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

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

Research output: Contribution to journalJournal articleResearchpeer-review


This paper proposes a new decentralized datadriven load restoration (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 datadriven 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 independent operated systems, and ensures the feasibility of a decentralized load restoration strategy even in the worstcase condition. The effectiveness of the proposed method is validated using a small-scale TD system 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 pages9
Publication statusAccepted/In press - 2021

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