Abstract: Systems and method are disclosed for monitor long-term voltage stability in a power system by estimating post-contingency operating point(s) considering PV-PQ transitions based on the current operating point; predicting a sensitivity based Thevenin index (STI) for each contingency using the estimated operating condition; and determining a voltage stability assessment (VSA) considering N?1 contingency.

Abstract: Systems and methods are disclosed to manage a microgrid with a hybrid energy storage system (HESS) includes deriving a dynamic model of a bidirectional-power-converter (BPC)-interfaced HESS; applying a first neural network (NN) to estimate a system dynamic; and applying a second NN to calculate an optimal control input for the HESS through online learning based on the estimated system dynamics.

Abstract: Systems and methods are disclosed to control a power inverter by extracting photovoltaic (PV) maximum power under a plurality of operating conditions as one or more reference variables; predicting the future behaviors of a PV output voltage as one or more controlled variables based on a current operational condition; generating one or more control signals for the H5 inverter that minimizes an error between the reference variable and the controlled variables; and controlling the power inverter with the one or more control signals.

Abstract: A control system is disclosed with a control strategy for autonomous multi-bus hybrid microgrids based on Finite-Control-Set Model Predictive Control (FCS-MPC). The control loops are expedited by predicting the future states and determining the optimal control action before switching signals are sent to converters/inverters. The method eliminates PI and PWM components, and offers 1) accurate PV maximum power point tracking (MPPT) and battery charging/discharging control, 2) DC and AC bus voltage/frequency regulation, and 3) precise and flexible power sharing control among multiple DERs.

Abstract: Systems and methods are disclosed to manage a microgrid with a hybrid energy storage system (HESS) includes deriving a dynamic model of a bidirectional-power-converter (BPC)-interfaced HESS; applying a first neural network (NN) to estimate a system dynamic; and applying a second NN to calculate an optimal control input for the HESS through online learning based on the estimated system dynamics.

Abstract: Systems and methods are disclosed to control a power inverter by extracting photovoltaic (PV) maximum power under a plurality of operating conditions as one or more reference variables; predicting the future behaviors of a PV output voltage as one or more controlled variables based on a current operational condition; generating one or more control signals for the H5 inverter that minimizes an error between the reference variable and the controlled variables; and controlling the power inverter with the one or more control signals.

Abstract: Systems and method are disclosed for monitor long-term voltage stability in a power system by estimating post-contingency operating point(s) considering PV-PQ transitions based on the current operating point; predicting a sensitivity based Thevenin index (STI) for each contingency using the estimated operating condition; and determining a voltage stability assessment (VSA) considering N?1 contingency.

Abstract: A control system is disclosed with a control strategy for autonomous multi-bus hybrid microgrids based on Finite-Control-Set Model Predictive Control (FCS-MPC). The control loops are expedited by predicting the future states and determining the optimal control action before switching signals are sent to converters/inverters. The method eliminates PI and PWM components, and offers 1) accurate PV maximum power point tracking (MPPT) and battery charging/discharging control, 2) DC and AC bus voltage/frequency regulation, and 3) precise and flexible power sharing control among multiple DERs.