A system and method are provided for operating a power generating asset coupled to an electrical grid. Accordingly, a controller receives an environmental data set indicative of at least one environmental variable projected to affect the power generating asset over a plurality of potential modeling intervals. The controller then determines the variability of the environmental data set and a corresponding modeling-confidence level at each of the potential modeling intervals based on the variability. A modeling interval is thus selected corresponding to a desired modeling-confidence level. A computer-implemented model is employed to predict a future power profile for the power generating asset over the selected modeling interval. The future power profile is indicative of a power-delivery capacity of the power generating asset at each of a plurality of time intervals of the modeling interval. Based, at least in part, on the future power profile, the controller determines an obligated-power-production schedule for the power generating asset over the modeling interval. The obligated-power-production schedule corresponds to a power production agreement with the electrical grid. In accordance with the obligated-power-production schedule, the controller modifies at least one setpoint of the power generating asset to deliver electrical power to the electrical grid.

Systems and methods for identifying optimal siting locations for an energy storage system. Siting locations are identified based on a value index derived from pricing data associated with a plurality of nodes on an electrical grid. An index is derived for each selected node of the plurality of nodes to produce a siting recommendation.

A method of controlling a wind power plant including an energy storage device, the wind power plant being connected to a power grid and comprising one or more wind turbine generators that produce electrical power for delivery to the power grid, the method comprising: processing input data related to one or more inputs to the wind power plant to determine a probability forecast for each input; and controlling charging and discharging of the energy storage device in accordance with each probability forecast and a prescribed probability of violating one or more grid requirements.

According to embodiments described herein control of the auxiliary power system in a hybrid power plant is provided by determining a grid-draw threshold from an external power grid; monitoring power consumption for powered systems of the hybrid power plant; monitoring power generation of the hybrid power plant; discharging an alternative power source of one or more of an Energy Storage System (ESS) and an auxiliary generator in response to the power consumption exceeding the grid-draw threshold; and implementing prediction algorithms for power generation of the hybrid power plant and the power consumption. Accordingly, a source of power is managed between several alternative sources and the external power grid to meet plant operator defined criteria when maintaining power in various wind speed conditions.

In an energy system in a community provided with a plurality of unit grids, each of which is an energy transfer network of a single-unit facility including a power load, the unit grids each include a photovoltaic generator, supply power generated by the photovoltaic generator thereof to the power load thereof, and, as an electric vehicle moves, form a cooperative grid that transfers power stored in a mobile storage battery mounted on the electric vehicle to and from another of the unit grids, and some of the unit grids whose geographical positional relationship is not fixed form a virtual grid for transferring power as a combination of the unit grids that form the cooperative grids changes in accordance with a destination of the electric vehicle.

A method and a system for energy scheduling of shared energy storage considering degradation cost of energy storage. Provided herein relates to energy scheduling for multiple microgrids. The method includes: acquiring energy data of each of microgrids in a multi-microgrid system of shared energy storage; establishing a peer-to-peer trading model between each of the microgrids; establishing a shared energy storage trading model between the multi-microgrid system and the shared energy storage device thereof; establishing a utility grid trading model between the multi-microgrid system and the utility grid thereof; and based on the peer-to-peer trading model, the shared energy storage trading model and the utility grid trading model, setting an objective of minimizing a total operating cost of the multi-microgrid system, and solving an objective function corresponding to the objective to acquire power data of each of the microgrids at each stage.

Disclosed are various embodiments for optimizing energy management. A quantity of renewable power that will be generated by renewable energy generation sources can be forecasted. The energy demand for a building or a cluster of buildings can be forecasted. A pricing model for buying energy from a grid can be determined. A quantity of energy to import from the grid or export to the grid can be scheduled based on the quantity of renewable energy forecasted and the state of charge or health of battery energy storage system, current and future operations of building HVAC, lighting and plug loads system, the forecasted energy demand for the building, and the pricing of the energy from the grid.

The invention relates to a method for controlling power generation from a power plant which comprises a plurality of power generating units. The method involves setting a maximal allowed power production for each power generating unit in a selection of one or more power generating units of second priority to a non-zero value, where the maximal allowed power production is determined dependent on whether a first power production gap is greater or less than zero and dependent on a comparison of the first power production gap with a second available power production capability of the selection of the one or more power generating units of the second priority.

A system and method for optimizing power grid operations and enhancing the life of switching components therein is provided. Current meteorological information of a region of operation of the power grid is collected during operation thereof, along with historical meteorological data of the region. A plurality of prediction models are executed using the current meteorological information and/or the historical meteorological data and a meteorological parameter of the region is forecast by selectively combining outputs of at least some of the executed prediction models, the meteorological parameter being a parameter that causes a renewable energy source in the power grid to generate power. The forecasted meteorological parameter is compensated with physical models and the historical meteorological data, and optimal switching operations of switching components in the power grid are computed based on the compensated forecasted meteorological parameter, with the switching components being controlled based on the computed optimal switching operations.

This power supply and demand planning device includes: an output range calculation unit that calculates the output range of a power generator that satisfies a plurality of restriction conditions; a power generation output range output calculation unit that calculates the power generator output in a single cross section on the basis of the calculated output range calculated; and a past specified cross section output correction unit that calculates a target output in the single cross section when a restriction condition violation occurs in the power generator output in the calculated single cross section calculated, and in order to eliminate a restriction condition violation, corrects the output range and the power generator output in the single cross section and a past cross section further in the past than the single cross section so that the power generator output in the single cross section becomes the target output.