Risk-based multi-objective optimization of distributed generation based on GPSO-BFA algorithm

Xiaoping Xiong, Wenliang Wu, Ning Li, Lu Yang, Jie Zhang, Zhi Wei

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


With the rapid evolution of technology, growing accessibility, and environmental appeal of wind and solar electric systems, distributed generation (DG) has been pushed from the fringe to a mainstream factor in the grid. However, due to the randomness and uncertainty of environmental and operational conditions, DG also brings many risks and may adversely affect the reliability and safety of the power grid when connected to the distribution network. Therefore, it is necessary to introduce the risk theory into the allocation and placement of DGs. This paper establishes a comprehensive set of risk and economic indexes by modeling the randomness and uncertainty of DG outputs. In addition, islanded operation, which is a promising development direction of microgrids, is explicitly studied and the related indexes are modeled. Putting them together, we propose a risk-based multi-objective optimal allocation model to optimize the placement and configuration of DGs and provide a reliable and cost-effective system. We solve the formulated multi-objective optimization problem by combining the gradient particle swarm optimization algorithm and the bacterial foraging algorithm. We demonstrate the validity and rationality of the proposed method through the analyses of the American PGE 69-node system.

Original languageEnglish (US)
Article number8672434
Pages (from-to)30563-30572
Number of pages10
JournalIEEE Access
StatePublished - 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Materials Science
  • General Engineering


  • Distribution generation
  • distributed network
  • multi-objective optimization
  • risk theory


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