Flocking-based adaptive granular control strategy for autonomous microgrids in emergency situations

Abstract

In this study, the authors study the operation of autonomous microgrids (MGs) in emergency situations such as the presence of large physical disturbances or cyber attacks. Traditional approaches to enhance system-wide stability, such as automatic generation control, are insufficient for stabilising MGs in some emergency situations due to the correspondingly lower capacity of distributed energy resources. To address this challenge, in this study, they develop an adaptive flocking-based framework that provides control-based MG resilience. The contribution of the authors’ work is three-fold. First, they effectively model the complex and dynamic dependencies amongst MG components by exploiting flocking theory. Second, they propose an adaptive granular control strategy based on the modelled dynamic dependencies. Third, they also explore the role of energy storage systems to facilitate distributed generations in achieving autonomous MG power balance in the presence of disruptions of different natures. Case studies demonstrate the effectiveness of the proposed strategy in stabilising MGs in response to physical disturbances and cyber attacks.

Keywords

• power generation control
• energy storage
• distributed power generation
• MG
• autonomous MG power balance
• distributed generations
• energy storage systems
• modelled dynamic dependencies
• flocking theory
• control-based MG resilience
• adaptive flocking-based framework
• distributed energy resources
• correspondingly lower capacity
• automatic generation control
• system-wide stability
• traditional approaches
• cyber attacks
• physical disturbances
• emergency situations
• autonomous microgrids
• adaptive granular control strategy