Abstract: The power flexibility of energy loads is maximized using a value function for each load and outputting optimal control parameters. Loads are aggregated into a virtual load by maximizing a global value function. The solution yields a dispatch function providing: a percentage of energy for each individual load, a time-varying power level for each load, and control parameters and values. An economic term represents the value of the power flexibility to different players. A user interface includes for each time interval upper and lower bounds representing respectively the maximum power that may be reduced to the virtual load and the maximum power that may be consumed. A trader modifies an energy level in a time interval relative to the reference curve for the virtual load. Automatically, energy compensation for other intervals and recalculation of upper and lower boundaries occurs. The energy schedule for the virtual load is distributed to the actual loads.

Abstract: A central site, using grid operator requirements to reduce portfolio power according to frequency decreases within a frequency band, determines the optimal frequency triggers at which each load within a portfolio should reduce power and by how much power. Power availability of each load is optimized. These triggers and individual load power reductions are periodically dispatched from the central site to the individual loads. Each load detects when a frequency deviation occurs and is able to independently and rapidly reduce its power consumption according to the triggers and corresponding power reductions it received previous to the frequency deviation. Reliance upon the central site to detect a frequency deviation and then to dispatch power reductions in real time is not needed. The system also detects frequency increases and directs a portfolio of loads to consume more power. The system applies to energy loads and detection of a grid signal in general.

Abstract: A central site, using grid operator requirements to reduce portfolio power according to frequency decreases within a frequency band, determines the optimal frequency triggers at which each load within a portfolio should reduce power and by how much power. Power availability of each load is optimized. These triggers and individual load power reductions are periodically dispatched from the central site to the individual loads. Each load detects when a frequency deviation occurs and is able to independently and rapidly reduce its power consumption according to the triggers and corresponding power reductions it received previous to the frequency deviation. Reliance upon the central site to detect a frequency deviation and then to dispatch power reductions in real time is not needed. The system also detects frequency increases and directs a portfolio of loads to consume more power. The system applies to energy loads and detection of a grid signal in general.

Abstract: The power flexibility of energy loads is maximized using a value function for each load and outputting optimal control parameters. Loads are aggregated into a virtual load by maximizing a global value function. The solution yields a dispatch function providing: a percentage of energy for each individual load, a time-varying power level for each load, and control parameters and values. An economic term represents the value of the power flexibility to different players. A user interface includes for each time interval upper and lower bounds representing respectively the maximum power that may be reduced to the virtual load and the maximum power that may be consumed. A trader modifies an energy level in a time interval relative to the reference curve for the virtual load. Automatically, energy compensation for other intervals and recalculation of upper and lower boundaries occurs. The energy schedule for the virtual load is distributed to the actual loads.

Abstract: The power flexibility of energy loads are maximized using a value function for each load and outputting optimal control parameters per load. These loads are aggregated into a virtual load by maximizing a global value function that includes the value function for each individual load. The solution yields a dispatch function providing: a percentage of energy to be assigned to each individual load, a possible time-varying power level within a time interval for each load, and control parameters and values. An economic term of the global value function represents the value of the power flexibility to different energy players. A user interface includes for each time interval upper and lower bounds representing respectively the maximum power that may be reduced to the virtual load and the maximum power that may be consumed by the virtual load. An energy trader modifies an energy level in a time interval relative to the reference curve for the virtual load.

Abstract: The power flexibility of energy loads are maximized using a value function for each load and outputting optimal control parameters per load. These loads are aggregated into a virtual load by maximizing a global value function that includes the value function for each individual load. The solution yields a dispatch function providing: a percentage of energy to be assigned to each individual load, a possible time-varying power level within a time interval for each load, and control parameters and values. An economic term of the global value function represents the value of the power flexibility to different energy players. A user interface includes for each time interval upper and lower bounds representing respectively the maximum power that may be reduced to the virtual load and the maximum power that may be consumed by the virtual load. An energy trader modifies an energy level in a time interval relative to the reference curve for the virtual load.

Abstract: The power flexibility of energy loads is maximized using a value function for each load and outputting optimal control parameters. Loads are aggregated into a virtual load by maximizing a global value function. The solution yields a dispatch function providing: a percentage of energy for each individual load, a time-varying power level for each load, and control parameters and values. An economic term represents the value of the power flexibility to different players. A user interface includes for each time interval upper and lower bounds representing respectively the maximum power that may be reduced to the virtual load and the maximum power that may be consumed. A trader modifies an energy level in a time interval relative to the reference curve for the virtual load. Automatically, energy compensation for other intervals and recalculation of upper and lower boundaries occurs. The energy schedule for the virtual load is distributed to the actual loads.

Abstract: In a method for charging a fleet of batteries (110) from a power grid (100), a fleet charge schedule (301) is determined and thereafter individual battery charge schedules are dynamically optimized. The fleet charge schedule (301) for the entire fleet (110) is determined optimizing an energy portfolio balance of an energy portfolio manager. Thereafter, battery charge schedules for individual batteries (111, 112, 113, 114, 115, 116, 117, 118) in the fleet (110) are dynamically optimized such that the battery charge schedules in aggregation realize the fleet charge schedule (301). The battery charge schedules are dynamically optimized in consideration of at least technical specifications of assets in the battery fleet and user constraints of the battery users.