Decentralized Data-Driven Load Restoration in Cou-pled Transmission and Distribution System with Wind Power

Jin Zhao, Qiuwei Wu, Fangxing Fran Li, Nikos D. Hatziargyriou, Fei Teng

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This paper proposes a new decentralized data-driven load res-toration (DDLR) scheme for transmission and distribution (TD) systems with high penetration of wind power. Robust DDLR models are constructed in order to handle uncertainties and ensure the feasibility of decentralized schemes. The Wasserstein metric is used to describe the ambiguity sets of probability distributions in order to build the complete DDLR model and realize computationally tractable formulation. A data-driven model-nested analytical target cascading (DATC) algorithm is developed to obtain the final load restoration result by iteratively solving small-scale mathematical models. The proposed DDLR scheme provides load restoration results with adjustable robustness, and performance efficiency is independent from the amount of data. The DDLR scheme makes full use of the available data while respecting information privacy requirements of independently operated systems, and ensures the feasibility of the decentralized load restoration strategy even in the worst-case condition. The effectiveness of the proposed method is validated using a small-scale TDS and a large-scale system with the IEEE 118-bus TS and thirty IEEE-33 DSs, showing high computational efficiency and superior restoration performance.

Original languageEnglish
JournalIEEE Transactions on Power Systems
Number of pages10
Publication statusAccepted/In press - 2021

Bibliographical note

Publisher Copyright:


  • Computational modeling
  • Data-driven optimization
  • Distributionally robust
  • Load modeling
  • Mathematical model
  • Measurement
  • Optimization
  • Power system restoration
  • Transmission and distribution system
  • Uncertainty
  • Wind power
  • Wind power generation


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