A method for electrical grid monitoring using aggregated smart meter data includes receiving, from an electric grid monitoring device, electrical data aggregated from a plurality of electric meters each connected to a secondary side of at least one electrical transformer within a local geographic region. Each electric meter is configured to generate measurement data based on measurements taken at a respective electrical load. The electrical data indicates one or more characteristics of a secondary electrical distribution system connected to the at least one electrical transformer. The method includes determining, based on the electrical data aggregated from the plurality of electric meters, one or more characteristics of the primary electrical distribution system connected to the electrical transformer.

A data processing apparatus is provided comprising a data acquisition unit configured to acquire sensor data of a sensor that is capable of measuring physical quantities of a measurement target, an interpretation unit configured to attempt to interpret, in accordance with a definition for each field of the sensor data, sensor data in the respective fields, and an output unit configured to output the sensor data interpreted by the interpretation unit for a field of which a definition is known, and to output the sensor data acquired by the data acquisition unit as is for a field of which a definition is unknown along with an indicator indicating unidentified. The data processing apparatus further comprises a confirming unit configured to later confirm a definition for at least one field among fields of which the definition is unknown.

A system for transferring energy based on predicted outages is described herein.

Operational planning of energy storage systems using batteries, e.g., Lithium-Ion batteries, is disclosed. A method of operating at least one server node includes: obtaining one or more load profiles associated with one or more interfacing modes of a battery energy storage system with an electrical utility distribution system, and predicting one or more degradations of the battery energy storage system, the one or more degradations being associated with operating the battery energy storage system in the one or more interfacing modes, the one or more degradations being predicted using an aging model of batteries of the battery energy storage system, the aging model being based on the one or more load profiles.

The energy management system is an energy management system for a microgrid including at least either one of an electrical load and a renewable energy power-generation system, and an energy storage device, the energy management system including: a prediction unit that predicts, at a fixed time interval, at least either one of an electricity demand and an amount of electricity generated from renewable energy, and an electricity fee, within a predetermined period; and an optimization unit that performs optimization for an optimum charging/discharging plan for the energy storage device by using a prediction result from the prediction unit and in consideration of uncertainty in prediction.

The present disclosure provides a method and a system for estimating capacity and usage pattern of behind-the-meter energy storage in electric networks. Conventional techniques on estimating an effective capacity of behind-the-meter energy storage of a consumer, in presence of distributed energy generation units is limited, computationally intensive and provide inaccurate prediction. The present disclosure provides an accurate estimate of the effective capacity and usage pattern of behind-the-meter energy storage of a target consumer utilizing data samples received from a utility in presence of one or more distributed energy generation units, using an energy balance equation with less computation and accurate prediction. Based on accurate estimation of the effective capacity and usage pattern, the utility may plan for proper infrastructure to meet power demands of the consumers.

A solar energy system comprising a photovoltaic array, an inverter, and an energy storage device is operated according to the following steps: (a) accessing irradiance data for a past time period; (b) acquiring an irradiance forecast for a future time period having a length of not more than 15 minutes and having a cumulative irradiance forecast of between 20% and 80% of clear-sky irradiance; (c) calculating a smoothed energy target-value for an imminent time step within the future time period, using the irradiance data for the past time period and the forecasted irradiance for the future time period; and (d) during the time step, delivering a quantity of energy to the inverter based on the calculated smoothed energy target-value. The energy storage device stores energy when more is produced than delivered, and discharges energy when more is delivered than is produced.

This invention provides an energy storage device manager, a system comprising the energy storage device manager, computer-readable media configured for providing the energy storage device manager, and methods of using the energy storage device manager. The energy storage device manager can optionally control charge buses and/or load buses to modulate the state of charge of an energy storage device. The energy storage device manager can optionally be configured with a plurality of modes that target different states of charge. The plurality of modes can optionally comprise a maintain mode which targets a nominal (e.g. 50%) charge state and a high-charge mode that targets a state of charge greater than the maintain mode. The plurality of modes can optionally further include an in-use mode which targets a state of charge greater than the maintain mode, and turns on a load bus that is turned off in the preparation mode. The energy storage device manager can optionally be configured to determine a charge start time to execute the preparation mode. The energy storage device manager can optionally be configured to determine the charge start time based on forecast data (e.g. power prediction forecast determined based on weather forecast).

Disclosed is a method for processing an irradiation forecast. The method includes: acquiring irradiation forecast data corresponding to a target time period; calling a stacked generalization model including a first-layer generalizer and a second-layer generalizer; determining, using the first-layer generalizer, intermediate forecast data based on the irradiation forecast data corresponding to the target time period; and determining, using the second-layer generalizer, an output forecast value corresponding to the target time period based on the intermediate forecast data. In a technical solution according to an embodiment of the present disclosure, a method for processing an irradiation forecast is achieved. In addition, in a technical solution according to the embodiment of the present disclosure, intermediate forecast data outputted by the first-level generalizer acts as an input of the second-level generalizer, such that

A module controller and a method for controlling operation of power producing modules in a power producing system are provided. The module controller comprises a processor and a memory, configured to store instructions, which when executed by the processor performs the method by causing the module controller to identify each power producing module connected to the module controller, retrieve a control logic for and associated with each of the identified power producing modules, determining the order in which the power producing modules are to be controlled by the module controller, allocate processor time to each power producing module and control the operation of each power producing module by executing, in the processor, the associated control logic.