Static Security Assessment for Smart Microgrids based on unstable working points clustering

This paper proposes a method for the offline Static Security Assessment of smart MicroGrids (MGs). The developed approach relies on defining, through Load Flow analyses and machine learning techniques, a manifold in the hyperspace described by the active and reactive powers of the system components where the MG can securely operate. This allows for measuring the closeness of a generic steady-state MG operating condition to the boundary of the identified stability manifold (i.e., the proximity of the actual working point to the instability region) and for evaluating proper corrective actions, if needed. The proposed method is tested by simulation on a case study reproducing the experimental MG installed at the Polytechnic University of Bari (the PrInCE MG), proving its effectiveness.