201results about "Load forecast in ac network" patented technology

The application relates to the field of information technology, in particular to a smart charging pile control method and system supporting photovoltaic storage linkage. The method comprises the following steps: acquiring real-time output data of a photovoltaic power generation system in a park, short-time predicted load of the park, charging state information of new energy vehicles, energy storage state data of fast charging and energy storage devices, and driving time of users; according to a preset scheduling period, the real-time output data, the short-time predicted load of the park and the energy storage state data are read, and whether there is light abandonment and power gap in the next scheduling period is judged; when there is light abandonment, new energy vehicles in a chargeable state are dispatched to consume abandoned light power for charging, and the consumed power is recorded; when there is a power gap, new energy vehicles meeting preset discharge conditions are controlled to reversely discharge to the park through charging piles, and the discharged power is recorded; the charging power, the consumed power and the discharged power of the new energy vehicles are respectively subjected to cost measurement, and the final charging cost is obtained.

In the context of an electrical utility system, changing cloud conditions may cause a customer having solar panels to greatly increase or decrease electrical demand in a difficult-to-predict manner. Accordingly, a spinning reserve maintained by an electric utility company must be larger, and is therefore more expensive. In an example, the spinning reserve may be managed by: calculating a stable sequence of forecasts of smoothed real-time consumption, wherein the calculating is based at least in part on smoothed estimates of consumption data. A stable sequence of forecasts of real-time measured load may be calculated by subtracting forecasts of real-time distributed solar photovoltaic (PV) generation data from the stable sequence of forecasts of smoothed real-time consumption. The spinning reserve of the electricity system may be controlled based at least in part on the stable sequence of forecasts of real-time measured load.

Embodiments relate to a virtual power plant system based on self-demand response. A system according to an embodiment relates to a virtual power plant system that analyzes in real time at least one of power demand data, power generation amount data, electricity price data, and distributed power information and provides the result of the analysis in order to enable an individual consumer to perform self-demand response (Self-DR) using a user terminal.

The application discloses a kind of based on load prediction turbine power generation propulsion system dynamic regulation method, comprising: the operating state information in flight task is collected and power consumption record;Information is input to trained data processing unit, and the expected power demand of each power consumption node is generated;According to the matching relationship of expected demand and generator current output, it is judged whether there is high load fluctuation sign;If there is, then with expected demand as constraint, the power transmission path is compared by optimization algorithm to multiple schemes iteration, and the optimal power allocation strategy is determined;According to optimal strategy adjustment generator set output parameter and distribution passage state, and continuously collect feedback data;When feedback data shows that actual state deviates from expected target, according to the deviation degree, expected demand is regenerated and returned to strategy determination step of execution.The application realizes prospective regulation by load prediction, identifies fluctuation risk and dynamically optimizes power allocation, and combines feedback iteration to correct deviation.

A building control system for at least one building. The building control system has a system controller and at least one building operation device with a respective operation controller. Electrical supply power is fed from a supply grid to the at least one building operation device. The system controller is set up to temporarily adapt a setpoint value for each building operation parameter in order to adapt the electrical supply power to the state of the supply grid and to stabilize the supply grid. The direct specification of a temporarily modified setpoint value for a building operation parameter is a simple and efficient option of integrating the subordinate open-loop or closed-loop controllers of the building operation devices into the building control system. This measure also makes simple retrofitting possible.

An example operation includes at least one of receiving data comprising at least one of weather data, grid load data, or energy usage data related to an area, executing an Artificial Intelligence (AI) model to predict energy production in the area and energy consumption in the area, based on the received data, determining an optimized energy-related output in a timeframe, based on the predicted energy production and the predicted energy consumption, and setting, by a device associated with a location in the area, at least one control at the location based on a parameter associated with the optimized energy-related output, to produce the optimized energy-related output within the timeframe.

A multi-timescale coordinated optimization scheduling method for an electrical power system that includes a number of distributed energy resources (DERs) is disclosed. The method includes performing long-timescale optimization scheduling for the electrical power system based at least on renewable energy generation forecast data of the DERs to obtain long-timescale operation planning data. The method further includes performing mid-timescale optimization scheduling for the electrical power system based on the long-timescale operation planning data and measured data of the DERs to obtain mid-timescale operation planning data. The method further includes performing at least close to real-time optimization scheduling for the electrical power system based on the mid-timescale operation planning data, the measured data of the DERs and grid signals of the electrical power system to obtain short-timescale power setpoints for the DERs. An electrical power system in which the method is used is also disclosed.

A grid distribution system aggregates energy resources of multiple distributed energy resources (DERs) and provides service to one or more energy markets with the DERs as a single market resource. The DERs can create data to indicate realtime local demand and local energy capacity of the DERs. Based on DER information and realtime market information, the system can compute how to provide one or more services to the power grid based on an aggregation of DER energy capacity.

The application discloses a kind of comprehensive flexible resource reliability optimization methods considering dynamic multi-energy demand response.Comprehensive flexible resource is divided into four kinds of multi-state comprehensive flexible resource, construct the multi-state model of comprehensive flexible resource considering time sequence characteristics, obtain the multi-energy load demand of each comprehensive flexible resource;Respectively, the multi-state model of comprehensive flexible resource reduction, transfer, alternative response considering time sequence characteristics is established, obtain the multi-energy load demand of comprehensive flexible resource after reduction, transfer, alternative response end in each state;Fusion three kinds of multi-state model, establish the multi-state model of total demand response, obtain the amount of demand side operation reserve provided in multiple states, and then calculate the reliability of the amount of demand side operation reserve provided by comprehensive flexible resource.The application adopts multi-state model to more accurately characterize the dynamic characteristics of comprehensive flexible resource, and accurately obtains the operation risk parameters of the amount of demand side operation reserve provided by comprehensive flexible resource.

Devices and methods are provided for adaptively optimizing load sharing to minimize power consumption. A controller receives an initial input power supplied by multiple power stages of a power supply unit to an electronic device. The controller adaptively configures load sharing settings of one or more power stages based on the initial input power and one or more parameters associated with the electronic device to achieve a minimum input power consumption. The power stage(s) exhibits uneven load sharing based on the load sharing settings. To supply an updated input power to the electronic device, the controller controls the power stage(s) based on the load sharing settings. The initial input power and the updated input power correspond to a total power consumption of the electronic device. The controller performs continuous monitoring, real-time analysis, and adaptive adjustments to optimize power usage based on specific hardware characteristics, environmental conditions, and dynamic load behavior.

The application provides a cluster baseline load prediction method and device, electronic equipment and storage medium, wherein the method comprises: obtaining sample data for cluster baseline load prediction; using a K-Means algorithm to divide users into photovoltaic users and non-photovoltaic users according to weather state characteristics of the sample data; using a Shapley Value method to calculate a cluster baseline load prediction value of the non-photovoltaic users according to a first baseline load prediction value and a second baseline load prediction value of the non-photovoltaic users; and summing the cluster baseline load prediction value of the non-photovoltaic users and a cluster baseline load prediction value of photovoltaic users determined based on an SVR model to obtain a cluster baseline load prediction result. Through the application, the problem of low accuracy of cluster baseline load prediction for a cluster containing distributed photovoltaics in the related art is solved.

This invention belongs to the technical fields of intelligent transportation systems, urban computing, and geographic information systems. It provides a method, device, equipment, and medium for predicting the utilization rate of electric vehicle charging piles. The method includes: preprocessing the raw data of the target urban area; determining a geographic adjacency graph and a travel tidal flow graph based on the obtained static and dynamic features; then performing feature weighting fusion using a time feature embedding method; performing two-layer graph attention aggregation on the geographic adjacency graph and the travel tidal flow graph based on the obtained node fusion features to obtain spatial feature embedding; using a gated recurrent unit to learn the evolution of the charging load of the grid over time; using the prediction error of the effective grid as a loss function and training with a dataset to obtain a grid charging load prediction model for subsequent predictions. This invention can effectively improve the model's environmental adaptability and prediction accuracy in different time periods.

An energy management system for microgrid systems. The energy management system includes a controller. The controller is configured to receive forecasted weather condition data, predict a power generation profile and / or a power demand profile of a microgrid system in response to the forecasted weather condition data, determine a performance model and / or a degradation model of one or more system components of the microgrid system in response to the power generation profile and / or the power demand profile, derive an energy management optimization strategy in response to the performance model and / or the degradation model of the one or more system components of the microgrid system, and control an operation mode of the one or more system components of the microgrid system in response to the energy management optimization strategy.

The application discloses a power price prediction method and device, which can acquire all electric appliance plans in a block area; generates a total demand load curve corresponding to the block area according to each electric appliance plan, and generates a power supply plan according to the total demand load curve; generates a power price plan according to the total demand load curve and the power supply plan; calculates a predicted electricity charge corresponding to each user based on the power price plan and the electric appliance plan corresponding to each user, and issues the power price plan and each predicted electricity charge to a terminal of the corresponding user; updates the total demand load curve according to the latest uploaded electric appliance plan of one or more users, returns to the step of generating the power supply plan corresponding to the block area according to the total demand load curve until the power price plan converges; it can be seen that the application can combine the energy consumption scheduling game of the demand side and the dynamic economic scheduling game of the supply side, further improve the accuracy of the predicted power price plan, and thus improve the practicability of the power price plan.

A method for operating a microgrid system (100) comprising: receiving a microgrid on or off schedule input (208) containing a schedule of required electrical power generation for the microgrid system (100) for a given period; filtering the scheduled on or off input and converting the scheduled on or off input into power levels (608) to meet the power demand for required electrical power generation of the on or off schedule input for the microgrid system (100); receiving an actual load level for the microgrid system (100) based on one or more electrical loads (116) electrically coupled to the microgrid system (100); and comparing the schedule of required electrical power generation with the actual electrical power generation required for the microgrid system (100).and switching one or more electrical systems on or off in real time to compensate for a difference between the planned required electrical energy generation and the actual required electrical energy generation.

Disclosed is a localised energy generation and distribution microgrid network 100 suitable for supplying local energy needs, comprising one or more network electricity generating components 110, 112, 114 for generating local electricity, and comprising an electrical storage component 140 for storing the local electricity, each of said components in use transmitting and / or receiving direct current (DC) local electricity, the microgrid network further comprising a controller 120 in two way data communication with said components and operable to: a) selectively direct the flow of the local DC electricity from the or each local electricity generating component 110,112,114 to the electrical storage component 140, and / or to meet the local energy needs, and b) selectively direct the flow of the local electricity out of the storage component 140 to meet said local energy needs, said controller 120 being operable to perform said selective direction according to the current state of the network components, historic data and periodic forecasts of local energy demand and local electricity generating capacity. The network may comprise additional components in the form of a heater 180 and a thermal storage component 190, and / or a chemical energy generator such as a hydrogen gas generator 130 and a chemical energy storage component such as a gas storage component 132, as well as an inverter 122 for converting the local DC electricity into local AC electricity to supply local energy needs. Forecasting and optimisation routines are used to aid the operation of the microgrid, based on predefined priorities and operational constraints.

The source network load storage collaborative multi-time scale self-balancing scheduling method and system of the active power distribution network maximizes the difference between the maximum value of the operation income of the day-ahead market transaction of the active power distribution network and the minimum value of the tie line power as a day-ahead scheduling objective function; takes the scheduling power balance constraint as a day-ahead scheduling constraint condition; obtains the error of the intraday prediction value and the day-ahead prediction value of the new energy unit output power and the adjustment amount of the intraday planning value compared with the day-ahead planning value of the building load power, and minimizes the difference between the error and the adjustment amount as an intraday scheduling objective function; takes the day-ahead scheduling constraint condition as an intraday scheduling constraint condition; takes the day-ahead scheduling objective function, the day-ahead scheduling constraint condition, the intraday scheduling objective function and the intraday scheduling constraint condition to form a multi-time scale self-balancing optimization scheduling model; iteratively solves the multi-time scale self-balancing optimization scheduling model to obtain the scheduling scheme of the active power distribution network under the source network load storage collaboration, and optimizes the tie line power to improve the safety of the active power distribution network.

The application discloses a charging station charging load prediction method based on an attention mechanism, including a prediction model, the prediction model including a feature embedding module, a historical data coding module and a fusion prediction module; feature data of a charging station site period t and a charging load value y corresponding to the period t are acquired from historical data t The prediction model calculates a predicted charging load value, the converged prediction model is obtained through historical data training, and thus the charging load of a future period is predicted. According to historical charging load data of the charging station, a novel prediction method is adopted to predict the charging load change curve of each charging station in a given future period, better data support is provided for construction planning and daily operation of the charging station, and then quantitative analysis basis is provided for load prediction of a power distribution network.