Multi-objective optimization framework for the selection of microgrid policies

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.