An autonomous real-time remedial action scheme (RAS) control system may receive electrical measurements of a power system. The RAS control system may determine active power and reactive power of each bus in the power system based on the received electrical measurements. The RAS control system may dynamically determine whether to shed one or more loads, generators, or both in the power system by optimizing an objective function to maintain maximum critical load and maximum critical generation in the electrical system based on the active and reactive power of each bus in the power system and the generation of each generator in the power system. The RAS control system may send a command to trip at least one breaker to cause the at least one breaker to shed the one or more loads, generators, or both. The RAS control system may send a command to runback one or more generators.

A software-defined control (SDC)-enabled microgrid system includes a physical plane having multiple distributed energy resources (DERs), the DERs being operatively coupled together via a bus, and a control plane. The control plane includes at least one virtual controller running on a hardware server in the control plane, a system analysis module in communication with the physical plane, and an SDC manager coupled with the virtual controller and the system analysis module. The virtual controller includes multiple software-defined functional modules configured to control prescribed parameters of the microgrid. The system analysis module is configured to generate system analytics information as a function of operational information associated with one or more DERs in the physical plane. The SDC manager is configured to generate one or more virtual controllers for controlling an operation of at least a subset of the DERs in the physical plane as a function of the system analytics information.

There is provided a load controller for a system, the system comprising a first sub-system arranged to deliver a first load, the load controller being operable to: acquire a first target load profile, being the load initially desired for delivery by the first sub-system over an operational period; measure in real time during an update window within the operational period: a first parameter of the first sub-system, to obtain a first measured Load Controller monitor signal; and the first load, to obtain a first measured load signal; develop in real time a model of the first sub-system, using the first measured monitor signal and the first measured load signal, the model relating the first load to the first parameter; given the first target load profile, and the model of the first sub-system, generate for a future period a first predicted monitor signal, the future period being ahead of the update window; and determine whether the first predicted monitor signal satisfies at least one predetermined criterion.

Aspects relate to a charger for an electric aircraft with failure monitoring and method for its use. An exemplary charger for an electric aircraft with failure monitoring includes a charging circuit. Included within the charging circuit is a connector configured to mate with an electric aircraft port of an electric aircraft and at least a current conductor configured to conduct a current. At least a conductor comprises a direct current conductor configured to conduct a direct current; and an alternating current conductor configured to conduct an alternating current. A charger may include a control circuit configured to command the charging circuit of an electric aircraft as a function of charging datum. A charger may also include a failure monitor circuit, the failure monitor circuit configured to: detect a failure and initiate a failure mitigation procedure as a function of failure detection.

This evaluation device comprises: a mathematical model acquisition unit that acquires a mathematical model expressing the state of a power storage element; an operation data acquisition unit that acquires operation data which includes time-series input data input during operation of a system constructed on the basis of the numerical model, and time-series output data output by the system on the basis of the time-series input data; a processing unit that inputs the time-series input data to the numerical model and executes processing causing time-series model output data to be output from the numerical model; and an evaluation unit that evaluates the design and the operation of the system on the basis of the time-series output data and the time-series model output data.

A method and system for controlling voltage and reactive power for electrical grids includes monitoring the output of the energy generation facilities at the point of interconnection (POI) of each energy generation facility to the power transmission system of the electrical grid. In addition, the voltage at a point of utilization (POU) is monitored to determine when the output voltage of the energy generation facilities must be adjusted to maintain voltage at the POU. In addition, when it is determined that energy generation facilities are exchanging reactive power, the voltage set points of the energy generation facilities are adjusted to reduce the exchanged power.

A method and system for controlling voltage and reactive power for electrical grids includes monitoring the output of the energy generation facilities at the point of interconnection (POI) of each energy generation facility to the power transmission system of the electrical grid. In addition, the voltage at a point of utilization (POU) is monitored to determine when the output voltage of the energy generation facilities must be adjusted to maintain voltage at the POU. In addition, when it is determined that energy generation facilities are exchanging reactive power, the voltage set points of the energy generation facilities are adjusted to reduce the exchanged power.

The invention involves an energy monitoring and control system that monitors and controls the peak energy demand so that demand charges are reduced. The system includes an electrical load which receives an aggregate power signal having grid, battery, and solar power supply components. An energy monitoring panel determines the grid and solar power components. A battery-demand modulator panel determines a charge cycle for a battery power supply. An energy gateway processor receives information from the battery-demand modulator panel regarding the charge cycle for the battery power supply and information from the energy monitoring panel regarding the grid and solar power components. The battery-demand modulator panel modulates the charge cycle for the battery power supply, in response to a signal from the energy gateway processor, to adjust the battery power supply component of the aggregate power signal.

A method, a system and a tangible product and non-transitory computer program are provided to automatically identify electrical installations in an electrical distribution system that are likely to exhibit an electrical non-conformity (ENC). The method requires only electrical profiles collected from meters and IT tools, without the need for any other sub-metering equipment. The method includes the steps of recovering electrical profiles generated by the meters; applying algorithmic processing associated with indicators of an ENC on the profiles; and identifying electrical installations likely to exhibit an ENC, according to the indicators that have met their target conditions. The method may include the recovery of local meteorological data and nominal data related to the electrical installations to confirm or deny that the identified electrical installations are likely to be non-conforming.

This invention is directed to systems and methods that track a specified stored energy level profile for a BESS in a microgrid. The systems and methods including using a control algorithm that tracks the stored energy level profile for the BESS. The controller algorithm includes a Kalman Filter design for a model-based state reconstruction to overcome sensor/communication errors during real-time operation. The latter is important to guarantee the ability of the microgrid to continue its seamless operation during periods of erroneous sensor measurements or flawed communication.