Provided is an aerosol generating device distributing and transmitting power from a battery to two heaters, wherein the aerosol generating device includes the battery, a first heater heating a first aerosol generating substrate, a second heater heating a second aerosol generating substrate, and a controller controlling power supplied from the battery to the first heater and the second heater, wherein the controller controls power to be supplied from the battery to the first heater and the second heater at different times.

Examples relate to adjusting load power consumption based on a power option agreement. A computing system may receive power option data that is based on a power option agreement and specify minimum power thresholds associated with time intervals. The computing system may determine a performance strategy for a load (e.g., set of computing systems) based on a combination of the power option data and one or more monitored conditions. The performance strategy may specify a power consumption target for the load for each time interval such that each power consumption target is equal to or greater than the minimum power threshold associated with each time interval. The computing system may provide instructions the set of computing systems to perform one or more computational operations based on the performance strategy.

An electric power control system for controlling supply and consumption of electric power in a system power supply, a storage battery and an electric power load, said electric power control system including: an estimated value correction unit configured to obtain a difference between a past power control estimated value and a past actual performance value, and to shift a power control estimated value obtained as a result of estimation in a predetermined period to an extent corresponding to said difference, thereby correcting the power control estimated value, wherein said past power control estimated value is a value obtained as a result of estimation performed in a past time relative to said predetermined period, and said past actual performance value is a value obtained as an actual result in the past time; and a power control unit configured to control supply and consumption of electric power in the system power supply, the storage battery, and the electric power load, based on the power control estimated value corrected by the estimated value correction unit.

Systems and methods are directed controlling components of a utility grid. The system can receive signals. The system can determine one or more statistical metrics based on the signals. The system can generate an input matrix. The system can input the input matrix into a machine learning model. The system can predict, based on the input matrix and via the machine learning model, the value for the signal of the utility grid at a time period for which the value is not provided in the input matrix. The system can provide a command to control a component of the utility grid responsive to the value for the signal of the utility grid predicted by the machine learning model.

A method for determining a physical model of an energy installation from a plurality of components linked together according to one or more constraints to form a tree, called tree of constraints, each component including one or more output ports, each output port being associated with a physical quantity of which the value depends on one or more variables internal to the component and/or on one or more variables external to the component, each external variable being communicated to the component through an input port. A second aspect relates to a method for controlling an electrical installation including a first phase of determining a physical model of the installation using the described method; and a second control phase during which each set point is determined as a function of a simulation carried out using the physical model obtained during the phase of determining a physical model of the energy installation.

A method and controller for controlling electrical activation of elements in a system. A method includes identifying (710) a first element (102) of a system (100) by a control system (600), among a plurality of elements (102, 110, 122) of the system (100), that is to be powered. The method includes determining (712) connected elements (110, 122) of the system (100) by the control system (600). The connected elements (110, 122) are connected to deliver power to the first element (102) directly or indirectly, based on an adjacency matrix (400), and the adjacency matrix (400) identifies connections between each of plurality of elements of the system (100). The method includes identifying (714) at least one of the connected elements (110, 122) to activate by the control system (600), based on the adjacency matrix (400), a health table (500), and the connected elements (110, 122), to deliver power to the first element (102). The method includes activating (716) the at least one of the connected elements (110, 122) by the control system (600), thereby delivering power to the first element (102).

A method for evaluating an energy efficiency of a second energy consumption scenario of a site includes obtaining a first energy consumption scenario, which comprises a first time-series of energy consumption data of at least one device, and a quality measure of the first energy consumption scenario; obtaining the second energy consumption scenario, which comprises a second time-series of energy consumption data, wherein the second energy consumption scenario has a same or a shorter duration than the first energy consumption scenario; comparing the second time-series of energy consumption data to the first time-series of energy consumption data; and if or when the second time-series of energy consumption data is similar to the first time-series of energy consumption data, outputting the quality measure of the first energy consumption scenario.

To provide a hydrogen system operation planning device capable of accurately preparing an operation plan that achieves efficient operation in a hydrogen system. In the hydrogen system operation planning device of the embodiment, a classification unit receives input of DR commands regarding demand for electric power in the hydrogen system and classifies the input DR commands into a first DR group and a second DR group with a lower priority than the first DR group. A first planning unit prepares a first operation plan to reflect the DR command classified into the first DR group. A second planning unit prepares a second operation plan by reflecting contents of the DR command classified into the second DR group on the first operation plan so that contents of the DR command classified into the first DR group have priority over the contents of the DR command classified into the second DR group.

Embodiments of the application disclose a temperature control method, system and a temperature controller. The method includes: obtaining a target temperature value set by a user; obtaining a current energy-saving level, the current energy-saving level being used to limit an adjustment speed at which a HVAC system controls a temperature change; determining a target difference between indoor and outdoor temperatures based on a current indoor temperature value and a current outdoor temperature value; determining a target adjustment speed corresponding to the target difference between indoor and outdoor temperatures; determining a target operating mode based on a relationship between the current energy-saving level and the target adjustment speed; and controlling the HVAC system to perform a temperature adjustment operation in the target operating mode. The method can improve the user's comfort experience and effectively reduce energy consumption.

Techniques are described for implementing automated control systems that manipulate operations of specified target systems, such as by modifying or otherwise manipulating inputs or other control elements of the target system that affect its operation (e.g., affect output of the target system). An automated control system may in some situations have a distributed architecture with multiple decision modules that each controls a portion of a target system and operate in a partially decoupled manner with respect to each other, such as by each decision module operating to synchronize its local solutions and proposed control actions with those of one or more other decision modules, in order to determine a consensus with those other decision modules. Such inter-module synchronizations may occur repeatedly to determine one or more control actions for each decision module at a particular time, as well as to be repeated over multiple times for ongoing control.