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We present a fast block direct solver for the unified dynamic simulations ofpower systems. This solver uses a novel Q-learning based method for taskscheduling. Unified dynamic simulations of power systems represent a method inwhich the electric-mechanical transient, medium-term and long-term dynamicphenomena are organically united. Due to the high rank and large numbers insolving, fast solution of these equations is the key to speeding up thesimulation. The sparse systems of simulation contain complex nested blockstructure, which could be used by the solver to speed up. For the scheduling ofblocks and frontals in the solver, we use a learning based task-tree schedulingtechnique in the framework of Markov Decision Process. That is, we could learnoptimal scheduling strategies by offline training on many sample matrices. Thenfor any systems, the solver would get optimal task partition and scheduling onthe learned model. Our learning-based algorithm could help improve theperformance of sparse solver, which has been verified in some numericalexperiments. The simulation on some large power systems shows that our solveris 2-6 times faster than KLU, which is the state-of-the-art sparse solver forcircuit simulation problems.

Fast Block Linear System Solver Using Q-Learning Schduling for Unified Dynamic Power System Simulations