One embodiment provides method comprising forecasting energy consumption of a consumer located in a first geographical location utilizing an artificial intelligence (AI) smart device, forecasting energy production in an environment of the consumer utilizing the AI smart device, and balancing the energy consumption with the energy production utilizing the AI smart device. The balancing comprises trading energy on an online energy trading platform with one or more entities located in one or more other geographical locations utilizing crypto commodity.

Provided are techniques for predictively generating demand response (DR) events based on grid operations and faults, based on optimization and self-learning routines. The techniques include obtaining grid status information, and determining a fault condition based at least in part on the grid status information. The techniques also include generating a DR event based at least in part on the fault condition, and responsive to generating the DR event, transmitting a notification of the DR event.

The disclosure provides a distributed dispatch method for ubiquitous power Internet of Things based on a transition matrix. The ubiquitous power Internet of Things includes generators. The method includes: S1, setting a marginal cost function of each of the generators, and extracting key cost parameters in the marginal cost function; S2, establishing an optimization model based on the key cost parameters of each of the generators and a communication topology of the ubiquitous power Internet of Things, and solving the optimization model to obtain an optimized transition matrix; and S3, generating a plan of a power output of each of the generators based on the optimized transition matrix and a distributed dispatch protocol to perform a distributed dispatch.

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.

An electrical grid control system includes a number of load tap changers (2), a number of voltage regulators (4), a number of capacitor banks (6), a number of distributed generators (10), and a centralized control unit (12) structured to generate settings information for the load tap changers (2), the voltage regulators (4), the capacitor banks (6), and the distributed generators (10) based on forecasted data. The distributed generators (10) are structured to use the settings information and a distributed algorithm to control power provisioning from each of the distributed generators (10). The load tap changers (2), the voltage regulators (4), and the capacitor banks (6) are structured to adjust their settings based on the settings information and local voltage measurements.

A method for determining a dynamic rating for a conductive path includes using a sensor to measure a value for a load parameter and selecting a heating process associated with the load parameter. A rated temperature change is changed by removing temperature changes due to a heating process other than the selected heating process to produce an impaired rated temperature change. A thermal load percentage is determined from the impaired rated temperature change. The thermal load percentage and the measured value are then used to determine the dynamic rating for the load parameter. A method also includes measuring a temperature rise, a current, and a voltage on the conductive path multiple times. Using at least two basis functions and the multiple measured temperature rises, currents and voltages, the values for at least two variables are determined. Trends in each variable are determined to determine a condition of electric equipment.

Regarding an independent system, a prediction value of charged/discharged power of a storage battery is calculated, based on a prediction value of generated power of a renewable energy power generator, a prediction value of demanded power of a control device, and a prediction value of demanded power of a load on an assumption that a power supply limit is applied to the load. Whether or not charge or discharge of the storage battery with charged/discharged power matching the prediction value of the charged/discharged power of the storage battery is possible is determined. The power supply limit is tightened when it is determined that the charge or discharge of the storage battery is not possible. A limit data indicating a detail of the power supply limit is output when it is determined that the charge or discharge of the storage battery is possible.

An energy optimization system for participating in a capacity market program (CMP) includes one or more memory devices storing instructions, that, when executed on one or more processors, cause the one or more processors to receive a nominated capacity value, generate an objective function and one or more CMP constraints, wherein the one or more CMP constraints cause an optimization of the objective function with the CMP constraints to generate a resource allocation that reduces the load of the facility by the nominated capacity value in response to receiving the dispatch from the utility, receive the dispatch from the utility, optimize the objective function based on the nominated capacity value, the dispatch, and the one or more CMP constraints to determine the resource allocation, and control one or more pieces of building equipment based on the determined resource allocation.

Embodiments for localizing abnormal energy consumption at a facility in a cloud computing environment by a processor. One or more residuals for both one or more predictors and a prediction may be generated according to one or more energy consumption measurements, weather data, and one or more characteristics of the one or more facilities, or a combination thereof. An energy consumption anomaly may be localized according to those of the one or more residuals associated with one or more predictors having an actual energy measurement deviating from a predicted actual energy measurement.

A method for generating a net load forecast for a utility grid, the grid including intermittent distributed energy resources and loads, comprising: defining two or more load forecast zones, each zone being associated with a load profile type and a climate zone type; assigning each of the loads to one of the zones based on the load profile and climate zone types associated with the load; assigning each of the energy resources to at least one of the zones based on the climate zone type associated with the energy resource; for each zone, generating an electrical energy consumption forecast for loads, an electric power generation forecast for energy resources, and a net load forecast from the electrical energy consumption and electric power generation forecasts; combining the net load forecast for each zone to generate the net load forecast for the grid; and, presenting the net load forecast on a display. The utility grid may be or may include a microgrid.