Abstract: Energy loads, sources or batteries exchange mathematical models with each other to form clusters of devices that together provide a service (self-reliance, frequency control, etc.) to a grid operator. Models are exchanged before or after forming clusters; a particular model is used to control its own device and is also used by another load/source to influence its control policy. Heuristics and an optimization technique (using models) are used to form a cluster of devices. Exchanging models obviates the need for a central entity to directly control loads/sources, and the need to exchange real-time data between loads/sources, providing resilience against communication failure. A service manager (demand-response aggregator) sends a service or technical constraints to loads/sources to form clusters on their own. Negotiation between manager and clusters occurs to form consensus on a response.

Abstract: Energy loads, sources or batteries exchange mathematical models with each other to form clusters of devices that together provide a service (self-reliance, frequency control, etc.) to a grid operator. Models are exchanged before or after forming clusters; a particular model is used to control its own device and is also used by another load/source to influence its control policy. Heuristics and an optimization technique (using models) are used to form a cluster of devices. Exchanging models obviates the need for a central entity to directly control loads/sources, and the need to exchange real-time data between loads/sources, providing resilience against communication failure. A service manager (demand-response aggregator) sends a service or technical constraints to loads/sources to form clusters on their own. Negotiation between manager and clusters occurs to form consensus on a response.

Abstract: A power metering system includes any number of sites and a central database. Loads within a site are unmetered and a sensor data vector over time includes sensor data, operational data and external data. Iteration occurs over time intervals for all unmetered loads and the unmeasured power for each interval is disaggregated by matching a load type with a computer model in the central database having the same feature domain in order to predict the power usage of each load. Iteration again occurs over those intervals in which only a single load is operating; the power of that load is determined to be the unmeasured power minus miscellaneous power, and a new computer model is fitted for that single load and also uploaded to the database. The feature domain of an existing model may also be increased. Both iteration steps repeat until no more unmeasured power can be disaggregated.

Abstract: A power metering system includes any number of sites and a central database. Loads within a site are unmetered and a sensor data vector over time includes sensor data, operational data and external data. Iteration occurs over time intervals for all unmetered loads and the unmeasured power for each interval is disaggregated by matching a load type with a computer model in the central database having the same feature domain in order to predict the power usage of each load. Iteration again occurs over those intervals in which only a single load is operating; the power of that load is determined to be the unmeasured power minus miscellaneous power, and a new computer model is fitted for that single load and also uploaded to the database. The feature domain of an existing model may also be increased. Both iteration steps repeat until no more unmeasured power can be disaggregated.

Abstract: A power metering system includes any number of sites and a central database. Loads within a site are unmetered and a sensor data vector over time includes sensor data, operational data and external data. Iteration occurs over time intervals for all unmetered loads and the unmeasured power for each interval is disaggregated by matching a load type with a computer model in the central database having the same feature domain in order to predict the power usage of each load. Iteration again occurs over those intervals in which only a single load is operating; the power of that load is determined to be the unmeasured power minus miscellaneous power, and a new computer model is fitted for that single load and also uploaded to the database. The feature domain of an existing model may also be increased. Both iteration steps repeat until no more unmeasured power can be disaggregated.

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 power metering system includes any number of sites and a central database. Loads within a site are unmetered and a sensor data vector over time includes sensor data, operational data and external data. Iteration occurs over time intervals for all unmetered loads and the unmeasured power for each interval is disaggregated by matching a load type with a computer model in the central database having the same feature domain in order to predict the power usage of each load. Iteration again occurs over those intervals in which only a single load is operating; the power of that load is determined to be the unmeasured power minus miscellaneous power, and a new computer model is fitted for that single load and also uploaded to the database. The feature domain of an existing model may also be increased. Both iteration steps repeat until no more unmeasured power can be disaggregated.

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.