An energy storage system is provided. The energy storage system includes a battery, a power conversion system, and an energy management system configured to control the power conversion system to supply power to the power consumption system using at least one of a power received from an outside or a power charged in the battery. The energy management system estimates power consumption amounts of the power consumption system for each unit time period in a predetermined time period, a reference power amount based on the battery power charged in the battery and the power consumption amounts, and based on the power consumption amounts and the reference power amount, controls the power conversion system to supply power to the power consumption system using the external power and the battery power during the predetermined time period.

Embodiments of systems and methods for power demand management are described herein. In some embodiments the system uses demand response bidding to reduce or increase the load of a power system. Day ahead or real-time bids may be performed. In other embodiments, a virtual power plant is disclosed that dynamically aggregates multiple power assets to provide demand response services, frequency response services, and fast ramp services to the grid.

In an example method, first electrical power is generated using one or more solar panels, and a water level rise of a sea is mitigated, at least in part, using a water processing system that is at least partially powered by the first electrical power. Mitigating the water level rise of the sea includes extracting saline water from the sea, desalinating the saline water, directing the desalinated water to one or more turbine generators, generating second electrical power using the one or more turbine generators, and directing the desalinated water from the one or more turbine generators into one or more aquifers. The one or more aquifers are hydraulically isolated from the sea.

In one embodiment, the method includes obtaining, by a first processing device, energy demand data representative of the energy consumption of respective tasks of a processing pipeline, obtaining, by the first processing device, battery availability data representative of the available energy of the batteries of other respective processing devices, for respective tasks of the processing pipeline, selecting, by the first processing device, one of the processing devices for executing the task, as a function of the energy demand data and the battery availability data, and controlling, by the first processing device, the execution of the respective tasks on the selected processing devices.

The present disclosure discloses a method for optimizing equipment capacity and equipment power of an energy hub system. The method includes establishing an energy hub model containing natural gas boilers, electric boilers, coolers and heat pumps, establishing a bilevel optimized upper model to solve the optimal heat pump capacity, and establishing a bilevel optimized lower model to solve the optimal power utilization of each energy device based on the binary search algorithm of the quadratic function solves the upper model by using the multi-objective evolutionary algorithm NSGA-II to solve the lower model. The optimization method of the present invention can solve the multi-objective bilevel model problem without the help of commercial optimization software. Obtaining a reasonable, efficient and green planning scheme makes the total operating cost and total exhaust gas emissions of the energy hub relatively optimal.

A grid power configuration may provide a reliable, efficient, inexpensive and environmentally conscious power source to a site, for example, a remote site such as a well services environment. Grid power may be provided for one or more operations at the site by coupling a main breaker to a switchgear unit coupled to one or more loads. The switchgear unit may be coupled to the main breaker via a main power distribution unit and may also be coupled to one or more loads. At least one of a grid power unit and a switchgear unit may be coupled to the main breaker via the main power distribution unit and may also be coupled to one or more additional loads. A control center may be communicatively coupled to the main breaker or any one or more other components to control one or more operations of the grid power configuration.

A method and apparatus for detecting and prioritizing loads comprising a load analyzer configured to receive at least one load monitoring signal from at least one channel, where each channel is configured to be coupled to at least one load, and analyze at least one load signature derived from the at least one load monitoring signal to detect a load type that is connected to the at least one channel and assign an energy consumption priority to the at least one load.

The present disclosure is directed to systems and methods for controlling an electrical system using setpoints. Some embodiments include control methods that monitor an adjusted net power associated with the electrical system and adjust the setpoint based on a comparison of the adjusted demand to the setpoint. If the adjusted demand has not exceeded the demand setpoint, the setpoint is reduced. If the adjusted demand has exceeded the demand setpoint, the setpoint is increased.

The present application discloses a chance constrained extreme learning machine method for nonparametric interval forecasting of wind power, which belongs to the field of renewable energy generation forecasting. The method combines an extreme learning machine with a chance constrained optimization model, ensures that the interval coverage probability is no less than the confidence level by chance constraint, and takes minimizing the interval width as the training objective. The method avoids relying on the probability distribution hypothesis or limiting the interval boundary quantile level, so as to directly construct prediction intervals with well reliability and sharpness. The present application also proposes a bisection search algorithm based on difference of convex functions optimization to achieve efficient training for the chance constrained extreme learning machine.

Systems and methods of managing power distribution in a portion of an electrical power grid with at least one power storage, including: receiving at least one power consumption rule from at least one consumer of the power grid, analyzing power consumption data from at least one power consumption meter connected to the power grid, applying the at least one power consumption rule on the analyzed power consumption data, based on forecasted data, and managing power consumption for the at least one consumer, based on the result of the at least one power consumption rule, and also based on a power capacity status of the at least one power storage.