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Forecasting-aided state estimation based on deep learning for hybrid AC/DC distribution systems

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  • Huang, Manyun
  • Wei, Zhinong
  • Lin, Yuzhang

Abstract

To accommodate a higher penetration of distributed energy resources, distribution systems are moving toward hybrid AC/DC configurations for secure and economic operation. In this regard, this paper proposes a forecasting-aided state estimator (FASE) for hybrid AC/DC distribution systems to obtain accurate estimates for online security monitoring and control. The proposed FASE is designed in a distributed framework, with decomposition into several subproblems and solution by a constrained ensemble Kalman filter algorithm. In the proposed methodology, a deep neural network-based state forecasting model is developed to imitate the complex temporal and spatial relationship between system states, avoiding the state transition model built by unfounded explicit formulations. Furthermore, smart meter data is integrated by deep regression learning to obtain power injections of consumers and address the system observability issue. Extensive comparisons with two alternatives are carried out on a sample 33-node hybrid AC/DC distribution system to show the effectiveness and benefits of the proposed FASE, and on a larger 106-node hybrid AC/DC distribution system to demonstrate scalability.

Suggested Citation

  • Huang, Manyun & Wei, Zhinong & Lin, Yuzhang, 2022. "Forecasting-aided state estimation based on deep learning for hybrid AC/DC distribution systems," Applied Energy, Elsevier, vol. 306(PB).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pb:s0306261921013982
    DOI: 10.1016/j.apenergy.2021.118119
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    References listed on IDEAS

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    1. 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.
    2. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
    3. Zhang, Tong & Li, Zhigang & Wu, Q.H. & Zhou, Xiaoxin, 2019. "Decentralized state estimation of combined heat and power systems using the asynchronous alternating direction method of multipliers," Applied Energy, Elsevier, vol. 248(C), pages 600-613.
    4. Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2016. "State estimation of medium voltage distribution networks using smart meter measurements," Applied Energy, Elsevier, vol. 184(C), pages 207-218.
    5. Ghadikolaee, Ebad Talebi & Kazemi, Ahad & Shayanfar, Heydar Ali, 2020. "Novel multi-objective phasor measurement unit placement for improved parallel state estimation in distribution network," Applied Energy, Elsevier, vol. 279(C).
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    Cited by:

    1. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    2. Song, Shaojian & Xiong, Hao & Lin, Yuzhang & Huang, Manyun & Wei, Zhinong & Fang, Zhi, 2022. "Robust three-phase state estimation for PV-Integrated unbalanced distribution systems," Applied Energy, Elsevier, vol. 322(C).
    3. Dong Yu & Shan Gao & Xin Zhao & Yu Liu & Sicheng Wang & Tiancheng E. Song, 2023. "Alternating Iterative Power-Flow Algorithm for Hybrid AC–DC Power Grids Incorporating LCCs and VSCs," Sustainability, MDPI, vol. 15(5), pages 1-22, March.

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