Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation
This addresses faster power grid optimization for grid operators, but it is incremental as it builds on existing AC-OPF methods.
The paper tackled accelerating AC-OPF power grid simulations by using a neural network to generate intelligent initial solutions, achieving an average 49.2% performance improvement without losing optimality.
The optimal power flow (OPF) problem is one of the most important optimization problems for the operation of the power grid. It calculates the optimum scheduling of the committed generation units. In this paper, we develop a neural network approach to the problem of accelerating the current optimal power flow (AC-OPF) by generating an intelligent initial solution. The high quality of the initial solution and guidance of other outputs generated by the neural network enables faster convergence to the solution without losing optimality of final solution as computed by traditional methods. Smart-PGSim generates a novel multitask-learning neural network model to accelerate the AC-OPF simulation. Smart-PGSim also imposes the physical constraints of the simulation on the neural network automatically. Smart-PGSim brings an average of 49.2% performance improvement (up to 91%), computed over 10,000 problem simulations, with respect to the original AC-OPF implementation, without losing the optimality of the final solution.