The present invention relates to an MPC-based hierarchical coordinated control method and device for a wind-hydrogen coupling system. The method comprises the following steps: (1) dividing the wind-hydrogen coupling system into upper-layer grid-connected control and lower-layer electrolytic cell control; (2) controlling grid-connected power to track a wind power prediction curve by adopting an MPC control algorithm for upper-layer grid-connected control, and obtaining an electrolytic cell power control quantity for the lower-layer electrolytic cell control at the same time; (3) dividing operation states of electrolytic cell monomers into four operation states of rated power operation, fluctuating power operation, overload power operation and shutdown; and (4) determining the operation states of various electrolytic cell monomers by adopting a time-power double-line rotation control strategy based on the electrolytic cell power control quantity, thus making the electrolytic cell monomers operate in one of the four operating states in turn.

Methods, systems, and apparatus are disclosed for electrical power grid visualization. A computer-implemented method includes: obtaining power grid data including different temporal and spatially dependent characteristics of a power grid, the characteristics including a first characteristic, a second characteristic, and a third characteristic; and generating a graphical user interface (GUI) representing a visualization of the power grid data. The GUI includes a line-diagram representation of power lines in the power grid overlaid on a map of a geographic region in which the power grid is located, the line-diagram including a plurality of line segments, wherein attributes of each line segment represent the power grid data at a particular spatial location of the power grid. The attributes include a time-changing thickness of the line segment representing the first characteristic; a plurality of time-changing directional arrows on the line segment representing the second characteristic; and a color shading representing the third characteristic.

A power management system includes a photovoltaic power generation device installed in a predetermined area and connected to a power grid disposed in the predetermined area, an acquisition device configured to acquire a wind direction at a reference point at which the photovoltaic power generation device is installed in the predetermined area, and an arithmetic device configured to calculate a predicted value of a solar radiation amount at the reference point at a prediction target time and calculate generated power of the photovoltaic power generation device by using the predicted value.

Techniques are described for implementing an automated control system to control operations of a target physical system, such as production of electrical power in an electrical grid. The techniques may include determining how much electrical power for each of multiple producers to supply for each of a series of time periods, such as to satisfy projected demand for that time period while maximizing one or more indicated goals, and initiating corresponding control actions. The techniques may further include repeatedly performing automated modifications to the control system's ongoing operations to improve the target system's functionality, by using reinforcement learning to iteratively optimize particles generated for a time period that represent different state information within the target system, to learn one or more possible solutions for satisfying projected electrical power load during that time period while best meeting the one or more defined goals.

A system and method for performing novel wind forecasting that is particularly accurate for forecasting over short-term time periods, e.g., over the next 1-5 hours. Such wind forecasting is particularly advantageous in wind energy applications. The disclosed method is anchored in a robust physical model of the wind variability in the atmospheric boundary layer (ABL). The disclosed method approach leverages a physical framework based on the unsteady dynamics of earth's atmosphere, and drives forecasting as a function of previously-observed atmospheric condition data observed at the same location for which a wind forecast is desired.

Systems and methods are directed to controlling components of a utility grid. The system can receive data samples including signals detected at one or more portions of a utility grid. The system can construct a matrix having a first dimension and a second dimension. The system can train a machine learning model based on the matrix to predict values for signals of the utility grid not provided in the matrix. The system can receive bounds for one or more input variables, constraints on one or more output variables, and a performance objective for the utility grid. The system can determine, based on the machine learning model and via an optimization technique, an adjustment to a component of the utility grid that satisfies the performance objective. The system can provide the adjustment to the component of the utility grid to satisfy the performance objective.

An electric power control system for controlling supply and consumption of electric power in a system power supply, a storage battery and an electric power load, said electric power control system including: an estimated value correction unit configured to obtain a difference between a past power control estimated value and a past actual performance value, and to shift a power control estimated value obtained as a result of estimation in a predetermined period to an extent corresponding to said difference, thereby correcting the power control estimated value, wherein said past power control estimated value is a value obtained as a result of estimation performed in a past time relative to said predetermined period, and said past actual performance value is a value obtained as an actual result in the past time; and a power control unit configured to control supply and consumption of electric power in the system power supply, the storage battery, and the electric power load, based on the power control estimated value corrected by the estimated value correction unit.

Systems and methods for automatically selecting actions to take on a utility grid to simultaneously reduce uncertainty while selecting actions that improve one or more effectiveness metrics. Grid action effects are represented as confidence intervals, the overlap of which is used as a weight when selecting actions within a constrained search space of grid actions. The response of the utility grid to the grid actions may be measured and parsed by the temporal and spatial reach of the grid action, then used to update the confidence intervals for that particular selected grid action.

A facility providing systems and methods for managing and optimizing energy consumption and/or production is provided. The facility provides techniques for optimizing energy-consuming and energy-producing systems to meet specified demands or goals in accordance with various constraints. The facility relies on models to generate an optimization for an energy system. In order to use generic models to simulate and optimize energy consumption for an energy system, the generic models are calibrated to properly represent or approximate conditions of the energy system during the optimization period. After the appropriate models have been calibrated for a given situation using one or more modeling parameter sets, the facility can simulate inputs and responses for the corresponding system. The facility uses the generated simulations to generate a plan or control schedule to be implemented by the energy system during the optimization period.

A method for determining a converter Thevenin equivalent model for a converter system, includes: receiving measurement values of a coupling point voltage and a coupling point current measured at a point of common coupling between a grid emulator system and the converter system, wherein the grid emulator system supplies the converter system with a supply voltage; and determining a converter Thevenin impedance and a converter Thevenin voltage source of the converter Thevenin equivalent model by inputting the measurement values of the coupling point voltage and of the coupling point current into a coupled system model, which includes equations modelling the converter system and the grid emulator system and from which the converter Thevenin impedance and a converter Thevenin voltage source are calculated.