IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v353y2024ipas0306261923014022.html
   My bibliography  Save this article

An intelligent power grid emergency allocation technology considering secondary disaster and public opinion under typhoon disaster

Author

Listed:
  • Wu, Wenjie
  • Hou, Hui
  • Zhu, Shaohua
  • Liu, Qin
  • Wei, Ruizeng
  • He, Huan
  • Wang, Lei
  • Luo, Yingting

Abstract

Typhoon disaster may not only cause power outage physically, but also breed negative sentiments in public opinion and affect social stability. Therefore, an intelligent power grid allocation technology considering secondary disaster and public opinion under typhoon disaster is proposed. Firstly, an Extra-Tree (ET) method is used to predict the power outage in time sequence (e.g., landing period, 6 h later, and 12 h later) after typhoon lands. Secondly, a typhoon secondary disaster potential assessment model is established based on disaster intensity and geographical environment. Since power outages may also cause public opinion polarization, a public opinion analysis model is proposed to mine Micro-blog (similar to Twitter) tweet information and analyze the demand for power restoration. Then, according to the results of secondary disaster potential assessment and public opinion analysis, the allocation strategy optimization is described as a Mixed Integer Nonlinear Programming (MINLP) problem. It is solved by the Stud Genetic Algorithm (Stud GA) and A-star path optimization algorithm. Finally, Yangjiang city in China under Typhoon “Chaba” (2022) is selected as the study area to verify the effectiveness and feasibility. It shows the proposed method can respond to public electricity demand in time and eliminate public negative sentiments, while the repair process is not disturbed by secondary disasters.

Suggested Citation

  • Wu, Wenjie & Hou, Hui & Zhu, Shaohua & Liu, Qin & Wei, Ruizeng & He, Huan & Wang, Lei & Luo, Yingting, 2024. "An intelligent power grid emergency allocation technology considering secondary disaster and public opinion under typhoon disaster," Applied Energy, Elsevier, vol. 353(PA).
    • Handle: RePEc:eee:appene:v:353:y:2024:i:pa:s0306261923014022
    DOI: 10.1016/j.apenergy.2023.122038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923014022
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.122038?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. Hou, Hui & Liu, Chao & Wei, Ruizeng & He, Huan & Wang, Lei & Li, Weibo, 2023. "Outage duration prediction under typhoon disaster with stacking ensemble learning," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Shaopan Li & Yan Wang & Hong Huang & Lida Huang & Yang Chen, 2023. "Study on typhoon disaster assessment by mining data from social media based on artificial neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(2), pages 2069-2089, March.
    3. Zhan, Xianwen & Han, Song & Rong, Na & Cao, Yun, 2023. "A hybrid transfer learning method for transient stability prediction considering sample imbalance," Applied Energy, Elsevier, vol. 333(C).
    4. Zhang, Heng & Zhang, Shenxi & Cheng, Haozhong & Li, Zheng & Gu, Qingfa & Tian, Xueqin, 2022. "Boosting the power grid resilience under typhoon disasters by coordinated scheduling of wind energy and conventional generators," Renewable Energy, Elsevier, vol. 200(C), pages 303-319.
    5. Shi, Qingxin & Li, Fangxing & Dong, Jin & Olama, Mohammed & Wang, Xiaofei & Winstead, Chris & Kuruganti, Teja, 2022. "Co-optimization of repairs and dynamic network reconfiguration for improved distribution system resilience," Applied Energy, Elsevier, vol. 318(C).
    6. Hou, Hui & Tang, Junyi & Zhang, Zhiwei & Wang, Zhuo & Wei, Ruizeng & Wang, Lei & He, Huan & Wu, Xixiu, 2023. "Resilience enhancement of distribution network under typhoon disaster based on two-stage stochastic programming," Applied Energy, Elsevier, vol. 338(C).
    7. Hui Hou & Shiwen Yu & Hongbin Wang & Yong Huang & Hao Wu & Yan Xu & Xianqiang Li & Hao Geng, 2019. "Risk Assessment and Its Visualization of Power Tower under Typhoon Disaster Based on Machine Learning Algorithms," Energies, MDPI, vol. 12(2), pages 1-23, January.
    8. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
    9. 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.
    10. Qiu, Dawei & Wang, Yi & Zhang, Tingqi & Sun, Mingyang & Strbac, Goran, 2023. "Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience," Applied Energy, Elsevier, vol. 336(C).
    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. Mao, Ding & Wang, Peng & Fang, Yi-Ping & Ni, Long, 2024. "Securing heat-supply against seismic risks: A two-staged framework for assessing vulnerability and economic impacts in district heating networks," Applied Energy, Elsevier, vol. 369(C).
    2. Yao, Qi & Tang, Jie & Ke, Yiming & Li, Li & Lu, Xiaoqin & Hu, Yang & Fang, Fang & Liu, Jizhen, 2024. "Anti-tropical cyclone load reduction control of wind turbines based on deep neural network yaw algorithm," Applied Energy, Elsevier, vol. 376(PB).

    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. Zhang, Guozhou & Hu, Weihao & Zhao, Yincheng & Cui, Zhengjie & Chen, Jianjun & Tang, Chao & Chen, Zhe, 2024. "Meta-learning and proximal policy optimization driven two-stage emergency allocation strategy for multi-energy system against typhoon disasters," Renewable Energy, Elsevier, vol. 237(PC).
    2. Dong, Lei & Sun, Shiting & Zhang, Shiming & Zhang, Tao & Pu, Tianjiao, 2024. "Distributed restoration for integrated electricity-gas-heating energy systems with an iterative loop scheme," Energy, Elsevier, vol. 304(C).
    3. Zoltan Varga & Ervin Racz, 2022. "Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System," Energies, MDPI, vol. 15(19), pages 1-18, October.
    4. Li, Wei & Han, Song & Guo, Xi & Xie, Shufan & Rong, Na & Zhang, Qingling, 2025. "Transient modeling and switching logic analysis of a power-electronic-assisted OLTC based Sen transformer," Applied Energy, Elsevier, vol. 378(PA).
    5. Yin, Linfei & Ge, Wei, 2024. "Mobileception-ResNet for transient stability prediction of novel power systems," Energy, Elsevier, vol. 309(C).
    6. Xie, Haipeng & Tang, Lingfeng & Zhu, Hao & Cheng, Xiaofeng & Bie, Zhaohong, 2023. "Robustness assessment and enhancement of deep reinforcement learning-enabled load restoration for distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Tang, Liangyu & Han, Yang & Zalhaf, Amr S. & Zhou, Siyu & Yang, Ping & Wang, Congling & Huang, Tao, 2024. "Resilience enhancement of active distribution networks under extreme disaster scenarios: A comprehensive overview of fault location strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    8. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2024. "Real-time automatic control of multi-energy system for smart district community: A coupling ensemble prediction model and safe deep reinforcement learning," Energy, Elsevier, vol. 304(C).
    9. Yang, Sen & Zhang, Yi & Lu, Xinzheng & Guo, Wei & Miao, Huiquan, 2024. "Multi-agent deep reinforcement learning based decision support model for resilient community post-hazard recovery," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    10. Cui, Feifei & An, Dou & Xi, Huan, 2024. "Integrated energy hub dispatch with a multi-mode CAES–BESS hybrid system: An option-based hierarchical reinforcement learning approach," Applied Energy, Elsevier, vol. 374(C).
    11. Chengqing, Yu & Guangxi, Yan & Chengming, Yu & Yu, Zhang & Xiwei, Mi, 2023. "A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks," Energy, Elsevier, vol. 263(PE).
    12. Yang, Yesen & Li, Zhengmao & Mandapaka, Pradeep V. & Lo, Edmond Y.M., 2023. "Risk-averse restoration of coupled power and water systems with small pumped-hydro storage and stochastic rooftop renewables," Applied Energy, Elsevier, vol. 339(C).
    13. Wang, Xiaowei & Kang, Qiankun & Gao, Jie & Zhang, Fan & Wang, Xue & Qu, Xinyu & Guo, Liang, 2024. "Distribution network restoration supply method considers 5G base station energy storage participation," Energy, Elsevier, vol. 289(C).
    14. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    15. Huang, Xiubing & Wang, Naiyu, 2024. "An adaptive nested dynamic downscaling strategy of wind-field for real-time risk forecast of power transmission systems during tropical cyclones," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    16. Mohammadniaei, M. & Namdari, F. & Shakarami, M.R., 2024. "A probabilistic distributed digital twins approach for short-term stability and islanding of smart grid," Applied Energy, Elsevier, vol. 374(C).
    17. Cao, Shankang & Wei, Fanrong & Lin, Xiangning & Yuan, Xitao & Huang, Qiyuan & Xiang, Hong, 2025. "Risk prediction based preventive typhoon defending for semi-independent power system," Applied Energy, Elsevier, vol. 377(PC).
    18. Lee, Keunmin & Park, Bongjoon & Kim, Jeongwon & Hong, Jinkyu, 2024. "Day-ahead wind power forecasting based on feature extraction integrating vertical layer wind characteristics in complex terrain," Energy, Elsevier, vol. 288(C).
    19. Sun, Xiaojun & Lin, Sheng & Feng, Ding & Zhang, Qiang, 2024. "Post-disaster repair optimization method for traction power supply system of electrified railways based on train operation loss," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    20. G., Varathan & J., Belwin Edward, 2024. "A review of uncertainty management approaches for active distribution system planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 205(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:appene:v:353:y:2024:i:pa:s0306261923014022. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.