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A deep spatial-temporal data-driven approach considering microclimates for power system security assessment

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  • Huang, Tian-en
  • Guo, Qinglai
  • Sun, Hongbin
  • Tan, Chin-Woo
  • Hu, Tianyu

Abstract

With the integration of renewable energy and microclimate-sensitive loads, secure and economic power system operation is becoming an increasingly important and complex problem. Therefore, based on big data from power systems and meteorological systems, a deep spatial-temporal data-driven model is proposed to predict and detect power system security weak spots during a future period. First, microclimates are considered in the proposed model. Then, a deep neural network structure is designed to extract deep features layer by layer for security weak spot detection. Furthermore, model simplification and parallelism as well as data parallelism are applied. Finally, the proposed model is evaluated based on the Guangdong Power Grid in China. The simulation results demonstrate that (1) power system security weak spots have spatial-temporal and microclimate-sensitive characteristics; (2) the deep model considering microclimates can greatly improve the task accuracy of online applications; and (3) simplification and parallelism can significantly enhance the training efficiency.

Suggested Citation

  • Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.
    • Handle: RePEc:eee:appene:v:237:y:2019:i:c:p:36-48
    DOI: 10.1016/j.apenergy.2019.01.013
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    as
    1. Guo, Yabin & Tan, Zehan & Chen, Huanxin & Li, Guannan & Wang, Jiangyu & Huang, Ronggeng & Liu, Jiangyan & Ahmad, Tanveer, 2018. "Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving," Applied Energy, Elsevier, vol. 225(C), pages 732-745.
    2. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    3. Wei, Zhinong & Chen, Sheng & Sun, Guoqiang & Wang, Dan & Sun, Yonghui & Zang, Haixiang, 2016. "Probabilistic available transfer capability calculation considering static security constraints and uncertainties of electricity–gas integrated energy systems," Applied Energy, Elsevier, vol. 167(C), pages 305-316.
    4. Chapman, Andrew J. & McLellan, Benjamin C. & Tezuka, Tetsuo, 2018. "Prioritizing mitigation efforts considering co-benefits, equity and energy justice: Fossil fuel to renewable energy transition pathways," Applied Energy, Elsevier, vol. 219(C), pages 187-198.
    5. Wu, Zhi & Liu, Pengxiang & Gu, Wei & Huang, He & Han, Jun, 2018. "A bi-level planning approach for hybrid AC-DC distribution system considering N-1 security criterion," Applied Energy, Elsevier, vol. 230(C), pages 417-428.
    6. Li, Canbing & Zhou, Jinju & Cao, Yijia & Zhong, Jin & Liu, Yu & Kang, Chongqing & Tan, Yi, 2014. "Interaction between urban microclimate and electric air-conditioning energy consumption during high temperature season," Applied Energy, Elsevier, vol. 117(C), pages 149-156.
    7. Kumar, Subhash, 2016. "Assessment of renewables for energy security and carbon mitigation in Southeast Asia: The case of Indonesia and Thailand," Applied Energy, Elsevier, vol. 163(C), pages 63-70.
    8. Kontokosta, Constantine E. & Tull, Christopher, 2017. "A data-driven predictive model of city-scale energy use in buildings," Applied Energy, Elsevier, vol. 197(C), pages 303-317.
    9. Kong, Xiangrui & Xu, Xiaoyuan & Yan, Zheng & Chen, Sijie & Yang, Huoming & Han, Dong, 2018. "Deep learning hybrid method for islanding detection in distributed generation," Applied Energy, Elsevier, vol. 210(C), pages 776-785.
    10. Rahman, Aowabin & Smith, Amanda D., 2018. "Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms," Applied Energy, Elsevier, vol. 228(C), pages 108-121.
    11. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    12. Narula, Kapil & Sudhakara Reddy, B. & Pachauri, Shonali & Mahendra Dev, S., 2017. "Sustainable energy security for India: An assessment of the energy supply sub-system," Energy Policy, Elsevier, vol. 103(C), pages 127-144.
    13. Gouveia, João Pedro & Dias, Luís & Martins, Inês & Seixas, Júlia, 2014. "Effects of renewables penetration on the security of Portuguese electricity supply," Applied Energy, Elsevier, vol. 123(C), pages 438-447.
    14. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    15. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    16. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    17. Duenas, Pablo & Ramos, Andres & Tapia-Ahumada, Karen & Olmos, Luis & Rivier, Michel & Pérez-Arriaga, Jose-Ignacio, 2018. "Security of supply in a carbon-free electric power system: The case of Iceland," Applied Energy, Elsevier, vol. 212(C), pages 443-454.
    18. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    19. Narula, Kapil & Sudhakara Reddy, B. & Pachauri, Shonali, 2017. "Sustainable Energy Security for India: An assessment of energy demand sub-system," Applied Energy, Elsevier, vol. 186(P2), pages 126-139.
    20. Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.
    21. Fan, Cheng & Xiao, Fu & Zhao, Yang & Wang, Jiayuan, 2018. "Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data," Applied Energy, Elsevier, vol. 211(C), pages 1123-1135.
    22. Wang, D. & Parkinson, S. & Miao, W. & Jia, H. & Crawford, C. & Djilali, N., 2012. "Online voltage security assessment considering comfort-constrained demand response control of distributed heat pump systems," Applied Energy, Elsevier, vol. 96(C), pages 104-114.
    23. Liddle, Brantley & Sadorsky, Perry, 2017. "How much does increasing non-fossil fuels in electricity generation reduce carbon dioxide emissions?," Applied Energy, Elsevier, vol. 197(C), pages 212-221.
    24. Zheng, Yanchong & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2018. "A novel real-time scheduling strategy with near-linear complexity for integrating large-scale electric vehicles into smart grid," Applied Energy, Elsevier, vol. 217(C), pages 1-13.
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    Cited by:

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