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On the Solution Uniqueness of Data-Driven Modeling of Flexible Loads

  • Shuai Lu
  • , Jiayi Ding
  • , Mingji Chen
  • , Wei Gu*
  • , Junpeng Zhu
  • , Yijun Xu
  • , Zhaoyang Dong
  • , Zezheng Sun
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

This letter first explores the solution uniqueness of the data-driven modeling of price-responsive flexible loads (PFL). The PFL on the demand side is critical in modern power systems. An accurate PFL model is fundamental for system operations. However, whether the PFL model can be uniquely and correctly identified from operational data remains unclear. To address this, we analyze the structural and practical identifiability of the PFL model, deriving the dataset condition that guarantees the solution uniqueness. Besides, we point out the practical implications of the results. Numerical tests validate this work. © 2024 IEEE.
Original languageEnglish
Pages (from-to)1993-1996
JournalIEEE Transactions on Smart Grid
Volume16
Issue number2
Online published16 Dec 2024
DOIs
Publication statusPublished - Mar 2025

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 52207080; in part by the Zhishan Young Scholar Support Program of Southeast University under Grant 2242024RCB0044; and in part by Global STEM Professorship and a Startup Grant of City University of Hong Kong

Research Keywords

  • data-driven modeling
  • Flexible loads
  • identifiability
  • inverse optimization
  • solution uniqueness

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