A Multi-Agent FLISR Model for Smart Grids

Resilient smart grids aim to quickly restore service in the face of unexpected faults and natural disasters. This is achieved by isolating faults and forming microgrids (MGs) around black-start generators, which can restore critical loads by optimally controlling distributed energy resources and remote switches through a mixed-integer optimization problem. However, neglecting the transient response of the resulting power system can compromise the effectiveness of this approach. To address this limitation, this research employs transient analysis of the power system to assess the disturbance experienced by network elements, including loads and generators. The resulting feedback is then used to limit the amount of load picked up in the next stage to ensure power system stability. The proposed solution relies on a distributed multi-agent system (MAS) that tackles the challenges of information discovery, real-time task scheduling, communication network congestion, and DER control during the restoration process. This framework's efficacy is demonstrated through a validation exercise conducted over a modified IEEE-123 node test feeder, which evaluates multiple fault scenarios. The results presented in this research show the potential of this approach to improve the resilience of distribution systems.