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The optimal power flow (OPF) problem is one of the most importantoptimization problems for the operation of the power grid. It calculates theoptimum scheduling of the committed generation units. In this paper, we developa neural network approach to the problem of accelerating the current optimalpower flow (AC-OPF) by generating an intelligent initial solution. The highquality of the initial solution and guidance of other outputs generated by theneural network enables faster convergence to the solution without losingoptimality of final solution as computed by traditional methods. Smart-PGSimgenerates a novel multitask-learning neural network model to accelerate theAC-OPF simulation. Smart-PGSim also imposes the physical constraints of thesimulation on the neural network automatically. Smart-PGSim brings an averageof 49.2% performance improvement (up to 91%), computed over 10,000 problemsimulations, with respect to the original AC-OPF implementation, without losingthe optimality of the final solution.

Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation