Various embodiments of the teachings herein include an energy system comprising: a central control unit; and an energy subsystem including an energy storage unit having a total storage capacity. The central control unit is programmed to control the energy storage unit based on an optimization. The total storage capacity of the energy storage unit is divided into a first partial storage capacity and a second partial storage capacity by the control unit for the optimization. The first partial storage capacity is designated for internal use with respect to the energy subsystem. The second partial storage capacity is designated for external use with respect to the energy subsystem.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for shaping compute load using virtual capacity. In one aspect, a method includes obtaining a load forecast that indicates forecasted future compute load for a cell, obtaining a power model that models a relationship between power usage and computational usage for the cell, obtaining a carbon intensity forecast that indicates a forecast of carbon intensity for a geographic area where the cell is located, determining a virtual capacity for the cell based on the load forecast, the power model, and the carbon intensity forecast, and providing the virtual capacity for the cell to the cell.

A learning device, for constructing a learning model for generating a prior acceleration command value for controlling a control target in advance of the time a load on a boiler used for thermal power generation changes, includes: a learner configured to generate the learning model by mechanically learning, as learning data, a data set including a power generation command value for the thermal power generation and the prior acceleration command value which have been used in a previous operation of the boiler.

A power supply system according to the present embodiment is a control apparatus for a power supply system that includes a hydrogen power generator adapted to generate hydrogen using electric power supplied from a power generator and supply a load with electric power generated using the generated hydrogen, and a storage battery configured to be quicker in control response than the hydrogen power generator and adapted to get charged and discharged to offset excess and deficiency of the electric power supplied to the load from the power generator, the control apparatus including: an acquiring unit adapted to acquire information about remaining energy in the storage battery; and a controller adapted to control the hydrogen power generator so as to start generating electric power when it is expected, based on the information, that the remaining energy in the storage battery reaches a first threshold within a predetermined time.

A computing device, a power consumption prediction method thereof, and a non-transitory computer-readable storage medium are provided. In one embodiment, leakage power consumption of a graphics processor is obtained. Switching power consumption data corresponding to the graphics processor running a frame of image is obtained. Switching power consumption is estimated according to the switching power consumption data. Overall power consumption of the graphics processor is obtained according to the leakage power and the switching power consumption. Overall power consumption of the graphics processor processing one frame of image is estimated based on the overall power consumption. Power consumption performance of the graphics processor is therefore predicted in real-time.

A method is for analyzing the correlation between different line loss actions, which records, for each individual to which line loss actions occur, all the line loss actions that occur to the individual; and counts, based on two categorization manners: by individual and by line loss action, line loss actions recorded in the database, to obtain line loss action counting results corresponding to respective categorization manners; then, based on the two counting results, collects respective multi-line loss action sequences; and acquires probability data as a data source for probability calculation, to obtain line loss action occurrence conditional probabilities for different line loss actions, so that a targeted a targeted loss reduction plan can be formulated.

This invention relates to a method for optimising usage of electricity from the grid and energy storage comprising: forecasting a predetermined number of hours of load at a predetermined interval to determine a forecasted load profile; subtracting a peak load from the forecasted load profile by a maximum amount of electricity that can be charged into or discharged from the energy storage to determine a subtracted peak; smoothening peaks within 30 minutes period that are more than the subtracted peak to obtain a final forecast result, and determining optimised battery setpoint actions based on the final forecast result and energy storage operating constraints.

A town storage battery power conversion device outputs an AC voltage to a distribution system during a power failure. Electric power generated by a solar cell installed in each consumer house is converted into an AC voltage by a solar cell power conversion device and output to a consumer premises distribution system to which a load is connected. In an autonomous operation during a power failure, an operation plan for a distributed power supply is updated in a cycle longer than a cycle of an operation plan for a town storage battery. In the autonomous operation, the town storage battery power conversion device changes an AC voltage frequency according to a difference between electric power output from the town storage battery and the operation plan. The solar cell power conversion device has a function of modifying a control target value for the solar cell according to the AC voltage frequency.

A computer-implemented method for providing an estimated utility expenditure to a user may include: obtaining, via one or more processors, historical transactional data of one or more customers other than the user from one or more transactional entities, wherein the historical transactional data includes: at least one address of a given customer of the one or more customers; and a historical utility expenditure associated with the at least one address; generating, via the one or more processors, a heatmap based on the historical transactional data of the one or more customers via one or more algorithms, wherein the heatmap is indicative of at least the estimated utility expenditure associated with the at least one address during a predetermined period; and causing a display of a user device associated with the user to demonstrate the heatmap.

A reference capacity determiner determines a reference capacity of each power storage apparatus, under an assumption that power is not transmitted/received among the customer facilities. An actual capacity determiner determines an actual capacity of each power storage apparatus, under an assumption that power is transmitted/received among the customer facilities. A characteristic value determiner determines a first number of charging/discharging cycles, under an assumption that power is not transmitted/received among the customer facilities and each power storage apparatus has the reference capacity. A charging/discharging plan determiner determines a second number of charging/discharging cycles, under an assumption that power is transmitted/received among the customer facilities and each power storage apparatus has the actual capacity, and determine a charging/discharging plan of the power storage apparatuses so as to minimize an objective function indicating a degree of degradation from the first number of cycles to the second number of cycles.