DeepOPF
Xiang Pan
Abstract
We develop a Deep Neural Network (DNN) approach, namely DeepOPF, for solving optimal power flow (OPF) problems that are critical for daily power system operation. DeepOPF leverages a DNN model to depict the high-dimensional load-to-solution mapping and can directly solve the OPF problem upon given load, excelling in fast computation process and desirable scalability. Simulation results for IEEE test cases show that DeepOPF generates feasible solutions with negligible (<0.2%) optimality loss and accelerates the computation time by up to two orders of magnitude as compared to a state-of-the-art solver.
Topics & Concepts
ScalabilityComputationSolverComputer scienceProcess (computing)Artificial neural networkElectric power systemPower (physics)Mathematical optimizationPower flowState (computer science)AlgorithmArtificial intelligenceMathematicsProgramming languagePhysicsDatabaseQuantum mechanicsOperating systemPower System Optimization and StabilityOptimal Power Flow DistributionEnergy Load and Power Forecasting