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Power systems solvers are vital tools in planning, operating, and optimizingelectrical distribution networks. The current generation of solvers employcomputationally expensive iterative methods to compute sequential solutions. Toaccelerate these simulations, this paper proposes a novel method that replacesthe physics-based solvers with data-driven models for many steps of thesimulation. In this method, computationally inexpensive data-driven modelslearn from training data generated by the power flow solver and are used topredict system solutions. Clustering is used to build a separate model for eachoperating mode of the system. Heuristic methods are developed to choose betweenthe model and solver at each step, managing the trade-off between error andspeed. For the IEEE 123 bus test system this methodology is shown to reducesimulation time for a typical quasi-steady state time-series simulation byavoiding the solver for 86.7% of test samples, achieving a median predictionerror of 0.049%.

Fast Solutions in Power System Simulation through Coupling with Data-Driven Power Flow Models for Voltage Estimation