LAUSR

Abstract The smart grid innovation targets at the problems of integrating large portion of intermittent generation resources such as utility-scale and rooftop photovoltaics (PVs), while ensuring both the security and reliability of the electric power grid operation during severe disturbances. To enhance the operators’ performance, the information processing of the large-scale widely interconnected electric power grid needs to be simplified and presented with situational awareness (SA) in a meaningful way, for timely response by the system operators in control centers. Coherency group identification separates the generators in the system into groups that feature similar behavior, which is a method for grid simplification. Meanwhile, wide area measurement systems (WAMS) using data from phasor measurement units (PMUs) provides a solution – enhanced situational awareness to power operation control centers addressing the concerns of capturing and transmitting dynamic data in near real-time. Based upon these technologies, a synchrophasor based identification of generator coherency groups and new visualizations for SA are proposed and developed for online application with using K-harmonic means clustering. Furthermore, a generator vulnerability index (GVI) and a corresponding power system vulnerability index (SVI) are proposed based on the dynamic coherency groups identified. These indices alert system operators on the coherency behavior of the synchronous generators in the system for variations of PV power and/or under severe system disturbances. Typical results are provided for four different disturbances including sudden PV power increase and decrease, unexpected bus faults on major tie-line (inter-area) transmission corridors. In summary, new algorithms, indices and visualization tools have been developed to enhance SA and provide actionable information to system operators in control centers in the presence of variable utility-scale PV power in a power system.