Multi-Period Active Distribution Network Planning Using Multi-Stage Stochastic Programming and Nested Decomposition by SDDIP

  • Tao Ding
  • , Ming Qu
  • , Can Huang
  • , Zekai Wang
  • , Pengwei Du
  • , Mohammad Shahidehpour

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

This paper presents a multi-period active distribution network planning (ADNP) with distributed generation (DG). The objective of the proposed ADNP is to minimize the total planning cost, subject to both investment and operation constraints. The paper proposes a multi-stage stochastic optimization model to address DG uncertainties over several periods, in which the decisions are made sequentially by only using the present-stage information. A nested decomposition method is proposed which applies the stochastic dual dynamic integer programming (SDDIP) method to address computational intractabilities of the proposed ADNP approach. The presented numerical results and discussions on a 33-bus distribution system and a large-scale 906-bus system verify the effectiveness of the proposed ADNP method and its solution method.

Original languageEnglish
Article number9234713
Pages (from-to)2281-2292
Number of pages12
JournalIEEE Transactions on Power Systems
Volume36
Issue number3
DOIs
StatePublished - May 2021

Keywords

  • Distribution network planning
  • distributed energy resources
  • multi-stage stochastic programming
  • uncertainty

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