An Online Data-Driven Method for Microgrid Secondary Voltage and Frequency Control With Ensemble Koopman Modeling

Low inertia, nonlinearity and a high level of uncertainty (varying topologies operating conditions) pose challenges to microgrid (MG) systemwide operation. This paper proposes an online adaptive Koopman operator optimal control (AKOOC) method for MG secondary voltage frequency control. Unlike typical data-driven methods that are data-hungry lack guaranteed stability, the proposed AKOOC requires no warm-up training yet with bounded-input-bounded-output (BIBO) stability even asymptotical under some mild conditions. The is developed based on ensemble state space modeling full basis functions combines both linear nonlinear bases without need event detection or switching. An iterative learning also exploit model parameters, ensuring effectiveness adaptiveness designed Simulation studies in 4-bus (with detailed inner-loop control) system 34-bus showed improved accuracy control, verifying subject various changes conditions time delay, measurement noise, missing measurements.