Abstract: A system and method for energy optimization is disclosed. The system may collect information from an information collector data including energy usage and storage data of at least one renewable energy generation system and battery energy storage system (BESS). The system may identify historical events that result in curtailment of renewable energy production, determine whether there is a curtailment of renewable energy production based at least on one historical event supervise the charge and discharge cycles of the at least one BESS; and ensuring that the diesel generators minimum up/down time is satisfied based on controlling at least one parameter of the BESS.

Abstract: Disclosed herein are embodiments for optimization of an energy grid system. First and second prediction models associated with a first energy grid system and a second energy grid system, respectively, may be trained based on historical data associated with each energy grid system. A prediction model basis may be created including the first and second prediction models. Training data associated with a third energy grid system may be input into each prediction model of the prediction model basis, and an accuracy of the prediction models may be evaluated to determine whether the prediction model basis is complete. When complete, a context-matching model may be trained based on subsequent energy grid systems until the context-matching model is determined to be sufficiently accurate. Then, the context-matching model may be used to identify a prediction model matching a new energy grid system, which may be used to warm-start the new energy grid system.

Abstract: Disclosed herein are embodiments for optimization of an energy grid system. First and second prediction models associated with a first energy grid system and a second energy grid system, respectively, may be trained based on historical data associated with each energy grid system. A prediction model basis may be created including the first and second prediction models. Training data associated with a third energy grid system may be input into each prediction model of the prediction model basis, and an accuracy of the prediction models may be evaluated to determine whether the prediction model basis is complete. When complete, a context-matching model may be trained based on subsequent energy grid systems until the context-matching model is determined to be sufficiently accurate. Then, the context-matching model may be used to identify a prediction model matching a new energy grid system, which may be used to warm-start the new energy grid system.

Abstract: A system and method for energy optimization is disclosed. The system may collect information from an information collector data including energy usage and storage data of at least one renewable energy generation system and battery energy storage system (BESS). The system may identify historical events that result in curtailment of renewable energy production, determine whether there is a curtailment of renewable energy production based at least on one historical event supervise the charge and discharge cycles of the at least one BESS; and ensuring that the diesel generators minimum up/down time is satisfied based on controlling at least one parameter of the BESS.

Abstract: Systems and methods are provided for dynamically selecting a control policy from among several available control policies for controlling an electric vehicle fleet charging system. A control policy may take into account fluctuating local renewable generation and/or time of use electricity pricing. The performance of the selected control policy is monitored and a different control policy may be deployed in its place if the different control policy has a higher chance of providing better performance given the current control environment. Thus, as the control environment changes, the control policy that controls the power system may also be changed in an adaptive manner. In this way, the control policies may be changed as the control environment changes to provide an improved real-time performance compared to the use of a single control policy.

Abstract: Disclosed herein are embodiments for optimization of an energy grid system. First and second prediction models associated with a first energy grid system and a second energy grid system, respectively, may be trained based on historical data associated with each energy grid system. A prediction model basis may be created including the first and second prediction models. Training data associated with a third energy grid system may be input into each prediction model of the prediction model basis, and an accuracy of the prediction models may be evaluated to determine whether the prediction model basis is complete. When complete, a context-matching model may be trained based on subsequent energy grid systems until the context-matching model is determined to be sufficiently accurate. Then, the context-matching model may be used to identify a prediction model matching a new energy grid system, which may be used to warm-start the new energy grid system.

Abstract: A system and method for energy optimization is disclosed. The system may collect information from an information collector data including energy usage and storage data of at least one renewable energy generation system and battery energy storage system (BESS). The system may identify historical events that result in curtailment of renewable energy production, determine whether there is a curtailment of renewable energy production based at least on one historical event supervise the charge and discharge cycles of the at least one BESS; and ensuring that the diesel generators minimum up/down time is satisfied based on controlling at least one parameter of the BESS.

Abstract: Systems and methods are described for distributed hierarchical artificial intelligence (AI) in smart grids using two levels. At a higher level, the AI center module sits at the high-voltage transmission or distribution substation level, and manages a few points of aggregations (POA). At a lower hierarchy, each POA consists of all controllable and non-controllable elements in distribution feeder, distribution transformer, or microgrid level. These elements include distributed energy resources, energy storage systems, residential and commercial energy management systems, electric vehicle charging stations, etc. Each POA may be logically and/or physically connected to other POAs. Within each POA, AI edge module calculates the optimal disaggregation of set-points received from the AI center module to the controllable elements based on local information, and information gathered from the AI center module.

Abstract: Disclosed herein are embodiments for optimization of an energy grid system. First and second prediction models associated with a first energy grid system and a second energy grid system, respectively, may be trained based on historical data associated with each energy grid system. A prediction model basis may be created including the first and second prediction models. Training data associated with a third energy grid system may be input into each prediction model of the prediction model basis, and an accuracy of the prediction models may be evaluated to determine whether the prediction model basis is complete. When complete, a context-matching model may be trained based on subsequent energy grid systems until the context-matching model is determined to be sufficiently accurate. Then, the context-matching model may be used to identify a prediction model matching a new energy grid system, which may be used to warm-start the new energy grid system.

Abstract: A method and system for short term wind power prediction using real time wind speed measurements is disclosed. The method includes receiving at least one real-time characteristic associated with at least one wind turbine, maintaining a database of characteristics associated with the at least one wind turbines, training a machine learning model based on one or both of the database of characteristics and the at least one characteristic, testing the accuracy of the at least one machine learning model and outputting from the machine learning model generated output data based on the training and testing data. Responsive to determining that the accuracy exceeds a predetermined value, one or both of wind speed and energy output of the at least one wind turbine can be calculated.

Abstract: A system and method for energy optimization is disclosed. The system may collect information from an information collector data including energy usage and storage data of at least one renewable energy generation system and battery energy storage system (BESS). The system may identify historical events that result in curtailment of renewable energy production, determine whether there is a curtailment of renewable energy production based at least on one historical event supervise the charge and discharge cycles of the at least one BESS; and ensuring that the diesel generators minimum up/down time is satisfied based on controlling at least one parameter of the BESS.

Abstract: Disclosed herein are embodiments for optimization of an energy grid system. First and second prediction models associated with a first energy grid system and a second energy grid system, respectively, may be trained based on historical data associated with each energy grid system. A prediction model basis may be created including the first and second prediction models. Training data associated with a third energy grid system may be input into each prediction model of the prediction model basis, and an accuracy of the prediction models may be evaluated to determine whether the prediction model basis is complete. When complete, a context-matching model may be trained based on subsequent energy grid systems until the context-matching model is determined to be sufficiently accurate. Then, the context-matching model may be used to identify a prediction model matching a new energy grid system, which may be used to warm-start the new energy grid system.