Abstract: A virtual power plant is presented. A virtual power plant couples to one or more virtual power plant units that provide power and/or storage of power to a power grid. In some embodiments, a method of operating a virtual power plant includes receiving a request from a requester; determining whether the request can be performed by a set of units; reporting the result to the requester; and if a subsequent execution request is received from the requester, then executing the request.

Abstract: A software platform in communication with networked distributed energy resource energy storage apparatus, configured to deliver various specific applications related to offset demand monitoring, methods of virtual power plant and orchestration, load shaping services, methods of reducing demand at aggregated level, prioritizing computer programs related to virtual energy pool, energy cloud controllers methods, charge discharge orchestration plans of electric vehicles, distributed energy resources, machine learning predictive algorithms, value optimizing algorithms, autonomous sensing event awareness, mode selection methods, capacity reservation monitoring, virtual power plant methods, advanced DER-ES apparatus features, energy management system for governing resources and methods, aggregated energy cloud methods, load shaping methods, marginal cost cycle-life degradation, load shaping API, forward event schedule, on demand request, and load service state request methods.

Abstract: A microgrid controller for controlling one or more edge units in a distributed energy system is presented. In some embodiments, the microgrid controller communicates with one or more network adjacent edge units, each of the one or more network adjacent edge units being coupled to a power grid, determines an instruction set to execute with the one or more network adjacent edge units, provides operating parameters for the one or more network adjacent edge units to execute the instruction set, and monitors the one or more edge units over a network that is not associated with the internet. In situations where the internet fails, microgrid controller determines a set of instructions to execute and provides instructions to the network adjacent edge units.

Abstract: A virtual power plant is presented. A virtual power plant couples to one or more virtual power plant units that provide power and/or storage of power to a power grid. In some embodiments, a method of operating a virtual power plant includes receiving a request from a requester; determining whether the request can be performed by a set of units; reporting the result to the requester; and if a subsequent execution request is received from the requester, then executing the request.

Abstract: In accordance with aspects of the present invention a distributed energy system edge unit is presented. An edge unit includes a power grid interface; one or more device interfaces; a processing unit coupled to the power grid interface and the one or more device interfaces, the processing unit including a communication state that allows communications with an external entity; a control and monitor state that communicates with the communication state; a check unit state that communicates with the control and monitor state and provides a unit state data; wherein the control and monitor state and the communication state provide an instruction data set, current operating parameters according to the unit state data, the instruction set data, and a characterization parameter data, and wherein the control and monitor state provides control signals to the power grid interface and the one or more device interfaces.

Abstract: A software platform in communication with networked distributed energy resource energy storage apparatus, configured to deliver various specific applications related to offset demand monitoring, methods of virtual power plant and orchestration, load shaping services, methods of reducing demand at aggregated level, prioritizing computer programs related to virtual energy pool, energy cloud controllers methods, charge discharge orchestration plans of electric vehicles, distributed energy resources, machine learning predictive algorithms, value optimizing algorithms, autonomous sensing event awareness, mode selection methods, capacity reservation monitoring, virtual power plant methods, advanced DER-ES apparatus features, energy management system for governing resources and methods, aggregated energy cloud methods, load shaping methods, marginal cost cycle-life degradation, load shaping API, forward event schedule, on demand request, and load service state request methods.

Abstract: A method for processing risk factors for a user is disclosed. The method may include receiving protocol data for creating a risk factor engine by an application stored and executed at a computing device. The risk factor engine may be stored in memory and executable by a processor to process a first set of stored user health data for a user. The protocol data may include a rule and one or more health attribute values. The rule and the one or more health attribute values may each include a computer programming expression editable by an administrator. The method may further include determining a user health score through execution of the risk factor engine by the processor and based on the user health data.

Abstract: A method for processing risk factors for a user is disclosed. The method may include receiving protocol data for creating a risk factor engine by an application stored and executed at a computing device. The risk factor engine may be stored in memory and executable by a processor to process a first set of stored user health data for a user. The protocol data may include a rule and one or more health attribute values. The rule and the one or more health attribute values may each include a computer programming expression editable by an administrator. The method may further include determining a user health score through execution of the risk factor engine by the processor and based on the user health data.

Abstract: A method for processing risk factors for a user is disclosed. The method may include receiving protocol data for creating a risk factor engine by an application stored and executed at a computing device. The risk factor engine may be stored in memory and executable by a processor to process a first set of stored user health data for a user. The protocol data may include a rule and one or more health attribute values. The rule and the one or more health attribute values may each include a computer programming expression editable by an administrator. The method may further include determining a user health score through execution of the risk factor engine by the processor and based on the user health data.

Abstract: A method for processing risk factors for a user is disclosed. The method may include receiving protocol data for creating a risk factor engine by an application stored and executed at a computing device. The risk factor engine may be stored in memory and executable by a processor to process a first set of stored user health data for a user. The protocol data may include a rule and one or more health attribute values. The rule and the one or more health attribute values may each include a computer programming expression editable by an administrator. The method may further include determining a user health score through execution of the risk factor engine by the processor and based on the user health data.

Abstract: Risk factor data can be processed by a risk factor coaching engine to determine health risk for a user. The risk factor coaching engine may be executed within a health coaching protocol to perform actions that provide a user with information, recommendations and alerts via other coaching engines, and appointments with health care professionals. The risk factor coaching engine may also predict attribute values for a user based on a time period and goals for user health data upon which the predicted attribute value is based.

Abstract: Risk factor data can be processed by a risk factor coaching engine to determine health risk for a user. The risk factor coaching engine may be executed within a health coaching protocol to perform actions that provide a user with information, recommendations and alerts via other coaching engines, and appointments with health care professionals. The risk factor coaching engine may also predict attribute values for a user based on a time period and goals for user health data upon which the predicted attribute value is based.