Low-carbon optimal scheduling method based on source network load storage integration

Abstract

The invention discloses a low-carbon optimal scheduling method based on source-grid-load-storage integration, and relates to the technical field of energy scheduling, and the method comprises the steps: obtaining historical power demands, renewable energy power generation, energy storage equipment states and power grid load data based on an environment sensor of a source-grid-load-storage integrated system; obtaining historical time sequence characteristic parameter data of the operation mode of the source-network-load-storage integrated system, and determining an interval vector range of the operation mode of the source-network-load-storage integrated system; acquiring real-time data; obtaining a real-time operation vector matrix; matching degree verification is carried out according to the real-time operation vector matrix and the interval vector range of the operation modes of the source-network-load-storage integrated system, and the operation mode with the maximum matching degree is screened out to serve as a scheduling scheme. According to the invention, through source-grid-load-storage integrated operation mode matching, the scheduling scheme is optimized, and the use efficiency of the power grid load and the energy storage equipment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy scheduling, and particularly to a low-carbon optimal scheduling method based on source-grid-load-storage integration. Background Art

[0002] With the transformation of the energy structure and the increasing requirements for low-carbon environmental protection, traditional energy scheduling methods have been difficult to meet the ever-changing power demand and the uncertainty of renewable energy generation. The source-grid-load-storage integration system, that is, the coordinated operation of integrated power generation, load, energy storage devices and the power grid, plays an increasingly important role in the optimal scheduling of the power system. By real-time monitoring and data analysis of power demand, renewable energy generation, grid load and the state of energy storage devices, the scheduling flexibility, efficiency and low-carbon nature of the power system can be improved. However, traditional scheduling methods usually rely on single historical data and lack in-depth mining of system operation modes and data correlation characteristics.

[0003] Therefore, how to combine the actual operation mode of the source-grid-load-storage integration system, optimize the scheduling scheme based on the trend analysis of historical time-series data and real-time data, and reduce carbon emissions has become an important issue in the intelligent power system. Summary of the Invention

[0004] To solve the above technical problems, a low-carbon optimal scheduling method based on source-grid-load-storage integration is provided, and the present technical solution solves the above problems.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows: A low-carbon optimal scheduling method based on source-grid-load-storage integration, comprising: Based on the environmental sensors of the source-grid-load-storage integration system, obtaining historical power demand, renewable energy generation, the state of energy storage devices and grid load data; According to the operation mode of the source-grid-load-storage integration system, screening the historical power demand, renewable energy generation, the state of energy storage devices and grid load data to obtain historical time-series characteristic parameter data of the operation mode of the source-grid-load-storage integration system; Based on the historical time-series characteristic parameter data of the source-grid-load-storage integration system, determining the range of interval vectors of the operation mode of the source-grid-load-storage integration system; Obtaining real-time power demand, renewable energy generation, the state of energy storage devices and grid load data; According to the operation mode of the source-grid-load-storage integration system, performing correlation mapping on the real-time power demand, renewable energy generation, the state of energy storage devices and grid load data to obtain a real-time operation vector matrix; Verify the matching degree according to the range of the interval vector of the real-time operation vector matrix and the operation mode of the source-grid-load-storage integrated system, and select the operation mode with the largest matching degree as the scheduling plan.

[0006] Preferably, screen the historical power demand, renewable energy power generation, energy storage device status, and grid load data according to the operation mode of the source-grid-load-storage integrated system to obtain the historical time-series characteristic parameter data of the source-grid-load-storage integrated system, specifically including: Based on the historical power demand, renewable energy power generation, energy storage device status, and grid load data, calculate the correlation coefficient between each operation parameter and the operation mode of the source-grid-load-storage integrated system; Cluster the correlation coefficients between each operation parameter and the operation mode of the source-grid-load-storage integrated system to obtain an array of correlation characteristic parameters of the operation mode of the source-grid-load-storage integrated system; Based on the timestamps in the historical power demand, renewable energy power generation, energy storage device status, and grid load data, use the timestamp as the observation window and the array of correlation characteristic parameters of the operation mode of the source-grid-load-storage integrated system as the observation object to establish the historical time-series characteristic parameter data of the source-grid-load-storage integrated system.

[0007] Preferably, the calculation of the correlation coefficient between each operation parameter and the operation mode of the source-grid-load-storage integrated system based on the historical power demand, renewable energy power generation, energy storage device status, and grid load data specifically includes: Based on the operation mode of the source-grid-load-storage integrated system, mark the key operation parameters concerned by the system; Assign a weighted mean to each key operation parameter; Standardize the historical power demand, renewable energy power generation, energy storage device status, and grid load data, and construct a scatter plot of the historical operation data; Based on the scatter plot of the historical operation data, key operation parameters, and weighted mean, calculate the correlation coefficient between each operation parameter and the key operation parameter in the historical operation data per unit time.

[0008] Preferably, the correlation coefficient is specifically: ; where is the correlation coefficient between the th key operation parameter and the th operation parameter, is the value of the th operation parameter, is the weighted mean of the key operation parameter, is the total number of operation parameters.

[0009] Preferably, the correlation coefficients between each operating parameter and the operating modes of the source-grid-load-storage integrated system are clustered to obtain an array of correlation characteristic parameters for the system operating modes, specifically including: Based on the correlation coefficients between each operating parameter and the source-grid-load-storage integrated system, a correlation coefficient matrix is established; Iterative clustering is performed through the k-means clustering algorithm: Step a Initialization: Randomly select K clustering centers in the correlation coefficient matrix as the initial cluster centroids ; i represents the index of the i-th clustering cluster, i = 1, 2,..., K; Step b Assign data points: Calculate the Euclidean distance between each data point and the current cluster centroid , and assign each point to the nearest cluster to form a new set of clusters ; k represents the number of iterations, that is, the clustering algorithm is currently in the k-th round of iteration; Step c Update cluster centers: Recalculate the centroid of each cluster ; is the number of data points in the i-th cluster; Step d Iteration termination: Repeat steps b - c until the change in the centroid is less than the threshold or the maximum number of iterations is reached. At this time, the centroid is the array of correlation characteristic parameters for the system operating modes.

[0010] Preferably, based on the historical time series characteristic parameter data of the source-grid-load-storage integrated system, the interval vector range of the operating modes of the source-grid-load-storage integrated system is determined, specifically including: Substitute the historical time series data into the autoregressive moving average model to predict the operating mode in the future unit time, and determine the interval vector range through the confidence interval of the error function.

[0011] Preferably, the autoregressive moving average model is specifically: ; where is the predicted value at time, is the unit time, is the time offset, are all model coefficients, is the white noise error term; P and Q are the orders of autoregression and moving average respectively, determined by fitting historical data; The interval vector range is determined by the predicted value and the confidence interval; where ; where is the standard normal distribution quantile corresponding to the confidence level α, and σ is the standard deviation of the prediction error.

[0012] Preferably, according to the interval vector range of the real-time operation vector matrix and the operation mode of the source-network-load-storage integrated system, the matching degree is verified, and the operation mode with the highest matching degree is selected, specifically including: Based on the real-time operation vector matrix, calculate the score recurrence value of each operation mode per unit time; Verify the matching degree between the score recurrence value and the interval vector range of the operation mode to obtain the matching degree value; Based on the matching degree value, select the operation mode with the highest matching degree as the scheduling scheme.

[0013] Preferably, through the formula Quantify the real-time operation vector matrix to obtain the real-time parameter value ; is the original value of the th parameter at time t; is the th historical mean of the parameter, used for centering the data; is the th historical standard deviation of the parameter, used for scaling the data distribution; Calculate the score recurrence value through the formula ; In the formula, the score recurrence value represents the dynamic score of the th operation mode at the th time unit, reflecting the matching degree between the real-time data and the historical mode. The lower the score, the higher the matching degree; , are the upper and lower limits of the interval vector range respectively, is the real-time data fitting value, λ is the weight coefficient, 0 < λ < 1, reflecting the collaborative influence of the predicted value and the real-time parameter value ; When verifying the matching degree between the score recurrence value and the interval vector range of the operation mode, if the real-time parameter falls within the interval vector range , the matching degree value is 1; otherwise it is 0; Calculate the distance between the real-time parameter and the midpoint of the interval vector range, calculated through the formula ; The closer the

[0014] Preferably, combining the score recurrence value and the interval matching result, calculate the final matching degree value through the formula by weighted calculation ; In the formula, is the interval matching weight;​​ Select the largest mode as the scheduling scheme.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes to deeply analyze the changing trends of historical power demand, renewable energy power generation, energy storage device status, and grid load data. This method can accurately predict future power demand and power generation, providing a reliable basis for power scheduling. Through the operation mode matching of source-network-load-storage integration, the scheduling scheme is optimized, and the utilization efficiency of grid load and energy storage devices is improved; at the same time, considering the uncertainty of renewable energy, through real-time monitoring and optimized scheduling strategies, the consumption capacity of renewable energy is improved; in addition, low-carbon optimized scheduling can preferentially use green energy, reduce high carbon emissions, and promote the construction of a low-carbon power system. Based on the autoregressive moving average model and iterative clustering algorithm, the system dynamically adjusts the scheduling strategy, enhances the stability and flexibility of the power system, and effectively responds to the risks brought by load fluctuations and energy storage imbalances. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flowchart of a low-carbon optimized scheduling method based on source-network-load-storage integration of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations.

[0018] Referring to Figure 1 as shown, a low-carbon optimized scheduling method based on source-network-load-storage integration includes: Based on the environmental sensors of the source-network-load-storage integration system, obtain historical power demand, renewable energy power generation, energy storage device status, and grid load data; According to the operation mode of the source-network-load-storage integration system, screen the historical power demand, renewable energy power generation, energy storage device status, and grid load data to obtain the historical time series characteristic parameter data of the operation mode of the source-network-load-storage integration system; Based on the historical time series characteristic parameter data of the source-network-load-storage integration system, determine the interval vector range of the operation mode of the source-network-load-storage integration system; Obtain real-time power demand, renewable energy power generation, energy storage device status, and grid load data; According to the operation mode of the source-network-load-storage integration system, perform correlation mapping on the real-time power demand, renewable energy power generation, energy storage device status, and grid load data to obtain a real-time operation vector matrix; Verify the matching degree according to the interval vector range of the real-time operation vector matrix and the operation mode of the source-grid-load-storage integrated system, and select the operation mode with the highest matching degree as the dispatching plan.

[0019] Filter the historical power demand, renewable energy power generation, energy storage device status, and grid load data according to the operation mode of the source-grid-load-storage integrated system to obtain the historical time-series characteristic parameter data of the source-grid-load-storage integrated system, specifically including: Based on the historical power demand, renewable energy power generation, energy storage device status, and grid load data, calculate the correlation coefficient between each operation parameter and the operation mode of the source-grid-load-storage integrated system; Cluster the correlation coefficients between each operation parameter and the operation mode of the source-grid-load-storage integrated system to obtain an array of correlation characteristic parameters for the operation mode of the source-grid-load-storage integrated system; Based on the timestamps in the historical power demand, renewable energy power generation, energy storage device status, and grid load data, use the timestamp as the observation window and the array of correlation characteristic parameters for the operation mode of the source-grid-load-storage integrated system as the observation object to establish the historical time-series characteristic parameter data of the source-grid-load-storage integrated system; By analyzing the changing trends of power demand, renewable energy power generation, energy storage device status, and grid load, combined with the historical time-series characteristic parameter data, identify the interval vector ranges of different operation modes, and through feature extraction and trend analysis of time series, make more accurate predictions for real-time operation, and further predict the future operation mode through the autoregressive moving average model.

[0020] Based on the historical power demand, renewable energy power generation, energy storage device status, and grid load data, calculating the correlation coefficient between each operation parameter and the operation mode of the source-grid-load-storage integrated system specifically includes: Based on the operation mode of the source-grid-load-storage integrated system, mark the key operation parameters concerned by the system; Assign a weighted mean to each key operation parameter; Standardize the historical power demand, renewable energy power generation, energy storage device status, and grid load data, and construct a scatter plot of historical operation data; Based on the scatter plot of historical operation data, key operation parameters, and weighted means, calculate the correlation coefficient between each operation parameter and the key operation parameter in the historical operation data per unit time; The correlation coefficient is specifically: ; where is the correlation coefficient between the th key operation parameter and the th operation parameter, is the value of the th operation parameter, is the weighted mean of the key operating parameters, is the total number of operating parameters.

[0021] By calculating the correlation coefficient between each operating parameter and the system operating mode, and using the mean clustering algorithm to cluster the data, an array of correlation characteristic parameters of the system is obtained. This method improves the identification ability of complex operating modes and helps to select the most appropriate scheduling scheme for different power demands and load changes.

[0022] Cluster the correlation coefficients between each operating parameter and the operating mode of the source-network-load-storage integrated system to obtain an array of correlation characteristic parameters of the system operating mode, specifically including: Based on the correlation coefficients between each operating parameter and the source-network-load-storage integrated system, establish a correlation coefficient matrix, Perform iterative clustering through the mean clustering algorithm, specifically including: Step a Initialization: Randomly select K clustering centers in the correlation coefficient matrix as the initial cluster centroids ; i represents the index of the i-th clustering cluster, i = 1, 2, …, K; Step b Assign data points: Calculate the Euclidean distance between each data point and the current cluster centroid and assign each point to the nearest cluster to form a new cluster set ; k represents the number of iterations, that is, the clustering algorithm is currently in the k-th round of iteration; Step c Update cluster centers: Recalculate the centroids of each cluster ; is the number of data points in the i-th cluster; Step d Iteration termination: Repeat steps b - c until the change in the centroid is less than the threshold or the maximum number of iterations is reached. At this time, the centroid is the array of correlation characteristic parameters of the system operating mode.

[0023] Based on the historical time series characteristic data of the source-network-load-storage integrated system, determine the range of the interval vector of the operating mode, specifically including: Substitute the historical time series data into the autoregressive moving average model to predict the operating mode in the future unit time, and determine the range of the interval vector through the confidence interval of the error function.

[0024] The autoregressive moving average model is specifically: ; where is the predicted value at time, is the unit time, is the time offset, are all model coefficients, is the white noise error term; P and Q are the orders of autoregression and moving average respectively, which are determined by fitting historical data; Interval vector range From the predicted value and the confidence interval; Among them, ; Among them, is the quantile of the standard normal distribution corresponding to the confidence level α, and σ is the standard deviation of the prediction error.

[0025] According to the real-time operation vector matrix and the interval vector range of the operation mode of the source-network-load-storage integrated system, the matching degree is verified, and the operation mode with the highest matching degree is selected, specifically including: Based on the real-time operation vector matrix, calculate the score recursion value of each operation mode per unit time; Verify the matching degree between the score recursion value and the interval vector range of the operation mode to obtain the matching degree value; Based on the matching degree value, select the operation mode with the highest matching degree as the scheduling scheme; Specifically, through the formula Quantify the real-time operation vector matrix to obtain the real-time parameter value ; is the original value of the th parameter at time t; is the th historical mean of the parameter, which is used to centralize the data; is the th historical standard deviation of the parameter, which is used to scale the data distribution; Calculate the score recursion value through the formula ; In the formula, the score recursion value represents the dynamic score of the th operation mode at the th time unit, reflecting the matching degree between the real-time data and the historical mode. The lower the score, the higher the matching degree; , are the upper and lower limits of the interval vector range respectively, is the real-time data fitting value, λ is the weight coefficient, 0 < λ < 1, reflecting the predicted value and the synergistic effect of the real-time parameter value ; When verifying the matching degree between the score recursion value and the interval vector range of the operation mode, if the real-time parameter falls within the interval vector range calculate the distance between the real-time parameter and the midpoint of the interval vector range ​, calculated by the formula ; The closer the value is to 1, the higher the matching degree.

[0026] Combining the score recursion value and the interval matching result, through the formula The final matching degree value is calculated by weighting ; In the formula, is the interval matching weight; it can take the value of 0.7; Select the largest mode as the scheduling scheme.

[0027] By verifying the matching degree between the real-time operation vector matrix and the interval vector range of the historical operation mode, the most suitable scheduling scheme is screened out. This process can significantly reduce the wrong selection of the scheduling scheme and improve the system scheduling efficiency and grid stability based on the score recursion value and the matching degree verification.

[0028] To sum up, the advantages of the present invention are as follows: By deeply analyzing the changing trends of data such as historical power demand, renewable energy power generation, energy storage device status, and grid load, it can more accurately predict future power demand and power generation, thereby providing a more reliable basis for power dispatching; Through the operation mode matching of source-network-load-storage integration, it can select the most suitable scheduling scheme for the current state according to the correlation between real-time operation data and historical data, and optimize the use of grid load and energy storage devices; This method can comprehensively consider the uncertainty of renewable energy power generation and effectively improve the consumption capacity of renewable energy through real-time monitoring and scheduling strategies; Through low-carbon optimized scheduling, it can not only optimize energy consumption, but also achieve the priority scheduling of green energy, reduce the energy consumption of high carbon emissions, and promote the construction of a low-carbon power system; Based on the autoregressive moving average model and the iterative clustering algorithm, the system can dynamically adjust the scheduling strategy, improve the stability and flexibility of the power system, and reduce the risks brought by load fluctuations and energy storage imbalance; This method conducts in-depth analysis on the historical time-series characteristic parameter data of the source-network-load-storage integration system, combines real-time data, and conducts dynamic optimized scheduling to ensure the efficient operation of the power system under different times and conditions; On the basis of the traditional scheduling model, a calculation method of the correlation coefficient based on key operation parameters and weighted mean is introduced. Through cluster analysis and data mining, the correlation characteristic parameter array that can best represent the system operation characteristics is found, so as to accurately formulate the scheduling strategy; By clustering the associated feature parameters through the mean clustering algorithm, different operating modes can be accurately classified, making the power dispatching not only more refined, but also able to quickly respond according to the actual needs of the power system and optimize the energy configuration; After obtaining the real-time operation vector matrix, the matching degree is verified in combination with the interval vector range of the source-network-load-storage integrated system, and the best-matched operation mode is selected. This method can ensure the real-time performance and accuracy of the dispatching scheme, thereby improving the dispatching efficiency of the system.

[0029] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. A low-carbon optimization scheduling method based on source-grid-load-storage integration, characterized in that: include: Based on the environmental sensors of the source-grid-load-storage integrated system, historical power demand, renewable energy generation, energy storage equipment status and grid load data are obtained; According to the operation mode of the source-grid-load-storage integrated system, historical power demand, renewable energy generation, energy storage equipment status and grid load data are screened to obtain historical time series characteristic parameter data of the operation mode of the source-grid-load-storage integrated system; Based on the historical time series characteristic parameter data of the source-grid-load-storage integrated system, the interval vector range of the source-grid-load-storage integrated system operation mode is determined; Obtain real-time power demand, renewable energy generation, energy storage equipment status and grid load data; According to the operation mode of the source-grid-load-storage integrated system, the real-time power demand, renewable energy generation, energy storage equipment status and grid load data are correlated and mapped to obtain the real-time operation vector matrix; The matching degree is verified based on the interval vector range of the real-time operation vector matrix and the operation mode of the source-grid-load-storage integrated system, and the operation mode with the greatest matching degree is selected as the scheduling plan.

2. A low-carbon optimization scheduling method based on source-grid-load-storage integration according to claim 1, characterized in that: According to the operation mode of the source-grid-load-storage integrated system, the historical power demand, renewable energy generation, energy storage equipment status and grid load data are screened to obtain the historical time series characteristic parameter data of the source-grid-load-storage integrated system, including: Based on historical power demand, renewable energy generation, energy storage equipment status and grid load data, the correlation coefficient between each operating parameter and the operating mode of the source-grid-load-storage integrated system is calculated; Clustering the correlation coefficient between each operating parameter and the operating mode of the source-grid-load-storage integrated system to obtain an array of associated characteristic parameters of the operating mode of the source-grid-load-storage integrated system; Based on the timestamps in the historical power demand, renewable energy generation, energy storage equipment status and grid load data, the timestamp is used as the observation window, and the associated characteristic parameter array of the source-grid-load-storage integrated system operation mode is used as the observation object to establish the historical time series characteristic parameter data of the source-grid-load-storage integrated system.

3. A low-carbon optimization scheduling method based on source-grid-load-storage integration according to claim 2, characterized in that: The calculation of the correlation coefficient between each operating parameter and the operating mode of the source-grid-load-storage integrated system based on historical power demand, renewable energy power generation, energy storage equipment status and grid load data specifically includes: Based on the operation mode of the integrated source-grid-load-storage system, key operation parameters of the system are marked; Assign a weighted mean value to each key operating parameter; Standardize historical power demand, renewable energy generation, energy storage equipment status, and grid load data to construct a scatter plot of historical operating data; Based on the scatter plot, key operating parameters and weighted mean of the historical operating data, the correlation coefficient between each operating parameter in the historical operating data and the key operating parameters per unit time is calculated.

4. A low-carbon optimization scheduling method based on source-grid-load-storage integration according to claim 3, characterized in that: The correlation coefficient is specifically: ; In the formula, For the The key operating parameters and The correlation coefficient between the operating parameters is For the The value of the operating parameter, is the weighted mean of key operating parameters, is the total number of running parameters.

5. A low-carbon optimization scheduling method based on source-grid-load-storage integration according to claim 4, characterized in that: The correlation coefficient between each operating parameter and the operating mode of the source-grid-load-storage integrated system is clustered to obtain an array of associated characteristic parameters of the system operating mode, including: Based on the correlation coefficient between each operating parameter and the source-grid-load-storage integrated system, a correlation coefficient matrix is ​​established; Iterative clustering using the mean clustering algorithm: Step a: Initialization: Randomly select K cluster centers in the correlation coefficient matrix as the initial cluster centroids ; i represents the index of the i-th cluster, i=1, 2,…, K; Step b: Assign data points: Calculate the With the current cluster centroid Euclidean distance, assign each point to the nearest cluster to form a new cluster set ; k represents the number of iterations, that is, the clustering algorithm is currently in the kth iteration; Step c: Update cluster centers: recalculate the centroid of each cluster ; is the number of data points in the i-th cluster; Step d Iteration termination: Repeat steps b-c until the centroid changes When the value is less than the threshold or reaches the maximum number of iterations, the centroid is the associated characteristic parameter array of the system operation mode.

6. A low-carbon optimization scheduling method based on source-grid-load-storage integration according to claim 5, characterized in that: Based on the historical time series characteristic parameter data of the source-grid-load-storage integrated system, the interval vector range of the source-grid-load-storage integrated system operation mode is determined, including: Substitute the historical time series data into the autoregressive moving average model to predict the operation mode in the future unit time, and determine the interval vector range through the confidence interval of the error function.

7. A low-carbon optimization scheduling method based on source-grid-load-storage integration according to claim 6, characterized in that: The autoregressive moving average model is specifically: ; In the formula, for The predicted value at time, is the unit time, is the time offset, are model coefficients, is the white noise error term; P and Q are the orders of autoregression and moving average, respectively, which are determined by fitting historical data; Interval vector range By predicted value and confidence interval determination; in, ;in, is the standard normal distribution quantile corresponding to the confidence level α, and σ is the standard deviation of the prediction error.

8. A low-carbon optimization scheduling method based on source-grid-load-storage integration according to claim 7, characterized in that: The matching degree is verified based on the interval vector range of the real-time operation vector matrix and the operation mode of the source-grid-load-storage integrated system, and the operation mode with the greatest matching degree is selected, including: Based on the real-time operation vector matrix, the score recursive value of each operation mode per unit time is calculated; Verify the matching degree between the score recursive value and the interval vector range of the operation mode to obtain the matching degree value; Based on the matching value, the operation mode with the greatest matching degree is selected as the scheduling solution.

9. A low-carbon optimization scheduling method based on source-grid-load-storage integration according to claim 8, characterized in that: By formula Quantize the real-time running vector matrix to obtain the real-time parameter value ; is the time t The original value of the parameter; For the The historical mean of the parameters is used to centralize the data; For the The historical standard deviation of the parameters is used to scale the data distribution; By formula Calculate the score recursively ; In the formula, the score recursive value Indicates Time unit The dynamic score of each operating mode reflects the matching degree between real-time data and historical mode. The lower the score, the higher the matching degree. , are the upper and lower limits of the interval vector range, is the real-time data fitting value, λ is the weight coefficient, 0<λ<1, reflecting the predicted value With real-time parameter values synergistic impact; When verifying the matching degree between the score recursive value and the interval vector range of the operation mode, if the real-time parameter Falling in the interval vector range If the value is within, the matching value is 1; Otherwise, 0; Calculate real-time parameters Distance from the midpoint of the interval vector range , through the formula Calculated; The closer the value is to 1, the higher the match degree is.

10. A low-carbon optimization scheduling method based on source-grid-load-storage integration according to claim 9, characterized in that: Combine the score recursion value and the interval matching result, through the formula Weighted calculation of the final matching value ; In the formula, is the interval matching weight; choose The largest mode is used as the scheduling scheme.

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