A capacity configuration method and system of energy storage in a microgrid. In this application, the time-series data related to photovoltaic power generation is acquired and processed to obtain the preprocessed time-series data; a time-series generative adversarial network (Time GAN) is trained based on the preprocessed time-series data to perform data enhancement to obtain enhanced time-series data; and based on the enhanced time-series data, a distributionally robust optimization model is used to perform capacity configuration of energy storage.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing thermal modeling. In one aspect, a method includes receiving monitoring data comprising temperature data measured inside a site, mode data, and state data, receiving weather data descriptive of weather at the site, and aligning the received temperature data, mode data, and state data with the received weather data. The method also includes determining an internal heat gain representing an amount of heat generated at the site irrespective of the heating or cooling system, determining at least one of a thermal product for the site or a thermal potential for the heating or cooling system, generating, based on the internal gain and the thermal product or the thermal potential, a thermal model for the site, and providing, as output, the generated thermal model.

An energy saving support system according to an embodiment is configured to provide a consumer, who has an electric load to which electric energy is supplied from an electric power supply system within a house, with energy consumption-related information through a photo frame or the like. The information providing apparatus is configured to acquire an acceptability level, which stepwise indicates a degree of interest of the consumer in the energy consumption-related information, and to determine the energy consumption-related information to be newly provided to the consumer based on the acceptability level.

Systems, methods, and devices may enable management of a renewable power asset. A control device may generate a Day-Ahead (DA) pricing model, a Real-Time (RT) pricing model and a renewable generation model for the renewable power asset. Optimal DA commitments may be determined, and an optimal RT schedule estimated. A DA power delivery strategy and an RT power delivery strategy may be determined. The determined DA and RT power delivery strategies may be evaluated based on obtained real power prices. The DA and RT power delivery strategies may be redetermined, and the renewable power asset may be controlled to deliver power the DA and RT power delivery strategies. The value of the renewable power asset may be maximized while bounding financial risks and returns associated with scheduling the renewable power asset as tailored to risk preferences of the renewable power asset owner or operator.

An integrated energy optimizer having an edge side and a cloud side. The edge side may incorporate an energy optimizer, a building management system connected to the energy optimizer, a controller connected to the building management system, and equipment connected to the controller. The cloud side may have a cloud connected to the energy optimizer and to the building management system, and a user interface connected to the cloud. Data from the field sensor may go to the optimizer and the building management system. The data may be processed at the optimizer and the building management system for proper settings at the building management system.

A system and method for energy distribution with decoupled by time and space domains that integrates energy storage capabilities that feature co-products utilization at the point of energy storage charging, byproduct utilization at the point of energy production, and time and space decoupling of vehicle shuttling energy storage media discharge to accelerate return on investment, reduce system energy consumption, and maximize utilization of existing energy infrastructure. Additionally, the system executes the energy transactions by controlling and integrating distributed energy producers and consumers with minimal grid dependence.

A method for controlling an energy storage system (ESS) and devices performing the method are disclosed. A method for controlling an ESS according to example embodiments, the method includes an operation of analyzing energy information of an area for which a peak power load is to be calculated received from a plurality of clients included in the area, an operation of generating, based on an analysis result, scheduling control information for controlling the ESS connected to the plurality of clients on a daily basis, an operation of determining, based on the scheduling control information, whether to generate real-time control information for controlling the ESS on a basis of a predetermined control time period, and an operation of correcting, based on the real-time control information, the scheduling control information.

In a control device, an instruction acquirer acquires an instruction to suppress in a specified period supplying of generated power generated by power generator installed in a power-consuming area to a commercial electrical power system. Upon the instruction acquirer acquiring the instruction, a water heater controller commands a water heater installed in the power-consuming area to perform a water heat-up operation when a predetermined condition is satisfied in the specified period.

A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

The present disclosure pertains to a system that may be configured to operate a power grid, including generation participants, consumers, and a controller that administers a market for the power generation participants and the consumers on the power grid. The operation of the power grid may include: performing power grid coordination calculations based upon, at least in part, constraints that pertain to the flow and/or congestion of power on the grid; and managing the coordination calculations by reducing a number of constraints used in the coordination calculations, the managing including a first step of coarse screening of the constraints, a second step of deterministic fine screening of the constraints, and a third step of statistical fine screening of the constraints. This second step may cause identification of constraints that are redundant and/or dominated by other constraints.