Due to reliability issues involved with the renewable energy integration and the random deviation of demand response profile from the generation response pattern, balancing of load flow and an assessment of transients stability become challenging research issues in super smart grids (SSGs). They are even more challenging, when an unexpected outage occurs due to occurrence of three phase (L-L-L) faults (TPFs), which also causes significant power quality disturbances in power systems. To address this problem, probabilistic and deterministic analysis based on super smart node (SSN), vehicle to grid (V2G) and continuous spinning reserve (CSR) are formulated to address the randomness complexity in terms of balancing load flow and an assessment of transient stability in SSGs. Through these techniques, future contingencies can be easily predicted in SSGs. Numerical results show that V2G probabilistic modeling provides better results for balancing of load flow and an enhancement in transients stability as compared to CSR deterministic modeling in SSGs. This observation is also validated by simulation results.