A system for monitoring electric energy amount according to one embodiment of the present disclosure includes an electric energy system including one or more loads receiving electric energy through an external electric energy source and a renewable energy generator, a home server for receiving information of a first electric energy amount that is an electric energy amount consumed by the one or more loads, a second electric energy amount that is supplied from the external electric energy source to the electric energy system, and a third electric energy amount that is supplied from the renewable energy generator to the electric energy system, and an electric energy amount monitoring server calculating the second electric energy amount or the third electric energy amount from electric energy consumption of the electric energy system, which is received from the home server, and supply electric energy amount information supplied to the electric energy system.

A measurement device that performs a predetermined measurement task together with a plurality of other measurement devices is provided. This measurement device is provided with a sampling phase generator for generating a sampling phase for instructing a timing of sampling, and a communication unit for communicating with at least one of the plurality of other measurement devices. The communication unit transmits the sampling phase generated by the sampling phase generator to at least one of the plurality of other measurement devices. The sampling phase generator is configured to generate a third sampling phase, using an operation that is based on a generated first sampling phase and a second sampling phase received by the communication unit from at least one of the plurality of other measurement devices.

A system of electrical distribution within a building, which selectively energizes power sockets only when an appliance is connected to the socket and in need of power.

In a direct load control system supporting frequency control of an electrical grid, loads are grouped into a plurality of load aggregations based on electrical power draw characteristics of the loads, e.g. power draw magnitude characteristic and transient slew rate. A best-fit dispatch signal is generated for each load aggregation to match a desired load response with a sum of predicted load responses of the aggregations each operating in accord with its best-fit dispatch signal. For each load aggregation, its best-fit dispatch signal is communicated to the loads of the load aggregation, and the loads of the load aggregation are operated in accord with the best-fit dispatch signal communicated to the loads. The desired load response may be generated based on historical Automatic Generation Control (AGC) signal data and at least one of (1) a weather forecast and (2) daily, weekly, and seasonal cycles.

System and methods for determining loading on supply lines of a three-phase power distribution system. Load meters are connected to the supply lines to receive power. A feeder meter is connected to a supply line designated the reference supply line having a known reference phase. A device generates a timing pulse received by the load meters and the feeder meter. The load meters and feeder meter each determine a respective time delay and feeder meter delay from receiving the timing pulse until a landmark value of the power received. The feeder meter delay corresponds to the reference phase. Each load meter and feeder meter includes a transmission device to transmit the time delay or feeder meter delay. An end server receives the time delays and feeder meter delay and assigns each load meter to a phase by comparing the time delay for a load meter to the feeder meter delay.

A machine learning method automates the generation of demand response messages to homeowners for utilities. Electric meter data is used to determine a confidence level that the house is occupied so that demand response messages are not sent to unoccupied houses. Demand response messages are associated with a profile. For an initial deployment of a demand response program, a variety of message profiles are tested to study the effectiveness of the communication. After each demand response event, the profile of each message sent to each user is evaluated to learn more effective approaches for future messages. Over time, the learning of the effectiveness of the message profile provides a more precise and complete behavioral model of the user to assess how to motivate energy savings. Continually updating the behavioral model of the user responses to demand response messages increases the likelihood to engage users to participate in demand response events.

This disclosure generally relates to a rechargeable battery for electric vehicles. The rechargeable battery may include a housing that includes a first temperature sensor to sense a first temperature of a first power connection terminal of the rechargeable battery and a second temperature sensor within the housing to sense a second temperature of a second power connection terminal of the rechargeable battery. The rechargeable battery may also include a battery management system located within the housing, where the battery management system causes a reduction in an amount of current through at least one of the first power connection terminal or the second power connection terminal when a temperature associated with at least one of the first power connection terminal or the second power connection terminal exceeds a threshold.

The present disclosure provides systems and methods for flexible renewable energy power generation. The present disclosure also provides systems and methods for firming power generation from multiple renewable energy sources.

Embodiments of this application disclose a backup power supply method and a related device. The method includes: calculating a battery capacity of a battery based on historical power outage alarm information and historical power consumption information of a station in which the battery is located; if a power outage occurs in the station, calculating a state of charge of the battery based on actual power consumption and power outage duration of the station, and the battery capacity; and sending a current target energy saving level of the battery to the station if it is determined, based on the state of charge, that an energy saving level of the battery changes, so that the station uses a power saving policy corresponding to the target energy saving level.

Systems and methods for meter-to-transformer connectivity. The system can include a data processing system comprising one or more processors coupled with memory. The data processing system can receive a time-series data set of voltage measured by meters at a plurality of loads in an electricity distribution grid. The data processing system can construct, based on the time-series data set, a matrix with values that indicate similarities between pairs of meters of the meters. The data processing system can generate, via clustering techniques applied to the matrix, a silhouette score for each of the meters in the matrix to group the meters into transformer groups. The data processing system can provide, for output via a graphical user interface, a digital map with an indication of a subset of meters of the meters associated with a transformer of the transformer groups generated based on the clustering techniques applied to the matrix.