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A novel deep learning based probabilistic power flow method for Multi-Microgrids distribution system with incomplete network information

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  • Xiao, Hao
  • Pei, Wei
  • Wu, Lei
  • Ma, Li
  • Ma, Tengfei
  • Hua, Weiqi

Abstract

With the massive deployment of microgrids (MGs) and energy communities, various stakeholders have been involved in distribution networks. Due to the underdeveloped information infrastructure, especially in rural distribution networks, there is an increasing number of “blind areas” in the operation of distribution networks. The calculation of probabilistic power flow (PPF) with incomplete parameters have become an urgent issue to be solved for ensuring safe operation. Based on the deep learning and mechanism models, a novel PPF method is proposed for multi-microgrids distribution systems considering incomplete network information. Firstly, accessible power exchange data as well as public and independent information are utilized to realize equivalent modeling for microgrids area with incomplete parameters, based on a novel Kriging surrogate enhanced Gate Recurrent Unit-Temporal Convolutional Network (GRU-TCN). Then, the PPF calculation is effectively conducted by the distribution system operator (DSO) through the point estimation method (PEM), in which the equivalent GRU-TCN models and model-based power flow are integrated. Therefore, the complicated interactive iteration of the power flow equation is avoided, and the PPF calculation efficiency is effectively improved. In addition, user privacy is protected because only the trained GRU-TCN deep learning models will be used by DSO for the PPF calculation. The proposed method is validated in a modified IEEE 33-node distribution network, a modified American PG&E 69-node distribution network as well as the modified three-phase unbalanced IEEE 123-node distribution network including several MGs with unknown internal network parameters. The results show that the proposed method can improve the PPF calculation efficiency greatly while ensuring high-precision calculation results. The required evaluation time can be reduced by 64.01% and 99.31% compared with the DNN-based Monte Carlo sampling method and the traditional mechanism model-based Monte Carlo sampling method with complete information, respectively.

Suggested Citation

  • Xiao, Hao & Pei, Wei & Wu, Lei & Ma, Li & Ma, Tengfei & Hua, Weiqi, 2023. "A novel deep learning based probabilistic power flow method for Multi-Microgrids distribution system with incomplete network information," Applied Energy, Elsevier, vol. 335(C).
    • Handle: RePEc:eee:appene:v:335:y:2023:i:c:s0306261923000806
    DOI: 10.1016/j.apenergy.2023.120716
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    References listed on IDEAS

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    1. Xiao, Hao & Pei, Wei & Deng, Wei & Ma, Tengfei & Zhang, Shizhong & Kong, Li, 2021. "Enhancing risk control ability of distribution network for improved renewable energy integration through flexible DC interconnection," Applied Energy, Elsevier, vol. 284(C).
    2. Haddadian, Hossein & Noroozian, Reza, 2017. "Multi-microgrids approach for design and operation of future distribution networks based on novel technical indices," Applied Energy, Elsevier, vol. 185(P1), pages 650-663.
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    Cited by:

    1. Hasanien, Hany M. & Alsaleh, Ibrahim & Alassaf, Abdullah, 2024. "Impact of electric vehicles and wave energy systems on OPF of power networks using hybrid Osprey-PSO approach," Energy, Elsevier, vol. 308(C).
    2. Li, Yutong & Hou, Jian & Yan, Gangfeng, 2024. "Exploration-enhanced multi-agent reinforcement learning for distributed PV-ESS scheduling with incomplete data," Applied Energy, Elsevier, vol. 359(C).
    3. Gang Mu & Yibo Zhou & Mao Yang & Jiahao Chen, 2023. "A Diagnosis Method of Power Flow Convergence Failure for Bulk Power Systems Based on Intermediate Iteration Data," Energies, MDPI, vol. 16(8), pages 1-16, April.
    4. Hasanien, Hany M. & Alsaleh, Ibrahim & Alassaf, Abdullah & Alateeq, Ayoob, 2023. "Enhanced coati optimization algorithm-based optimal power flow including renewable energy uncertainties and electric vehicles," Energy, Elsevier, vol. 283(C).
    5. Zhang, Zhaoyi & Han, Zixi & Hu, Hao & Fan, Youping & Fan, Jianbin & Shu, Yinbiao, 2024. "Self-adaptive system state optimization based on nonlinear affine transformation for renewable energy volatility," Renewable Energy, Elsevier, vol. 230(C).
    6. Yang, Weijia & Sparrow, Sarah N. & Wallom, David C.H., 2024. "A comparative climate-resilient energy design: Wildfire Resilient Load Forecasting Model using multi-factor deep learning methods," Applied Energy, Elsevier, vol. 368(C).
    7. Chen, Yujia & Pei, Wei & Ma, Tengfei & Xiao, Hao, 2023. "Asymmetric Nash bargaining model for peer-to-peer energy transactions combined with shared energy storage," Energy, Elsevier, vol. 278(PB).

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