Abstract: Systems and methods for making real-time predictions about an arc flash event on an electrical system are disclosed. A virtual system model database is operable for providing a virtual system model for the electrical system and continuously update the virtual system model with real-time data from the electrical system. An analytics server comprises an arch flash simulation engine. The arch flash simulation engine is operable to modify the virtual system model to introduce a short-circuit feature to an uninterrupted power supply bypass circuit branch; choose a standard to supply equations used for arc flash event simulation and energy calculation; simulate an arc flash event utilizing the modified virtual system model; calculate a quantity of arc energy released by the arc flash event using results from the simulation; and communicate a report that forecasts an aspect of the arc flash event.

Abstract: A system for conducting a real-time power capacity assessment of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system. The power analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a machine learning engine. The machine learning engine is configured to store and process patterns observed from the real-time data output and the predicted data output, forecasting power capacity of the electrical system subjected to a simulated contingency event.

Abstract: A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

Abstract: Systems and methods for making real-time predictions about an arc flash event on an electrical system are disclosed. A virtual system model database is operable for providing a virtual system model for the electrical system and continuously update the virtual system model with real-time data from the electrical system. An analytics server comprises an arch flash simulation engine. The arch flash simulation engine is operable to modify the virtual system model to introduce a short-circuit feature to an uninterrupted power supply bypass circuit branch; choose a standard to supply equations used for arc flash event simulation and energy calculation; simulate an arc flash event utilizing the modified virtual system model; calculate a quantity of arc energy released by the arc flash event using results from the simulation; and communicate a report that forecasts an aspect of the arc flash event.

Abstract: Systems and methods for making real-time predictions about the health, reliability, and performance of a monitored system are disclosed. A data acquisition component acquires real-time data output from the monitored system. A power analytics server comprises a virtual system modeling engine, an analytics engine, and an adaptive prediction engine. The virtual system modeling engine is operable to generate predicted data output for the monitored system utilizing a virtual system model of the monitored system. An analytics engine is operable to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold. The adaptive prediction engine is operable to forecast an aspect of the monitored system based on an adaptive neural network algorithm and automatically minimize a measure of error between the real-time data output and a corresponding forecasted data output by the adaptive prediction engine.

Abstract: A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

Abstract: A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

Abstract: Systems and methods for making real-time predictions about the health, reliability, and performance of a monitored system are disclosed. A data acquisition component acquires real-time data output from the monitored system. A power analytics server comprises a virtual system modeling engine, an analytics engine, and an adaptive prediction engine. The virtual system modeling engine is operable to generate predicted data output for the monitored system utilizing a virtual system model of the monitored system. An analytics engine is operable to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold. The adaptive prediction engine is operable to forecast an aspect of the monitored system based on an adaptive neural network algorithm and automatically minimize a measure of error between the real-time data output and a corresponding forecasted data output by the adaptive prediction engine.

Abstract: A system for making real-time predictions about an arc flash event on an electrical system comprises a data acquisition component communicatively connected to a sensor configured to acquire real-time data output from the electrical system; an analytics server communicatively connected to the data acquisition component and comprising a virtual system modeling engine configured to generate predicted data output for the electrical system using a virtual system model of the electrical system, an analytics engine configured to monitor the real-time data output and the predicted data output of the electrical system, and an arc flash simulation engine configured to use the virtual system model updated based in the real-tune data to forecast an aspect of the arc flash event.

Abstract: Systems and methods for making real-time predictions about an arc flash event on an electrical system are disclosed. A virtual system model database is operable for providing a virtual system model for the electrical system and continuously update the virtual system model with real-time data from the electrical system. An analytics server comprises an arch flash simulation engine. The arch flash simulation engine is operable to modify the virtual system model to introduce a short-circuit feature to an uninterrupted power supply bypass circuit branch; choose a standard to supply equations used for arc flash event simulation and energy calculation; simulate an arc flash event utilizing the modified virtual system model; calculate a quantity of arc energy released by the arc flash event using results from the simulation; and communicate a report that forecasts an aspect of the arc flash event.

Abstract: Systems and methods for performing power analytics on a microgrid. In an embodiment, predicted data is generated for the microgrid utilizing a virtual system model of the microgrid, which comprises a virtual representation of a topology of the microgrid. Real-time data is received via a portal from at least one external data source. If the difference between the real-time data and the predicted data exceeds a threshold, a calibration and synchronization operation is initiated to update the virtual system model in real-time. Power analytics may be performed on the virtual system model to generate analytical data, which can be returned via the portal.

Abstract: Systems and methods for predicting power output from photo-voltaic power generation resources are disclosed. An analytics server is communicatively connected to a data acquisition component and a virtual system model database. The analytics server is operable to receive real-time data via the data acquisition component from an electrical system comprising photo-voltaic power generation resources; generate predicted data based on a virtual model of the electrical system provided by the virtual system model database; continuously and automatically synchronize the virtual model with the electrical system based on a difference between the real-time data and the predicted data; receive solar irradiance forecast data; and forecast a power output of the photo-voltaic power generation resources based on the virtual model and the solar irradiance forecast data. The photo-voltaic power generation resources include at least one photo-voltaic inverter.

Abstract: Systems and methods for market-based electric power system optimization are disclosed. Real-time data is acquired from a microgrid. Predicted data is generated by a virtual system model of the microgrid. A virtual system model is updated in real time when a difference between the real-time data and the predicted data exceeds a threshold. A multiplicity of market-based scenarios is presented to the virtual system model to create a multiplicity of altered virtual system models. The multiplicity of market-based scenarios is based on demand data, pricing data and availability data. Simulations for the multiplicity of market-based scenarios are performed based on the multiplicity of altered virtual system models. Simulation results are compared for optimization of microgrid availability and reliability.

Abstract: Systems and methods for optimizing energy consumption in multi-energy sources sites are provided. These techniques include developing a real-time model and a virtual model of the electrical system of a multi-energy source site, such as a microgrid. The real-time model represents a current state of the electrical system can be developed by collecting data from sensors interfaced with the various components of the electrical system. The virtual model of the electrical system mirrors the real-time model of the electrical system and can be used to generate predictions regarding the performance, availability, and reliability of cost and reliability of various distributed energy sources and to predict the price of acquiring energy from these sources. The virtual model can be used to test “what if” scenarios, such as routine maintenance, system changes, and unplanned events that impact the utilization and capacity of the microgrid.

Abstract: A system for conducting a real-time power capacity assessment of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system. The power analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a machine learning engine. The machine learning engine is configured to store and process patterns observed from the real-time data output and the predicted data output, forecasting power capacity of the electrical system subjected to a simulated contingency event.

Abstract: Systems and methods for making real-time predictions about an arc flash event on an electrical system are disclosed. An analytics server is communicatively connected to a data acquisition component. The analytics server comprises a virtual system modeling engine configured to create a virtual system model reflecting current network topology of the electrical system; an analytics engine configured to monitor predicted output data from the virtual system model and real-time output data from the electrical system acquired by the data acquisition component and update the virtual system model when a difference between the real-time output data and the predicted output data exceeds a threshold; and an arc flash simulation engine configured to forecast at least one aspect of and arc flash event based on the virtual system model.

Abstract: Systems and methods for modeling an electrical power system are disclosed. A computer and an analytics server are in network connection. The computer comprises a processor coupled with a memory. The memory is configured to maintain at least one engine element and a component database. The analytics server comprises a virtual system modeling engine and an analytics engine. The component database is operable to store power system components. The at least one engine element is operable to generate a virtual system model of the electrical power system and generate predicted output based on the virtual system model. The analytics engine is operable to monitor the predicted output and real-time output from at least one sensor of the electrical power system, and calibrate the virtual system model based on a difference between the predicted output and the real-time output.

Abstract: A system for utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine, an adaptive prediction engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The adaptive prediction engine can be configured to forecast an aspect of the monitored system using a neural network algorithm.

Abstract: Systems and methods for model-based demand response are disclosed. An analytics server is communicatively connected to a data acquisition component and a virtual system model database. The data acquisition component is operable to acquire and transmit real-time data from a demand response (DR) power network to the analytic server. The virtual system model database is operable to provide a virtual system model of the DR power network. The analytics server is operable to generate predicted data based on the virtual system model of the DR power network and update the virtual system model in real time based on a difference between the predicted data and the real-time data. The analytics server is further operable to optimize DR output of the DR power network to a power grid.

Abstract: Systems and methods for predicting power output from photo-voltaic power generation resources are disclosed. An analytics server is communicatively connected to a data acquisition component and a virtual system model database. The analytics server is operable to receive real-time data via the data acquisition component from an electrical system comprising photo-voltaic power generation resources; generate predicted data based on a virtual model of the electrical system provided by the virtual system model database; continuously and automatically synchronize the virtual model with the electrical system based on a difference between the real-time data and the predicted data; receive solar irradiance forecast data; and forecast a power output of the photo-voltaic power generation resources based on the virtual model and the solar irradiance forecast data. The photo-voltaic power generation resources include at least one photo-voltaic inverter.