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Wireless AMI planning for guaranteed observability of medium voltage distribution grid

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  • Zhang, Jialun
  • Peng, Jimmy Chih-Hsien
  • Hug, Gabriela

Abstract

Due to the scarcity of measurement devices, distribution systems often suffer from unobservability. The recent expansion of smart meters (SMs) provides the means to enable system observability. To ensure continuous situational awareness of distribution grids, strategic planning of advanced metering infrastructure (AMI) is crucial. However, conventional AMI planning approaches only consider the economically optimal placement of communication devices without considering the need for observability even in case of failures. To address this challenge, this paper proposes a wireless AMI network planning framework that guarantees the observability of distribution grids in the face of potential communication failures. Specifically, given the failure scenario, a closed-form observability criterion is formulated and its observability fortification strategy is derived, based on which the AMI network planning problem is formulated as an integer linear programming (ILP) problem. In addition, a heuristic decomposition technique is applied to the ILP problem in order to address the scalability issues of large-size networks. Finally, case studies demonstrate the robustness and effectiveness of the proposed AMI system planning framework. The findings of this work assist distribution utilities in developing a reliable and economical AMI, while providing guaranteed situational awareness of their assets.

Suggested Citation

  • Zhang, Jialun & Peng, Jimmy Chih-Hsien & Hug, Gabriela, 2024. "Wireless AMI planning for guaranteed observability of medium voltage distribution grid," Applied Energy, Elsevier, vol. 370(C).
  • Handle: RePEc:eee:appene:v:370:y:2024:i:c:s0306261924009814
    DOI: 10.1016/j.apenergy.2024.123598
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    References listed on IDEAS

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    1. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
    2. Ahmad, Tanveer, 2017. "Non-technical loss analysis and prevention using smart meters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 573-589.
    3. Fekri, Mohammad Navid & Patel, Harsh & Grolinger, Katarina & Sharma, Vinay, 2021. "Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network," Applied Energy, Elsevier, vol. 282(PA).
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