Disclosed are various embodiments for optimizing energy management. A quantity of renewable power that will be generated by renewable energy generation sources can be forecasted. The energy demand for a building or a cluster of buildings can be forecasted. A pricing model for buying energy from a grid can be determined. A quantity of energy to import from the grid or export to the grid can be scheduled based on the quantity of renewable energy forecasted and the state of charge or health of battery energy storage system, current and future operations of building HVAC, lighting and plug loads system, the forecasted energy demand for the building, and the pricing of the energy from the grid.

A method for operating an appliance on a power grid operated by a utility is described. The method includes receiving, at a server associated with the utility, information indicating whether a person is present in a proximity of the appliance, determining, by the server, an operational state of the appliance based on whether the person is present in the proximity of the appliance, and transmitting to the appliance, via a communication link associated with the power grid, control information related to the operational state to control operation of the appliance. Related methods, devices and systems are described.

The present disclosure provides a new and improved method and apparatus of scheduling for a charging infrastructure serving a plurality of electric vehicles. A computer-implemented method for scheduling a charging infrastructure serving a plurality of electric vehicles is provided, in which a prediction for a usage pattern of the charging infrastructure is made with a context based on historical usage patterns of the charging infrastructure and the contexts of the historical usage patterns, and a schedule scheme for deciding a distribution of charging spots of the charging infrastructure among the electric vehicles is determined based on the predicted usage pattern.

A model is generating for predicting energy workloads to adjust electrical energy supply to meet varying short-term energy demands at a microcosm level. A model is developed, using a computer, to facilitate predicting energy workloads for adjusting energy supplies to meet an energy demand. The model includes receiving, at the computer, input parameters of dynamic values of workloads as historical data, and generating a predictive model by analyzing the input parameters. The model further includes predicting short-term energy demands based on the predictive model, the predicted short-term energy demands include identifying a predicted peak value. Also, the model includes initiating short term energy output in an electrical grid to a transformer level component in the electrical grid based on the predicted short term energy demands.

Aspects of the present invention relate to a method of controlling a renewable power plant comprising a plurality of renewable power generators electrically connected by a local network and configured to supply active power to a main network. The method comprises: in response to receiving a signal requesting substantially zero active power supply to the main network: categorizing a generator as power-supplying, and the remaining generators as power-consuming; operating the power-consuming generators to generate no active power and to have their auxiliary systems draw power from the local network; and operating the power-supplying generator to supply active power to the local network such that the plant supplies substantially zero active power to the main network.

A capacity configuration method and system of energy storage in a microgrid. In this application, the time-series data related to photovoltaic power generation is acquired and processed to obtain the preprocessed time-series data; a time-series generative adversarial network (Time GAN) is trained based on the preprocessed time-series data to perform data enhancement to obtain enhanced time-series data; and based on the enhanced time-series data, a distributionally robust optimization model is used to perform capacity configuration of energy storage.

This energy management device is provided at a customer and is capable of controlling an electric appliance of the customer by communicating with a server. The server is configured to send a request for demand response. The energy management device includes: a receiver configured to receive the request for the demand response from the server; a transmitter configured to transmit a response indicating participation or nonparticipation with respect to the request; an electric appliance control unit configured to control the electric appliance; and an information acquisition unit configured to acquire information of the electric appliance. When determination that the request for the demand response is not achievable has been made after the response indicating participation has been transmitted, a response indicating nonparticipation is transmitted.

Responding to grid events is provided. The system determines, based on an event, to modify an electrical load of a site. The system selects a parameter for the site to adjust to modify the electrical load. The system identifies a script constructed from previously processed interactions between a human-machine interface of the building management system to adjust the parameter for the site. The system establishes a communication session with a remote access agent executed by a computing device of the site to invoke the building management system of the site. The system generates a sequence of commands defined by the script to adjust the one or more parameters for the site. The system transmits the sequence of commands to cause the remote access agent to execute the sequence of commands on the human-machine interface of the building management system to modify the electrical load of the site.

A power supply assembly including a source connection system including a primary source connection, a load connection, a converter system including at least one converter controllable for reactive power compensation, an energy saving transfer route connecting the primary source connection electrically to the load connection, and bypassing the converter system, and a control system. The control system is adapted to provide an efficiency optimization operation including transferring energy through the energy saving transfer route, and controlling the converter system according to an optimal operating scheme that optimizes efficiency of the power supply assembly while keeping reactive power drawn from the source connection system within a required range, wherein the optimal operating scheme defines an optimal combination for the converters used for reactive power compensation such that each of the converters operates in a predetermined optimal efficiency range thereof.

The herein disclosed electrical grid system includes: a first obtaining controller that obtains the electric power demand or supply request information from the electric power transmission and distribution facility; a second obtaining controller that obtains an electric storage state information; a third obtaining controller that obtains a vehicle information of an electric vehicle; a supply and demand satisfaction level calculation controller that calculates a supply and demand satisfaction level based on the electric power demand or supply request information; a selection controller that selects an electric vehicle to be guided to the charge or discharge spot based on the vehicle information obtained by the third obtaining controller and the supply and demand satisfaction level calculated by the supply and demand satisfaction level calculation controller; and a communication controller that sends a guide information to the selected electric vehicle.