Systems and Methods for Automated Model-Based Real-Time Simulation of a Microgrid for Market-Based Electric Power System Optimization
Systems and methods for real-time modeling of a microgrid. In an embodiment, real-time data is acquired from a microgrid. Predicted data for the microgrid is generated using a first virtual system model of the microgrid, which comprises a virtual representation of energy sources within the microgrid. The real-time data and the predicted data are monitored, and a calibration and synchronization operation is initiated to update the first virtual system model in real-time when a difference between the real-time data and the predicted data exceeds a threshold. Parameters of the first virtual system model can be modified to create a second virtual system model, and aspects can be forecasted for the microgrid operating under the modified parameters of the second virtual system model. In a further embodiment, market price information can be received, and optimization solutions can be generated based on the market price information.
1 - 20 . (canceled)
21 . A system for real-time modeling of a microgrid, the system comprising:
a data acquisition component configured to acquire real-time data from a microgrid; and
an analytics server communicatively connected to the data acquisition component, comprising:
a modeling engine configured to generate a first virtual system model and generate predicted data of the microgrid;
an analytics engine configured to monitor the real-time data and the predicted data of the microgrid, and initiates a calibration and synchronization operation to update the first virtual system model in real-time when a difference between the real-time data and the predicted data exceeds a threshold; and
a simulation engine configured to create a second virtual system model by facilitating a modification of parameters of the first virtual system model.
22 . The system of claim 21 , wherein the threshold is a Defined Difference Tolerance (DDT) value for at least one of the frequency deviation, voltage deviation, power factor deviation, and other deviations between the real-time output data and simulated output data.
23 . The system of claim 21 , wherein the analytics server further comprises a machine learning engine that stores and processes patterns observed from the real-time data and the predicted data.
24 . The system of claim 21 , wherein the parameters comprise operating parameters of at least one of the one or more energy sources within the microgrid.
25 . The system of claim 21 , wherein the simulation engine comprises an interface that receives market price information.
26 . The system of claim 24 , wherein the simulation engine generates one or more optimization solutions based on the market price information.
27 . The system of claim 25 , wherein the one or more optimization solutions comprise a modification to an operating parameter of at least one of the one or more energy sources within the microgrid.
28 . The system of claim 24 , wherein the interface comprises an abstraction layer.
29 . The system of claim 21 , wherein the parameters comprises reliability of the microgrid.
30 . The system of claim 21 , wherein the parameters comprises availability of power the microgrid.
31 . The system of claim 21 , wherein the simulation engine facilitates modified parameters by a market modeling engine which comprises a model of an energy market.
32 . A system for real-time modeling of a microgrid, comprising:
a data acquisition component configured to acquire real-time data from a microgrid; and
an analytics server communicatively connected to the data acquisition component, comprising:
a modeling engine configured to generate a first virtual system model and generate predicted data of the microgrid;
an analytics engine configured to monitor the real-time data and the predicted data of the microgrid, and initiates a calibration and synchronization operation to update the first virtual system model in real-time when a difference between the real-time data and the predicted data exceeds a threshold; and
a simulation engine configured to create a second virtual system model by facilitating a modification of parameters of the first virtual system model, the simulation engine further configured to forecasts one or more aspects of the microgrid operating under the second virtual system model.
33 . The system of claim 32 , wherein the threshold is a Defined Difference Tolerance (DDT) value for at least one of the frequency deviation, voltage deviation, power factor deviation, and other deviations between the real-time output data and simulated output data.
34 . The system of claim 32 , further comprising a machine learning engine configured to store and process patterns observed from the real-time data and the predicted data.
35 . The system of claim 32 , wherein the parameters comprise operating parameters of at least one of the one or more energy sources within the microgrid.
36 . The system of claim 32 , wherein the simulation engine further configured to receive market price information.
37 . The system of claim 36 , wherein the simulation engine further configured to generate one or more optimization solutions based on the market price information.
38 . The system of claim 37 , wherein the one or more optimization solutions comprise a modification to an operating parameter of at least one of the one or more energy sources within the microgrid.
39 . The system of claim 36 , wherein the market price information is received through an abstraction layer.
40 . The system of claim 32 , wherein the parameters comprises reliability of the microgrid.
41 . The system of claim 32 , wherein the parameters comprises availability of the microgrid.
42 . The system of claim 32 , wherein the modified parameters is received from a market modeling engine which comprises a model of an energy market.