A Fast and Scalable Genetic Algorithm-Based Approach for Planning of Microgrids in Distribution Networks: Preprint

Abhijeet Sahu, Kumar Utkarsh, Fei Ding

Research output: Contribution to conferencePaper


As a result of climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution utilities and system operators to ensure that there is uninterrupted power supply to critical loads in their networks; thus, the level of proactive preparation of the distribution system to be able to handle severe impacts of extreme weather events represents the system's resilience. One method that distribution systems use to prepare for extreme events is to form multiple microgrids and thereby isolate themselves from the grid supply by using local generation as much as possible to supply critical loads. But partitioning an existing system into multiple feasible islands capable of supporting critical loads is still challenging for distribution systems - first, because of the size of the graph partitioning problem and, second, because of the difficulty in properly formulating the desired attributes of such islands or microgrids. Therefore, this paper presents a genetic algorithmbased approach that facilitates incorporating multiple objectives for grid partitioning by formulating two types of problems - node allocation and edge elimination - and it considers multiple topological and resilience-enhancing objectives. The performance of the proposed genetic algorithm-based approach is numerically evaluated on multiple test systems as well as on a real distribution feeder in Colorado, USA.
Original languageAmerican English
Number of pages8
StatePublished - 2022
Event2022 IEEE Power & Energy Society General Meeting - Denver, Colorado
Duration: 17 Jul 202221 Jul 2022


Conference2022 IEEE Power & Energy Society General Meeting
CityDenver, Colorado

Bibliographical note

See NREL/CP-5D00-84977 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5D00-80846


  • edge elimination
  • genetic algorithm
  • microgrid
  • multi-objective optimization


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