A method for forecasting wind power output of a target wind farm. The method includes normalizing, wind power output data for each wind farm of a group of wind farms, based, at least in part, on a respective installed capacity; transforming, the normalized power output data to yield transformed normalized wind power output data. Fitting, by the temporal module, each temporal model of at least one temporal model to model input data for each wind farm. The model input data corresponds to normalized wind power output data or transformed normalized wind power output data. The method further includes fitting, by a spatial module, a DVINE copula model for the group of wind farms, based, at least in part, on at least one residual value. Each residual value is determined based, at least in part on a selected fitted temporal model for each wind farm in the group.

A charging system utilizing energy storage multiplication is provided. An energy storage battery pack of the charging system is directly connected to a DC power transmission bus. When the charging request is initiated by the charging device, the charging device takes the power from the DC bus, the AC-DC converter and DC-DC converter connected to the energy generation device work as the energy source to deliver power to the DC bus, the power goes through the DC bus to the charging device, and the rest of the power goes to the energy storage device or goes out form the energy storage device when the charring power is higher than total energy from all other converters. The high C rate discharging of the energy storage device means high power capacity during discharging, this can provide much high power than AC-DC converter to fulfill the requirement of charging device.

A direct current power system. The direct current power system includes a direct current bus system, a solar power system, an energy storage system and a wind power system. The solar power system is configured to supply a first direct current power. The energy storage system has an input electrically coupled to the solar power system and is configured to supply a second direct current power at 380 volts to the direct current bus system. The wind power system includes is electrically coupled to the energy storage system and is configured to supply a third direct current power.

A computer-implemented method for power grid anomaly detection using a convolutional neural network (CNN) trained to detect anomalies in electricity demand data and electricity supply data includes receiving (i) electricity demand data comprising time series measurements of consumption of electricity by a plurality of consumers, and (ii) electricity supply data comprising time series measurements of availability of electricity by one or more producers. An input matrix is generated that comprises the electricity demand data and the electricity supply data. The CNN is applied to the input matrix to yield a probability of anomaly in the electricity demand data and the electricity supply data. If the probability of anomaly is above a threshold value, an alert message is generated for one or more system operators.

The present disclosure provides an energy storage system comprising a plurality of input ports connectable to receive electrical power from one or more energy sources, a plurality of output ports connectable to deliver electrical power to one or more loads, a plurality of battery modules, a switching matrix connected between the plurality of battery modules and the plurality of inputs, and between the plurality of battery modules and the plurality of outputs, the switching matrix configured to selectively connect each battery module to any number of the plurality of input ports or any number of the plurality of output ports, each input port to any number of battery modules, and each output port to any number of battery modules, and a main battery management controller operably coupled to the switching matrix for controlling connections between each battery module and any number of the plurality of input ports or any number of the plurality of output ports.

This invention provides an energy storage device manager, a system comprising the energy storage device manager, computer-readable media configured for providing the energy storage device manager, and methods of using the energy storage device manager. The energy storage device manager can optionally control charge buses and/or load buses to modulate the state of charge of an energy storage device. The energy storage device manager can optionally be configured with a plurality of modes that target different states of charge. The plurality of modes can optionally comprise a maintain mode which targets a nominal (e.g. 50%) charge state and a high-charge mode that targets a state of charge greater than the maintain mode. The plurality of modes can optionally further include an in-use mode which targets a state of charge greater than the maintain mode, and turns on a load bus that is turned off in the preparation mode. The energy storage device manager can optionally be configured to determine a charge start time to execute the preparation mode. The energy storage device manager can optionally be configured to determine the charge start time based on forecast data (e.g. power prediction forecast determined based on weather forecast).

Estimating components of a grid impedance, Z, of a power grid being coupled to a power generating unit at a point of interconnection is disclosed. A voltage, Vmeas, across the point of interconnection; an active current, IP, and/or an active power, P, delivered by the power generating unit to the power grid; and a reactive current, IQ, and/or a reactive power, Q, delivered by the power generating unit are determined. A parameter estimation vector is estimated using a recursive adaptive filter algorithm, and on the basis of Vmeas, IP, P, IQ and/or Q. A model representation of the power grid is created on the basis of the parameter estimation vector, and a system DC gain vector for the power grid is calculated, using the model representation. Finally, Z, and/or a resistance, R, of Z, and/or a reactance, X, of Z, is derived from the system DC gain vector.

A method for evaluating resonance stability of a flexible direct current (DC) transmission system in an offshore wind farm includes: establishing an s-domain equivalent circuit of a flexible DC transmission system in an offshore wind farm, constructing an s-domain node admittance matrix of the flexible DC transmission system in the offshore wind farm, determining a resonant mode of the system based on a zero root of a determinant of the node admittance matrix, and determining stability of the system. In the method, an s-domain impedance model is used to describe dynamic characteristics of a wind turbine, a flexible DC converter, and other power devices, avoiding coupling between device modeling and an operation mode of the system. In addition, the node admittance matrix is used for analysis so as to fully consider a plurality of power electronic devices and a grid structure of the offshore wind farm, realizing comprehensive analysis.

A system and method are provided for operating a power generating asset coupled to an electrical grid. Accordingly, a controller receives an environmental data set indicative of at least one environmental variable projected to affect the power generating asset over a plurality of potential modeling intervals. The controller then determines the variability of the environmental data set and a corresponding modeling-confidence level at each of the potential modeling intervals based on the variability. A modeling interval is thus selected corresponding to a desired modeling-confidence level. A computer-implemented model is employed to predict a future power profile for the power generating asset over the selected modeling interval. The future power profile is indicative of a power-delivery capacity of the power generating asset at each of a plurality of time intervals of the modeling interval. Based, at least in part, on the future power profile, the controller determines an obligated-power-production schedule for the power generating asset over the modeling interval. The obligated-power-production schedule corresponds to a power production agreement with the electrical grid. In accordance with the obligated-power-production schedule, the controller modifies at least one setpoint of the power generating asset to deliver electrical power to the electrical grid.

The application relates to an oscillation active damping control method and system for grid-tied type-4 wind turbine generator. The method comprises: based on an interconnection model of multiple subsystems, constructing a stored energy function and a dissipated energy function of a current inner loop control subsystem, and interaction energy functions between the current inner loop control subsystem and other subsystems are constructed, then establishing an energy feedback model of Type-4 wind turbine generator; when the oscillation occurs, obtaining instantaneous angular frequency of the PLL, and then based on the energy feedback model, adjusting the current reference value of the q-axis current inner loop generated by the reactive power outer loop control subsystem, to make the stored energy function decrease with time, so as to suppress the oscillation.