A control unit manages power sources and energy storage in a hybrid power generation system. The hybrid power generation system is configured to meet a power demand of one or more electrical loads, and has a primary power source with one or more fuel cells to convert hydrogen to electrical power, a secondary power source comprising a combustion engine and a generator to generate to convert fuel into electrical power, and an energy storage system to support the primary and secondary power sources. The control unit monitors the power demand of the one or more electrical loads, and upon detecting a spike in the power demand, connects the energy storage system to support at least one of the primary and secondary power sources.

In one implementation of a backup splice for anti-islanding, a system includes a wire cutter that severs an electrical wire (e.g., a main service line within a main electrical panel) within a microgrid of a residential unit, commercial unit, or other electrical system from the power grid. The system also includes multiple splices to provide an electrical connection on the electrical wire on each side of the wire cutter. A relay is configured to close to enable or complete the electrical connection along the electrical wire and open to disconnect one or more distributed energy resources (DERs) (e.g., a battery or solar panels) from the power grid. The system further includes a controller that opens the relay in response to detecting a power outage in the power grid, thereby preventing the islanding of the microgrid or the DER.

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.

Systems and methods are disclosed for tripping energy loads in an energy transmission system based on Rate of Change of Frequency (RoCoF). An initial RoCoF of electrical voltage in the energy transmission system is detected and a determination is performed of whether the initial RoCoF falls within a predetermined frequency band. A corresponding amount of energy load to trip is armed, and at least one tripping delay timer is started. While the at least one tripping delay timer is running, a deceleration RoCoF of electrical voltage is detected, and a determination is performed of whether a frequency of the electrical voltage has decayed past a tripping frequency threshold. A time at which to trip the amount of energy load is determined, based at least in part on the deceleration RoCoF of electrical voltage, and the amount of energy load is tripped at the determined time.

Systems, methods, and articles for a portable power case are disclosed. The portable power case is comprised of at least one battery and at least one PCB. The portable power case is operable to supply power to a transceiver. The portable power case is operable to be charged using a DC power source (e.g., solar panel, wind turbine, water turbine). A plurality of portable power cases, DC power sources, and transceivers are operable to form a mesh network.

A system is disclosed for monitoring a modular electrical power plant, the modular electrical power plant including at least two power generation units and an auxiliary system. The system for monitoring a modular electrical power plant includes a plurality of data collection channels for receiving data describing operating conditions of the at least two power generation units and a processing unit configured to generate a user interface. The user interface includes a single power generation unit view and a multiple power generation unit view. The processing unit is configured to select between the single power generation unit view and multiple power generation unit view based on the data describing the operating conditions of the at least two power generation units.

Disclosed herein are AI-based platforms for enabling intelligent orchestration and management of power and energy. In various embodiments, a set of edge devices is configured to communicate with at least one energy generation facility, energy storage facility, and/or energy consumption system and automatically execute a set of preconfigured policies that govern energy generation, energy storage, or energy consumption of the respective energy generation facilities, energy storage facilities, or energy consumption systems. In some embodiments, the automatically executed policies are a set of contextual policies that adjust based on the current status of a set of energy generation entities in an energy grid.

An optimization scheduling method for coupling microgrid considering electro-thermal load demand coordination, comprising: building hydrogen-containing microgrid operation model and building electro-thermal load demand coordination response model by considering peak-valley complementary characteristics of original electric load and thermal load in system, characterizing electric load by time-of-use electricity price demand response method, by considering thermal load having heat transfer inertia and fuzziness of user temperature perception, adjusting electro-thermal load demand flexibly based on different electricity prices; based on hydrogen-containing microgrid operation model and electro-thermal load demand coordination response model, building objective function by minimizing sum of total operation cost, wind and light curtailed cost of renewable energy and demand response compensation cost, building electro-hydrogen coupling microgrid optimal scheduling model according to constraint conditions and solving it by mixed integer linear programming, to generate and send instructions based on solved optimal operation scheduling strategy to the microgrid for controlling corresponding unit equipment operation.