A network reconfiguration approach for power system restoration based on preference-based multiobjective optimization

Runjia Sun, Yutian Liu, Hainan Zhu, Rasoul Azizipanah-Abarghooee, Vladimir Terzija

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

Rapid load restoration after large-scale power system blackouts is of virtual importance to reduce economic losses. Network reconfiguration is an essential foundation of load restoration. Incorporating the preference for different objectives, a network reconfiguration approach based on preference-based multiobjective optimization is proposed for network reconfiguration schemes. On the one hand, considering the influences of generators, lines and loads, three evaluation indicators are proposed as objectives to establish a preference multiobjective optimization model. On the other hand, a preference-based discrete nondominated sorting genetic algorithm II (PD-NSGA-II) is designed considering the preference and high discreteness of the suggested model. For efficient decision making, the algorithm is equipped with two sorts of population and two dominance relations to obtain solutions with required quantity and high quality. The simulation results demonstrate that the preference-based multiobjective optimization can reasonably leverage the tradeoff among different factors about network reconfiguration. Furthermore, comparison with other algorithms indicates the efficiency of PD-NSGA-II in solving network reconfiguration optimization problems.

Original languageEnglish
Article number105656
JournalApplied Soft Computing Journal
Volume83
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes

Keywords

  • Evolutionary computing
  • Network reconfiguration
  • Power system restoration
  • Preference-based multiobjective optimization

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