Predictive energy management across a plurality of microgrids situated at disparate facilities is disclosed. Each microgrid may include one or more distributed energy resources (DERs). Reception of profile data from these microgrids and the creation of aggregated profiles is enabled, which may incorporate the profile data, charts of accounts, energy transfer tariffs, and other energy-related attributes. An event detection engine may identify triggering events such as energy surpluses, deficits, or operational conditions, indicating a benefit to energy reallocation. A recommendation engine may generate energy allocation recommendations based on predictive models, optimizing factors like cost efficiency, carbon offset utilization, and energy availability. The recommendations may be executed through an aggregation server, which dynamically updates the aggregated profiles in real-time.

Predictive energy management comprising systems and methods to collect information about the configuration of energy resources and historical data about energy utilization as to make recommendations on how to optimize those energy resources are disclosed. Intelligent distributed energy resources (IDERs) which are devices that are energy producers or consumers that are automatable with application programming interfaces are aggregated together. An intelligent energy profile, which comprises a summary of energy resources and historical data about utilization is generated. A predictive algorithm is applied to the intelligent energy profile thereby generating a predicted future state, and a recommendation on how to optimize against that predicted future state is generated. In some embodiments generative artificial intelligence techniques are utilized, and in some embodiments the recommendations are automatically performed via the generation of computer script embodying the recommendations. The predictive energy management techniques scale from a single building, through microgrids, to the national level.

A backup apparatus includes a contactor, a first rectifier circuit, an auxiliary power supply circuit, a first switch, a second switch, and a controller. The contactor includes a coil connected to the auxiliary power supply circuit through the second switch and a main contact switch connected to a power grid. The first rectifier circuit is connected to the power grid through the first switch, and the first rectifier circuit is connected to the coil. When the first switch is turned on, the first rectifier circuit supplies power to the coil. After the main contact switch is turned on, the controller controls the second switch to be turned on and the first switch to be turned off. When the second switch is turned on and a voltage of the power grid is lower than a voltage threshold, an input capacitor in the auxiliary power supply circuit supplies power to the coil.

A computing system for functional integration with a distributed power grid having one or more inverters. The computing system includes processing device(s) to control operation of an inverter by executing a feedback controller with a grid-tied line, of the distributed power grid, treated as a plant and including a unified algebraic control system operating based on a pair of closed-loop variables carried in closed-loop signals of the unified algebraic control system. The processing device(s) control, using the feedback controller, transitions of the inverter between a plurality of operating modes by adjusting a magnitude of the closed-loop signals, which correspond to the pair of closed-loop variables, towards a setpoint of a plurality of setpoints. Each setpoint corresponds to a different operating mode of the plurality of operating modes.

The present disclosure relates to curtailment control technology for photovoltaic power generation, and more particularly, to a gridforming type curtailment control system and method in link with a photovoltaic inverter. According to the present disclosure, inertia control that cannot be performed in existing photovoltaic inverters may be performed more continuously in link with a gridforming inverter, thereby having an effect of efficiently managing and utilizing existing photovoltaic inverter facilities.

Disclosed herein are AI-based platforms for enabling intelligent orchestration and management of power and energy. In various embodiments, an artificial intelligence system us configured to analyze a data set of monitored local conditions and generate a recommended configuration of at least one distributed system of a set of distributed systems, each distributed system of the set of distributed systems being configurable both to produce energy and to consume energy, wherein the configuration causes the at least one distributed system to produce and/or consume energy based on the monitored local conditions. In some embodiments, the artificial intelligence system configures a plurality of the distributed systems in the set such that a set of aggregate performance requirements are satisfied across the plurality. In some embodiments, the aggregate performance requirements are a set of economic performance requirements and/or a set of regulatory performance requirements.

A refueling prediction mode in a microgrid power system may include inputting historical load and weather date of the microgrid power system to a forecasting block and determining forecasted site load and weather factors for the microgrid power system. The forecasted site load and weather factors may be used along with fuel efficiency curves and asset fuel levels for power assets of the microgrid power system, along with other information, to determine an asset dispatch schedule and a refueling timeline for the power assets. Asset dispatch commands based on the asset dispatch schedule and an actual site load may be output to the power assets to meet the load demand on the microgrid power system. A sustained reliability mode may use the information plus real-time fuel costs for the power assets to determine an asset dispatch schedule for longest microgrid sustenance for the power assets.

A smart load control device can include a network interface controller (NIC) including memory storing one or more distributed intelligence (DI) agents that configure the smart load control device to perform operations such as monitoring electricity consumption by a first load at a first service site; receiving operational status data representing an operational status of a second load at a second service site; and/or controlling provision of control circuit wiring or mains power to the first load based at least in part on monitoring the first load and the operational status of the second load, to maintain a total electricity consumption of the first service site and the second service site below a threshold electricity consumption.

A system and method for dynamic energy grid management with centralized intelligence and distributed edge execution is disclosed. An energy grid optimization computer continuously receives operational data from edge units distributed throughout an energy distribution network. The computer monitors the data to detect trigger events such as renewable generation fluctuations or predicted grid congestion, performs system-wide analysis to identify affected edge units, calculates updated parameters accounting for variable fault current contributions from renewable sources, generates lightweight configuration packages, and deploys them to edge units through secure communication networks. Edge units receive configurations, perform validation, execute atomic rolling updates without interrupting operations, continuously sample measurements, autonomously execute protection and control algorithms, coordinate actions using GPS/PTP synchronized timing, and stream filtered operational data back to the central platform. The continuous configuration update methodology enables dynamic adaptation to variable renewable generation and changing grid conditions while maintaining millisecond-level autonomous protection response.

Systems and methods for controlling dispatchable elements of a microgrid, such as generators or a point of common coupling, to achieve control objectives of the microgrid are provided. A monitoring and control system may determine a set of prioritized control objectives corresponding to a number of dispatchable elements of a microgrid and a combined dispatch region for the microgrid based on the prioritized control objectives. The monitoring and control system may control the plurality of dispatchable elements based on the combined dispatch region.