Data-Driven Probabilistic Voltage Risk Assessment of MiniWECC System with Uncertain PVs and Wind Generations Using Realistic Data

Ketian Ye, Junbo Zhao, Hongming Zhang, Yingchen Zhang

Research output: Contribution to journalArticlepeer-review

3 Scopus Citations

Abstract

It is found from actual data that due to generation dispatch and uncertain renewable generations and loads with complicated correlations, inferring the probabilistic distributions for uncertain inputs is challenging. Many probabilistic power flow approaches have been developed in the literature but their validations using realistic systems and data are lacking. This paper proposes a data-driven probabilistic analysis approach for system risk assessment of the miniWECC system using actual data. The sparse Gaussian process (SGP) is advocated to quantify the impacts of uncertain inputs on voltage security. SGP does not need the probability distribution function of uncertain inputs, can handle correlations and is highly computationally efficient. Results on the miniWECC system using realistic data show that SGP outperforms existing approaches and is able to quantify the voltage violation risks.
Original languageAmerican English
Pages (from-to)4121-4124
Number of pages4
JournalIEEE Transactions on Power Systems
Volume37
Issue number5
DOIs
StatePublished - 2022

NREL Publication Number

  • NREL/JA-5D00-83476

Keywords

  • MiniWECC
  • probabilistic power flow
  • renewable energy
  • sparse Gaussian process
  • uncertainty quantification

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