Systems and methods for load distribution, where a method can include: establishing an interface with a controlling architecture of an electric power distribution system, the electric power distribution system in communication with a set of outlets of utility-facing devices providing access to the electric power distribution system; returning a demand assessment from a demand model characterizing anticipated demand upon the one or more portions of the electric power distribution associated with the set of outlets, the set of outlets positioned at a set of sites; and executing an action for adjustment of load distribution throughout the set of sites, through the controlling architecture, based upon the demand assessment. The inventions can be used to achieve carbon emission goals by incentivizing better use of limited energy resources.

Methods, computer program products, and systems are presented. The methods include, for instance: obtaining historical data of demand response programs and demand response agreements respective to each of the users regarding a subject energy. Training dataset for a DR user pooling model includes attributes of the demand response data collected that are relevant to responsivities of the demand response programs. The DR user pooling model is trained by the training dataset by machine learning. A DR user pool is identified amongst users of the demand response program by the DR user pooling model. Users in the DR user pool respond to demands as a group and the DR user pool is adjusted to improve responsivities of the demand response programs.

A method and a system for managing power resources. One or more measurements received from one or more sensors communicatively coupled to at least one processor are processed. The sensors monitor and measure at least one of: one or more operational parameters associated with operation of at least one equipment, one or more external parameters associated with an environment of the equipment, and one or more power parameters associated with a power consumption by the equipment. Based on the processed one or more measurements, one or more future operational parameters associated with an operation of the are determined. The operation of the equipment is controlled using the determined future operational parameters.

Systems, methods, techniques and apparatuses of state estimation are disclosed. One exemplary embodiment is a method comprising determining, with a state estimator, a state estimate based on power grid data corresponding to characteristics of a power grid received from a plurality of local controllers; calculating, with the state estimator, a first gain matrix based on a Gauss-Newton method; updating, with the state estimator, the state estimate based on the first gain matrix; calculating, with the state estimator, a second gain matrix based on Newton's method; updating, with the state estimator, the state estimate based on the second gain matrix; and iteratively recalculating the second gain matrix and updating the state estimate based on the second gain matrix until the state estimate converges.

A building automation system that is comprised of a plurality of network connected electrical modules contained in standard receptacle gang boxes for outlets and switches is described. The system includes an AC to DC power supply, a bidirectional solid state dimmer switch, a microprocessor, and, interconnected sensors that are powered and controlled by microprocessors within the electrical modules. The electrical modules include a user interface for programming and control. The system provides enhanced safety and security, power metering, power control, and home diagnostics. The apparatus replaces existing outlet receptacles and switches with a familiar flush but elegantly updated and seamlessly integrated faceplate.

A method and system are provided for improving predictions of electrical power usage. In the method and system, load and/or environmental data is classified into data sets that correspond to different modes of operation of an electrical load. Different predictive models are also provided for each set of classified data. The predictive models may provide more efficient and/or more accurate predictions of power usage since each model is limited to a particular mode of operation.

A method for identifying report triggering events in a power grid to improve stability in the power grid is provided. Energy flow information in the power grid is measured and report triggering events during a predetermined period of time are identified. The report triggering events include at least a predetermined number of load flow transitions where energy flows back from a load associated with the power grid to the power grid during the predetermined period of time and/or exceeding a threshold of time where an energy flow phase angle between voltage and current remains at or near 90 or 270 degrees indicating pure volt-ampere reactive (VAR) operation during the predetermined period of time. A report associated with each of the identified report triggering events is generated and includes a location indicating where the report triggering event occurred in the power grid. The reports are transmitted.

Systems and methods are directed to controlling components of a utility grid. The system can receive data samples including signals detected at one or more portions of a utility grid. The system can construct a matrix having a first dimension and a second dimension. The system can train a machine learning model based on the matrix to predict values for signals of the utility grid not provided in the matrix. The system can receive bounds for one or more input variables, constraints on one or more output variables, and a performance objective for the utility grid. The system can determine, based on the machine learning model and via an optimization technique, an adjustment to a component of the utility grid that satisfies the performance objective. The system can provide the adjustment to the component of the utility grid to satisfy the performance objective.

A system and method to join distributed energy resources (DER) to achieve common objectives is provided. The present technology organizes and/or aggregates DERs by routing a (DER) program request for resources to DER contributors capable of responding to and performing the request using a routing system. The system accesses a plurality of DER profiles, each profile associated with a DER contributor capable of contributing a resource to the request, and calculates an initial value for each DER profile based on request attributes and scoring metrics associated with the profile. The system then calculates a fitness metric for each DER profile based on the initial value using a neural network having weights based on the plurality of performance indicators and selects the DER profile and contributors to whom to route the request.

A model identification system includes a device information acquiring unit that acquires device information used to identify a model of an electric device, an operation extracting unit that extracts data of a predetermined operation section, a feature quantity extracting unit that extracts a parameter used to identify the electric device, and a model identifying unit that identifies a model of an electric device, wherein the feature quantity extracting unit performs a machine learning process by sampling the data of the predetermined operation section extracted from the operation extracting unit a plurality of times, extracts a parameter corresponding to each sampling, and extracts a parameter appropriate to identify a model among a plurality of sampled parameters.