A control system (300) allows recognized standard premise electrical outlets, for example NEMA, CEE and BS, among others to be remotely monitored and/or controlled, for example, to intelligently execute blackouts or brownouts or to otherwise remotely control electrical devices. The system (300) includes a number of smart receptacles (302) that communicate with a local controller (304), e.g., via power lines using the TCP/IP protocol. The local controller (304), in turn, communicates with a remote controller (308) via the internet.

A method, apparatus, and system for identifying electrical phases connected to electricity meters are disclosed. Voltage time series data of electricity meters are collected over a preselected collection time period, and three initial kernels representing three line-to-neutral phases are generated based on voltage correlations of meter-to-meter combinations. Three new kernels are then generated based on correlation values calculated for each of the three initial kernels with each electricity meter, and electricity meters are clustered into three groups based on average correlation values associated with each electricity meter. Six new kernels representing six phases are then formed based on the average correlation value associated with each electricity meter, and a predicted phase is assigned to each electricity meter based on correlation values of the electricity meter with each of the six new kernels based on the voltage time series data.

Systems and methods dynamically assess energy efficiency by obtaining a minimum energy consumption of a system, receiving in a substantially continuous way a measurement of actual energy consumption of the system, and comparing the minimum energy consumption to the measurement of actual energy consumption to calculate a substantially continuous energy performance assessment. The system further provides at least one of a theoretical minimum energy consumption based at least in part on theoretical performance limits of system components, an achievable minimum energy consumption based at least in part on specifications for high energy efficient equivalents of the system components, and the designed minimum energy consumption based at least in part on specifications for the system components.

Systems, methods, and processor-readable storage media for AI continued learning in electrical power grid fault analysis use historical fault record data to generate a fault cause prediction model for predicting the cause of a fault, and modify the fault cause prediction model based on additional technician data received from power grid technicians. The systems disclosed herein additionally receive an indication of a fault which has occurred in a power grid, obtain a prediction of the cause of the fault by applying the indication of the fault to the fault cause prediction model, and cause the predicted cause of the fault to be remedied.

A controller of a power generation system provides, in response to a request or a determination to initiate a heating mode for a photovoltaic (PV) array, a first instruction to an inverter coupled between the PV array and a power grid to apply an initial backfeed voltage on PV modules of the PV array. The PV array comprises a plurality of strings of PV modules coupled in parallel, and a first subset of the strings of PV modules are online and a second subset of the strings of PV modules are offline. The controller monitors current data from a set of current sensors that each measure current provided from a corresponding set of strings of PV modules of the plurality of strings of PV modules of the PV array. The controller also provides a second command to the inverter to adjust the backfeed voltage.

Systems include one or more critical datacenter connected to behind-the-meter flexible datacenters. The critical datacenter is powered by grid power and not necessarily collocated with the flexboxes, which are powered “behind the meter.” When a computational operation to be performed at the critical datacenter is identified and determined that it can be performed at a lower cost at a flexible datacenter, the computational operation is instead routed to the flexible datacenters for performance. The critical datacenter and flexible datacenters preferably shared a dedicated communication pathway to enable high-bandwidth, low-latency, secure data transmissions.

Provided is a system and method for detecting source(s) of oscillation on a power grid. In one example, the method may include receiving measurements from one or more sensors on a power grid, the measurements including data of an oscillation within the power grid, determining, via execution of one or more machine learning model, a candidate set of power system components disposed on the power grid that are candidates for being the source(s) of the oscillation, identifying, via execution of an optimization model, a component from among the candidate set of power system components which is the source (e.g., location, controller type, and/or asset type) of the oscillation, and displaying, via a user interface, information about the identified component.

An underwater data center includes a data center positioned in a water environment, powered by one or more sustainable energy sources. One or more data center nodes is coupled to the data center or included in the data center. A controller is coupled to the one or more data center nodes. A housing member houses the data center node under water. A passive cooling system coupled to the data center. The passive cooling operates by at least one of convention or conduction without moving fluid in the housing. The underwater data center is coupled to a sustainable energy source that provides energy to the underwater data center. The controller is configured to redistribute excess power from the sustainable energy source to an alternate source responsive to determine that the power from the sustainable energy source is greater than an amount needed to power the system.

An underwater data center system includes a data center positioned in a water environment, powered by one or more sustainable energy sources. One or more data center nodes are coupled to the data center or included in the data center. A controller is coupled to the one or more data center nodes. A housing member houses the data center node under water. The underwater data center is coupled to a sustainable energy source that provides energy to the underwater data center. One or more cables are coupled to the one or more data center nodes or the sustainable energy source. The system includes an underwater lattice AIoT device.

A computer system provides online state estimation (SE) and topology identification (TI) using advanced metering infrastructure (AMI) measurements in a distribution network. The computer system obtains input data including the AMI measurements, a network configuration, and line parameters; solves an SE and TI problem formulated from the input data and power equations of the distribution network; and periodically updates states and topology of the distribution network during power system operation. To solve the SE and TI problem, the computer system constructs a mixed-integer convex approximation programming (MICP) model to obtain an initial topology; generates neighboring spanning trees according to the MICP model and the initial topology; evaluates performance of each neighboring spanning tree with a matching index that is an indication of power flow performance; and chooses a tree topology of a neighboring spanning tree having a minimum matching index as a final network topology of the distribution network.