages 1-28, September.
    5. Sun, M. & Teng, F. & Konstantelos, I. & Strbac, G., 2018. "An objective-based scenario selection method for transmission network expansion planning with multivariate stochasticity in load and renewable energy sources," Energy, Elsevier, vol. 145(C), pages 871-885.
    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. Adel A. Abou El-Ela & Ragab A. El-Sehiemy & Abdullah M. Shaheen & Aya R. Ellien, 2022. "Review on Active Distribution Networks with Fault Current Limiters and Renewable Energy Resources," Energies, MDPI, vol. 15(20), pages 1-30, October.
    2. Muhyaddin Rawa, 2022. "Towards Avoiding Cascading Failures in Transmission Expansion Planning of Modern Active Power Systems Using Hybrid Snake-Sine Cosine Optimization Algorithm," Mathematics, MDPI, vol. 10(8), pages 1-25, 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. Xinghua Wang & Fucheng Zhong & Yilin Xu & Xixian Liu & Zezhong Li & Jianan Liu & Zhuoli Zhao, 2023. "Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics," Energies, MDPI, vol. 16(18), pages 1-19, September.
    2. Faezeh Akhavizadegan & Lizhi Wang & James McCalley, 2020. "Scenario Selection for Iterative Stochastic Transmission Expansion Planning," Energies, MDPI, vol. 13(5), pages 1-18, March.
    3. Changrong Liu & Hanqing Wang & Zhiqiang Liu & Zhiyong Wang & Sheng Yang, 2021. "Research on a Bi-Level Collaborative Optimization Method for Planning and Operation of Multi-Energy Complementary Systems," Energies, MDPI, vol. 14(23), pages 1-20, November.
    4. Dagoumas, Athanasios S. & Koltsaklis, Nikolaos E., 2019. "Review of models for integrating renewable energy in the generation expansion planning," Applied Energy, Elsevier, vol. 242(C), pages 1573-1587.
    5. Finke, Jonas & Bertsch, Valentin, 2022. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," MPRA Paper 115504, University Library of Munich, Germany.
    6. Zhang, Heng & Hu, Xiao & Cheng, Haozhong & Zhang, Shenxi & Hong, Shaoyun & Gu, Qingfa, 2021. "Coordinated scheduling of generators and tie lines in multi-area power systems under wind energy uncertainty," Energy, Elsevier, vol. 222(C).
    7. Liu, Aaron & Miller, Wendy & Cholette, Michael E. & Ledwich, Gerard & Crompton, Glenn & Li, Yong, 2021. "A multi-dimension clustering-based method for renewable energy investment planning," Renewable Energy, Elsevier, vol. 172(C), pages 651-666.
    8. Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
    9. Sun, Mingyang & Cremer, Jochen & Strbac, Goran, 2018. "A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration," Applied Energy, Elsevier, vol. 228(C), pages 546-555.
    10. Zhang, Houwang & Wu, Qiuwei & Chen, Jian & Lu, Lina & Zhang, Jiangfeng & Zhang, Shuyi, 2023. "Multiple stage stochastic planning of integrated electricity and gas system based on distributed approximate dynamic programming," Energy, Elsevier, vol. 270(C).
    11. Julien Keutchayan & Janosch Ortmann & Walter Rei, 2023. "Problem-driven scenario clustering in stochastic optimization," Computational Management Science, Springer, vol. 20(1), pages 1-33, December.
    12. Wei, Zhinong & Yang, Li & Chen, Sheng & Ma, Zhoujun & Zang, Haixiang & Fei, Youdie, 2022. "A multi-stage planning model for transitioning to low-carbon integrated electric power and natural gas systems," Energy, Elsevier, vol. 254(PC).
    13. Zhang, Bin & Wu, Xuewei & Ghias, Amer M.Y.M. & Chen, Zhe, 2023. "Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: An improved soft actor–critic approach," Energy, Elsevier, vol. 271(C).
    14. Finke, Jonas & Bertsch, Valentin, 2023. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," Applied Energy, Elsevier, vol. 332(C).
    15. Masoud Khatibi & Abbas Rabiee & Amir Bagheri, 2023. "Integrated Electricity and Gas Systems Planning: New Opportunities, and a Detailed Assessment of Relevant Issues," Sustainability, MDPI, vol. 15(8), pages 1-32, April.
    16. Teichgraeber, Holger & Brandt, Adam R., 2022. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    17. Pombo, Daniel Vázquez & Martinez-Rico, Jon & Spataru, Sergiu V. & Bindner, Henrik W. & Sørensen, Poul E., 2023. "Decarbonizing energy islands with flexibility-enabling planning: The case of Santiago, Cape Verde," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    18. Wei Zhang & Kai Wang & Alexandre Jacquillat & Shuaian Wang, 2023. "Optimized Scenario Reduction: Solving Large-Scale Stochastic Programs with Quality Guarantees," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 886-908, July.
    19. Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
    20. Kyung-Taek Kim & Deok-Joo Lee & Donghyun An, 2020. "Real Option Valuation of the R&D Investment in Renewable Energy Considering the Effects of the Carbon Emission Trading Market: A Korean Case," Energies, MDPI, vol. 13(3), pages 1-17, February.

    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:jmathe:v:9:y:2021:i:21:p:2771-:d:669981. 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.