A hierarchical control method for an island power grid energy storage system for increasing new energy generation fluctuation is disclosed. The method includes implementing a rolling dispatch method based on an idea of model predictive control; comprehensively considering a response capability of the energy storage system to grid-wide economic dispatch instructions and life loss of energy storage lithium batteries, and constructing an objective function of an intraday control model of the energy storage system with economic optimization; and constructing, on a basis of satisfying the objective function of the intraday control model, a real-time control model of the energy storage system and solving the model, and determining charge and discharge plans of a lithium battery energy storage system and a supercapacitor energy storage system.

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.

A method includes simulating a process, with computer-based software, to produce virtual data about the process; identifying process parameters for a real-world version of the process; providing a real-world sensor to sense a parameter associated with the real-world version of the process; receiving sensor readings from the real-world sensor while the real-world version is being performed; and training a machine-learning software model to predict a behavior of the real-world sensor based on the virtual data about the process, the process parameters, and the sensor readings.

Computer-implemented methods for fault diagnosis in an industrial environment generally includes processing the plurality of sensor data values to determine a recognized pattern therefrom; retrieving at least one industrial-environment digital twin corresponding to the industrial environment, the at least one industrial-environment digital twin comprising a plurality of component digital twins, with each of the plurality of component digital twins corresponding to one of the plurality of components in the industrial environment, and wherein the at least one industrial-environment digital twin and the plurality of component digital twins are visual digital twins that are configured to be rendered in a visual manner; and rendering the at least one industrial-environment digital twin and the at least one respective component digital twin corresponding to the particular component in the client application in response to the received request and based on the operational condition of the particular component.

Processes and systems that control operation of a commercial refinery distillation column and/or splitter operable to separate hydrocarbons. An automated process controller (APC) receives signal from at least one analyzer that provides information about the concentration of at least a first chemical in a first fraction and a second chemical in a second fraction obtained from the distillation column. The APC comprises programming in the form of an algorithm that calculates real-time monetary values for the first chemical and the second chemical and alters the operation of the distillation column to change either the percentage of the first chemical in the second fraction or the percentage of the second chemical in the first fraction, thereby maximizing overall operational profit for the distillation column.

Provided is an operation system including: an evaluation model generation apparatus configured to generate, by machine learning, an evaluation model configured to output an indicator indicating a result of evaluating a state in a piece of equipment with respect to an intended target based on an operation target in the piece of equipment and a state in the piece of equipment; an operation model generation apparatus configured to generate an operation model configured to output an action corresponding to the state in the piece of equipment, by reinforcement learning in which an output of the evaluation model is set as at least a part of a reward; and a control apparatus configured to apply, to a controlled object in the piece of equipment, a manipulated variable based on the action that is output by the operation model according to the state in the piece of equipment.

Provided is an apparatus including a supply unit suppling a value of a state parameter to an operation model outputting a recommendation value of a control parameter of a piece of equipment in response to a value of a state parameter relating to the piece of equipment being input; a control parameter acquisition unit acquiring a recommendation value of a control parameter output from the operation model in response to the supply unit supplying a value of a state parameter to the operation model; an acquisition unit acquiring a model evaluation value corresponding to a result of having operated the piece of equipment according to the recommendation value acquired by the control parameter acquisition unit; and an evaluation unit evaluating the operation model, based on the model evaluation value and a reference evaluation value corresponding to a result of having operated the piece of equipment through manipulation by a human.

The present disclosure provides a multi-arm spacecraft model predictive control method based on the mixture of Gaussian processes, equipment, and a medium. Model predictive control has excellent performance in dealing with complex nonlinear systems such as multi-arm spacecrafts with various constraints, and is widely applied to ground robots, unmanned aerial vehicles, autonomous driving and other practical scenarios. Therefore, a task space controller is designed based on the model predictive control in the present disclosure. Besides, in order to enhance the anti-interference capability of the present disclosure, an interference model is established and compensation is carried out in the model predictive control by utilizing the characteristics of small training data volume and high training speed in the mixture of Gaussian processes. Finally, a thrust distribution method is designed to complete platform control. The method provided by the present disclosure is convenient and intuitive in design and has relatively high practicability.

A priori georeferenced vegetative index data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the georeferenced vegetative index data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

Systems and methods may use a low speed controller in addition to an economic optimizer to achieve long-term objectives without significantly disrupting or destabilizing an electrical system. Specific long-term objectives include maximizing a capacity factor incentive and regulating battery degradation, but the methods and systems herein can be extended to more long-term objectives. A low speed controller can adjust one or more parameters of a cost function based on the relation between the projected state of the electrical system and the one or more parameters to effectuate a change to the electrical system to attempt to comply with the long-term objective.