Distributionally Robust Optimal Reactive Power Dispatch with Wasserstein Distance in Active Distribution Network

Jun Liu*, Yefu Chen, Chao Duan, Jiang Lin, Jia Lyu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The uncertainties from renewable energy sources (RESs) will not only introduce significant influences to active power dispatch, but also bring great challenges to the analysis of optimal reactive power dispatch (ORPD). To address the influence of high penetration of RES integrated into active distribution networks, a distributionally robust chance constraint (DRCC)-based ORPD model considering discrete reactive power compensators is proposed in this paper. The proposed ORPD model combines a second-order cone programming (SOCP)-based model at the nominal operation mode and a linear power flow (LPF) model to reflect the system response under certainties. Then, a distributionally robust optimization (WDRO) method with Wasserstein distance is utilized to solve the proposed DRCC-based ORPD model. The WDRO method is data-driven due to the reason that the ambiguity set is constructed by the available historical data without any assumption on the specific probability distribution of the uncertainties. And the more data is available, the smaller the ambiguity would be. Numerical results on IEEE 30-bus and 123-bus systems and comparisons with the other three-benchmark approaches demonstrate the accuracy and effectiveness of the proposed model and method.

Original languageEnglish (US)
Article number9097573
Pages (from-to)426-436
Number of pages11
JournalJournal of Modern Power Systems and Clean Energy
Issue number3
StatePublished - May 2020


  • Active distribution network
  • chance constraint
  • optimal reactive power dispatch (ORPD)
  • renewable energy source

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology

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