IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v191y2020ics0360544219321371.html
   My bibliography  Save this article

Distribution system resilience enhancement by microgrid formation considering distributed energy resources

Author

Listed:
  • Gilani, Mohammad Amin
  • Kazemi, Ahad
  • Ghasemi, Mostafa

Abstract

Due to increasing in natural disasters in the recent years, the issue of distribution network resilience has become highly important. Microgrids with different types of distributed energy resources have the capabilities to improve distribution network resilience under extreme events. In this paper, we present a mixed-integer linear program to restore prioritized loads while satisfying topology and operational constraints. In the presented model, we study dynamic microgrid formation and optimal management of various smart grid technologies such as distributed generations, demand response programs, wind turbines and energy storage units. We also investigate the significant impact of renewable energy resources, loads and their uncertainty on distribution system resilience. In addition, we determine the required emergency budgets of operation to restore a distribution system from extreme events. We also intend to examine the effects of demand response programs on improving the distribution system performance in the recovery period. Finally, we verify the effectiveness of the presented model on a modified IEEE 33-node test system and a real distribution system.

Suggested Citation

  • Gilani, Mohammad Amin & Kazemi, Ahad & Ghasemi, Mostafa, 2020. "Distribution system resilience enhancement by microgrid formation considering distributed energy resources," Energy, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321371
    DOI: 10.1016/j.energy.2019.116442
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219321371
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.116442?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rosales-Asensio, Enrique & de Simón-Martín, Miguel & Borge-Diez, David & Blanes-Peiró, Jorge Juan & Colmenar-Santos, Antonio, 2019. "Microgrids with energy storage systems as a means to increase power resilience: An application to office buildings," Energy, Elsevier, vol. 172(C), pages 1005-1015.
    2. Al-Shammari, Eiman Tamah & Shamshirband, Shahaboddin & Petković, Dalibor & Zalnezhad, Erfan & Yee, Por Lip & Taher, Ros Suraya & Ćojbašić, Žarko, 2016. "Comparative study of clustering methods for wake effect analysis in wind farm," Energy, Elsevier, vol. 95(C), pages 573-579.
    3. Mousavizadeh, Saeed & Haghifam, Mahmoud-Reza & Shariatkhah, Mohammad-Hossein, 2018. "A linear two-stage method for resiliency analysis in distribution systems considering renewable energy and demand response resources," Applied Energy, Elsevier, vol. 211(C), pages 443-460.
    4. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    5. Ji, Haoran & Wang, Chengshan & Li, Peng & Song, Guanyu & Wu, Jianzhong, 2018. "SOP-based islanding partition method of active distribution networks considering the characteristics of DG, energy storage system and load," Energy, Elsevier, vol. 155(C), pages 312-325.
    6. Esteban, Miguel & Portugal-Pereira, Joana, 2014. "Post-disaster resilience of a 100% renewable energy system in Japan," Energy, Elsevier, vol. 68(C), pages 756-764.
    7. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    8. Hughes, Larry, 2015. "The effects of event occurrence and duration on resilience and adaptation in energy systems," Energy, Elsevier, vol. 84(C), pages 443-454.
    9. Tabar, Vahid Sohrabi & Ghassemzadeh, Saeid & Tohidi, Sajjad, 2019. "Energy management in hybrid microgrid with considering multiple power market and real time demand response," Energy, Elsevier, vol. 174(C), pages 10-23.
    10. Sadaei, Hossein Javedani & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha & Lee, Muhammad Hisyam, 2019. "Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series," Energy, Elsevier, vol. 175(C), pages 365-377.
    11. Xu, Beibei & Chen, Diyi & Venkateshkumar, M. & Xiao, Yu & Yue, Yan & Xing, Yanqiu & Li, Peiquan, 2019. "Modeling a pumped storage hydropower integrated to a hybrid power system with solar-wind power and its stability analysis," Applied Energy, Elsevier, vol. 248(C), pages 446-462.
    12. Ding, Tao & Lin, Yanling & Bie, Zhaohong & Chen, Chen, 2017. "A resilient microgrid formation strategy for load restoration considering master-slave distributed generators and topology reconfiguration," Applied Energy, Elsevier, vol. 199(C), pages 205-216.
    13. Lin, Yanling & Bie, Zhaohong, 2018. "Tri-level optimal hardening plan for a resilient distribution system considering reconfiguration and DG islanding," Applied Energy, Elsevier, vol. 210(C), pages 1266-1279.
    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. Wang, Yi & Rousis, Anastasios Oulis & Strbac, Goran, 2020. "On microgrids and resilience: A comprehensive review on modeling and operational strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    2. Liao, Shiwu & Yao, Wei & Han, Xingning & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu & He, Haibo, 2019. "An improved two-stage optimization for network and load recovery during power system restoration," Applied Energy, Elsevier, vol. 249(C), pages 265-275.
    3. Zhao, Jinli & Zhang, Mengzhen & Yu, Hao & Ji, Haoran & Song, Guanyu & Li, Peng & Wang, Chengshan & Wu, Jianzhong, 2019. "An islanding partition method of active distribution networks based on chance-constrained programming," Applied Energy, Elsevier, vol. 242(C), pages 78-91.
    4. Zhou, Yutian & Panteli, Mathaios & Moreno, Rodrigo & Mancarella, Pierluigi, 2018. "System-level assessment of reliability and resilience provision from microgrids," Applied Energy, Elsevier, vol. 230(C), pages 374-392.
    5. Mishra, Dillip Kumar & Ghadi, Mojtaba Jabbari & Azizivahed, Ali & Li, Li & Zhang, Jiangfeng, 2021. "A review on resilience studies in active distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    6. Mehrjerdi, Hasan & Hemmati, Reza, 2020. "Coordination of vehicle-to-home and renewable capacity resources for energy management in resilience and self-healing building," Renewable Energy, Elsevier, vol. 146(C), pages 568-579.
    7. Habibollah Raoufi & Vahid Vahidinasab & Kamyar Mehran, 2020. "Power Systems Resilience Metrics: A Comprehensive Review of Challenges and Outlook," Sustainability, MDPI, vol. 12(22), pages 1-24, November.
    8. Lee, J. & Razeghi, G. & Samuelsen, S., 2022. "Generic microgrid controller with self-healing capabilities," Applied Energy, Elsevier, vol. 308(C).
    9. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Matelli, José Alexandre & Goebel, Kai, 2018. "Conceptual design of cogeneration plants under a resilient design perspective: Resilience metrics and case study," Applied Energy, Elsevier, vol. 215(C), pages 736-750.
    11. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
    12. Sharifi, Ayyoob & Yamagata, Yoshiki, 2016. "Principles and criteria for assessing urban energy resilience: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1654-1677.
    13. Charani Shandiz, Saeid & Foliente, Greg & Rismanchi, Behzad & Wachtel, Amanda & Jeffers, Robert F., 2020. "Resilience framework and metrics for energy master planning of communities," Energy, Elsevier, vol. 203(C).
    14. Zhang, Qianzhi & Wang, Zhaoyu & Ma, Shanshan & Arif, Anmar, 2021. "Stochastic pre-event preparation for enhancing resilience of distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    15. Cagnano, A. & De Tuglie, E. & Mancarella, P., 2020. "Microgrids: Overview and guidelines for practical implementations and operation," Applied Energy, Elsevier, vol. 258(C).
    16. Wang, Zekai & Ding, Tao & Jia, Wenhao & Huang, Can & Mu, Chenggang & Qu, Ming & Shahidehpour, Mohammad & Yang, Yongheng & Blaabjerg, Frede & Li, Li & Wang, Kang & Chi, Fangde, 2022. "Multi-stage stochastic programming for resilient integrated electricity and natural gas distribution systems against typhoon natural disaster attacks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    17. Alex Guamán & Alex Valenzuela, 2021. "Distribution Network Reconfiguration Applied to Multiple Faulty Branches Based on Spanning Tree and Genetic Algorithms," Energies, MDPI, vol. 14(20), pages 1-16, October.
    18. Liu, Hanchen & Wang, Chong & Ju, Ping & Li, Hongyu, 2022. "A sequentially preventive model enhancing power system resilience against extreme-weather-triggered failures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    19. Younesi, Abdollah & Shayeghi, Hossein & Wang, Zongjie & Siano, Pierluigi & Mehrizi-Sani, Ali & Safari, Amin, 2022. "Trends in modern power systems resilience: State-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    20. Li, Peng & Ji, Jie & Ji, Haoran & Song, Guanyu & Wang, Chengshan & Wu, Jianzhong, 2020. "Self-healing oriented supply restoration method based on the coordination of multiple SOPs in active distribution networks," Energy, Elsevier, vol. 195(C).

    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:eee:energy:v:191:y:2020:i:c:s0360544219321371. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.