An Overvoltage-Averse Model for Renewable Rich AC/DC Distribution Networks Considering the Sensitivity of Voltage Violation Probability

  • Bo Tong
  • , Lu Zhang*
  • , Gen Li
  • , Bo Zhang
  • , Fang Xie
  • , Wei Tang
  • *Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

The increasing renewable generation increase the probability of voltage violation. The spatial and temporal power transfer can be achieved in AC/DC distribution networks based on voltage source converters (VSCs) and energy storage (ES), which can effectively avoid system voltage violation. However, the existing voltage violation mitigation methods in uncertain scenarios are either limited by the long solution time or complex modeling, which are difficult to meet the overvoltage probability reduction requirements of intra-day dispatch. In addition, the power transfer will simultaneously affect interconnected systems because the power is coupled through the VSCs. Overvoltage probability reduction on one line may lead to an increase on the other. This paper proposes a two-stage overvoltage-averse model considering the sensitivity of voltage violation probability. The proposed method analytically depicts the impact of power adjustment on the system overvoltage probability. The day-ahead optimization model is established as chance-constrained model. The intra-day optimization model is established as a quadratic convex model, which can be efficiently solved. Simulation results verify that the method proposed can effectively achieve the overvoltage probability reduction of renewable-rich AC/DC distribution networks.
Original languageEnglish
JournalIEEE Transactions on Sustainable Energy
Volume16
Issue number1
Pages (from-to)613-626
ISSN1949-3029
DOIs
Publication statusPublished - 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • AC/DC distribution networks
  • Probabilistic power flow
  • Power correlation
  • Risk sensitivity
  • Two-stage optimization model

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