Distributionally robust chance-constraint optimal power flow considering uncertain renewables with Wasserstein-moment metric

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

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Chance-constraint optimal power flow has been proven as an efficient method to manage the risk of volatile renewable energy sources. To address the uncertainties of renewable energy sources, a novel distributionally robust chance-constraint OPF model is proposed in this paper, in which the discrete reactive compensators are accurately modeled. Compared with the commonly used DC OPF equations, the proposed model considers the reactive parameters such as Q and V, and also their impacts on the dispatch of active power sources. Besides, a novel Wasserstein-Moment metric is utilized to solve the distributionally robust chance-constraint OPF model. The new Wasserstein-Moment metric is able to combine the advantages of both Wasserstein metric and Moment metric, and decrease the conservatism of the robust optimisation solution greatly. Furthermore, the proposed method is data-driven, which means that the more data is available, the less conservative the solution would be. Finally, numerical case studies are carried out on IEEE 118-bus system to verify the effectiveness of the proposed chance-constraint OPF model and the proposed WM metric.

Original languageEnglish (US)
Pages (from-to)192-197
Number of pages6
JournalEnergy Procedia
Volume158
DOIs
StatePublished - 2019
Event10th International Conference on Applied Energy, ICAE 2018 - Hong Kong, China
Duration: Aug 22 2018Aug 25 2018

Keywords

  • Chance-constraint
  • Data-driven
  • Distributionally robust
  • OPF
  • Renewable energy
  • Wasserstein-Moment metric

ASJC Scopus subject areas

  • Energy(all)

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