Distributed power generation resource scheduling method based on real-time data processing

Abstract

The invention relates to the field of power grid resource scheduling, and discloses a distributed power generation resource scheduling method based on real-time data processing, and the method comprises the steps: carrying out the preprocessing and feature extraction of distributed power generation resource information data collected in real time, and carrying out the analysis of power generation data, load data and operation data of a power grid, the comprehensive power generation resource scheduling prediction coefficient is used for predicting whether the power generation resources need to be subjected to optimal scheduling adjustment or not, the unreasonable power generation resource scheduling condition is controlled, adjusted, evaluated and analyzed according to the prediction result, and the power generation resource scheduling control and adjustment process is monitored in real time. And the monitoring result and the evaluation analysis result are fed back to the management personnel, so that the power generation amount and the load demand can be predicted more accurately, the resources can be allocated reasonably, the change of the scheduling effect can be reflected in time, and the accuracy and the reliability of the evaluation can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid resource scheduling, and more specifically to a distributed power generation resource scheduling method based on real-time data processing. Background Art

[0002] With the rapid development of new energy technologies, the proportion of distributed generation in the power system has gradually increased. Distributed generation has the characteristics of high flexibility and a wide range of energy sources, but it also brings challenges in resource scheduling. The distributed generation resource scheduling method based on real-time data processing can realize dynamic monitoring and optimal configuration of power generation resources, thereby improving energy utilization efficiency, reducing system operating costs, and enhancing the stability and reliability of the power system;

[0003] However, the above process still has the following disadvantages:

[0004] First, the existing technology does not have a comprehensive data monitoring scope for power generation resource scheduling. It is unable to comprehensively consider multiple aspects such as power generation, load demand, and grid stability, and conduct a comprehensive assessment of the scheduling effect, resulting in the inability to timely discover problems and deficiencies in the scheduling process;

[0005] Second, the existing technology cannot fully obtain and analyze the data in the scheduling process in real time, resulting in the inability to reflect the changes in scheduling effects in a timely manner, which may cause prediction errors and inaccurate evaluation results. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a distributed power generation resource scheduling method based on real-time data processing to solve the problems existing in the above-mentioned background technology.

[0007] The present invention provides the following technical solution: a distributed power generation resource scheduling method based on real-time data processing, comprising:

[0008] S1: collects distributed power generation resource information data in real time through sensors, including power generation data, load data and grid operation data, and transmits the collected distributed power generation resource information data to S2;

[0009] S2: by preprocessing and feature extraction of distributed generation resource information data, average power generation, average load, grid frequency deviation, grid voltage deviation and grid loss rate are obtained, and the distributed generation resource information data after feature extraction is transmitted to S3;

[0010] S3: Analyze the power generation data to obtain the power generation efficiency monitoring index, analyze the load data to obtain the load demand index, analyze the power grid operation data to obtain the power grid loss index, and transmit the analysis results to S4;

[0011] S4: Perform secondary analysis based on the analysis results to calculate the comprehensive power generation resource scheduling prediction coefficient, use the comprehensive power generation resource scheduling prediction coefficient to predict whether the power generation resources need to be optimized and adjusted, and transmit the prediction results to S5;

[0012] S5: According to the prediction results, control adjustment and evaluation analysis are performed on the unreasonable scheduling of power generation resources respectively. Through the evaluation and analysis of power generation resources, the state evaluation coefficient is calculated, and the control adjustment result is transmitted to S6, and the evaluation analysis result is transmitted to S7;

[0013] S6: by real-time monitoring of the power generation resource scheduling control adjustment process, until it is monitored that the power generation resource scheduling is within a range that does not require optimization, immediately issuing a stop control adjustment command signal, and feeding back the stop control adjustment command signal to S5;

[0014] S7: Feedback the monitoring results and evaluation and analysis results of the power generation resource dispatching control adjustment to the management personnel.

[0015] Preferably, the S1 deploys sensors in the distributed power generation system and key nodes of the power grid, including current transformers, voltage transformers, temperature sensors and power sensors, and uses the sensors to collect power generation data, load data and power grid operation data in real time according to a preset sampling frequency. The power generation data includes power generation, power generation efficiency and power generation time; the load data includes load; and the power grid operation data includes voltage, current and frequency.

[0016] Preferably, the preprocessing process of the distributed power generation resource information data by S2 includes data cleaning, data verification and data integration, and then extracting relevant parameters affecting the change of power generation resources from the preprocessed distributed power generation resource information data, and calculating the average power generation, average load, grid frequency deviation, grid voltage deviation and grid loss rate;

[0017] The calculation formula for the average power generation is: I i represents the power generation collected for the i-th time, and N represents the number of collections;

[0018] The calculation formula for the average load is: L i represents the load collected for the i-th time;

[0019] The grid frequency deviation Δf=f m -f n , f m Indicates the actual grid frequency, f n Indicates the standard frequency of the power grid;

[0020] The calculation formula of the grid voltage deviation is ΔV=V m -V n , V m Indicates the actual grid voltage, V n Indicates the standard voltage of the power grid;

[0021] The calculation formula of the power grid loss rate is: P loss Indicates the power loss of the power grid, P total Indicates the total grid input power.

[0022] Preferably, S3 performs specific analysis on the distributed power generation resource information data after feature extraction, and calculates the power generation efficiency monitoring index, load demand index and power grid loss index, so as to monitor the change of power generation resources in real time;

[0023] The power generation efficiency monitoring index is calculated by collecting the power generation at historical time 1, 2, 3...N respectively, and the specific calculation formula of the power generation efficiency monitoring index is calculated based on the power generation collected in the historical time: I i represents the power generation collected for the i-th time, represents the average power generation, and N represents the number of acquisitions;

[0024] The load demand index is calculated by collecting the load at each time point. The specific calculation formula of the load demand index is as follows: L i represents the load collected for the i-th time, Indicates the average load;

[0025] The specific calculation formula of the power grid loss index is W=k1×Δf+k2×ΔV+k3×λ, Δf represents the power grid frequency deviation, ΔV represents the power grid voltage deviation, λ represents the power grid loss rate, and k1, k2, k3 are weight coefficients.

[0026] Preferably, S4 performs a comprehensive analysis on the power generation efficiency monitoring index Q, the load demand index R and the power grid loss index W, and calculates the comprehensive power generation resource scheduling prediction coefficient for evaluating the scheduling efficiency and optimizing the adjustment of power generation resources. The specific calculation formula is:

[0027] By setting the maximum threshold C for dispatching generation resources max and the standard minimum threshold C for dispatching generation resources min , the standard maximum threshold of power generation resource scheduling C max and the standard minimum threshold C for dispatching generation resources min Compared with the comprehensive power generation resource scheduling prediction coefficient D, if Cmin ≤D≤C max When D <C min When D>C max When , it indicates that the power generation efficiency is high, the load demand is high, and the grid loss is relatively low. It is necessary to evaluate whether there is over-generation or resource waste, and transmit the prediction results to S5.

[0028] Preferably, the S5 controls and adjusts the power generation resources according to the received prediction results. <C min When D>C max When the power generation resource dispatching data is evaluated and analyzed, the formula for calculating the state evaluation coefficient is: Q represents the power generation efficiency monitoring index, Q 标准 represents the standard power generation efficiency monitoring index, R represents the load demand index, and R 标准 represents the standard load demand index, W represents the power grid loss index, and W 标准 It represents the standard power grid loss index. If E>0, it means that the actual power generation efficiency is higher than the optimal value, and the actual load demand is also higher than the optimal value, while the power grid loss is lower than the optimal value. There is over-generation or waste of resources. The evaluation and analysis results need to be transmitted to S7 immediately. If E≤0, it means that the actual power generation efficiency is lower than the optimal value, or the actual load demand is lower than the optimal value, or the power grid loss is higher than the optimal value. At this time, the power generation resource scheduling is in the optimal state, and the scheduling situation continues to be monitored.

[0029] Preferably, the S6 is adjusted by real-time monitoring of the power generation resource dispatch control until the comprehensive power generation resource dispatch prediction coefficient D ≥ the standard power generation resource dispatch minimum threshold C min , immediately and automatically generate a control adjustment command signal and transmit it to S5, triggering the transmission of the stop control adjustment result.

[0030] Preferably, the S7 automatically generates an adjustment monitoring report based on the monitoring results of the power generation resource scheduling control adjustment and sends it to the management personnel terminal, and displays the evaluation and analysis results to the management personnel terminal through a visual interface.

[0031] Technical effects and advantages of the present invention:

[0032] The present invention collects distributed power generation resource information data in real time through sensors, and then performs preprocessing and feature extraction on the collected distributed power generation resource information data. By analyzing the power generation data, load data and power grid operation data extracted by features, secondary analysis is performed according to the analysis results to calculate the comprehensive power generation resource scheduling prediction coefficient. The comprehensive power generation resource scheduling prediction coefficient is used to predict whether the power generation resources need to be optimized and scheduled. According to the prediction results, control adjustment and evaluation analysis are performed on the unreasonable power generation resource scheduling. By real-time monitoring of the power generation resource scheduling control adjustment process until it is monitored that the power generation resource scheduling is within the range that does not need to be optimized, the monitoring results and evaluation analysis results of the power generation resource scheduling control adjustment are finally fed back to the management personnel, which is conducive to real-time acquisition and analysis of the operation data of the distributed power generation resources, so as to more accurately predict the power generation and load demand, and reduce the prediction error. By real-time monitoring of the power grid operation state and load demand, it is conducive to more reasonable allocation of resources, so as to quickly respond to load changes, adjust the power generation output, and improve resource utilization. At the same time, it can comprehensively consider the power generation, load demand and power grid stability, which is conducive to comprehensive evaluation of the scheduling effect, more timely reflect the change of the scheduling effect, and improve the accuracy and reliability of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a diagram of the method steps of the present invention.

[0034] Figure 2 It is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0035] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are merely illustrative. The distributed power generation resource scheduling method based on real-time data processing involved in the present invention is not limited to the various structures recorded in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0036] like Figure 1 This embodiment provides a distributed generation resource scheduling method based on real-time data processing, including:

[0037] S1: Collect distributed power generation resource information data in real time through sensors, including power generation data, load data and grid operation data, and transmit the collected distributed power generation resource information data to S2.

[0038] In this embodiment, the S1 deploys sensors in the distributed power generation system and key nodes of the power grid, including current transformers, voltage transformers, temperature sensors and power sensors, and uses the sensors to collect power generation data, load data and power grid operation data in real time according to a preset sampling frequency. The power generation data includes power generation, power generation efficiency and power generation time; the load data includes load; and the power grid operation data includes voltage, current and frequency.

[0039] S2: By preprocessing and feature extracting the distributed power generation resource information data, the average power generation, average load, grid frequency deviation, grid voltage deviation and grid loss rate are obtained, and the distributed power generation resource information data after feature extraction is transmitted to S3.

[0040] In this embodiment, the preprocessing process of the distributed power generation resource information data by S2 includes data cleaning, data verification and data integration, and then extracts the relevant parameters affecting the change of power generation resources from the preprocessed distributed power generation resource information data, and calculates the average power generation, average load, grid frequency deviation, grid voltage deviation and grid loss rate;

[0041] The calculation formula for the average power generation is: I i represents the power generation collected for the i-th time, and N represents the number of collections;

[0042] The calculation formula for the average load is: L i represents the load collected for the i-th time;

[0043] The grid frequency deviation Δf=f m -f n , f m Indicates the actual grid frequency, f n Indicates the standard frequency of the power grid;

[0044] The calculation formula of the grid voltage deviation is ΔV=V m -V n , V m Indicates the actual grid voltage, V n Indicates the standard voltage of the power grid;

[0045] The calculation formula of the power grid loss rate is: P loss Indicates the power loss of the power grid, P total Indicates the total grid input power.

[0046] S3: By analyzing the power generation data, the power generation efficiency monitoring index is obtained, by analyzing the load data, the load demand index is obtained, by analyzing the operation data of the power grid, the power grid loss index is obtained, and the analysis results are transmitted to S4.

[0047] In this embodiment, S3 performs specific analysis on the distributed power generation resource information data after feature extraction, and calculates the power generation efficiency monitoring index, load demand index and power grid loss index, so as to monitor the change of power generation resources in real time;

[0048] The power generation efficiency monitoring index is calculated by collecting the power generation at historical time 1, 2, 3...N respectively, and the specific calculation formula of the power generation efficiency monitoring index is calculated based on the power generation collected in the historical time: I i represents the power generation collected for the i-th time, represents the average power generation, and N represents the number of acquisitions;

[0049] The load demand index is calculated by collecting the load at each time point. The specific calculation formula of the load demand index is as follows: L i represents the load collected for the i-th time, and L represents the average load;

[0050] The specific calculation formula of the power grid loss index is W=k1×Δf+k2×ΔV+k3×λ, Δf represents the power grid frequency deviation, ΔV represents the power grid voltage deviation, λ represents the power grid loss rate, and k1, k2, k3 are weight coefficients.

[0051] S4: Perform secondary analysis based on the analysis results to calculate the comprehensive power generation resource scheduling prediction coefficient. The comprehensive power generation resource scheduling prediction coefficient is used to predict whether the power generation resources need to be optimized and scheduled, and the prediction results are transmitted to S5.

[0052] In this embodiment, S4 performs a comprehensive analysis on the power generation efficiency monitoring index Q, the load demand index R, and the power grid loss index W, and calculates the comprehensive power generation resource scheduling prediction coefficient for evaluating the scheduling efficiency and optimizing the adjustment of power generation resources. The specific calculation formula is:

[0053] By setting the maximum threshold C for dispatching generation resources max and the standard minimum threshold C for dispatching generation resources min , the standard maximum threshold of power generation resource scheduling C max and the standard minimum threshold C for dispatching generation resources min Compared with the comprehensive power generation resource scheduling prediction coefficient D, if C min≤D≤C max When D <C min When D>C max When , it indicates that the power generation efficiency is high, the load demand is high, and the grid loss is relatively low. It is necessary to evaluate whether there is over-generation or resource waste, and transmit the prediction results to S5.

[0054] S5: According to the prediction results, control adjustment and evaluation analysis are performed on the unreasonable scheduling of power generation resources respectively. Through the evaluation and analysis of power generation resources, the state evaluation coefficient is calculated, and the control adjustment result is transmitted to S6, and the evaluation analysis result is transmitted to S7.

[0055] In this embodiment, S5 controls and adjusts the power generation resources according to the received prediction results. <C min When D>C max When the power generation resource dispatching data is evaluated and analyzed, the formula for calculating the state evaluation coefficient is: Q represents the power generation efficiency monitoring index, Q 标准 represents the standard power generation efficiency monitoring index, R represents the load demand index, and R 标准 represents the standard load demand index, W represents the power grid loss index, and W 标准 It represents the standard power grid loss index. If E>0, it means that the actual power generation efficiency is higher than the optimal value, and the actual load demand is also higher than the optimal value, while the power grid loss is lower than the optimal value. There is over-generation or waste of resources. The evaluation and analysis results need to be transmitted to S7 immediately. If E≤0, it means that the actual power generation efficiency is lower than the optimal value, or the actual load demand is lower than the optimal value, or the power grid loss is higher than the optimal value. At this time, the power generation resource scheduling is in the optimal state, and the scheduling situation continues to be monitored.

[0056] S6: By monitoring the power generation resource scheduling control adjustment process in real time until it is detected that the power generation resource scheduling is within a range that does not require optimization, a stop control adjustment command signal is immediately issued, and the stop control adjustment command signal is fed back to S5.

[0057] In this embodiment, S6 is adjusted by real-time monitoring of the power generation resource scheduling control until the comprehensive power generation resource scheduling prediction coefficient D ≥ the standard power generation resource scheduling minimum threshold C min, immediately and automatically generate a control adjustment command signal and transmit it to S5, triggering the transmission of the stop control adjustment result.

[0058] S7: Feedback the monitoring results and evaluation and analysis results of the power generation resource dispatching control adjustment to the management personnel.

[0059] In this embodiment, the S7 automatically generates an adjustment monitoring report based on the monitoring results of the power generation resource scheduling control adjustment and sends it to the management terminal, and displays the evaluation and analysis results to the management terminal through a visual interface.

[0060] like Figure 2 The present embodiment shown provides an implementation system corresponding to a distributed power generation resource scheduling method based on real-time data processing, including a data acquisition module, a data processing module, a data analysis module, a scheduling prediction module, a scheduling control module, a scheduling effect evaluation module and a result feedback module, wherein the data acquisition module is connected to the data processing module, the data processing module is connected to the data analysis module, the data analysis module is connected to the scheduling prediction module, the scheduling prediction module is connected to the scheduling control module, the scheduling control module is connected to the scheduling effect evaluation module, and the scheduling control module is connected to the result feedback module.

[0061] The data acquisition module collects distributed power generation resource information data in real time through sensors, including power generation data, load data and power grid operation data;

[0062] The data processing module obtains average power generation, average load, grid frequency deviation, grid voltage deviation and grid loss rate by preprocessing and feature extraction of distributed power generation resource information data;

[0063] The data analysis module obtains a power generation efficiency monitoring index by analyzing power generation data, obtains a load demand index by analyzing load data, and obtains a power grid loss index by analyzing power grid operation data;

[0064] The scheduling prediction module performs secondary analysis based on the analysis results to calculate the comprehensive power generation resource scheduling prediction coefficient, and uses the comprehensive power generation resource scheduling prediction coefficient to predict whether the power generation resources need to be optimized and scheduled;

[0065] The dispatch control module performs control adjustment and evaluation analysis on the unreasonable dispatch of power generation resources according to the prediction results, and calculates the state evaluation coefficient through the evaluation analysis of power generation resources;

[0066] The dispatch effect evaluation module monitors the dispatch control adjustment process of the power generation resource in real time until it detects that the dispatch of the power generation resource is within a range that does not require optimization, and immediately issues a stop control adjustment instruction signal;

[0067] The result feedback module feeds back the monitoring results and evaluation analysis results of the power generation resource scheduling control adjustment to the management personnel.

[0068] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0069] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A distributed generation resource scheduling method based on real-time data processing, characterized in that: include: S1: collects distributed power generation resource information data in real time through sensors, including power generation data, load data and grid operation data, and transmits the collected distributed power generation resource information data to S2; S2: by preprocessing and feature extraction of distributed generation resource information data, average power generation, average load, grid frequency deviation, grid voltage deviation and grid loss rate are obtained, and the distributed generation resource information data after feature extraction is transmitted to S3; S3: Analyze the power generation data to obtain the power generation efficiency monitoring index, analyze the load data to obtain the load demand index, analyze the power grid operation data to obtain the power grid loss index, and transmit the analysis results to S4; S4: Perform secondary analysis based on the analysis results to calculate the comprehensive power generation resource scheduling prediction coefficient, use the comprehensive power generation resource scheduling prediction coefficient to predict whether the power generation resources need to be optimized and adjusted, and transmit the prediction results to S5; S5: According to the prediction results, control adjustment and evaluation analysis are performed on the unreasonable scheduling of power generation resources respectively. Through the evaluation and analysis of power generation resources, the state evaluation coefficient is calculated, and the control adjustment result is transmitted to S6, and the evaluation analysis result is transmitted to S7; S6: by real-time monitoring of the power generation resource scheduling control adjustment process, until it is monitored that the power generation resource scheduling is within a range that does not require optimization, immediately issuing a stop control adjustment command signal, and feeding back the stop control adjustment command signal to S5; S7: Feedback the monitoring results and evaluation and analysis results of the power generation resource dispatching control adjustment to the management personnel.

2. A distributed generation resource scheduling method based on real-time data processing according to claim 1, characterized in that: The S1 deploys sensors at distributed power generation systems and key power grid nodes, including current transformers, voltage transformers, temperature sensors, and power sensors, and uses the sensors to collect power generation data, load data, and power grid operation data in real time at a preset sampling frequency. The power generation data includes power generation, power generation efficiency, and power generation time; the load data includes load; and the power grid operation data includes voltage, current, and frequency.

3. The method for scheduling distributed generation resources based on real-time data processing according to claim 1, characterized in that: The preprocessing process of the distributed power generation resource information data by S2 includes data cleaning, data verification and data integration, and then extracting the relevant parameters affecting the change of power generation resources from the preprocessed distributed power generation resource information data, and calculating the average power generation, average load, grid frequency deviation, grid voltage deviation and grid loss rate; The calculation formula for the average power generation is: I i represents the power generation collected for the i-th time, and N represents the number of collections; The calculation formula for the average load is: L i represents the load collected for the i-th time; The grid frequency deviation Δf=f m -f n , f m Indicates the actual grid frequency, f n Indicates the standard frequency of the power grid; The calculation formula of the grid voltage deviation is ΔV=V m -V n , V m Indicates the actual grid voltage, V n Indicates the standard voltage of the power grid; The calculation formula of the power grid loss rate is: P loss Indicates the power loss of the power grid, P total Indicates the total grid input power.

4. The method for scheduling distributed generation resources based on real-time data processing according to claim 1, characterized in that: The S3 performs specific analysis on the distributed power generation resource information data after feature extraction, and calculates the power generation efficiency monitoring index, load demand index and power grid loss index, so as to monitor the change of power generation resources in real time; The power generation efficiency monitoring index is calculated by collecting the power generation at historical time 1, 2, 3...N respectively, and the specific calculation formula of the power generation efficiency monitoring index is calculated based on the power generation collected in the historical time: I i represents the power generation collected for the i-th time, represents the average power generation, and N represents the number of acquisitions; The load demand index is calculated by collecting the load at each time point. The specific calculation formula of the load demand index is as follows: L i represents the load collected for the i-th time, Indicates the average load; The specific calculation formula of the power grid loss index is W=k1×Δf+k2×ΔV+k3×λ, Δf represents the power grid frequency deviation, ΔV represents the power grid voltage deviation, λ represents the power grid loss rate, and k1, k2, k3 are weight coefficients.

5. The method for scheduling distributed generation resources based on real-time data processing according to claim 1, characterized in that: The S4 performs a comprehensive analysis on the power generation efficiency monitoring index Q, the load demand index R and the power grid loss index W, and calculates the comprehensive power generation resource scheduling prediction coefficient for evaluating the scheduling efficiency and optimizing the adjustment of power generation resources. The specific calculation formula is: By setting the maximum threshold C for dispatching generation resources max and the standard minimum threshold C for dispatching generation resources min , the standard maximum threshold of power generation resource scheduling C max and the standard minimum threshold C for dispatching generation resources min Compared with the comprehensive power generation resource scheduling prediction coefficient D, if C min ≤D≤C max When D <C min When D>C max When , it indicates that the power generation efficiency is high, the load demand is high, and the grid loss is relatively low. It is necessary to evaluate whether there is over-generation or resource waste, and transmit the prediction results to S5.

6. A distributed generation resource scheduling method based on real-time data processing according to claim 1, characterized in that: The S5 controls and adjusts the power generation resources according to the received prediction results. <C min When D>C max When the power generation resource dispatching data is evaluated and analyzed, the formula for calculating the state evaluation coefficient is: Q represents the power generation efficiency monitoring index, Q 标准 represents the standard power generation efficiency monitoring index, R represents the load demand index, and R 标准 represents the standard load demand index, W represents the power grid loss index, W 标准 It represents the standard power grid loss index. If E>0, it means that the actual power generation efficiency is higher than the optimal value, and the actual load demand is also higher than the optimal value, while the power grid loss is lower than the optimal value. There is over-generation or waste of resources. The evaluation and analysis results need to be transmitted to S7 immediately. If E≤0, it means that the actual power generation efficiency is lower than the optimal value, or the actual load demand is lower than the optimal value, or the power grid loss is higher than the optimal value. At this time, the power generation resource scheduling is in the optimal state, and the scheduling situation continues to be monitored.

7. A distributed generation resource scheduling method based on real-time data processing according to claim 1, characterized in that: S6 monitors the power generation resource dispatch control and adjusts in real time until the comprehensive power generation resource dispatch prediction coefficient D is greater than the standard power generation resource dispatch minimum threshold C min , immediately and automatically generate a control adjustment command signal and transmit it to S5, triggering the transmission of the stop control adjustment result.

8. The method for scheduling distributed generation resources based on real-time data processing according to claim 1, characterized in that: The S7 automatically generates an adjustment monitoring report based on the monitoring results of the power generation resource scheduling control adjustment and sends it to the management terminal, and displays the evaluation and analysis results to the management terminal through a visual interface.

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