A method, apparatus, system and computer program is provided for controlling an electric power system, including implementation of a voltage control and conservation (VCC) system used to optimally control the independent voltage and capacitor banks using a linear optimization methodology to minimize the losses in the EEDCS and the EUS. An energy validation process system (EVP) is provided which is used to document the savings of the VCC and an EPP is used to optimize improvements to the EEDCS for continuously improving the energy losses in the EEDS. The EVP system measures the improvement in the EEDS a result of operating the VCC system in the “ON” state determining the level of energy conservation achieved by the VCC system. In addition the VCC system monitors pattern recognition events and compares them to the report-by-exception data to detect HVL events. If one is detected the VCC optimizes the capacity of the EEDS to respond to the HVL events by centering the piecewise linear solution maximizing the ability of the EDDS to absorb the HVL event.

A power prediction method includes obtaining evaluation metrics of models in a model pool, where the evaluation metrics are used to indicate precision of the models; selecting, based on the evaluation metrics, a first model for a power prediction on a first electrical unit; and presenting a result of the power prediction of the first electrical unit using the first model.

The present disclosure provides a photovoltaic (PV) disconnect device used in an electrical system. The electrical system includes an energy control system electrically coupled to a utility grid. The electrical system includes a PV power generation system electrically coupled to the energy control system. The electrical system includes an energy storage system electrically coupled to the energy control system. The PV disconnect device is electrically coupled to the PV power generation system and the energy control system. The PV disconnect device electrically disconnects the PV power generation system from the energy control system.

Methods, systems, and computer readable mediums determining a system state of a power system using a convolutional neural network using a convolutional neural network are disclosed. One method includes converting power grid topology data corresponding to a power system into a power system matrix representation input and applying the power system matrix representation input to a plurality of convolutional layers of a deep convolutional neural network (CNN) structure in a sequential manner to generate one or more feature maps. The method further includes applying the one or more feature maps to a fully connected layer (FCL) operation for generating a respective one or more voltage vectors representing a system state of the power system.

A method for remote monitoring includes (1) generating first sensor data from a first sensor at a first network node of a communications network and (2) sending the first sensor data from the first sensor to a second network node that is remote from the first network node, via the communications network. The first network node is powered from an electrical power grid that is separate from the communications network. The first sensor data may be raw sensor data and/or lossless sensor data.

A power system within a battery-powered node includes a primary cell, a secondary cell, and a battery controller. The battery controller includes a constant current source that draws power from the primary cell to charge the secondary cell. The battery-powered node draws power from the secondary cell across a wide range of current levels. When the voltage of the secondary cell drops beneath a minimum voltage level, the constant current source charges the secondary cell and a charging signal is sent to the battery-powered node. When the voltage of the second cell exceeds a maximum voltage level, the constant current source stops charging the secondary cell and the charging signal is terminated. The battery-powered node records the amount of time the charging signal is active and then determines a battery depletion level based on that amount of time. Battery replacement may then be efficiently scheduled based on the depletion level.

This application provides a method and apparatus for obtaining location information of a controller. The method includes: obtaining, by an inverter, signal feature information of each photovoltaic unit in a high-voltage direct current string, where the signal feature information of a photovoltaic unit includes a communication identifier of the photovoltaic unit and a signal feature of another photovoltaic unit, and the photovoltaic unit includes one controller and at least one photovoltaic module; determining an installation sequence of the photovoltaic units based on signal feature information of the photovoltaic units; and determining relative installation location information of each photovoltaic unit based on relative installation location information of a target photovoltaic unit in the high-voltage direct current string and the installation sequence of the photovoltaic units, where the target photovoltaic unit is at least one photovoltaic unit in the high-voltage direct current string.

A method for assisting control of an electrical supply grid) or a portion thereof is provided. The method includes recording system states of the grid and/or influencing the grid, transmitting the states to a central evaluation and/or control unit and/or between other subscribers, including wind turbines and/or wind farms that supply the grid, for use in controlling their supply to the grid. The method includes controlling the grid on the basis of the transmitted states. Fundamentally identical states are simultaneously recorded at multiple recording locations associated with the grid and the recording of at least one of the states at a respective recording location is performed by a turbine and/or farm and the turbine or farm recording a state at one recording location and the turbine or farm recording a state at another recording location are independent at least such that they supply to the grid at different points.

A hardware processor may be coupled to a communication network and receive charging requests and discharging requests from a plurality of prosumer facilities via the communication network. One or more energy storage systems may be coupled to an energy grid and able to charge from and discharge to the energy grid, and may communicate with the hardware processor via the communication network. Based on the charging requests and discharging requests, an energy schedule may be generated. The energy schedule may include a first set of the prosumer facilities from which charge requests are accepted, and a second set of prosumer facilities from which discharge requests are accepted. One or several energy storage systems may be controlled or triggered to charge or discharge repeatedly via the energy grid according to an updated energy schedule (e.g., regularly updated).

The present disclosure provides a new and improved method and apparatus of scheduling for a charging infrastructure serving a plurality of electric vehicles. A computer-implemented method for scheduling a charging infrastructure serving a plurality of electric vehicles is provided, in which a prediction for a usage pattern of the charging infrastructure is made with a context based on historical usage patterns of the charging infrastructure and the contexts of the historical usage patterns, and a schedule scheme for deciding a distribution of charging spots of the charging infrastructure among the electric vehicles is determined based on the predicted usage pattern.