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.

A method performed by a control node for a radio access network. The control node may select an energy source from a plurality of energy sources connected to a base station based on selecting an energy rate of one of the energy sources correlated in time with a radio traffic power usage of the base station. The selection may be made from a plurality of energy rates for the plurality of energy rates correlated in time for each energy source with a radio traffic power usage of the base station. The selected energy rate may improve at least one operating parameter of the selected energy source. The control node may activate the selected energy source for use by the base station. A further method performed by a global control node may be provided.

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.

A method may include obtaining irradiance data at a first time and a second time from sensors, determining whether one or more solar modules of a plurality of networked power plants will be covered by a shadow or shade at a third time based on the irradiance data, and generating, based on the determination, a power output prediction for each power plant of the networked power plants at the third time. The method may further include receiving power delivery profiles for first and second loads, adjusting a power output of one or more power plants of the networked power plants based at least in part on the power output prediction and the power delivery profiles for the first and second loads, and allocating a combined power output of the power plants to the first and second loads based on first and second load reliability thresholds.

There is provided a method, a system, a storage medium and an orchestrator to enable a computer resource of a computing pool. In particular, the methods and apparatuses of the present disclosure are configured to enable a computing resource (16) of one or more computing pools (17), the computing resource (16) being in communication with a converter (13) of a renewable energy source (12). The methods and apparatuses may be configured to: tracking an electrical consumption of one or more electrical appliances (14) in communication with the converter; enabling the computing resource (16) in response to determining a surplus of renewable electrical energy provided by the renewable energy source (12); and to determining that there is computation demand from the one or more computing pools (17); and to determining that an estimated efficiency of use of the renewable energy is greater when responding to the demand of the one or more computing pools (17) than when injecting the renewable energy into an electrical grid (11) in communication with the converter (13).

A collaborative aggregation trading method for consuming clean energy includes: acquiring related data of clean energy power generation companies, the demand-side resources and energy storage service providers; acquiring a trading quotation and acquiring self-regulating ability data and regulating fees of shared energy storage service providers; performing trading of collaboratively consuming the clean energy through the demand-side resources and the shared energy storage: when the power generation output value exceeds the maximum regulating ability of user demands and the demand-side resources, storing the redundant power into shared energy storage systems for standby application; when the power generation output value is lower than the minimum operation load level of the user demands and the demand-side resources, calling the shared energy storage service providers participating in the trading according to energy storage ranking indexes, to acquire required power from the shared energy storage systems; and performing fees settlement.

Embodiments of systems and methods for power demand management are described herein. More specifically, embodiments comprise systems and methods for powering, controlling, and/or operating various types of controllable load for integration with power fluctuations from intermittent power generation plants, such as photovoltaic arrays and wind turbine farms.

An energy evaluation system includes an energy system modeling framework and an Application Program Interface (API) as part of the energy system evaluation framework. The energy evaluation system can be configured to query a simulation application program interface (API) with PV system configuration parameters as arguments, generate a shade loss time series based on the system configuration parameters, simulate energy output for the PV system based on the shade loss time series, and output energy aggregations based on the simulation.

The present invention belongs to the technical field of information, particularly relates to the theories such as time series interval prediction, extreme learning machine modeling and Gaussian approximation solution, and is a wind power output interval prediction method. First, interval prediction of wind power output influencing factors is realized by time series analysis and normal exponential smoothing so as to consider an input noise factor. Then an extreme learning machine prediction model is established with an interval result as an input, output distribution is calculated based on iterative expectation and a conditional variance law, and thus an interval prediction result of wind power output is obtained. The method has advantages in interval prediction performance and calculation efficiency and can provide guidance for production, scheduling and safe operation of a power system.

Systems and methods for power generation forecasting in a microgrid are disclosed. In some embodiments, a system comprises at least one processor; and memory storing instructions executable by the at least one processor, the instructions when executed cause the system to: obtain weather information; extract, from the weather information, one or more weather events, the one or more weather events including a first event, wherein the first event causes reduction of power generation of a power generation system; determine a condition of one or more components of the power generation system based on the first weather event; and determine future power generation by the power generation system based on the determined condition of the power generation system.