A method for managing an energy storage device in an air conditioning system includes determining capacity of an AC power grid connected to the air conditioning system; in response to the AC power grid having high capacity, operating the air conditioning system in a first mode; and in response to the AC power grid having low capacity, operating the air conditioning system in a second mode.

The present invention relates to an energy management system for a building, comprising at least one heat pump, at least one first thermal energy storage device for providing domestic hot water, at least one second thermal energy storage device for providing space heating, at least one renewable energy generation device, at least one first state of charge analyser for determining the state of charge of the at least one first thermal energy storage device, at least one second state of charge analyser for determining the state of charge of the at least one second thermal energy storage device, and a controller configured to control the at least one heat pump, the at least one first thermal energy storage device, the at least one second thermal energy storage device, and the at least one renewable energy generation device. The controller is configured to control, in dependence on at least the state of charge of the at least one first thermal energy storage device and/or the state of charge of the at least one second thermal energy storage device, whether one of and which of the at least one first thermal energy storage device and the at least one second thermal energy storage device is charged with energy provided by (a heat pump operation of) the at least one heat pump and/or energy provided by the at least one renewable energy generation device. Furthermore, the present invention relates to a method of using the energy management system.

An AI-based platform for enabling intelligent orchestration and management of at least one operating process is provided herein. The AI-based platform includes an artificial intelligence system that is configured to generate a prediction of an energy pattern associated with the at least one operating process. The AI-based platform is also configured to manage the at least one operating process based on the prediction of the energy pattern.

Systems and methods are disclosed for preparing electrified vehicles to transfer energy to other structures. Weather related data and/or grid related data may be leveraged for predicting the likelihood of power outage conditions of a grid power source. When power outage conditions are predicted as being likely, the electrified vehicle may automatically enter a readiness state for transferring power to the structure without any time delays once an actual power outage condition occurs. Entering the readiness state may include steps such as waking up the electrified vehicle, initiating communications with electric vehicle supply equipment (EVSE), completing vehicle pre-checks, precharging certain power transfer system components, etc.

Electrical loads from an electrically powered unit plugged into a receptacle of a power distribution unit (PDU) can be detected, using a computer communicating with the PDU. The electrical load is analyzed by comparing an electrical cycle waveform of the electrical load to electrical cycle waveforms of problem waveforms accessible by the computer. When the electrical cycle waveform of the electrical load matches an electrical cycle waveform of one of the problem waveforms. In response to the electrical cycle waveform of the electrical load matching the electrical cycle waveform of one of the problem waveforms, the computer initiates an action to disable electrical power to the receptacle of the PDU.

The present invention relates to an inverter system (INV) for a photovoltaic system and to a method for operating the inverter system (INV). The inverter system (INV) comprises an inverter unit (WE), ), to which a predetermined number of DC-to-DC converters (B1, . . . , B4) is connected upstream via an intermediate circuit (ZK). The DC inputs (DC1, . . . , DC4) of the inverter system (INV) are formed by the DC-to-DC converters (B1, . . . , B4), which predetermines the number and properties of the DC inputs (DC1, . . . , DC4). The DC inputs (DC1, . . . , DC4) are connected to different direct-voltage units (PV1, PV2, BAT, EC, GE, VB), in particular PV units, energy storage units, etc. A switching unit (SE), which comprises inputs (E1, . . . , E6) for connecting the direct-voltage units (PV1, PV2, BAT, EC, GE, VB), is connected to the DC inputs (DC1, . . . , DC4). The switching unit (SE) is thus arranged between the DC-to-DC converters (B1, . . . , B4) forming the DC inputs (DC1, . . . , DC4) and the direct-voltage units (PV1, PV2, BAT, EC, GE, VB) which are connectable to the switching unit (SE). The different direct-voltage units (PV1, PV2, BAT, EC, GE, VB) connected to the inputs (E1, . . . , E6) are identified (101, 102), and for each direct-voltage unit (PV1, PV2, BAT, EC, GE, VB) connected to an input (E1, . . . , E6) of the switching unit (SE) a current value of at least one power variable is determined (103). The determined current value of the at least one power variable is then compared with at least one predetermined threshold value (104). Depending on a respective comparison result, the switching unit (SE) then establishes a connection between the respectively connected direct-voltage unit (PV1, PV2, BAT, EC, GE, VB) and at least one suitable DC input (DC1, . . . , DC4) and/or adapts the connection (105).

Technologies are provided for reporting, monitoring, analyzing performance of distributed energy resources (DERs). The monitoring and analysis of a DER can be performed in real-time or essentially real-time. The monitoring also can include health monitoring of a DER. The reporting of performance of a DER is configurable. The monitoring and analysis of performance of the DER also is configurable. Attributes that control the reporting, monitoring, and analysis can be interactively configured via user interfaces having one multiple control elements. Performance of a DER can be reported, monitored, and analyzed irrespective of manufacturer of the DER.

A system includes a management module which manages a plurality of vehicles by categorization into a plurality of groups based on combinations of deterioration levels and states of charge of a plurality of batteries included in the plurality of vehicles, and a selection module which selects, based on electric power supply/demand information in an electric power grid, a vehicle in which a battery is to be used for an electric power delivery. A method includes steps of acquiring information representing deterioration levels and states of charge of a plurality of batteries included in a plurality of vehicles, managing the plurality of vehicles by categorization into a plurality of groups based on combinations of the deterioration levels and the states of charge, and selecting, based on electric power supply/demand information in an electric power grid, a vehicle in which a battery is to be used for an electric power delivery.

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.