Abstract: Systems and techniques for monitoring, predicting and/or alerting for census periods in medical inpatient units are presented. A system can perform a first machine learning process to learn patterns in patient flow data related to a set of patient identities and a set of operations associated with a set of medical inpatient units. The system can also perform a second machine learning process to detect abnormalities associated with the patterns in the patient flow data. Furthermore, the system can determine patient census data associated with a prediction for a total number of patient identities in the set of medical inpatient units during a period of time based on the patterns and the abnormalities. The system can also generate an alert for a user interface in response to a determination that the patient census data satisfies a defined criterion.

Abstract: Systems and techniques for monitoring, predicting and/or alerting for census periods in medical inpatient units are presented. A system can perform a first machine learning process to learn patterns in patient flow data related to a set of patient identities and a set of operations associated with a set of medical inpatient units. The system can also perform a second machine learning process to detect abnormalities associated with the patterns in the patient flow data. Furthermore, the system can determine patient census data associated with a prediction for a total number of patient identities in the set of medical inpatient units during a period of time based on the patterns and the abnormalities. The system can also generate an alert for a user interface in response to a determination that the patient census data satisfies a defined criterion.

Abstract: Aspects of the present disclosure relate to a system comprising a computer-readable storage medium storing at least one program and a method for optimizing and controlling the physical and business aspects of an industrial system. In example embodiments, the method may include assessing criteria to be applied to an industrial system, and generating simulation scenarios based on the criteria. The method may further include simulating each of the simulation scenarios over a period of time to generate simulated physical aspects and simulated business aspects of the industrial system for each of the plurality of simulation scenarios. The method may further include identifying at least one of the simulation scenarios for use with the industrial system based on a comparison of the simulated physical aspects and the simulated business aspects corresponding to each simulation scenario.

Abstract: A method and system for controlling demand events in a utility network with multiple customer sites. The value of a demand response parameter threshold for invoking a demand response event is calculated based on the number of available demand response events and the number of opportunities remaining to issue the available demand response events. This parameter represents the utility objectives for using the demand response program (e.g., cost savings, reliability, avoided costs). A current value of the demand response parameter is compared to the threshold value, and a determination is made whether or not to call a demand response event for the current opportunity, or to save the event for a future opportunity based upon this comparison.

Abstract: A system may include a boundary-less machine-to-machine (“M2M”) service layer, which may reside between an application layer and a core network layer. The boundary-less M2M service layer may allow M2M applications to interact with multiple applications (e.g., multiple M2M applications, multiple non-M2M applications, or a mix of both). The boundary-less M2M service layer may also allow M2M devices and applications to interact over various types of networks, such as wireless and wireline networks.

Abstract: Method and system to provide co-optimized utilization of demand response and energy storage resources in an electrical grid system. The system may include a module to evaluate a marginal savings relative to a dispatch cost, if a demand response event is performed. The system may further include a module to evaluate a marginal savings relative to the dispatch cost, if an energy storage event is performed, and a controller including circuitry may be configured to determine a control strategy to perform a dispatch including a demand response event and/or an energy storage event, based at least in part on the respective marginal savings of the demand response event and/or the energy storage event, which may be selected to co-optimize an integrated utilization of the demand response and energy storage resources to meet a given objective.

Abstract: A method includes receiving at least one event parameter for a demand response event and identifying a pool of customers. The pool of customers includes a plurality of customers potentially usable to support the demand response event. Characteristics of each customer in the pool of customers are retrieved and a plurality of groups of customers are created from the pool of customers based on the retrieved characteristics and the at least one event parameter.

Abstract: A system for optimal planning of electric power demand is presented. The system includes a node comprising one or more smart charging plug-in electric vehicles (SCPEVs), a processing subsystem, wherein the processing subsystem receives relevant data from one or more sources; and determines an optimized SCPEV load and optimal charging schedule for the node by applying an operations research technique on the relevant data.

Abstract: A power forecasting module determines a load forecast of a distribution system (DS) and a power generation forecast of distributed energy resources (DERs) A demand response (DR) module estimates an available DR based on the load and the power generation forecast and modifies the load forecast by dispatching an optimal DR. An energy storage commitment module determines a charging/discharging schedule of energy storage devices in the system based on the modified load profile and generates a second load profile by incorporating the charging/discharging schedule of energy storage devices. An unit commitment (UC) module determines a DER schedule of supplying electrical power to the loads in the second load profile based on a first objective function. An economic dispatch (ED) module determines an optimum operating point for each DER and an Integrated Volt-VAR Control (IVVC) module controls distribution assets to maintain a voltage profile in the system.

Abstract: A system may include a boundary-less machine-to-machine (“M2M”) service layer, which may reside between an application layer and a core network layer. The boundary-less M2M service layer may allow M2M applications to interact with multiple applications (e.g., multiple M2M applications, multiple non-M2M applications, or a mix of both). The boundary-less M2M service layer may also allow M2M devices and applications to interact over various types of networks, such as wireless and wireline networks.

Abstract: A method and system for controlling load in a utility distribution network are provided that initiates a shed event for a node in the distribution network by selecting premisess associated with the node that are participating in a demand response program to reduce the load at the node to desired levels. More specifically, the system and method provide for selecting only enough participating premisess associated with the node that are necessary to reduce the load to desired levels.

Abstract: A method for demand response management includes determining a number of available demand response events and a number of opportunities available to issue the available demand response events. A priority for each demand response event is provided and a threshold value for each demand response vent is determined. A highest priority demand response event among the available demand response events whose threshold value is lower than an observed value of a selected demand response trigger is selected and control signals to utilize the selected demand response event for a current opportunity are transmitted to customer sites.

Abstract: Method and system to provide co-optimized utilization of demand response and energy storage resources in an electrical grid system. The system may include a module to evaluate a marginal savings relative to a dispatch cost, if a demand response event is performed. The system may further include a module to evaluate a marginal savings relative to the dispatch cost, if an energy storage event is performed, and a controller including circuitry may be configured to determine a control strategy to perform a dispatch including a demand response event and/or an energy storage event, based at least in part on the respective marginal savings of the demand response event and/or the energy storage event, which may be selected to co-optimize an integrated utilization of the demand response and energy storage resources to meet a given objective.

Abstract: A power forecasting module determines a load forecast of a distribution system (DS) and a power generation forecast of distributed energy resources (DERs). A demand response (DR) module estimates an available DR based on the load and the power generation forecast and modifies the load forecast by dispatching an optimal DR. An energy storage commitment module determines a charging/discharging schedule of energy storage devices in the system based on the modified load profile and generates a second load profile by incorporating the charging/discharging schedule of energy storage devices. An unit commitment (UC) module determines a DER schedule of supplying electrical power to the loads in the second load profile based on a first objective function. An economic dispatch (ED) module determines an optimum operating point for each DER and an Integrated Volt-VAR Control (IVVC) module controls distribution assets to maintain a voltage profile in the system.

Abstract: A method includes receiving at least one event parameter for a demand response event and identifying a pool of customers. The pool of customers includes a plurality of customers potentially usable to support the demand response event. Characteristics of each customer in the pool of customers are retrieved and a plurality of groups of customers are created from the pool of customers based on the retrieved characteristics and the at least one event parameter.

Abstract: A system for implementing congestion pricing in a power distribution network includes a monitoring and control engine configured to receive an actual power consumption value for a node in the power distribution network and a congestion engine coupled to the monitoring and control engine configured to determine that a power consumption value exceeds a capacity related set point of the node. The system further includes a pricing calculator that establishes a congestion price for power to be applied while the actual power consumption value exceeds the set point of the node and a credit calculator that provides credits to consumers coupled to the node that consume power at a rate below a threshold while the actual power consumption value exceeds the set point of the node.

Abstract: A system for optimal planning of electric power demand is presented. The system includes a node comprising one or more smart charging plug-in electric vehicles (SCPEVs), a processing subsystem, wherein the processing subsystem receives relevant data from one or more sources; and determines an optimized SCPEV load and optimal charging schedule for the node by applying an operations research technique on the relevant data.

Abstract: A system and method for use in a network of utility customers to estimate demand response for various utility DR programs. The system first aggregates the premises into clusters based upon customer information for each of the premises. A response estimation function is developed for each of the clusters. The demand response event request is then processed according to the clusters and the response estimation functions.

Abstract: A system and method for receiving demand response data and for grouping customers into customer groups based on the demand response data. One or more of the customer groups is scheduled for a demand response event based on the demand response data and forecast data. The selected customer groups are notified and the demand response is implemented for the selected customer groups.

Abstract: A method of load dispatch generates a load dispatch curve in response to both the opportunity cost of dispatching resources with a contractually limited number of dispatches in a given time period and estimated rebound effect data. The method identifies a best time period for dispatch based on the load dispatch curve and also supply curve data and generates a resultant load dispatch schedule. The resultant load dispatch schedule is transmitted to one or more smart home meters to dispatch loads in a manner that provides the greatest economic benefit.