Multi-objective optimization framework for the selection of microgrid policies

Juan Pablo Sáenz, Nurcin Celik

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Distributed energy generation resources provide multiple benefits to electrical networks, including increases in reliability and power quality, increased energy efficiency, and reduced emissions. However, the deployment of a potentially very large number of distributed energy generation resources creates a daunting challenge for the safe operation of the energy network. Microgrids, entities that control locally clustered distributed energy generation resources, may be used to address this challenge since they behave as a single producer or load, from the grid's perspective. The optimal operation of microgrids must ensure that the energy dispatch is done economically while enabling adequate responses to disruptions in the main grid. The deployment of different operational and market policies has a profound impact on the operation of the distributed energy generation sources within the microgrids. To this end, in this work, we develop a multi-objective optimization framework leveraging particle filtering 1) to evaluate impacts of different operational and market policies on microgrids, and 2) to determine the best feasible secure and economic dispatch of distributed energy resources within these systems. The proposed framework has been successfully demonstrated using the Study Case LV Network for various types of network disruptions.

Original languageEnglish
Title of host publicationIIE Annual Conference and Expo 2013
PublisherInstitute of Industrial Engineers
Number of pages10
StatePublished - Jan 1 2013
EventIIE Annual Conference and Expo 2013 - San Juan, Puerto Rico
Duration: May 18 2013May 22 2013


OtherIIE Annual Conference and Expo 2013
Country/TerritoryPuerto Rico
CitySan Juan


  • Best compromise solution
  • Microgrids
  • Particle filtering
  • Real time dispatching
  • Steady state security

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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