Systems and methods for operating a hybrid power system are disclosed. A controller may perform operations, including: obtaining load data for the hybrid power system; obtaining power availability data and energy cost data for each power asset in each power asset group of a plurality of power asset groups; and determining active power commands for each power asset by performing at least one optimization, such that the determined active power commands optimize a total operating cost, wherein: the at least one optimization is based on at least one cost function that accounts for asset degradation, asset maintenance cost, asset operation efficiency cost, and the energy cost data; and the at least one optimization is constrained by a plurality of constraints based on the load data, the power availability data, and characteristics of the power assets; and operating each power asset based on the determined active power commands.

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.

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 photovoltaic system with an inverter, at least one solar panel for providing electrical power, and electrical wiring for coupling electrical power from the at least one solar panel to the inverter. Also included is a transmitter for transmitting a messaging protocol along the electrical wiring, where the protocol includes a multibit wireline signal. Also included is circuitry for selectively connecting the electrical power from the at least one solar panel along the electrical wiring to the inverter in response to the messaging protocol.

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 invention relates to an electrical power distribution system, including: an energy storage unit connectable to a power supply and configured to store electrical energy supplied by the power supply; a splitter connected to the energy storage unit and connectable to a grid and a micro-grid, the splitter being configured to split electrical energy supplied by the energy storage unit to allow electrical energy to be controllably supplied to the grid and/or the micro-grid; a splitter meter connected to the splitter, the splitter meter being configured to measure an amount of electrical energy supplied by the energy storage unit to the micro-grid; and electronic controlling devices coupled to the splitter, the one or more electronic controlling devices being configured to: determine a micro-grid load requirement; and control the splitter to cause electrical energy to be supplied to the micro-grid in accordance with the micro-grid load requirement.

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.

The disclosure provides a method for calculating a parameter changing domain of loads under a case that guarantees a constant locational marginal price in an electricity market, which relates to the electricity market field of the power system. With the method in the disclosure, the clearing model on the locational marginal price in the general form is established, and the safe changing domain of the locational marginal price with respect to the loads may be derived and calculated based on the first-order KKT condition expansion of the clearing model on the locational marginal price in the general form. When the increment of the nodal loads is subordinate to the changing domain, the locational marginal price may remain unchanged. The parameter changing domain of loads in the power system may be used for the comprehensive evaluation of power market clearing results and assisting the operation of the power market.

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.

Described herein is a monitoring installation and a monitoring system for monitoring and/or controlling at least one electrical parameter in an electrical supply system, as well as to an associated computer program.