CN117937638B - Wind power plant cluster power generation control method and device, electronic equipment and storage medium - Google Patents
Wind power plant cluster power generation control method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a wind farm cluster power generation control method, a device, electronic equipment and a storage medium, and relates to the technical field of power generation control. The method comprises the steps of clustering wind power plants according to numerical weather forecast data of each wind power plant in a subsequent period, predicting predicted power of the wind power plant cluster in the subsequent period through a power prediction model based on a clustering result, performing rolling optimization on power of the wind power plant in each period, performing rolling optimization on power of each cluster wind power plant in each sub-period of each period, performing rolling optimization on power of each wind power plant in a plurality of unit time periods of each sub-period, and finally adjusting an output mode of each wind power plant in each unit time period according to the obtained predicted power and power scheduling index of each wind power plant in each unit time period. The method, the device, the electronic equipment and the storage medium provided by the invention can effectively balance the power fluctuation of the wind power climbing event and improve the stability of a system of a wind power plant cluster.
Description
Technical Field
The invention belongs to the technical field of power generation control, and particularly relates to a method and a device for controlling power generation of a wind farm cluster, electronic equipment and a storage medium.
Background
In recent years, the permeability of wind power generation steadily rises, the fluctuation and uncertainty of the permeability are also continuously increased, and wind power climbing events often occur, namely, the wind power amplitude greatly fluctuates in a short time. The wind power climbing event is difficult to predict because of low occurrence probability, once the wind power climbing event occurs, the power system cannot formulate timely and effective adjustment measures, and the stable operation and the safe power supply of the power system are seriously affected. Therefore, the suitable control strategy is selected to limit the climbing rate of the wind power, the damage caused by wind power climbing events is reduced, and the method is a hotspot problem for researching the wind power climbing events.
At present, the power generation control of the wind farm cluster mostly adjusts the output mode of the wind farm cluster in a future period according to numerical weather forecast (numerical weather prediction, NWP) data in the future period of the region where the wind farm cluster is located. However, because the numerical weather forecast data of the positions of different wind power plants in the wind power plant clusters are different, and the numerical weather forecast data of the same wind power plant in the region in different periods within a period of time are also different, the wind power plant clusters cannot be accurately predicted and controlled, wind power climbing events cannot be effectively stabilized, and the stability of the wind power plant clusters is poor.
Therefore, how to provide an effective solution to stabilize the wind climbing event and improve the stability of the wind farm cluster has become a challenge in the prior art.
Disclosure of Invention
The invention aims to provide a wind farm cluster power generation control method, a device, electronic equipment and a storage medium, which are used for solving the problems in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the present invention provides a wind farm cluster power generation control method, including:
acquiring numerical weather forecast data of each wind power plant in a wind power plant cluster in a subsequent period;
performing cluster analysis on each wind power plant in the wind power plant cluster based on the numerical weather forecast data of the wind power plant in the subsequent period to obtain at least one cluster;
Inputting numerical weather forecast data of wind power plants in each cluster in a subsequent period into a corresponding pre-trained power prediction model to obtain predicted power of each wind power plant in the subsequent period;
Obtaining predicted power of a wind power plant cluster in a subsequent period based on predicted power of each wind power plant in the subsequent period;
Rolling and optimizing the power of the wind power plant cluster in each period of a subsequent period by taking the scheduling plan constraint of the wind power plant cluster, the output limit constraint of the wind power plant cluster and the output climbing constraint of the wind power plant cluster as constraint conditions to obtain the predicted power of the wind power plant cluster in each period, wherein the output climbing constraint is used for representing the change rate constraint of the power in one time scale, and the scheduling plan constraint is used for representing the power generation constraint in one time scale;
Based on the predicted power of the wind power plant clusters in each period, rolling optimization is carried out on the power of each sub-period of each cluster wind power plant in each period by taking the scheduling plan constraint of each cluster wind power plant, the output limit constraint of each cluster wind power plant and the output climbing constraint of each cluster wind power plant as constraint conditions to obtain the predicted power of each cluster wind power plant in each sub-period;
Based on the predicted power of each cluster of wind power plants in each sub-period, rolling optimization is carried out on the power of each wind power plant in a plurality of unit time periods of each sub-period by taking the scheduling plan constraint of each wind power plant, the output limit constraint of each wind power plant and the output climbing constraint of each wind power plant as constraint conditions, so that the predicted power of each wind power plant in each unit time period is obtained;
and adjusting the output mode of each wind power plant in each unit time based on the predicted power of each wind power plant in each unit time and the power scheduling index of each wind power plant in each unit time.
According to the wind power plant cluster power generation control method, numerical weather forecast data of each wind power plant in the wind power plant cluster in a subsequent period are obtained; performing cluster analysis on each wind power plant in the wind power plant cluster based on the numerical weather forecast data of the wind power plant in the subsequent period to obtain at least one cluster; inputting numerical weather forecast data of wind power plants in each cluster in a subsequent period into a corresponding pre-trained power prediction model to obtain predicted power of each wind power plant in the subsequent period; obtaining predicted power of a wind power plant cluster in a subsequent period based on predicted power of each wind power plant in the subsequent period; rolling and optimizing the power of the wind power plant cluster in each period of a subsequent period by taking the scheduling plan constraint of the wind power plant cluster, the output limit constraint of the wind power plant cluster and the output climbing constraint of the wind power plant cluster as constraint conditions to obtain the predicted power of the wind power plant cluster in each period, wherein the output climbing constraint is used for representing the change rate constraint of the power in one time scale, and the scheduling plan constraint is used for representing the power generation constraint in one time scale; based on the predicted power of the wind power plant clusters in each period, rolling optimization is carried out on the power of each sub-period of each cluster wind power plant in each period by taking the scheduling plan constraint of each cluster wind power plant, the output limit constraint of each cluster wind power plant and the output climbing constraint of each cluster wind power plant as constraint conditions to obtain the predicted power of each cluster wind power plant in each sub-period; based on the predicted power of each cluster of wind power plants in each sub-period, rolling optimization is carried out on the power of each wind power plant in a plurality of unit time periods of each sub-period by taking the scheduling plan constraint of each wind power plant, the output limit constraint of each wind power plant and the output climbing constraint of each wind power plant as constraint conditions, so that the predicted power of each wind power plant in each unit time period is obtained; and adjusting the output mode of each wind power plant in each unit time based on the predicted power of each wind power plant in each unit time and the power scheduling index of each wind power plant in each unit time. In this way, in the process of controlling the power generation of the wind power plant clusters, the wind power plants can be clustered according to the numerical weather forecast data of each wind power plant in the subsequent period, so that the predicted power of the wind power plant clusters in the subsequent period can be accurately predicted, then the power of the wind power plants in each period is subjected to rolling optimization through dynamic optimization, the power of each cluster of wind power plants in each sub-period is subjected to rolling optimization, the power of each wind power plant in each sub-period in a plurality of unit time periods is subjected to rolling optimization, and the prediction time and space scale are gradually reduced in each layer of optimization, so that the control accuracy can be improved, the influence of prediction errors is reduced, the accurate prediction and control of the wind power plants are further realized, the power fluctuation of wind power climbing events can be effectively restrained, and the stability of a system of the wind power plant clusters is improved.
In one possible design, the scheduling plan constraints for a wind farm cluster are:
the output limit constraint of the wind farm cluster is:
the output climbing constraint of the wind power plant cluster is as follows:
Wherein J clu represents the sum of the predicted powers of the wind farm clusters, T represents the number of cycles, m represents the number of clusters corresponding to the wind farm clusters, n represents the number of wind farm clusters, Representing the power value of an ith wind farm in a wind farm cluster in one period,Representing a power predicted value of an ith wind power plant in a wind power plant cluster in one period; Representing a minimum output threshold for an ith wind farm in the wind farm cluster, Representing the installed capacity of the ith wind farm in the wind farm cluster, P j+1 representing the total power of the wind farm cluster in the j+1th cycle, P j representing the total power of the wind farm cluster in the j-th cycle, β max representing the maximum power slope in the cycle, |t j+1-Tj | representing the time length of one cycle, β representing the minimum power slope in the cycle, and γ and λ both representing the power threshold.
In one possible design, the scheduling plan constraints for any cluster of wind farms are:
the output limit constraint of any cluster of wind farms is:
The output climbing constraint of any cluster of wind power plants is as follows:
wherein, Representing the power value of the ith wind farm in any cluster in one sub-period,Representing the power planning value of the ith wind farm in any cluster in one period, n' representing the number of wind farms in any cluster,Representing the minimum output threshold of the ith wind farm in any cluster,Representing a predicted power value of an ith wind farm in any cluster in one sub-period, P i N representing an installed capacity of the ith wind farm in any cluster in one sub-period, P 'j+1 representing a total power of the wind farms in any cluster in a j+1th period, P j' representing the total power of the wind farms in any cluster in the j-th period, |T 'j+1-Tj' | representing a time length of one sub-period, β 'max representing a maximum power slope value in the sub-period, β' representing a minimum power slope value in the sub-period, and λ 'and γ' both representing power thresholds.
In one possible design, the scheduling plan constraints for a wind farm are:
the output limit constraints of the wind farm are:
the output climbing constraint of the wind power plant is as follows:
wherein, Indicating the power value of the wind farm at a unit time,A power schedule value representing a wind farm for a unit length of time,Representing a minimum output threshold for the wind farm,Representing a predicted value of the power of the wind power plant in a unit time period, P i′N representing the installed capacity of the wind power plant in a unit time period, P 'j+1 representing the total power of the wind power plant in a j+1th unit time period, P' representing the total power of the wind power plant in a j unit time period, T "j+1-Tj" represents a time length of one unit time period, β "max represents a maximum value of the power slope in the unit time period, β" represents a minimum value of the power slope in the unit time period, and γ "and λ" each represent a power threshold.
In one possible design, the numerical weather forecast data includes wind speed data.
In one possible design, the numerical weather forecast data further includes temperature data, barometric pressure data, and/or wind direction data.
In one possible design, the power prediction model is an LSTM model.
In a second aspect, the present invention provides a wind farm cluster power generation control device, including:
the acquisition unit is used for acquiring numerical weather forecast data of each wind power plant in the wind power plant cluster in a subsequent period;
The clustering unit is used for carrying out clustering analysis on each wind power plant in the wind power plant cluster based on the numerical weather forecast data of the wind power plant in the subsequent period to obtain at least one cluster;
the prediction unit is used for inputting numerical weather forecast data of the wind power plants in each cluster in a subsequent period into a corresponding pre-trained power prediction model to obtain predicted power of each wind power plant in the subsequent period;
the calculation unit is used for obtaining the predicted power of the wind power plant cluster in the subsequent period based on the predicted power of each wind power plant in the subsequent period;
The first optimizing unit is used for carrying out rolling optimization on the power of each period of the wind power plant cluster in a subsequent period by taking the scheduling plan constraint of the wind power plant cluster, the output limit constraint of the wind power plant cluster and the output climbing constraint of the wind power plant cluster as constraint conditions to obtain the predicted power of the wind power plant cluster in each period, wherein the output climbing constraint is used for representing the change rate constraint of the power in a time scale, and the scheduling plan constraint is used for representing the generation power constraint in the time scale;
The second optimizing unit is used for performing rolling optimization on the power of each sub-period of each cluster of wind power plants in each period based on the predicted power of each cluster of wind power plants in each period and taking scheduling plan constraint of each cluster of wind power plants, output limit constraint of each cluster of wind power plants and output climbing constraint of each cluster of wind power plants as constraint conditions to obtain the predicted power of each sub-period of each cluster of wind power plants;
The third optimizing unit is used for carrying out rolling optimization on the power of each wind power station in a plurality of unit time periods of each sub-period by taking the scheduling plan constraint of each wind power station, the output limit constraint of each wind power station and the output climbing constraint of each wind power station as constraint conditions based on the predicted power of each cluster wind power station in each sub-period to obtain the predicted power of each wind power station in each unit time period;
The control unit is used for adjusting the output mode of each wind power plant in each unit time based on the predicted power of each wind power plant in each unit time and the power scheduling index of each wind power plant in each unit time.
In a third aspect, the present invention provides an electronic device, including a memory, a processor and a transceiver, which are sequentially communicatively connected, where the memory is configured to store a computer program, the transceiver is configured to send and receive a message, and the processor is configured to read the computer program, and execute the wind farm cluster power generation control method according to the first aspect or any one of the possible designs of the first aspect.
In a fourth aspect, the present invention provides a computer readable storage medium having instructions stored thereon which, when run on a computer, perform the wind farm cluster generation control method of the first aspect or any of the possible designs of the first aspect.
In a fifth aspect, the invention provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the wind farm cluster generation control method according to the first aspect or any of the possible designs of the first aspect.
The beneficial effects are that:
According to the wind power plant cluster power generation control method, the device, the electronic equipment and the storage medium, in the wind power plant cluster power generation control process, the wind power plants can be clustered according to the numerical weather forecast data of each wind power plant in the subsequent period, so that the predicted power of the wind power plant cluster in the subsequent period can be accurately predicted, then the power of the wind power plant in each period is subjected to rolling optimization through dynamic optimization, the power of each sub-period of each cluster wind power plant in each period is subjected to rolling optimization, the power of each wind power plant in a plurality of unit time lengths of each sub-period is subjected to rolling optimization, the prediction time and the spatial scale are gradually reduced in each layer of optimization, the control accuracy can be improved, the influence of prediction errors is reduced, the accurate prediction and control of the wind power plant cluster are further realized, the power fluctuation of wind power climbing events can be effectively flattened, the stability of the wind power plant cluster system is improved, and the wind power plant cluster system is convenient to be practically applied and popularized.
Drawings
FIG. 1 is a flowchart of a wind farm cluster power generation control method provided by an embodiment of the application;
FIG. 2 is a schematic block diagram of a wind farm cluster power generation control device according to an embodiment of the present application;
fig. 3 is a schematic block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly described below with reference to the accompanying drawings and the description of the embodiments or the prior art, and it is obvious that the following description of the structure of the drawings is only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art. It should be noted that the description of these examples is for aiding in understanding the present invention, but is not intended to limit the present invention.
It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments of the present invention.
It should be understood that for the term "and/or" that may appear herein, it is merely one association relationship that describes an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a alone, B alone, and both a and B; for the term "/and" that may appear herein, which is descriptive of another associative object relationship, it means that there may be two relationships, e.g., a/and B, it may be expressed that: a alone, a alone and B alone; in addition, for the character "/" that may appear herein, it is generally indicated that the context associated object is an "or" relationship.
In order to improve stability of a wind farm cluster, the embodiment of the application provides a wind farm cluster power generation control method, a device, electronic equipment and a storage medium.
The wind farm cluster power generation control method provided by the embodiment of the application can be used for a background management terminal or server for carrying out power generation control on the wind farm cluster. It will be appreciated that the execution body is not to be construed as limiting the embodiments of the application.
The method for controlling the cluster power generation of the wind farm provided by the embodiment of the application is described in detail below.
As shown in fig. 1, a flowchart of a wind farm cluster power generation control method according to the first aspect of the present application may include, but is not limited to, the following steps S101 to S108.
S101, acquiring numerical weather forecast data of each wind power plant in the wind power plant cluster in a subsequent period.
The numerical weather forecast data at least comprises wind speed data, and can also comprise temperature data, air pressure data, wind direction data and the like. In the embodiment of the application, the numerical weather forecast data comprises wind speed data, temperature data, air pressure data and wind direction data. The numerical weather forecast data may be obtained from a weather forecast system.
The subsequent time period may be one or more time periods after the current point in time. For example, in one embodiment, numerical weather forecast data for each wind farm in a cluster of wind farms may be obtained for 24 hours in the future, and the 24 hours in the future may be divided into 16 time periods every 90 minutes.
S102, carrying out cluster analysis on each wind power plant in the wind power plant cluster based on numerical weather forecast data of the wind power plant in a subsequent period to obtain at least one cluster.
The cluster analysis may be performed on each wind farm in the wind farm cluster, but is not limited to, a K-means clustering algorithm, a sliding window-based clustering algorithm, or a density-based clustering algorithm, which is not specifically limited in the embodiment of the present application.
S103, inputting numerical weather forecast data of the wind power plants in each cluster in a subsequent period into a corresponding pre-trained power prediction model to obtain predicted power of each wind power plant in the subsequent period.
In the embodiment of the application, a plurality of prediction models for power prediction are trained in advance, and when training is performed, historical numerical weather forecast data and historical power generation data of each wind power plant in a wind power plant cluster can be obtained as training samples, clustering analysis is performed on each wind power plant based on the historical numerical weather forecast data to obtain a plurality of clusters, then the historical numerical weather forecast data of each cluster of wind power plants is used as model input, the historical power generation data of the corresponding wind power plant is used as model output to perform training, and the power prediction model corresponding to each cluster is obtained.
After cluster analysis is performed on each wind farm in the wind farm cluster based on the numerical weather forecast data of the wind farm in the subsequent period to obtain at least one cluster, the numerical weather forecast data of the wind farm in each cluster in the subsequent period can be input into a corresponding and pre-trained power prediction model to obtain the predicted power of each wind farm in the subsequent period.
The power prediction model may be, but is not limited to, a Long Short-Term Memory (LSTM) model, a convolutional neural network (Convolutional Neural Networks, CNN) model, which is not specifically limited in the embodiment of the present application.
And S104, obtaining the predicted power of the wind power plant cluster in the subsequent period based on the predicted power of each wind power plant in the subsequent period.
Specifically, the predicted power of each wind farm in the subsequent period can be added to obtain the predicted power of the wind farm cluster in the subsequent period.
S105, rolling and optimizing the power of the wind power plant cluster in each period of a subsequent period by taking the scheduling plan constraint of the wind power plant cluster, the output limit constraint of the wind power plant cluster and the output climbing constraint of the wind power plant cluster as constraint conditions to obtain the predicted power of the wind power plant cluster in each period.
The output climbing constraint is used for representing the change rate constraint of power in a time scale, and the scheduling plan constraint is used for representing the generation power constraint in the time scale.
In an embodiment of the present application, the scheduling constraint of a wind farm cluster may be expressed as:
the output limit constraint of a wind farm cluster can be expressed as:
the output ramp constraint of a wind farm cluster can be expressed as:
Wherein J clu represents the sum of the predicted powers of the wind farm clusters, T represents the number of cycles, m represents the number of clusters corresponding to the wind farm clusters, n represents the number of wind farm clusters, Representing the power value of an ith wind farm in a wind farm cluster in one period,Representing a power predicted value of an ith wind power plant in a wind power plant cluster in one period; Representing a minimum output threshold for an ith wind farm in the wind farm cluster, The method comprises the steps of representing the installed capacity of an ith wind farm in a wind farm cluster, P j+1 representing the total power of the wind farm cluster in a j+1th period, P j representing the total power of the wind farm cluster in the j period, beta max representing the maximum value of power slope in the period, T j+1-Tj representing the time length of one period, beta representing the minimum value of power slope in the period, and gamma and lambda representing preset power thresholds.
The time length of one cycle may be set according to the actual situation, and may be 30 minutes, 15 minutes, 10 minutes, or the like, for example. In the embodiment of the application, the time length of one period is 15 minutes.
In the embodiment of the present application, the rolling optimization may use the existing rolling optimization algorithm, and detailed description is not given in the embodiment of the present application.
S106, rolling optimization is carried out on the power of each sub-period of each cluster wind power plant in each period based on the predicted power of each cluster wind power plant in each period and taking scheduling plan constraint of each cluster wind power plant, output limit constraint of each cluster wind power plant and output climbing constraint of each cluster wind power plant as constraint conditions, so that the predicted power of each cluster wind power plant in each sub-period is obtained.
In the embodiment of the application, the scheduling plan constraint of any cluster of wind farms can be expressed as follows:
The output limit constraints of any cluster of wind farms can be expressed as:
the output ramp constraint of any cluster of wind farms can be expressed as:
wherein, Representing the power value of the ith wind farm in any cluster in one sub-period,Representing the power planning value of the ith wind farm in any cluster in one period, n' representing the number of wind farms in any cluster,Representing the minimum output threshold of the ith wind farm in any cluster,Representing a power predicted value of an ith wind power plant in any cluster in one sub-period, P i N representing an installed capacity of the ith wind power plant in any cluster in one sub-period, P 'j+1 representing total power of the wind power plant in any cluster in a j+1th period, P j' representing total power of the wind power plant in any cluster in the j-th period, |T 'j+1-Tj' | representing a time length of one sub-period, β 'max representing a maximum value of power slope in the sub-period, β' representing a minimum value of power slope in the sub-period, and λ 'and γ' both representing preset power thresholds.
In the embodiment of the application, the time length of one sub-period can be set according to the actual situation, and the time length of one period is a positive integer of the time length of one sub-period. For example, the time length of one cycle is 15 minutes, and the time length of one sub-cycle may be 5 minutes, 3 minutes, or the like. In the embodiment of the application, the time length of one sub-period is 5 minutes.
S107, rolling optimization is carried out on the power of each wind power station in a plurality of unit time periods of each sub-period based on the predicted power of each cluster wind power station in each sub-period by taking the scheduling plan constraint of each wind power station, the output limit constraint of each wind power station and the output climbing constraint of each wind power station as constraint conditions, so that the predicted power of each wind power station in each unit time period is obtained.
Wherein, the scheduling plan constraint of the wind farm can be expressed as:
the output limit constraints of a wind farm can be expressed as:
The output ramp constraint of a wind farm can be expressed as:
wherein, Indicating the power value of the wind farm at a unit time,A power schedule value representing a wind farm for a unit length of time,Representing a minimum output threshold for the wind farm,Representing a predicted value of the power of the wind power plant in a unit time period, P i′N representing the installed capacity of the wind power plant in a unit time period, P 'j+1 representing the total power of the wind power plant in a j+1th unit time period, P' representing the total power of the wind power plant in a j unit time period, T "j+1-Tj" represents a time length of one unit time period, β "max represents a maximum value of the power slope in the unit time period, β" represents a minimum value of the power slope in the unit time period, and γ "and λ" each represent a power threshold.
The time length of the unit time length may be set according to the actual situation, for example, the time length of the unit time length may be 1 minute, 30 seconds, or the like. The length of time per unit time in the embodiment of the application is 1 minute.
S108, adjusting the output mode of each wind power plant in each unit time based on the predicted power of each wind power plant in each unit time and the power scheduling index of each wind power plant in each unit time.
After the predicted power of each wind power plant in each unit time period is obtained, the predicted power of each wind power plant in each unit time period can be compared with the power scheduling index of each wind power plant in each unit time period, and if the predicted power of the wind power plant in a certain unit time period is too high, the output model of the wind power plant in the unit time period is adjusted to reduce the output power of the wind power plant in the unit time period, so that the wind power climbing event is avoided.
According to the wind power plant cluster power generation control method, numerical weather forecast data of each wind power plant in the wind power plant cluster in a subsequent period are obtained; performing cluster analysis on each wind power plant in the wind power plant cluster based on the numerical weather forecast data of the wind power plant in the subsequent period to obtain at least one cluster; inputting numerical weather forecast data of wind power plants in each cluster in a subsequent period into a corresponding pre-trained power prediction model to obtain predicted power of each wind power plant in the subsequent period; obtaining predicted power of a wind power plant cluster in a subsequent period based on predicted power of each wind power plant in the subsequent period; rolling and optimizing the power of the wind power plant cluster in each period of a subsequent period by taking the scheduling plan constraint of the wind power plant cluster, the output limit constraint of the wind power plant cluster and the output climbing constraint of the wind power plant cluster as constraint conditions to obtain the predicted power of the wind power plant cluster in each period, wherein the output climbing constraint is used for representing the change rate constraint of the power in one time scale, and the scheduling plan constraint is used for representing the power generation constraint in one time scale; based on the predicted power of the wind power plant clusters in each period, rolling optimization is carried out on the power of each sub-period of each cluster wind power plant in each period by taking the scheduling plan constraint of each cluster wind power plant, the output limit constraint of each cluster wind power plant and the output climbing constraint of each cluster wind power plant as constraint conditions to obtain the predicted power of each cluster wind power plant in each sub-period; based on the predicted power of each cluster of wind power plants in each sub-period, rolling optimization is carried out on the power of each wind power plant in a plurality of unit time periods of each sub-period by taking the scheduling plan constraint of each wind power plant, the output limit constraint of each wind power plant and the output climbing constraint of each wind power plant as constraint conditions, so that the predicted power of each wind power plant in each unit time period is obtained; and adjusting the output mode of each wind power plant in each unit time based on the predicted power of each wind power plant in each unit time and the power scheduling index of each wind power plant in each unit time. In this way, in the process of controlling the power generation of the wind power plant clusters, the wind power plants can be clustered according to the numerical weather forecast data of each wind power plant in the subsequent period, so that the predicted power of the wind power plant clusters in the subsequent period can be accurately predicted, then the power of the wind power plants in each period is subjected to rolling optimization through dynamic optimization, the power of each cluster of wind power plants in each sub-period is subjected to rolling optimization, the power of each wind power plant in each sub-period in a plurality of unit time periods is subjected to rolling optimization, and the prediction time and space scale are gradually reduced in each layer of optimization, so that the control accuracy can be improved, the influence of prediction errors is reduced, the accurate prediction and control over the wind power plants can be further realized, the power fluctuation of wind power climbing events can be effectively restrained, the stability of the wind power plant cluster system is improved, and the wind power plant cluster system is convenient to apply and popularize practically.
Referring to fig. 2, a second aspect of the embodiment of the present application provides a wind farm cluster power generation control device, where the wind farm cluster power generation control device includes:
the acquisition unit is used for acquiring numerical weather forecast data of each wind power plant in the wind power plant cluster in a subsequent period;
The clustering unit is used for carrying out clustering analysis on each wind power plant in the wind power plant cluster based on the numerical weather forecast data of the wind power plant in the subsequent period to obtain at least one cluster;
the prediction unit is used for inputting numerical weather forecast data of the wind power plants in each cluster in a subsequent period into a corresponding pre-trained power prediction model to obtain predicted power of each wind power plant in the subsequent period;
the calculation unit is used for obtaining the predicted power of the wind power plant cluster in the subsequent period based on the predicted power of each wind power plant in the subsequent period;
The first optimizing unit is used for carrying out rolling optimization on the power of each period of the wind power plant cluster in a subsequent period by taking the scheduling plan constraint of the wind power plant cluster, the output limit constraint of the wind power plant cluster and the output climbing constraint of the wind power plant cluster as constraint conditions to obtain the predicted power of the wind power plant cluster in each period, wherein the output climbing constraint is used for representing the change rate constraint of the power in a time scale, and the scheduling plan constraint is used for representing the generation power constraint in the time scale;
The second optimizing unit is used for performing rolling optimization on the power of each sub-period of each cluster of wind power plants in each period based on the predicted power of each cluster of wind power plants in each period and taking scheduling plan constraint of each cluster of wind power plants, output limit constraint of each cluster of wind power plants and output climbing constraint of each cluster of wind power plants as constraint conditions to obtain the predicted power of each sub-period of each cluster of wind power plants;
The third optimizing unit is used for carrying out rolling optimization on the power of each wind power station in a plurality of unit time periods of each sub-period by taking the scheduling plan constraint of each wind power station, the output limit constraint of each wind power station and the output climbing constraint of each wind power station as constraint conditions based on the predicted power of each cluster wind power station in each sub-period to obtain the predicted power of each wind power station in each unit time period;
The control unit is used for adjusting the output mode of each wind power plant in each unit time based on the predicted power of each wind power plant in each unit time and the power scheduling index of each wind power plant in each unit time.
The working process, working details and technical effects of the wind farm cluster power generation control device provided in the second aspect of the present embodiment may be referred to in the first aspect of the present embodiment, and will not be described herein again.
As shown in fig. 3, a third aspect of the embodiment of the present application provides an electronic device, which includes a memory, a processor and a transceiver that are sequentially communicatively connected, where the memory is configured to store a computer program, the transceiver is configured to send and receive a message, and the processor is configured to read the computer program, and execute the wind farm cluster power generation control method according to the first aspect of the embodiment.
By way of specific example, the Memory may include, but is not limited to, random Access Memory (RAM), read Only Memory (ROM), flash Memory (Flash Memory), first-in-first-out Memory (FIFO), and/or first-in-last-out Memory (FILO), etc.; the processor may not be limited to a microprocessor of the STM32F105 family, ARM (Advanced RISC Machines), X86 or other architecture processor or a processor of an integrated NPU (neural-network processing units); the transceiver may be, but is not limited to, a WiFi (wireless fidelity) wireless transceiver, a bluetooth wireless transceiver, a General Packet Radio Service (GPRS) wireless transceiver, a ZigBee (low power local area network protocol based on the ieee802.15.4 standard) wireless transceiver, a 3G transceiver, a 4G transceiver, and/or a 5G transceiver, etc.
A fourth aspect of the present embodiment provides a computer readable storage medium storing instructions comprising the method for controlling power generation of a wind farm cluster according to the first aspect of the present embodiment, i.e. the computer readable storage medium has instructions stored thereon, which when run on a computer, perform the method for controlling power generation of a wind farm cluster according to the first aspect. The computer readable storage medium refers to a carrier for storing data, and may include, but is not limited to, a floppy disk, an optical disk, a hard disk, a flash Memory, and/or a Memory Stick (Memory Stick), etc., where the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable devices.
A fifth aspect of the present embodiment provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the wind farm cluster power generation control method according to the first aspect of the embodiment, wherein the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus.
It should be understood that specific details are provided in the following description to provide a thorough understanding of the example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. For example, a system may be shown in block diagrams in order to avoid obscuring the examples with unnecessary detail. In other instances, well-known processes, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the example embodiments.
Finally, it should be noted that: the foregoing description is only of the preferred embodiments of the invention and is not intended to limit the scope of the invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The cluster power generation control method for the wind farm is characterized by comprising the following steps of:
acquiring numerical weather forecast data of each wind power plant in a wind power plant cluster in a subsequent period;
performing cluster analysis on each wind power plant in the wind power plant cluster based on the numerical weather forecast data of the wind power plant in the subsequent period to obtain at least one cluster;
Inputting numerical weather forecast data of wind power plants in each cluster in a subsequent period into a corresponding pre-trained power prediction model to obtain predicted power of each wind power plant in the subsequent period;
Obtaining predicted power of a wind power plant cluster in a subsequent period based on predicted power of each wind power plant in the subsequent period;
Rolling and optimizing the power of the wind power plant cluster in each period of a subsequent period by taking the scheduling plan constraint of the wind power plant cluster, the output limit constraint of the wind power plant cluster and the output climbing constraint of the wind power plant cluster as constraint conditions to obtain the predicted power of the wind power plant cluster in each period, wherein the output climbing constraint is used for representing the change rate constraint of the power in one time scale, and the scheduling plan constraint is used for representing the power generation constraint in one time scale;
Based on the predicted power of the wind power plant clusters in each period, rolling optimization is carried out on the power of each sub-period of each cluster wind power plant in each period by taking the scheduling plan constraint of each cluster wind power plant, the output limit constraint of each cluster wind power plant and the output climbing constraint of each cluster wind power plant as constraint conditions to obtain the predicted power of each cluster wind power plant in each sub-period;
Based on the predicted power of each cluster of wind power plants in each sub-period, rolling optimization is carried out on the power of each wind power plant in a plurality of unit time periods of each sub-period by taking the scheduling plan constraint of each wind power plant, the output limit constraint of each wind power plant and the output climbing constraint of each wind power plant as constraint conditions, so that the predicted power of each wind power plant in each unit time period is obtained;
and adjusting the output mode of each wind power plant in each unit time based on the predicted power of each wind power plant in each unit time and the power scheduling index of each wind power plant in each unit time.
2. The wind farm cluster power generation control method of claim 1, wherein the scheduling plan constraints of the wind farm cluster are:
the output limit constraint of the wind farm cluster is:
the output climbing constraint of the wind power plant cluster is as follows:
Wherein J clu represents the sum of the predicted powers of the wind farm clusters, T represents the number of cycles, m represents the number of clusters corresponding to the wind farm clusters, n represents the number of wind farm clusters, Representing the power value of an ith wind farm in a wind farm cluster in one period,Representing a power predicted value of an ith wind power plant in a wind power plant cluster in one period; Representing a minimum output threshold for an ith wind farm in the wind farm cluster, Representing the installed capacity of the ith wind farm in the wind farm cluster, P j+1 representing the total power of the wind farm cluster in the j+1th cycle, P j representing the total power of the wind farm cluster in the j-th cycle, β max representing the maximum power slope in the cycle, |t j+1-Tj | representing the time length of one cycle, β representing the minimum power slope in the cycle, and γ and λ both representing the power threshold.
3. The method for controlling cluster power generation of a wind farm according to claim 1, wherein the scheduling plan constraint of any cluster wind farm is:
the output limit constraint of any cluster of wind farms is:
The output climbing constraint of any cluster of wind power plants is as follows:
wherein, Representing the power value of the ith wind farm in any cluster in one sub-period,Representing the power planning value of the ith wind farm in any cluster in one period, n' representing the number of wind farms in any cluster,Representing the minimum output threshold of the ith wind farm in any cluster,Representing a predicted power value of an ith wind farm in any cluster in one sub-period, P i N representing an installed capacity of the ith wind farm in any cluster in one sub-period, P ' j+1 representing a total power of the wind farms in any cluster in a j+1th period, P ' j representing the total power of the wind farms in any cluster in the j-th period, |T ' j+1-T′j | representing a time length of one sub-period, β ' max representing a maximum power slope value in the sub-period, β ' representing a minimum power slope value in the sub-period, and λ ' and γ ' both representing power thresholds.
4. The wind farm cluster power generation control method of claim 1, wherein the scheduling plan constraints of the wind farm are:
the output limit constraints of the wind farm are:
the output climbing constraint of the wind power plant is as follows:
wherein, Indicating the power value of the wind farm at a unit time,A power schedule value representing a wind farm for a unit length of time,Representing a minimum output threshold for the wind farm,Representing a predicted value of the power of the wind power plant in a unit time period, P i′N representing the installed capacity of the wind power plant in a unit time period, P 'j+1 representing the total power of the wind power plant in a j+1th unit time period, P' representing the total power of the wind power plant in a j unit time period, T "j+1-T″j" represents a time length of one unit time period, β "max represents a maximum value of the power slope in the unit time period, β" represents a minimum value of the power slope in the unit time period, and γ "and λ" each represent a power threshold.
5. The wind farm cluster power generation control method of claim 1, wherein the numerical weather forecast data includes wind speed data.
6. The wind farm cluster power generation control method of claim 5, wherein the numerical weather forecast data further comprises temperature data, barometric pressure data, and/or wind direction data.
7. The method for controlling cluster power generation of a wind farm according to claim 1, wherein the power prediction model is an LSTM model.
8. A wind farm cluster power generation control device, comprising:
the acquisition unit is used for acquiring numerical weather forecast data of each wind power plant in the wind power plant cluster in a subsequent period;
The clustering unit is used for carrying out clustering analysis on each wind power plant in the wind power plant cluster based on the numerical weather forecast data of the wind power plant in the subsequent period to obtain at least one cluster;
the prediction unit is used for inputting numerical weather forecast data of the wind power plants in each cluster in a subsequent period into a corresponding pre-trained power prediction model to obtain predicted power of each wind power plant in the subsequent period;
the calculation unit is used for obtaining the predicted power of the wind power plant cluster in the subsequent period based on the predicted power of each wind power plant in the subsequent period;
The first optimizing unit is used for carrying out rolling optimization on the power of each period of the wind power plant cluster in a subsequent period by taking the scheduling plan constraint of the wind power plant cluster, the output limit constraint of the wind power plant cluster and the output climbing constraint of the wind power plant cluster as constraint conditions to obtain the predicted power of the wind power plant cluster in each period, wherein the output climbing constraint is used for representing the change rate constraint of the power in a time scale, and the scheduling plan constraint is used for representing the generation power constraint in the time scale;
The second optimizing unit is used for performing rolling optimization on the power of each sub-period of each cluster of wind power plants in each period based on the predicted power of each cluster of wind power plants in each period and taking scheduling plan constraint of each cluster of wind power plants, output limit constraint of each cluster of wind power plants and output climbing constraint of each cluster of wind power plants as constraint conditions to obtain the predicted power of each sub-period of each cluster of wind power plants;
The third optimizing unit is used for carrying out rolling optimization on the power of each wind power station in a plurality of unit time periods of each sub-period by taking the scheduling plan constraint of each wind power station, the output limit constraint of each wind power station and the output climbing constraint of each wind power station as constraint conditions based on the predicted power of each cluster wind power station in each sub-period to obtain the predicted power of each wind power station in each unit time period;
The control unit is used for adjusting the output mode of each wind power plant in each unit time based on the predicted power of each wind power plant in each unit time and the power scheduling index of each wind power plant in each unit time.
9. An electronic device, comprising a memory, a processor and a transceiver, which are in communication connection in sequence, wherein the memory is used for storing a computer program, the transceiver is used for receiving and transmitting a message, and the processor is used for reading the computer program and executing the wind farm cluster power generation control method according to any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon instructions which, when run on a computer, perform the wind farm cluster power generation control method according to any of claims 1-7.
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| CN108898251A (en) * | 2018-06-29 | 2018-11-27 | 上海电力学院 | Consider the marine wind electric field power forecasting method of meteorological similitude and power swing |
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