Output prediction method and system of distributed new energy power station, equipment and medium

Abstract

The invention discloses an output prediction method and system for a distributed new energy power station, equipment and a medium, and the method comprises the steps: calculating a fluctuation characteristic value based on the output data of each node in a plurality of historical time sections after the fluctuation characteristic value is set, and judging whether the generalized fluctuation exists or not according to the calculation result of the fluctuation characteristic value of the plurality of historical time sections, and if yes, predicting the fluctuation center position in the to-be-predicted time section and the fluctuation characteristic value of each node based on the fluctuation characteristic value of each node in a plurality of historical time sections, and finally predicting the actual output values of all nodes in the fluctuation region based on the predicted fluctuation center position and the fluctuation characteristic value of each node. According to the method, space-time coupling characteristics and generalized volatility analysis between the distributed new energy power stations are combined, the increased or decreased output value of the output of each node under the influence of volatility at the prediction moment is compensated, and the output prediction accuracy of the distributed new energy power stations under the influence of group randomness factors is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of output prediction of distributed renewable energy power stations, and in particular, to an output prediction method and system for distributed renewable energy power stations, electronic equipment, and a computer-readable storage medium. Background Art

[0002] Distributed renewable energy (including distributed photovoltaic and distributed / distributed wind power) power stations are characterized by huge number and fixed geographical location (adjacent location). These distributed renewable energy power stations form a giant network across a huge geographical space. Each renewable energy power station has a metering device, and each renewable energy power station can be regarded as a sensor node. Its sensor data is the output data of the renewable energy power station. Therefore, a distributed renewable energy system across a huge geographical space can be regarded as a giant sensor network.

[0003] Affected by external factors, the output of new energy power stations has great randomness. Among them, the randomness of the output of distributed new energy power stations can be divided into individual randomness and group randomness. Taking photovoltaics as an example, a large bird lands on a photovoltaic module, which is an individual randomness that only affects the output of the photovoltaic power station and has no effect on adjacent photovoltaic power stations; while an unpredictable cloud brings group randomness. The aggregation, deformation, movement, and dissipation of this cloud have an impact on a certain number of photovoltaic power stations. The impact of this group randomness on the output of photovoltaic nodes can also be regarded as a fluctuation, running on related distributed photovoltaic nodes. This individual randomness is a random change caused by a specific event of an individual power station, similar to the independent behavior of particles, and can be defined as generalized particle properties, while this group randomness is a random change caused by a large-scale event or collective event, similar to the diffusion and transmission of waves, and can be defined as generalized volatility. The influence of individual randomness is limited, and only acts on individual new energy power stations. The changes of each individual event are relatively independent and have no direct correlation with the changes of other power stations. Moreover, the changes occur at the micro level, and the output of the overall distributed new energy system is less affected. However, group randomness will affect multiple new energy power stations or even the entire system, and has greater volatility. The changes have a certain correlation between different new energy power stations, and the influence range is wider. Moreover, the changes occur at the macro level, and the output of the overall system is significantly affected. The characteristic of volatility is that in a time span, the fluctuation will propagate far away. When the fluctuation runs in the sensor network composed of distributed new energy power stations, a strong spatiotemporal coupling is formed. The changes of a certain node at a certain moment can be propagated to another node at the next moment to form a corresponding change. This spatiotemporal coupling characteristic can be used as an important influencing factor for the output prediction of distributed new energy power stations. However, the current research does not consider the influence of spatiotemporal coupling characteristics on the output prediction of distributed new energy power stations, which leads to poor accuracy of output prediction of new energy power stations. Summary of the invention

[0004] The present invention provides a method and system for predicting the output of a distributed new energy power station, an electronic device, and a computer-readable storage medium, which can improve the output prediction accuracy of the distributed new energy power station under the influence of group random factors.

[0005] According to one aspect of the present invention, a method for predicting the output of a distributed renewable energy power station is provided, comprising the following contents:

[0006] Construct the geographical topology between distributed renewable energy power stations;

[0007] Set the fluctuation characteristic value;

[0008] Calculate the fluctuation characteristic value based on the output data of each node in multiple time sections, and judge whether there is generalized volatility according to the calculation results of the fluctuation characteristic values ​​of multiple time sections. If there is generalized volatility, obtain the fluctuation center position and the fluctuation characteristic value of each node in multiple time sections;

[0009] Based on the fluctuation center position and the fluctuation characteristic value of each node in multiple time sections, the fluctuation center position and the fluctuation characteristic value of each node in the next time section are predicted;

[0010] Based on the fluctuation center position in the next time section and the fluctuation characteristic value of each node, the actual output value of all nodes in the fluctuation area is predicted.

[0011] Furthermore, the fluctuation characteristic value is the power fluctuation rate, wherein the power fluctuation rate = (baseline output difference - actual output difference) / baseline mean, the baseline output difference is the difference between the baseline value at the next moment and the baseline value at the previous moment, the actual output difference is the difference between the output value at the next moment and the output value at the previous moment, and the baseline mean is the average value of the baseline value at the next moment and the baseline value at the previous moment.

[0012] Furthermore, the process of judging whether generalized volatility exists according to the calculation results of the fluctuation characteristic values ​​of multiple time sections includes the following contents:

[0013] In a certain time section, if an extreme value region appears in the power fluctuation rate of each node in a certain area of ​​the topological structure, and the power fluctuation rate gradually decreases outside the extreme value region, the extreme value region is regarded as the fluctuation center. In the subsequent time sections, if the distance between the new fluctuation center and the old fluctuation center does not exceed the preset threshold, the power fluctuation rate of the nodes near the fluctuation center migration line is higher than that of other areas, and the power fluctuation rate sequence change of a single node is negatively correlated with the change in the distance between the node and the fluctuation center, it is determined that generalized volatility exists.

[0014] Furthermore, the process of predicting the actual output values ​​of all nodes in the fluctuation area based on the fluctuation center position and the fluctuation characteristic value of each node in the next time section includes the following contents:

[0015] For each node, a multivariate regression model is constructed with the actual output of the node as the variable and the output baseline and power fluctuation rate of the node as the influencing factors. The actual output data, output baseline and power fluctuation rate of the node in the known time section are substituted into the multivariate regression model to calculate the coefficients of the multivariate regression model. The predicted power fluctuation rate and output baseline of the node in the next time section are substituted into the multivariate regression model to calculate the actual output value of the node in the next time section. The above process is repeated to obtain the actual output values ​​of all nodes in the fluctuation area.

[0016] Furthermore, the fluctuation characteristic values ​​are the node influence ratio and the node correlation ratio, wherein the node influence ratio is the ratio of the total node influence of a certain node to the average of the total node influence of all nodes, the total node influence of each node is the sum of the absolute values ​​of the standardized covariance sequence of the output value of the node and the output values ​​of all other nodes in multiple time sections, and the node correlation ratio is the ratio of the number of nodes in the standardized covariance sequence whose absolute values ​​are greater than a preset correlation threshold to (N-1), where N is the number of nodes.

[0017] Furthermore, the process of judging whether generalized volatility exists according to the calculation results of the fluctuation characteristic values ​​of multiple time sections includes the following contents:

[0018] The node with the highest node correlation ratio in multiple time sections is taken as the fluctuation center. If the movement of the new and old fluctuation centers meets the continuity requirements, it is determined that generalized volatility exists.

[0019] Furthermore, the process of predicting the fluctuation center position and the fluctuation characteristic value of each node in the next time section based on the fluctuation center position and the fluctuation characteristic value of each node in multiple time sections includes the following contents:

[0020] Based on the standardized covariance sequence of each node in the most recent multiple time sections, the standardized covariance sequence of each node in the next period of time is predicted, and the predicted value of the fluctuation characteristic value of each node is calculated according to the predicted standardized covariance sequence, and the new fluctuation center is determined.

[0021] In addition, the present invention also provides an output prediction system for a distributed new energy power station, comprising:

[0022] Topology building module, used to build the geographical topology between distributed renewable energy power stations;

[0023] A fluctuation characteristic value setting module, used for setting the fluctuation characteristic value;

[0024] A generalized volatility judgment module is used to calculate the fluctuation characteristic value based on the output data of each node in multiple time sections, and judge whether there is generalized volatility according to the calculation results of the fluctuation characteristic values ​​of multiple time sections. If generalized volatility exists, the fluctuation center position and the fluctuation characteristic value of each node in multiple time sections are obtained;

[0025] A fluctuation center prediction module is used to predict the fluctuation center position and the fluctuation characteristic value of each node in the next time section based on the fluctuation center position and the fluctuation characteristic value of each node in multiple time sections;

[0026] The node output prediction module is used to predict the actual output values ​​of all nodes in the fluctuation area based on the fluctuation center position and the fluctuation characteristic value of each node in the next time section.

[0027] In addition, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the above method by calling the computer program stored in the memory.

[0028] In addition, the present invention also provides a computer-readable storage medium for storing a computer program for output prediction of a distributed renewable energy power station, wherein the computer program executes the steps of the method described above when running on a computer.

[0029] The present invention has the following beneficial effects:

[0030] The output prediction method of the distributed new energy power station of the present invention calculates the fluctuation characteristic value based on the output data of each node in multiple historical time sections after setting the fluctuation characteristic value, and judges whether there is generalized volatility according to the calculation results of the fluctuation characteristic values ​​of multiple historical time sections. If so, the fluctuation center position and the fluctuation characteristic value of each node in the predicted time section are predicted based on the fluctuation characteristic value of each node in the multiple historical time sections. Finally, the actual output values ​​of all nodes in the fluctuation area are predicted based on the predicted fluctuation center position and the fluctuation characteristic value of each node. It combines the spatiotemporal coupling characteristics between distributed new energy power stations and the generalized volatility analysis, and compensates for the output value of each node that is increased or decreased due to the influence of volatility at the prediction time, thereby improving the output prediction accuracy of the distributed new energy power station under the influence of group random factors.

[0031] In addition, the output prediction system of the distributed new energy power station of the present invention also has the above advantages.

[0032] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0034] Figure 1 It is a flow chart of the output prediction method of the distributed new energy power station in the first embodiment of the present application.

[0035] Figure 2 It is a schematic diagram of the module structure of an output prediction system of a distributed new energy power station according to another embodiment of the present application. DETAILED DESCRIPTION

[0036] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0037] Embodiment 1

[0038] Reference Figure 1 Embodiment 1 of the present application provides a method for predicting the output of a distributed renewable energy power station, including the following contents:

[0039] Step S1: constructing a geographical topology structure between distributed new energy power stations;

[0040] Step S2: setting the fluctuation characteristic value;

[0041] Step S3: Calculate the fluctuation characteristic value based on the output data of each node in multiple time sections, and determine whether there is generalized volatility according to the calculation results of the fluctuation characteristic values ​​of multiple time sections. If there is generalized volatility, obtain the fluctuation center position and the fluctuation characteristic value of each node in the multiple time sections;

[0042] Step S4: based on the fluctuation center positions and fluctuation characteristic values ​​of each node in multiple time sections, predict the fluctuation center position and fluctuation characteristic values ​​of each node in the next time section;

[0043] Step S5: Based on the fluctuation center position in the next time section and the fluctuation characteristic value of each node, the actual output value of all nodes in the fluctuation area is predicted.

[0044] It can be understood that the output prediction method of the distributed new energy power station in this embodiment, after setting the fluctuation characteristic value, calculates the fluctuation characteristic value based on the output data of each node in multiple historical time sections, and judges whether there is generalized volatility based on the calculation results of the fluctuation characteristic values ​​of multiple historical time sections. If so, the fluctuation center position and the fluctuation characteristic value of each node in the predicted time section are predicted based on the fluctuation characteristic value of each node in multiple historical time sections. Finally, the actual output values ​​of all nodes in the fluctuation area are predicted based on the predicted fluctuation center position and the fluctuation characteristic value of each node. It combines the spatiotemporal coupling characteristics and generalized volatility analysis between distributed new energy power stations, and compensates for the output value of each node that is increased or decreased due to the influence of volatility at the prediction time, thereby improving the output prediction accuracy of distributed new energy power stations under the influence of group random factors.

[0045] It can be understood that in step S1, a topological structure diagram of the new energy power station system is constructed based on the geographical location relationship between each distributed new energy power station, so as to provide an analysis basis for the subsequent generalized volatility analysis and volatility center prediction. In addition, the nodes referred to below are new energy power stations.

[0046] It can be understood that in step S2, optionally, the fluctuation characteristic value is the power fluctuation rate, wherein the power fluctuation rate = (baseline output difference - actual output difference) / baseline mean, the baseline output difference is the difference between the baseline value at the next moment and the baseline value at the previous moment, the actual output difference is the difference between the output value at the next moment and the output value at the previous moment, and the baseline mean is the average value of the baseline value at the next moment and the baseline value at the previous moment.

[0047] Among them, the baseline output of each node is a theoretical value predicted based on the historical output data of the node without considering the influence of volatility and randomness. For example, the historical output data of a certain node within a period of time is collected at a sampling frequency of every 15 minutes, and then combined with the weather forecast of the future time span, the node output within the next time span (for example, 24 hours) is predicted, and the prediction result is the output baseline prediction of the node. Of course, in other embodiments of the present invention, the influence of weather conditions can also be ignored, and the output baseline can be predicted based solely on historical output data. The improvement idea of ​​the present application is to compensate for the influence of local volatility on the basis of the baseline output prediction, and to optimize the output prediction result by utilizing the spatiotemporal coupling characteristics between distributed new energy power stations.

[0048] It can be understood that the power fluctuation rate is the rate of change of the fluctuation influence of the node output power. The higher the value, the greater the impact of volatility on the node. When making output forecasts, the power fluctuation rate at the prediction moment is added to the baseline output forecast value, that is, the output power value of the node output at the prediction moment that is increased or decreased due to the fluctuation can be compensated, thereby improving the accuracy of the output forecast of distributed new energy power stations.

[0049] It can be understood that in step S3, the power fluctuation rate of each node in multiple historical time sections is calculated after the baseline output prediction is performed based on the output data of each node in multiple historical time sections, where a time section can be understood as a data collection moment. The process of judging whether generalized volatility exists based on the calculation results of the fluctuation characteristic values ​​of multiple time sections includes the following contents:

[0050] In a certain time section, if an extreme value region appears in the power fluctuation rate of each node in a certain area of ​​the topological structure, and the power fluctuation rate gradually decreases outside the extreme value region, the extreme value region is regarded as the fluctuation center. In the subsequent time sections, if the distance between the new fluctuation center and the old fluctuation center does not exceed the preset threshold, the power fluctuation rate of the nodes near the fluctuation center migration line is higher than that of other areas, and the power fluctuation rate sequence change of a single node is negatively correlated with the change in the distance between the node and the fluctuation center, it is determined that generalized volatility exists.

[0051] Specifically, within a certain time section, if the power fluctuation rate of each node in a certain area of ​​the topological structure has an extreme point or an extreme area, and the power fluctuation rate outside the extreme area gradually decreases, the extreme area is regarded as the fluctuation center. Among them, a certain area of ​​the topological structure represents the fluctuation influence area, that is, the fluctuation area, which can be divided according to actual needs. The entire distributed new energy power station system can be selected, or a local area of ​​the distributed new energy power station system can be selected. In addition, the extreme point refers to the maximum power fluctuation rate of a node in the fluctuation area, and the extreme area refers to the area formed by these node clusters when multiple nodes with similar distances are at the maximum level. The maximum level refers to the power fluctuation rate of the node is in the top 5% of the highest. The specific percentage can be set according to actual conditions, for example, set to the highest 3%. In addition, for the extreme area, the center point of the extreme area in the topological structure diagram can be selected as the fluctuation center, or the node where the mean power fluctuation rate in the extreme area is located can be selected as the fluctuation center, or any node in the extreme area can be selected as the fluctuation center. It can be selected according to actual needs. In the subsequent time section, due to the change of the power fluctuation rate of the node, new fluctuation centers will appear one after another. If the new fluctuation center is adjacent to the old fluctuation center, that is, the distance between the new fluctuation center and the old fluctuation center does not exceed the preset threshold, the preset threshold can be set to three times the average distance between adjacent nodes in the entire distributed new energy power station system, and the power fluctuation rate of the nodes near the fluctuation center migration line is higher than other areas, and the power fluctuation rate sequence change of a single node is negatively correlated with the change of the distance between the node and the fluctuation center, that is, the farther the node is from the fluctuation center, the smaller its power fluctuation rate, and the closer the node is to the fluctuation center, the greater its power fluctuation rate. If there is a fluctuation center and the above conditions are met, it is determined that the output of the distributed new energy power station system has generalized volatility.

[0052] In addition, in step S3, after determining that there is generalized volatility, the fluctuation center position and the power volatility of each node in multiple historical time sections are obtained, so as to facilitate the subsequent prediction of the fluctuation center position and the power volatility of each node in the predicted time section. In addition, based on the power volatility of each node in multiple time sections, the change law of the fluctuation characteristic value outside the fluctuation center in the fluctuation area can be analyzed, including spatial law and temporal law. The spatial law refers to whether the fluctuation characteristic value is positively correlated or negatively correlated with the distance from the node to the fluctuation center from the fluctuation center to the periphery, and the size of the correlation can be calculated. The temporal law refers to whether the change rate of the fluctuation characteristic value of each node gradually increases or decreases over time, thereby characterizing whether the influence of the fluctuation area is increasing or weakening.

[0053] It can be understood that in step S4, the power fluctuation rate sequence of each node can be obtained based on the power fluctuation rate of each node in multiple time sections, and then the power fluctuation rate sequence of each node is predicted based on the linear regression, curve fitting and other data methods, so that the power fluctuation rate of each node at the prediction time can be obtained. Then, the fluctuation center is judged based on the power fluctuation rate of each node at the prediction time. If the fluctuation center remains unchanged, the prediction time is extended and the prediction is repeated until a new fluctuation center appears. In addition, if the new fluctuation center cannot be obtained even if the prediction time is extended, it means that the volatility is greatly reduced or even disappears, which is not considered in the present invention.

[0054] It can be understood that in step S5, the process of predicting the actual output values ​​of all nodes in the fluctuation area based on the fluctuation center position and the fluctuation characteristic value of each node in the next time section includes the following contents:

[0055] For each node, a multivariate regression model is constructed with the actual output of the node as the variable and the output baseline and power fluctuation rate of the node as the influencing factors. The actual output data, output baseline and power fluctuation rate of the node in the known time section are substituted into the multivariate regression model to calculate the coefficients of the multivariate regression model. The predicted power fluctuation rate and output baseline of the node in the next time section are substituted into the multivariate regression model to calculate the actual output value of the node in the next time section. The above process is repeated to obtain the actual output values ​​of all nodes in the fluctuation area.

[0056] Specifically, a prediction model is constructed for each node in the fluctuation area to facilitate output prediction for each node. For each node, a multiple regression model is constructed with the actual output of the node as a variable and the output baseline and power fluctuation rate of the node as influencing factors. The actual output value, output baseline and power fluctuation rate of the node in the historical time section are then substituted into the constructed multiple regression model for calculation to obtain the coefficients of the multiple regression model. The power fluctuation rate and output baseline of the node in the predicted time section are then substituted into the multiple regression model to calculate the actual output value of the node in the predicted time section. Repeat the above process to calculate the actual output values ​​of all nodes in the fluctuation area.

[0057] Embodiment 2

[0058] The difference between the second embodiment and the first embodiment lies in the different specific contents of steps S2, S3, S4 and S5.

[0059] It can be understood that in step S2, the fluctuation characteristic value can also be a node influence ratio and a node correlation ratio, wherein the node influence ratio is the ratio of the total node influence of a certain node to the average of the total node influence of all nodes, the total node influence of each node is the sum of the absolute values ​​of the standardized covariance sequence of the output value of the node and the output values ​​of all other nodes in multiple time sections, and the node correlation ratio is the ratio of the number of nodes in the standardized covariance sequence whose absolute values ​​are greater than the preset correlation threshold to (N-1), where N is the number of nodes.

[0060] In addition, the standardized covariance sequence refers to the sequence composed of the standardized covariance coefficients of the output value of a certain node and the output values ​​of other nodes in a series of time sections. The covariance between nodes can be defined as: COV(X, Y) = E(XY)-E(X)E(Y), X and Y represent the output value sequences of the two nodes respectively, E(X) and E(Y) represent the means or mathematical expectations of the sequences X and Y respectively, and E(XY) represents the mean or mathematical expectation of the product of the sequences X and Y, and the standardized covariance = covariance / (first standard deviation × second standard deviation), the first standard deviation and the second standard deviation represent the standard deviations of the sequences X and Y respectively. The standardized covariance between two nodes represents the consistency of the output changes of the two nodes with the influence of fluctuations, and its value is between [-1,1]. The closer the absolute value is to 1, the stronger the correlation between the output changes of the two nodes is, and the closer it is to 0, the weaker the correlation is. The correlation threshold is used to determine whether there is a correlation between nodes. The absolute value of the standardized covariance between nodes is between [0,1]. The closer it is to 0, the lower the data correlation is. Therefore, by setting the correlation threshold, only when the absolute value of the standardized covariance is greater than the correlation threshold, the output data of the two nodes are considered to be correlated. Therefore, the standardized covariance between two nodes reflects the correlation of the output series between the two nodes. When affected by the same fluctuation area, the output time series between the two nodes will show a certain degree of similarity. The closer the two nodes are and the closer they are to the fluctuation center, the higher the output correlation is, and vice versa. The total influence of the node represents the sum of the correlations between the node and the rest of the nodes. The node influence ratio is to standardize the total influence of the node. One of the functions of the two is to use the size of the total influence of the node / node influence ratio to determine the node position (i.e., whether it is located in the center). The second function is to quantify the influence of the fluctuation area through the output correlation between the nodes. When predicting the output of the node at the next moment, the influence of the fluctuation area can be considered on the basis of the predicted value. The purpose of the node correlation ratio is to exclude nodes with weak correlation (i.e., the outermost nodes that may not be affected by the fluctuation area) and avoid randomness when searching for the fluctuation center.

[0061] It can be understood that the present invention, by adopting the node influence ratio and the node correlation ratio as the fluctuation characteristic values, can reflect the influence of the fluctuation area in the time series changes of the node characteristic values ​​and the correlation changes between the nodes. By extracting the laws of spatiotemporal changes and adding them to the basic prediction, the output value of each node at the prediction time that is increased or decreased due to the influence of volatility is compensated, thereby improving the accuracy of the output prediction.

[0062] It can be understood that in step S3, the output data of each node in multiple historical time sections are collected, and the fluctuation characteristic value is calculated according to the definition formula of the node influence ratio and the node correlation ratio, and the output value of the corresponding node in the prediction time section is predicted based on the historical output data of each node. The purpose of this embodiment is to consider more detailed volatility effects on the basis of the output prediction value, so as to correct the prediction value and improve the accuracy of the output prediction.

[0063] The process of judging whether there is generalized volatility based on the calculation results of the fluctuation characteristic values ​​of multiple time sections includes the following contents:

[0064] The node with the highest node correlation ratio in multiple time sections is taken as the fluctuation center. If the movement of the new and old fluctuation centers meets the continuity requirements, it is determined that generalized volatility exists.

[0065] It can be understood that the node with the highest node correlation ratio is usually the central node or key node of the distributed new energy power station system. The node has a strong correlation with the remaining nodes and reflects a large fluctuation influence. Therefore, in a series of time sections, if there are always one or more nodes with the highest node correlation ratio, they can be regarded as the fluctuation center. In addition, if the movement of the new and old fluctuation centers meets the continuity requirement, that is, the new fluctuation center is adjacent to the old fluctuation center, and there are no other nodes between the two, then it is determined that the output of the distributed new energy power station system has generalized volatility. For example, assuming that the output data of each node in 50 time sections are collected to calculate the fluctuation characteristic value, according to the calculation result of the fluctuation characteristic value, it is obtained that there is a fluctuation center A in the time section 1-25, and there is a fluctuation center B in the time section 26-50, and A and B are adjacent, then it is determined that there is generalized volatility. In addition, after determining that there is generalized volatility, the fluctuation center position in the nearest multiple time sections, the fluctuation characteristic value of each node (including the node influence ratio and the node correlation ratio) and the standardized covariance sequence are obtained to facilitate the prediction of the fluctuation center and the characteristic value of each node in the next period of time.

[0066] It can be understood that in step S4, the process of predicting the fluctuation center position and the fluctuation characteristic value of each node in the next time section based on the fluctuation center position and the fluctuation characteristic value of each node in multiple time sections includes the following contents:

[0067] Based on the standardized covariance sequence of each node in the most recent multiple time sections, linear regression, curve fitting and other data processing methods are used to predict the standardized covariance sequence of each node in the next period of time. The predicted value of the fluctuation characteristic value of each node is calculated based on the predicted standardized covariance sequence, and the new fluctuation center is determined.

[0068] For example, based on the standardized covariance sequence of each node in time sections 26-50, the standardized covariance sequence of each node in time section 51 and a series of subsequent time sections is predicted, and then the node influence ratio prediction value and the node correlation ratio prediction value of each node are calculated based on the predicted standardized covariance sequence of each node, and the new fluctuation center position is determined according to the above definition of the fluctuation center.

[0069] It can be understood that this embodiment makes predictions based on a standardized covariance sequence with time series characteristics, and then indirectly calculates the predicted value of the fluctuation characteristic value. Compared with directly using the fluctuation characteristic value sequence for prediction, this can reduce calculation errors and is conducive to more accurate prediction of the new fluctuation center position.

[0070] It can be understood that the standardized covariance sequence, fluctuation characteristic value prediction value and new fluctuation center of each node in the future period of time have been predicted in step S4. In step S5, the actual output of each node needs to be predicted based on the standardized covariance sequence, fluctuation characteristic value prediction value and new fluctuation center of each node in the prediction period. Among them, firstly, according to the rule that the fluctuation characteristic value prediction value is from high to low and the position is from the fluctuation center (i.e., the new fluctuation center) to the periphery, the fluctuation area is first layered using a clustering method (such as the existing SVM clustering algorithm, K-means clustering algorithm, etc.), and the highest cardinality of each layer is 1, that is, it represents the fluctuation center node, and it decreases as the distance from the fluctuation center increases, and the lowest is 0; then, according to the volatility law, the fluctuation center node's own output is affected by the fluctuation and the volatility influence factor on each node in the layer is calculated, wherein the volatility influence factor of the fluctuation center on each node in the layer = the layer cardinality * the standardized covariance between the fluctuation center node and the calculation node. In addition, the output of the fluctuation center node itself is affected by the fluctuation = the power change rate of the fluctuation center × the change in the fluctuation characteristic value of the fluctuation center (for example, the change in the node influence ratio). Based on the known series of output power values ​​and a series of fluctuation characteristic values ​​of the previous fluctuation center, the linear regression model is used to describe the relationship between the output power and the fluctuation characteristic value, which can be expressed as: P = β0 + β1x, x represents the fluctuation characteristic value, P represents the output power, β0 and β1 are coefficients, β0 represents the intercept, and β1 represents the power change rate of the fluctuation center. The values ​​of the coefficients β0 and β1 can be estimated by the least squares method, and the change in the fluctuation characteristic value of the fluctuation center = the fluctuation characteristic value of the old fluctuation center - the fluctuation characteristic value of the new fluctuation center, so that the output of the fluctuation center node itself can be calculated.

[0071] First, the output impact of the fluctuation is added to the output forecast value of the fluctuation center node at the prediction time as a correction value for the output forecast of the fluctuation center, wherein the output impact of the fluctuation center node itself is also the standard value of other levels. The output forecast value of the fluctuation center node is obtained in step S3, that is, it is predicted based on the historical output data of the fluctuation center node.

[0072] Then, starting from the level with the greatest fluctuation impact, the output forecast correction value is calculated using the level's volatility impact factor and the node output forecast value. The output forecast correction value of the remaining nodes = the node's output forecast value × (1 + the output fluctuation impact rate of the fluctuation center node × the fluctuation impact factor), and the output fluctuation impact rate of the fluctuation center node = the output fluctuation impact amount of the fluctuation center node / the output forecast correction value of the fluctuation center. Iterate all levels until the corrected output forecast values ​​of all nodes are obtained.

[0073] In addition, after completing the correction of the predicted value at this moment, the predicted corrected value at this moment is added to the power data sequence of the actual output to form a new power data sequence, and then the standardized covariance sequence and the fluctuation characteristic value of each node are calculated based on the new power data sequence, and the error between the standardized covariance sequence and the predicted value of the fluctuation characteristic value predicted in step S4 is required not to exceed 5% (the specific value can be set according to actual needs). If the condition is not met, the fluctuation influence of the fluctuation center node at the predicted moment and the level cardinality of each level (selective depending on the specific situation) are adjusted and re-iterated. For example, if the error exceeds 5%, or there is a fluctuation characteristic value after correction of more than half of the nodes and the fluctuation characteristic value predicted value The error exceeds 5%, the fluctuation influence of the fluctuation center node is adjusted, and the new influence amount = the old influence amount × exp (-α*k), α represents the center adjustment rate, and k represents the number of iterations.

[0074] Repeat the above steps until the corrected forecast value sequence within the required forecast time period is obtained, and the output forecast is completed.

[0075] In addition, if Figure 2 As shown, another embodiment of the present invention further provides an output prediction system for a distributed new energy power station, preferably using the output prediction method as described above, including:

[0076] Topology building module, used to build the geographical topology between distributed renewable energy power stations;

[0077] A fluctuation characteristic value setting module, used for setting the fluctuation characteristic value;

[0078] A generalized volatility judgment module is used to calculate the fluctuation characteristic value based on the output data of each node in multiple time sections, and judge whether there is generalized volatility according to the calculation results of the fluctuation characteristic values ​​of multiple time sections. If generalized volatility exists, the fluctuation center position and the fluctuation characteristic value of each node in multiple time sections are obtained;

[0079] A fluctuation center prediction module is used to predict the fluctuation center position and the fluctuation characteristic value of each node in the next time section based on the fluctuation center position and the fluctuation characteristic value of each node in multiple time sections;

[0080] The node output prediction module is used to predict the actual output values ​​of all nodes in the fluctuation area based on the fluctuation center position and the fluctuation characteristic value of each node in the next time section.

[0081] It can be understood that the output prediction system of the distributed new energy power station in this embodiment, after setting the fluctuation characteristic value, calculates the fluctuation characteristic value based on the output data of each node in multiple historical time sections, and judges whether there is generalized volatility based on the calculation results of the fluctuation characteristic values ​​of multiple historical time sections. If so, the fluctuation center position and the fluctuation characteristic value of each node in the predicted time section are predicted based on the fluctuation characteristic value of each node in multiple historical time sections. Finally, the actual output values ​​of all nodes in the fluctuation area are predicted based on the predicted fluctuation center position and the fluctuation characteristic value of each node. It combines the spatiotemporal coupling characteristics and generalized volatility analysis between distributed new energy power stations, and compensates for the output value of each node that is increased or decreased due to volatility at the prediction time, thereby improving the output prediction accuracy of distributed new energy power stations under the influence of group random factors.

[0082] In addition, another embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the above method by calling the computer program stored in the memory.

[0083] In addition, another embodiment of the present invention further provides a computer-readable storage medium for storing a computer program for output prediction of a distributed renewable energy power station, wherein the computer program executes the steps of the method described above when running on a computer.

[0084] The general form of computer readable storage media includes: floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with a pattern of holes, random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash erasable programmable read-only memory (FLASH-EPROM), any other memory chip or cartridge, or any other medium that can be read by a computer. The instructions can further be transmitted or received by a transmission medium. The term transmission medium can include any tangible or intangible medium that can be used to store, encode or carry instructions for execution by a machine, and includes digital or analog communication signals or other intangible media that facilitate the communication of the above instructions. Transmission media include coaxial cables, copper wires and optical fibers, which include the wires of a bus used to transmit a computer data signal.

[0085] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0086] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0089] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0090] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting the output of a distributed renewable energy power station, characterized in that: Includes the following: Construct the geographical topology between distributed renewable energy power stations; Set the fluctuation characteristic value; Calculate the fluctuation characteristic value based on the output data of each node in multiple time sections, and judge whether there is generalized volatility according to the calculation results of the fluctuation characteristic values ​​of multiple time sections. If there is generalized volatility, obtain the fluctuation center position and the fluctuation characteristic value of each node in multiple time sections; Based on the fluctuation center position and the fluctuation characteristic value of each node in multiple time sections, the fluctuation center position and the fluctuation characteristic value of each node in the next time section are predicted; Based on the fluctuation center position in the next time section and the fluctuation characteristic value of each node, the actual output value of all nodes in the fluctuation area is predicted.

2. The method for predicting the output of a distributed renewable energy power station according to claim 1, characterized in that: The fluctuation characteristic value is the power fluctuation rate, wherein the power fluctuation rate = (baseline output difference - actual output difference) / baseline mean, the baseline output difference is the difference between the baseline value at the next moment and the baseline value at the previous moment, the actual output difference is the difference between the output value at the next moment and the output value at the previous moment, and the baseline mean is the average value of the baseline value at the next moment and the baseline value at the previous moment.

3. The method for predicting the output of a distributed renewable energy power station according to claim 2, characterized in that: The process of judging whether generalized volatility exists according to the calculation results of the fluctuation characteristic values ​​of multiple time sections includes the following contents: In a certain time section, if an extreme value region appears in the power fluctuation rate of each node in a certain area of ​​the topological structure, and the power fluctuation rate gradually decreases outside the extreme value region, the extreme value region is regarded as the fluctuation center. In the subsequent time sections, if the distance between the new fluctuation center and the old fluctuation center does not exceed the preset threshold, the power fluctuation rate of the nodes near the fluctuation center migration line is higher than that of other areas, and the power fluctuation rate sequence change of a single node is negatively correlated with the change in the distance between the node and the fluctuation center, it is determined that generalized volatility exists.

4. The method for predicting the output of a distributed renewable energy power station according to claim 2, characterized in that: The process of predicting the actual output values ​​of all nodes in the fluctuation area based on the fluctuation center position and the fluctuation characteristic value of each node in the next time section includes the following contents: For each node, a multiple regression model is constructed with the actual output of the node as a variable and the output baseline and power fluctuation rate of the node as influencing factors. The actual output data, output baseline and power fluctuation rate of the node in the known time section are then substituted into the multiple regression model to calculate the coefficients of the multiple regression model. The power fluctuation rate and output baseline of the node in the next time section that are predicted are then substituted into the multiple regression model to calculate the actual output value of the node in the next time section. Repeat the above process to obtain the actual output values ​​of all nodes in the fluctuation area.

5. The method for predicting the output of a distributed renewable energy power station according to claim 1, characterized in that: The fluctuation characteristic values ​​are the node influence ratio and the node correlation ratio, wherein the node influence ratio is the ratio of the total node influence of a certain node to the average of the total node influence of all nodes, the total node influence of each node is the sum of the absolute values ​​of the standardized covariance sequence of the output value of the node and the output values ​​of all other nodes at multiple time sections, and the node correlation ratio is the ratio of the number of nodes in the standardized covariance sequence whose absolute values ​​are greater than the preset correlation threshold to (N-1), where N is the number of nodes.

6. The method for predicting the output of a distributed renewable energy power station according to claim 5, characterized in that: The process of judging whether generalized volatility exists according to the calculation results of the fluctuation characteristic values ​​of multiple time sections includes the following contents: The node with the highest node correlation ratio in multiple time sections is taken as the fluctuation center. If the movement of the new and old fluctuation centers meets the continuity requirements, it is determined that generalized volatility exists.

7. The method for predicting the output of a distributed renewable energy power station according to claim 6, characterized in that: The process of predicting the fluctuation center position and the fluctuation characteristic value of each node in the next time section based on the fluctuation center position and the fluctuation characteristic value of each node in multiple time sections includes the following contents: Based on the standardized covariance sequence of each node in the most recent multiple time sections, the standardized covariance sequence of each node in the next period of time is predicted, and the predicted value of the fluctuation characteristic value of each node is calculated according to the predicted standardized covariance sequence, and the new fluctuation center is determined.

8. An output prediction system for a distributed new energy power station, characterized in that: include: Topology building module, used to build the geographical topology between distributed renewable energy power stations; A fluctuation characteristic value setting module, used for setting the fluctuation characteristic value; A generalized volatility judgment module is used to calculate the fluctuation characteristic value based on the output data of each node in multiple time sections, and judge whether there is generalized volatility according to the calculation results of the fluctuation characteristic values ​​of multiple time sections. If generalized volatility exists, the fluctuation center position and the fluctuation characteristic value of each node in multiple time sections are obtained; A fluctuation center prediction module is used to predict the fluctuation center position and the fluctuation characteristic value of each node in the next time section based on the fluctuation center position and the fluctuation characteristic value of each node in multiple time sections; The node output prediction module is used to predict the actual output values ​​of all nodes in the fluctuation area based on the fluctuation center position and the fluctuation characteristic value of each node in the next time section.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method according to any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium for storing a computer program for predicting the output of a distributed renewable energy power station, characterized in that: When the computer program is run on a computer, the steps of the method according to any one of claims 1 to 7 are executed.

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