Comprehensive Evaluation Method, System, Equipment and Medium for Adjustable Resources of Power Grid Source and Load

Abstract

The present invention discloses a comprehensive evaluation method, system, device and medium for adjustable resources of power grid source and load. By classifying the adjustable resources of the power grid in the target area and obtaining the historical power data of various types of adjustable resources; using the density peak clustering algorithm based on natural nearest neighbors to cluster these power data to generate a set of typical operation scenarios and their probabilities; constructing a complementary evaluation index system for adjustable resources of power grid source and load including supply-demand complementarity evaluation index, fluctuation complementarity evaluation index and energy complementarity evaluation index to quantify each complementary evaluation index under the set of typical operation scenarios; determining the weights of each complementary evaluation index through an improved CRITIC entropy weight combination weighting method, and calculating the comprehensive complementary score among various types of adjustable resources of the power grid based on the weighted complementary evaluation index and probability, so as to refine the overall source-load matching degree analysis to the adjustable resources of power grid source and load, and improve the evaluation accuracy of the adjustable resources of power grid source and load.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a comprehensive evaluation method, system, device and medium for adjustable resources of power grid source and load. Background Art

[0002] With the construction of a new power system, the proportion of renewable energy power generation represented by wind power and photovoltaic power generation has increased rapidly, resulting in enhanced volatility and uncertainty on the power generation side of the power system, posing higher requirements for the flexible regulation ability of the power system, and also making the evaluation of adjustable resources in the power grid more important.

[0003] In the prior art, for the evaluation of adjustable resources in the power grid, most focus on the matching evaluation of the overall new energy output and the overall load of the regional power grid, that is, clustering and source-load matching degree evaluation are carried out for a single source side or load side scenario through a clustering algorithm, without considering refining the overall source-load matching degree analysis to the adjustable resources of the source and load, resulting in inaccurate evaluation results.

[0004] Therefore, how to improve the evaluation accuracy of the existing scheme for the adjustable resources of the source and load in the regional power grid has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides a comprehensive evaluation method, system, device and medium for adjustable resources of power grid source and load, to solve the problem of how to improve the evaluation accuracy of the existing scheme for the adjustable resources of the source and load in the regional power grid.

[0006] To solve the above technical problem, in the first aspect of the present invention, a comprehensive evaluation method for adjustable resources of power grid source and load is provided, including:

[0007] Classify the adjustable resources of the source and load in the target regional power grid according to the power supply and consumption characteristics, and obtain the historical power curves of various adjustable resources to construct a power supply and consumption power database of the adjustable resources of the source and load;

[0008] Cluster the historical operation scenarios in the power supply and consumption power database of the adjustable resources of the source and load through a density peak clustering algorithm based on natural nearest neighbors to generate a set of typical operation scenarios and their probabilities;

[0009] Construct a complementary evaluation index system for the adjustable resources of the source and load in the target regional power grid to quantify each complementary evaluation index under the set of typical operation scenarios; the complementary evaluation index system for the adjustable resources of the source and load includes a supply-demand complementary evaluation index, a fluctuation complementary evaluation index and an energy complementary evaluation index;

[0010] Determine the weights of each of the complementary evaluation indicators by improving the CRITIC entropy weight combination weighting method, and quantify the comprehensive complementary score among various adjustable resources in the target regional power grid based on each of the weighted complementary evaluation indicators and each of the probabilities.

[0011] As one of the preferred solutions, the classification results of the source-load adjustable resources include source-side adjustable resources and load-side adjustable resources; the source-side adjustable resources include distributed photovoltaic and distributed wind power; the load-side adjustable resources include transferable load, curtailable load, and shiftable load in residential load, commercial load, and industrial load.

[0012] As one of the preferred solutions, the clustering of the historical operation scenarios in the source-load adjustable resources power supply and consumption database by using the density peak clustering algorithm based on natural nearest neighbors to generate a set of typical operation scenarios and their probabilities includes:

[0013] Obtain the historical power curves of various adjustable resources in the source-load adjustable resources power supply and consumption database, and perform normalization and combination processing on the historical power curves of the adjustable resources on the same day to generate a number of historical operation scenarios;

[0014] Use the density peak clustering algorithm based on natural nearest neighbors to cluster each of the historical operation scenarios, and use the clustering centers as typical operation scenarios to construct a set of typical operation scenarios;

[0015] Quantify the probabilities of each of the typical operation scenarios in the set of typical operation scenarios according to the number of historical operation scenarios contained in each of the typical operation scenarios.

[0016] As one of the preferred solutions, the use of the density peak clustering algorithm based on natural nearest neighbors to cluster each of the historical operation scenarios and using the clustering centers as typical operation scenarios to construct a set of typical operation scenarios includes:

[0017] Quantify the Euclidean distance between each of the historical operation scenarios to obtain a distance matrix, and quantify the natural nearest neighbor sets of each of the historical operation scenarios based on the Euclidean distance matrix;

[0018] Quantify the local density and relative distance of each of the historical operation scenarios through each of the natural nearest neighbor sets and the distance matrix, and select a number of clustering centers according to the product of each of the local densities and each of the relative distances;

[0019] Determine a number of typical operation scenarios according to each of the clustering centers, and construct the set of typical operation scenarios according to each of the typical operation scenarios.

[0020] As one of the preferred solutions, the supply-demand complementarity evaluation indicator is expressed by the following formula:

[0021]

[0022] Wherein, is the evaluation index of supply-demand complementarity; is the power of the a-th adjustable resource on the source side at time t; is the power of the b-th adjustable resource on the load side at time t; N is an integer;

[0023] The evaluation index of fluctuation complementarity is expressed by the following formula:

[0024]

[0025]

[0026] Wherein, is the evaluation index of fluctuation complementarity; is the power change rate of the a-th adjustable resource on the source side at time t; is the power change rate of the b-th adjustable resource on the load side at time t; is the time interval;

[0027] The evaluation index of energy complementarity is expressed by the following formula:

[0028]

[0029] Wherein, is the evaluation index of energy complementarity.

[0030] As one of the preferred solutions, before determining the weights of the complementarity evaluation indexes by the improved CRITIC entropy weight combination weighting method, it includes:

[0031] Standardize each of the complementarity evaluation indexes to obtain standardized indexes, and quantify the correlation between each of the complementarity evaluation indexes to obtain index correlations;

[0032] Quantify the information content of each of the complementarity evaluation indexes based on each of the index correlations to obtain index information contents, and determine the entropy values of each of the complementarity evaluation indexes according to each of the standardized indexes and each of the index information contents.

[0033] As one of the preferred solutions, determining the weights of each of the complementarity evaluation indexes by the improved CRITIC entropy weight combination weighting method, and quantifying the comprehensive complementarity score between various adjustable resources in the target regional power grid based on each of the weighted complementarity evaluation indexes and each of the probabilities, includes:

[0034] Based on each of the entropy values and each of the index information amounts, determine the weights of each of the complementary evaluation indicators by improving the CRITIC entropy weight combination weighting method; wherein, the weights are calculated by the following formula:

[0035]

[0036] In the formula, is the weight of the j-th complementary evaluation indicator; and are the entropy value and the index information amount of the j-th complementary evaluation indicator respectively; K is 3;

[0037] Weight each of the complementary evaluation indicators according to each of the weights, and determine the complementarity synthesis among various adjustable resources in the target regional power grid through each of the weighted complementary evaluation indicators and each of the probabilities, so as to output the score of the comprehensive evaluation of the source-load adjustable resources; wherein, the complementary synthesis score is calculated by the following formula:

[0038]

[0039] In the formula, is the complementary synthesis score between the a-th type of source-side adjustable resource and the b-th type of load-side adjustable resource; is the probability of the i-th typical operation scenario; is the value of the j-th complementary evaluation indicator under the i-th typical operation scenario.

[0040] The second aspect of the present invention provides a comprehensive evaluation system for power grid source-load adjustable resources, including:

[0041] A database construction module, configured to classify the source-load adjustable resources in the target regional power grid according to the power supply and consumption characteristics, and obtain the historical power curves of various adjustable resources, so as to construct a power supply and consumption power database of the source-load adjustable resources;

[0042] A scenario clustering module, configured to cluster the historical operation scenarios in the power supply and consumption power database of the source-load adjustable resources by using a density peak clustering algorithm based on natural nearest neighbors, and generate a set of typical operation scenarios and their probabilities;

[0043] An index construction module, configured to construct a complementary evaluation index system for the source-load adjustable resources in the target regional power grid to quantify each complementary evaluation index under the set of typical operation scenarios; the complementary evaluation index system for the source-load adjustable resources includes a supply-demand complementarity evaluation index, a fluctuation complementarity evaluation index, and an energy complementarity evaluation index;

[0044] A resource evaluation module is used to determine the weights of the complementary evaluation indicators by improving the CRITIC entropy weight combination weighting method, and quantify the comprehensive complementary score among various adjustable resources in the target regional power grid based on the weighted complementary evaluation indicators and various probabilities.

[0045] A third aspect of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the above-mentioned comprehensive evaluation method for adjustable power grid source and load resources is implemented.

[0046] A fourth aspect of the present invention provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. When the device where the computer-readable storage medium is located executes the computer program, the above-mentioned comprehensive evaluation method for adjustable power grid source and load resources is implemented.

[0047] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0048] (1) The original operation scenario set is clustered by using the density peak clustering algorithm based on natural nearest neighbors. On the basis of retaining the advantages of high efficiency and fast clustering of the original density peak clustering algorithm, the local density definition is improved based on natural nearest neighbors, and there is no need to artificially specify the truncation distance, realizing the rapid and accurate identification of data;

[0049] (2) A complementary evaluation index system based on supply-demand matching degree, fluctuation matching degree, and energy matching degree is established to quantify the complementary degree of adjustable source and load resources; at the same time, the complementary evaluation based on typical operation scenarios can represent the complementary situation of the operation scenarios of adjustable source and load resources throughout the cycle, thereby improving the accuracy and efficiency of complementary evaluation, and reducing complexity and calculation amount;

[0050] (3) The improved CRITIC entropy weight combination weighting method is used to determine the weights. The calculation process of this method is simple and not affected by subjective factors, and can objectively and accurately calculate the weights of evaluation indicators; and the present invention refines the overall source-load matching degree analysis to adjustable source and load resources, and can evaluate the complementarity between adjustable source and load resources in the regional power grid, providing guidance for the optimal scheduling of adjustable resources. Description of the Drawings

[0051] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for implementation will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0052] Figure 1 is a flowchart of a comprehensive evaluation method for adjustable resources of power grid source and load provided by an embodiment of the present invention;

[0053] Figure 2 is a structural diagram of a comprehensive evaluation system for adjustable resources of power grid source and load provided by an embodiment of the present invention;

[0054] Figure 3 is a structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0055] Next, in combination with the accompanying drawings and embodiments, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0056] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0057] In the description of the present application, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of illustration, rather than indicating or implying that the system or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation of the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0058] In the description of the present application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0059] In one embodiment, as Figure 1 shown, the first aspect of the present invention provides a comprehensive evaluation method for grid source-load adjustable resources, including:

[0060] S1. Classify the source-load adjustable resources in the target area power grid according to the power supply and consumption characteristics, and obtain the historical power curves of various types of adjustable resources to construct a power supply and consumption power database for source-load adjustable resources;

[0061] Specifically, the present invention classifies the source-load adjustable resources in the target area power grid according to the power supply and consumption characteristics, regards the adjustable resources for power supply as the source-side adjustable resources, and regards the adjustable resources for power consumption as the load-side adjustable resources. Then, in one embodiment, the classification results of the source-load adjustable resources include source-side adjustable resources and load-side adjustable resources; the source-side adjustable resources include distributed photovoltaic and distributed wind power; the load-side adjustable resources include transferable loads, reducible loads, and shiftable loads in residential loads, commercial loads, and industrial loads; for the source-load power supply and consumption adjustable resources with both source-side power supply characteristics and load-side power consumption characteristics (such as new loads such as electric vehicles and energy storage devices such as batteries), the present invention can use the daily power interaction volume of the resource as the judgment basis to add it to the source side or the load side. For example, if the discharge amount of the battery on a certain day is A1 and the charge amount is A2, then when A1 < A2, it means that the battery discharges more than it charges on that day, so the battery can be determined as a source-side adjustable resource on that day. Similarly, when A1 > A2, the battery can be determined as a load-side adjustable resource on that day. Based on the above principle, the source-load power supply and consumption adjustable resources can be allocated to the source-side adjustable resources or the load-side adjustable resources at the corresponding time. Then, on an annual basis, obtain the historical daily power curves of various types of adjustable resources in the same period from channels such as the power grid dispatching system, power generation enterprises, and power consumption enterprises, respectively, and use the threshold detection method and the maximum likelihood method to detect and correct the abnormal data in these historical daily power curves to unify the data granularity, and design a reasonable database structure, including table structure, field definition, index, etc. After sorting and plotting the obtained historical power curve data, import it into the database to form a power supply and consumption power database for source-load adjustable resources. The present invention refines the previous overall matching degree analysis of the source and load to the source-side and load-side adjustable resources, can evaluate the complementarity between various source-load adjustable resources in the regional power grid, and provides guidance for the optimal dispatching of adjustable resources.

[0062] S2. Cluster the historical operation scenarios in the power supply and consumption database of the source-load adjustable resources through the density peak clustering algorithm based on natural nearest neighbors to generate a set of typical operation scenarios and their probabilities;

[0063] In one embodiment, step S2 includes:

[0064] Obtain the historical power curves of various adjustable resources in the power supply and consumption database of the source-load adjustable resources, and perform normalization and combination processing on the historical power curves of the adjustable resources on the same day to generate a number of historical operation scenarios;

[0065] Cluster each of the historical operation scenarios by using the density peak clustering algorithm based on natural nearest neighbors, and use the clustering center as a typical operation scenario to construct a set of typical operation scenarios;

[0066] Quantify the probabilities of the typical operation scenarios according to the number of historical operation scenarios included in each typical operation scenario in the set of typical operation scenarios.

[0067] Specifically, due to the strong volatility and uncertainty of the source-load adjustable resources, the output curves on the source side and the load curves in the same year lack regularity. It is impossible to accurately evaluate the complementarity of the source-load resources based on the historical data in a single scenario. Moreover, clustering the historical power curves of the source-load adjustable resources separately will ignore the power time-series correlation of the source-load adjustable resources. Based on this, the present invention combines the power curves on the source side and the load side to form a structured set of original operation scenarios, and clusters them, reducing a large amount of historical data to several typical operation scenarios, which includes:

[0068] Obtain n historical daily power curves of the a-th type of source-side adjustable resource from the power supply and consumption database of the source-load adjustable resources, and denote the i-th daily curve as , where N is an integer, taking 1, 2,..., 24; then obtain n historical daily power curves of the b-th type of load-side adjustable resource that is on the same day as the source-side adjustable resource a from the power supply and consumption database of the source-load adjustable resources, and denote the i-th daily curve as ; and perform normalization processing on the daily power curve data of the two types of adjustable resources to unify the data magnitude and ensure data comparability; among them, the normalization method is as shown in the following formula:

[0069]

[0070] In the formula, and are the data before and after normalization, that is, the historical daily power curve data of various adjustable resources; is the mean value of the data , is the standard deviation of the data .

[0071] Subsequently, the power curve data of the source-side and load-side adjustable resources on the i-th day after normalization are combined to obtain a number of historical operation scenarios, which are represented by the following formula:

[0072]

[0073] In the formula, is the i-th historical operation scenario.

[0074] Finally, the density peak clustering algorithm based on natural nearest neighbors is used to cluster these historical operation scenarios, and the cluster centers are used as typical operation scenarios to form a set of typical operation scenarios. And the probability p of each typical operation scenario is calculated according to the number of historical operation scenarios included in each type of typical operation scenario in the set of typical operation scenarios i , which is calculated by the following formula: , in the formula, v i is the number of historical operation scenarios included in each type of typical operation scenario; n is the total number of historical operation scenarios.

[0075] The present invention realizes the effective integration of a large amount of historical data by extracting the historical power curves of various adjustable resources from the power supply and consumption database of source-load adjustable resources; normalizes the historical power curves of the adjustable resources on the same day to eliminate the differences in power magnitudes of different resources, making the data more comparable; the historical operation scenarios generated after combined processing provide a rich and standardized data set for subsequent clustering analysis; the generated historical operation scenarios can comprehensively reflect the operation states of the source-load adjustable resources at different times and under different conditions, which helps to more accurately grasp the operation rules and potential risks of the resources; the set of typical operation scenarios can summarize and reflect the main operation states and change trends of the source-load adjustable resources, providing an important reference basis for the planning, dispatching and optimization of the power system, and helping to improve the operation efficiency and safety of the system.

[0076] In another embodiment, the clustering of typical scenarios can also be realized by combining the time series alignment method, the clustering algorithm and the probability model, including:

[0077] By using the Dynamic Time Warping (DTW) method to calculate the similarity between the power curves of various adjustable resources, that is, using the DTW distance as the similarity metric, calculate the similarity between the power curve data of the adjustable resources on the source side and the load side after normalization on the i-th day; then adopt K-Shape clustering, a clustering based on shape similarity, and adapt to the morphological differences of power curves by iteratively optimizing the cluster centers and data alignment: 1. Initialize the centroids, and use the similarity calculated by DTW to align all curves with the centroids; 2. Calculate the shape similarity based on the normalized cross-correlation, that is, use the cross-correlation distance to calculate the distance between each power curve and the cluster center, and assign each curve to the cluster of the nearest cluster center based on this distance; 3. Update the centroids to the average shape of the curves within the current cluster, that is, for each cluster, recalculate its cluster center, and the update of the cluster center can be obtained by the average or median of all curves in the cluster under a certain distance metric; repeat steps 2 and 3 until convergence, and output the clustering result; then, based on the K-Shape clustering result, use the Gaussian Mixture Model GMM (that is, a probability model used to represent the data distribution, which can assign the probability of belonging to different clusters to each data point) to perform probability correction on the clustering result. Through GMM, the probability distribution of each power curve belonging to different typical operation scenarios can be obtained; finally, according to the result of K-Shape clustering and the probability distribution of GMM, extract the typical operation scenarios to construct a set of typical operation scenarios, and each typical operation scenario corresponds to a probability value, indicating the frequency or possibility of this scenario appearing in the historical data.

[0078] The present invention combines the DTW and K-Shape algorithms, which can fully consider the characteristics of time series data such as time series, trend, and local changes, thereby improving the accuracy of clustering; by using DTW to capture the non-linear alignment relationship between time series, the clustering result can better reflect the morphological characteristics of the power curve; using GMM for probability correction can assign the probability of belonging to different clusters to each power curve, thereby enhancing the reliability of the clustering result; the generated set of typical operation scenarios and their probabilities can provide strong support for the scheduling, optimization, and management of the power system, helping decision-makers better understand and predict the operation behavior of adjustable resources; this method combines the advantages of multiple algorithms, has strong adaptability and flexibility, and can be applied to the historical power curve data of different types and scales of adjustable resources.

[0079] In an embodiment, clustering the historical operation scenarios by using the density peak clustering algorithm based on natural nearest neighbors, and taking the cluster centers as the typical operation scenarios to construct a set of typical operation scenarios, includes:

[0080] Quantify the Euclidean distance between each of the historical operation scenarios to obtain a distance matrix, and quantify the natural nearest neighbor set of each of the historical operation scenarios based on the Euclidean distance matrix;

[0081] Quantify the local density and relative distance of each of the historical operation scenarios through each of the natural nearest neighbor sets and the distance matrix, and select a number of clustering centers according to the product of each of the local densities and each of the relative distances;

[0082] Determine a number of typical operation scenarios according to each of the clustering centers, so as to construct the set of typical operation scenarios according to each of the typical operation scenarios.

[0083] In this embodiment, first calculate the Euclidean distance between the i-th and j-th historical operation scenarios , and form a distance matrix; then calculate the natural nearest neighbor set of each historical operation scenario : Let the number of the nearest neighbors of each scenario be KN, the value of KN starts from 1 and increases by 1 each time. For each operation scenario, sort the Euclidean distances between this scenario and other scenarios, and select KN nearest neighbors in ascending order of the Euclidean distance to form the nearest neighbor set of this scenario. Calculate the number of times each scenario appears in the nearest neighbor sets of other scenarios. When all scenarios have appeared at least once, the value of KN no longer changes. Based on this KN value, the natural nearest neighbor set corresponding to each scenario can be obtained; subsequently, quantify its local density and relative distance based on the natural nearest neighbor sets and the distance matrix of each historical operation scenario, as shown in the following formula:

[0084]

[0085]

[0086] In the formula, is the local density of the i-th historical operation scenario; is the relative distance of the i-th historical operation scenario;

[0087] Finally, take the product of the local density and relative distance under the i-th historical operation scenario as the clustering center, that is, the i-th typical operation scenario, and construct the set of typical operation scenarios based on this , where represents the -th clustering center, that is, the -th typical operation scenario, and M represents the number of clustering centers, that is, the number of typical operation scenarios.

[0088] The present invention uses a density peak clustering algorithm based on natural nearest neighbors to cluster the historical operation scenario set. This algorithm improves the definition of local density based on natural nearest neighbors while retaining the advantages of high efficiency and fast clustering of the original density peak clustering algorithm. It does not require manual specification of the truncation distance and is applicable to the rapid and accurate identification of various data sets. Moreover, the complementary evaluation based on typical operation scenarios can represent the complementary situation of the source-load adjustable resources in the entire cycle, thereby improving the accuracy and efficiency of the complementary evaluation, and reducing the complexity and computational amount.

[0089] S3. Construct a complementary evaluation index system for the source-load adjustable resources of the target regional power grid to quantify each complementary evaluation index under the typical operation scenario set; the complementary evaluation index system for the source-load adjustable resources includes a supply-demand complementary evaluation index, a fluctuation complementary evaluation index, and an energy complementary evaluation index.

[0090] Specifically, the present invention constructs a complementary evaluation index system for the source-load adjustable resources based on the time-series characteristics of the power curves on the source and load sides, and based on the supply-demand matching degree, fluctuation matching degree, and energy matching degree. Through this index system, the complementary degree of the source-load adjustable resources can be quantified. Among them, the supply-demand complementary evaluation index uses the Euclidean distance to calculate the distance between the source-load power time series, and can measure the overall complementary degree of power supply and demand within a certain period, that is, it is used to measure the similarity of the daily power curves of the source-load adjustable resources, and is expressed by the following formula:

[0091]

[0092] In the formula, is the supply-demand complementary evaluation index; is the power of the a-th type of adjustable resource on the source side at time t; is the power of the b-th type of adjustable resource on the load side at time t; the value range of the supply-demand complementary evaluation index is , the closer it is to 1, the greater the similarity between the daily power curves of the source-load adjustable resources and the stronger the supply-demand complementarity.

[0093] In addition to the power supply-demand complementary relationship, the source-load adjustable resources may also have consistency in the fluctuation trend. Therefore, the present invention defines a fluctuation complementary evaluation index based on the source-load fluctuation change rate to analyze the source-load complementarity from the perspective of fluctuation matching, that is, to measure the similarity of the fluctuation trends of the daily power curves of the source-load adjustable resources. The fluctuation complementary evaluation index is expressed by the following formula:

[0094]

[0095]

[0096] In the formula, is the fluctuation complementary evaluation index; is the power change rate of the a-th type of adjustable resource on the source side at time t; is the power change rate of the b-th type of adjustable resource on the load side at time t; is the time interval; the value range of the fluctuation complementarity evaluation index is , the closer it is to 1, the greater the fluctuation similarity between the daily power curves of the adjustable resources on the source and load sides, and the stronger the fluctuation complementarity.

[0097] To more comprehensively reflect the power supply-demand relationship between the source and load, and evaluate the consumption of the output of the adjustable resources on the source side throughout the day, the present invention sets an energy complementarity evaluation index to analyze the source-load complementarity from the perspective of energy matching, that is, to measure the consumption degree of the output of the adjustable resources on the source side. The energy complementarity evaluation index is expressed by the following formula:

[0098]

[0099] In the formula, is the energy complementarity evaluation index; the value range of the energy complementarity evaluation index is , the closer it is to 1, the higher the consumption degree of the output of the adjustable resources on the source side, and the stronger the energy complementarity between the source and load. When it is equal to 1, it indicates that the output of the adjustable resources on the source side is fully consumed.

[0100] Then, based on the constructed complementary evaluation index system of the adjustable resources on the source and load sides, each complementary evaluation index in the typical operation scenario set is quantified. The present invention constructs a complementary evaluation index system of the adjustable resources on the source and load sides from three perspectives: supply-demand complementarity, fluctuation complementarity, and energy complementarity. By quantifying the differences and complementarities between supply and demand, it helps to optimize the power source layout and load management, ensuring the stable operation and efficient utilization of the power grid; by quantifying the fluctuation complementarity, it can guide the configuration and scheduling of the energy storage system to suppress the fluctuations of new energy power generation and improve the stability and reliability of the power grid; by quantifying the energy complementarity, it helps to optimize the energy structure, improve the energy utilization efficiency, and reduce the dependence on traditional energy; and it can comprehensively, intuitively, and quantitatively reflect the complementary relationship of the adjustable resources on the source and load sides, providing assistance for the planning and construction of future distributed new energy power generation or demand response, thus ensuring the safe and stable operation of the power system.

[0101] S4. Determine the weights of each of the complementary evaluation indexes by improving the CRITIC entropy weight combination weighting method, and quantify the comprehensive complementary score among various types of adjustable resources in the target regional power grid based on each of the weighted complementary evaluation indexes and each of the probabilities;

[0102] In one embodiment, before determining the weights of each of the complementary evaluation indexes by improving the CRITIC entropy weight combination weighting method, it includes:

[0103] Standardize each of the complementary evaluation indicators to obtain standardized indicators, and quantify the correlation between each of the complementary evaluation indicators to obtain the indicator correlation;

[0104] Quantify the information content of each of the complementary evaluation indicators based on each of the indicator correlations to obtain the indicator information content, and determine the entropy value of each of the complementary evaluation indicators according to each of the standardized indicators and each of the indicator information contents.

[0105] Specifically, the present invention first standardizes each of the calculated complementary evaluation indicators, which is carried out by the following formula:

[0106]

[0107] In the formula, and are the values of the j-th complementary evaluation indicator in the i-th typical operation scenario before and after standardization, respectively; where k = 1, 2, 3, then and and are the values of the supply-demand complementarity evaluation indicator, the fluctuation complementarity evaluation indicator, and the energy complementarity evaluation indicator in the i-th typical operation scenario, respectively.

[0108] Then quantify the correlation between each of the complementary evaluation indicators , which is carried out by the following formula:

[0109]

[0110] In the formula, is the average value of the p-th complementary indicator; is the average value of the q-th complementary indicator; is the value of the p-th complementary indicator in the i-th typical operation scenario; is the value of the q-th complementary indicator in the i-th typical operation scenario; where p and q can both take 1, 2, 3. The closer it is to 1, the greater the correlation between the indicators.

[0111] Then quantify the information content of each of the complementary evaluation indicators based on the calculated indicator correlations, which is carried out by the following formula:

[0112]

[0113] In the formula, is the indicator information content of the j-th complementary evaluation indicator; is the standard deviation of the j-th complementary evaluation indicator; is the correlation between the k-th and j-th complementary evaluation indicators; K is 3.

[0114] Finally, the entropy value of each complementary evaluation index is determined according to each standardized index and the information amount of each index, which is carried out by the following formula:

[0115]

[0116] In the formula, is the entropy value of the j-th complementary evaluation index; m ij is the proportion of the j-th complementary evaluation index in the i-th typical operation scenario, which can also be called the normalization result.

[0117] By standardizing each complementary index in the present invention, comparability is achieved among the indexes, laying a foundation for subsequent quantification of the correlation degree and calculation of the entropy value; by quantifying the correlation degree among the complementary evaluation indexes, the internal connection and mutual influence among the indexes can be revealed, which helps to deeply understand the overall structure of the evaluation system for subsequent optimization of the weight assignment to ensure that the weight assignment is more reasonable and scientific, thereby improving the accuracy and reliability of the evaluation result; by calculating the entropy value of each complementary evaluation index, the dispersion degree and uncertainty of the information contained in the index can be reflected, serving as an important basis for decision-making analysis to help decision-makers identify key indexes and potential risks, thereby formulating a more effective decision-making scheme.

[0118] In an embodiment, step S4 includes:

[0119] Based on each of the entropy values and the information amount of each index, the weight of each complementary evaluation index is determined by an improved CRITIC entropy weight combination weighting method; wherein, the weight is calculated by the following formula:

[0120]

[0121] In the formula, is the weight of the j-th complementary evaluation index;

[0122] Each complementary evaluation index is weighted according to each of the weights, and the comprehensive complementary score among various adjustable resources in the target regional power grid is determined through the weighted complementary evaluation indexes and each of the probabilities, and is output as the comprehensive evaluation result of the source-load adjustable resources; wherein, the comprehensive complementary score is calculated by the following formula:

[0123]

[0124] In the formula, is the comprehensive complementary score between the a-th type of source-side adjustable resource and the b-th type of load-side adjustable resource, and the higher its value, the higher the complementarity between the two types of source-load resources a and b.

[0125] In the comprehensive evaluation process, the weights of each evaluation index are closely related to the credibility of the evaluation results. To ensure the accuracy and objectivity of the weights, it is necessary to fully consider the differences and correlations among the evaluation indexes. Therefore, the present invention uses an improved CRITIC entropy weight combination weighting method to determine the weights. This method has a simple calculation process and is not affected by subjective factors, and can objectively and accurately calculate the weights of the evaluation indexes.

[0126] In another embodiment, the present invention can also use the entropy weight method to determine the weights of each complementary evaluation index; it can also adopt an innovative hybrid algorithm framework that combines subjective and objective methods, that is, combines the coefficient of variation method, the analytic hierarchy process and fuzzy comprehensive evaluation, and optimizes the weight distribution through game theory combined weighting, and then obtains the weights of each complementary evaluation index, which includes the following steps: First, perform data standardization processing on each complementary evaluation index to eliminate the influence of dimensions, calculate the mean and standard deviation of each complementary evaluation index to obtain the corresponding coefficient of variation, and normalize the coefficient of variation to obtain the objective weight W obj ; Then construct a fuzzy judgment matrix: Experts use triangular fuzzy numbers (such as "(2, 3, 4)" corresponding to "slightly important") to replace the traditional 1-9 scale to reduce the fuzziness of subjective judgment. At the same time, verify the rationality of the matrix through the fuzzy consistency index (FCR). If it fails, adjust the fuzzy number boundary and use the fuzzy analytic hierarchy process (FAHP) to solve the subjective weight W sub ; Finally, based on the subjective and objective weights, define the subjective and objective weight vectors (that is, W 1 = W obj , W 2 = W sub ), and construct a combined weight (W = αW 1 + βW 2 , α and β are combined coefficients) based on this. Through game theory optimization, minimize the deviation between the combined weight and each single weight to obtain a deviation optimization model (min‖W - W 1 ‖ 2 + ‖W - W 2 ‖ 2 ). After solving this model, obtain the optimal coefficients α and β, and obtain the final weights after normalization. The present invention realizes multi-angle weight fusion by taking into account data objectivity, expert experience and fuzzy information processing, improves the scientificity of weight distribution, and effectively improves the evaluation accuracy of grid source-load adjustable resources.

[0127] In the embodiments of the present application, in view of the problem of how to improve the evaluation accuracy of the existing solution for the adjustable resources of the regional power grid source and load, a comprehensive evaluation method for the adjustable resources of the power grid source and load is designed. It classifies the adjustable resources of the source and load according to the power supply and consumption characteristics to construct a database, and uses the density peak clustering algorithm based on natural nearest neighbors to cluster the original operation scenario set in the database to obtain typical operation scenarios. On the basis of retaining the advantages of the efficient and fast clustering of the original density peak clustering algorithm, the local density definition is improved based on natural nearest neighbors, and there is no need to artificially specify the truncation distance, realizing the rapid and accurate identification of data; a complementary evaluation index system based on the supply-demand matching degree, fluctuation matching degree and energy matching degree is established to quantify the complementary degree between the adjustable resources of the source and load under each typical operation scenario, representing the complementary situation of the adjustable resources of the source and load in the operation scenario during the whole cycle, thereby improving the evaluation accuracy and evaluation efficiency of the complementarity, and reducing the complexity and calculation amount; finally, the improved CRITIC entropy weight combination weighting method is used to determine the weights to weight each calculated complementary evaluation index, and a comprehensive score is obtained as the output of the comprehensive evaluation result of the adjustable resources of the source and load in the target regional power grid; the overall source-load matching degree analysis is refined to the adjustable resources of the source and load, which can evaluate the complementarity between the adjustable resources of the source and load in the regional power grid, provide guidance for the optimal dispatching of adjustable resources, and effectively improve the evaluation accuracy of the adjustable resources of the power grid source and load.

[0128] It should be noted that although the steps in the above flowchart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders.

[0129] In another embodiment, as Figure 2 shown, the second aspect of the present invention provides a comprehensive evaluation system for the adjustable resources of the power grid source and load, including:

[0130] A database construction module 10, configured to classify the adjustable resources of the source and load in the target regional power grid according to the power supply and consumption characteristics, and obtain the historical power curves of various types of adjustable resources to construct a power supply and consumption power database for the adjustable resources of the source and load;

[0131] A scenario clustering module 20, configured to cluster the historical operation scenarios in the power supply and consumption power database of the adjustable resources of the source and load through a density peak clustering algorithm based on natural nearest neighbors, and generate a typical operation scenario set and its probability;

[0132] An index construction module 30 is configured to construct a complementary evaluation index system for the adjustable resources of the power source and load in the target regional power grid to quantify each complementary evaluation index under the set of typical operation scenarios. The complementary evaluation index system for the adjustable resources of the power source and load includes a supply-demand complementarity evaluation index, a fluctuation complementarity evaluation index, and an energy complementarity evaluation index.

[0133] A resource evaluation module 40 is configured to determine the weights of the complementary evaluation indexes by an improved CRITIC entropy weight combination weighting method, and quantify the comprehensive complementary score among various types of adjustable resources in the target regional power grid based on the weighted complementary evaluation indexes and probabilities.

[0134] It should be noted that each module in the above comprehensive evaluation system for the adjustable resources of the power source and load in the power grid can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules. For the specific limitations of the comprehensive evaluation system for the adjustable resources of the power source and load in the power grid, refer to the limitations of the comprehensive evaluation method for the adjustable resources of the power source and load in the power grid in the above text. They have the same functions and effects and will not be elaborated here.

[0135] A third aspect of the present invention provides an electronic device, which includes:

[0136] A processor, a memory, and a bus;

[0137] The bus is used to connect the processor and the memory;

[0138] The memory is used to store operation instructions;

[0139] The processor is configured to execute the operations corresponding to the comprehensive evaluation method for the adjustable resources of the power source and load in the power grid as shown in the first aspect of the present application by calling the operation instructions.

[0140] In an optional embodiment, an electronic device is provided, as Figure 3 shown, Figure 3 The electronic device 5000 shown includes a processor 5001 and a memory 5003. Among them, the processor 5001 and the memory 5003 are connected, such as through a bus 5002. Optionally, the electronic device 5000 may further include a transceiver 5004. It should be noted that in practical applications, the transceiver 5004 is not limited to one, and the structure of the electronic device 5000 does not constitute a limitation to the embodiments of the present application.

[0141] The processor 5001 can be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in connection with the disclosure of this application. The processor 5001 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0142] The bus 5002 can include a path for transmitting information between the above components. The bus 5002 can be a PCI bus or an EISA bus, etc. The bus 5002 can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 3 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0143] The memory 5003 can be a ROM or other types of static storage devices that can store static information and instructions, a RAM, or other types of dynamic storage devices that can store information and instructions. It can also be an EEPROM, a CD-ROM, or other optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0144] The memory 5003 is used to store the application program code for executing the solution of this application, and is controlled by the processor 5001 to execute. The processor 5001 is used to execute the application program code stored in the memory 5003 to implement the content shown in any of the foregoing method embodiments.

[0145] Among them, the electronic device includes but is not limited to: mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0146] The fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements a comprehensive evaluation method for grid source-load adjustable resources shown in the first aspect of this application.

[0147] Another embodiment of this application provides a computer-readable storage medium, on which a computer program is stored, and when it runs on a computer, it enables the computer to execute the corresponding content in the foregoing method embodiments.

[0148] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

[0149] In summary, the present invention relates to the technical field of data processing, and discloses a comprehensive evaluation method, system, device and medium for adjustable power sources and loads in a power grid. By classifying the adjustable power sources and loads in the target area power grid and obtaining the historical power data of various adjustable power sources; using the density peak clustering algorithm based on natural nearest neighbors to cluster these power data to generate a set of typical operation scenarios and their probabilities; constructing a complementary evaluation index system for adjustable power sources and loads in the power grid, including supply-demand complementarity evaluation indexes, fluctuation complementarity evaluation indexes and energy complementarity evaluation indexes, to quantify each complementary evaluation index under the set of typical operation scenarios; determining the weights of each complementary evaluation index by improving the CRITIC entropy weight combination weighting method, and calculating the comprehensive complementary score between various adjustable power sources in the power grid based on the weighted complementary evaluation indexes and probabilities, so as to refine the overall source-load matching degree analysis to the adjustable power sources and loads in the power grid, and improve the evaluation accuracy of the adjustable power sources and loads in the power grid.

[0150] Each embodiment in this specification is described in a progressive manner. For parts that are the same or similar in each embodiment, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for the relevant parts, reference can be made to the partial description of the method embodiment. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered that the scope described in this specification.

[0151] The above embodiments only represent several preferred implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be pointed out that for those of ordinary skill in the art in this technical field, without departing from the technical principle of the present invention, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the protection scope of the claimed rights.

Claims

1. A comprehensive evaluation method for adjustable power grid source and load resources, characterized in that: include: Classify the source-load adjustable resources in the target area power grid according to the power supply and consumption characteristics, and obtain the historical power curves of various adjustable resources to build a power supply and consumption database of source-load adjustable resources; Clustering the historical operation scenarios in the source-load adjustable resource power supply and consumption database by using a density peak clustering algorithm based on natural nearest neighbors to generate a set of typical operation scenarios and their probabilities; Constructing a source-load adjustable resource complementarity evaluation index system of the target regional power grid to quantify various complementarity evaluation indicators under the typical operation scenario set; the source-load adjustable resource complementarity evaluation index system includes supply-demand complementarity evaluation index, fluctuation complementarity evaluation index and energy complementarity evaluation index; The weights of the complementarity evaluation indicators are determined by improving the CRITIC entropy weight combination weighting method, and the comprehensive scores of complementarity between various adjustable resources in the target area power grid are quantified based on the weighted complementarity evaluation indicators and the probabilities; The supply-demand complementarity evaluation index is expressed by the following formula: In the formula, It is an evaluation indicator of supply and demand complementarity; is the power of the adjustable resource on the a-th source side at time t; is the power of the b-th load-side adjustable resource at time t; N is an integer; The volatility complementarity evaluation index is expressed by the following formula: In the formula, It is the evaluation index of volatility complementarity; is the power change rate of the adjustable resource on the a-th source side at time t; is the power change rate of the b-th load-side adjustable resource at time t; is the time interval; The energy complementarity evaluation index is expressed by the following formula: In the formula, It is an evaluation index of energy complementarity.

2. A comprehensive evaluation method for power grid source-load adjustable resources according to claim 1, characterized in that: The classification results of the source-load adjustable resources include source-side adjustable resources and load-side adjustable resources; the source-side adjustable resources include distributed photovoltaic and distributed wind power; the load-side adjustable resources include transferable loads, curtailable loads and shiftable loads among residential loads, commercial loads and industrial loads.

3. A comprehensive evaluation method for power grid source-load adjustable resources according to claim 1, characterized in that: The historical operation scenarios in the source-load adjustable resource power supply and consumption database are clustered by a density peak clustering algorithm based on natural nearest neighbors to generate a typical operation scenario set and its probability, including: Obtaining historical power curves of various adjustable resources in the power supply and consumption database of the source-load adjustable resources, and normalizing and combining the historical power curves of the adjustable resources on the same day to generate several historical operation scenarios; Clustering each of the historical operation scenarios using a density peak clustering algorithm based on natural nearest neighbors, and taking the cluster center as a typical operation scenario to construct a typical operation scenario set; The probability of each typical operation scenario is quantified according to the number of historical operation scenarios contained in each typical operation scenario in the typical operation scenario set.

4. A comprehensive evaluation method for power grid source-load adjustable resources according to claim 3, characterized in that: The method of clustering the historical operation scenarios by using a density peak clustering algorithm based on natural nearest neighbors, and taking the cluster center as a typical operation scenario to construct a typical operation scenario set includes: Quantifying the Euclidean distances between the historical operation scenarios to obtain a distance matrix, and quantifying the natural nearest neighbor sets of the historical operation scenarios based on the Euclidean distance matrix; quantifying the local density and relative distance of each of the historical operation scenarios through each of the natural nearest neighbor sets and the distance matrix, and selecting a number of cluster centers according to the product of each of the local densities and each of the relative distances; A number of typical operation scenarios are determined according to each of the cluster centers, so as to construct the typical operation scenario set according to each of the typical operation scenarios.

5. A comprehensive evaluation method for power grid source-load adjustable resources according to claim 1, characterized in that: Before determining the weights of the complementary evaluation indicators by improving the CRITIC entropy weight combination weighting method, the method includes: Standardizing the complementary evaluation indicators to obtain standardized indicators, and quantifying the correlation between the complementary evaluation indicators to obtain indicator correlation; The information amount of each of the complementary evaluation indicators is quantified based on the correlation of each of the indicators to obtain the indicator information amount, and the entropy value of each of the complementary evaluation indicators is determined according to each of the standardized indicators and the indicator information amount.

6. A comprehensive evaluation method for power grid source-load adjustable resources according to claim 5, characterized in that: The weights of the complementarity evaluation indicators are determined by improving the CRITIC entropy weight combination weighting method, and the comprehensive scores of complementarity between various adjustable resources in the target regional power grid are quantified based on the weighted complementarity evaluation indicators and the probabilities, including: Based on the entropy values ​​and the information content of each indicator, the weight of each complementarity evaluation indicator is determined by improving the CRITIC entropy weight combination weighting method; wherein the weight is calculated by the following formula: In the formula, is the weight of the jth complementarity evaluation index; , are the entropy value and information content of the jth complementarity evaluation index respectively; K is 3; The complementarity evaluation indicators are weighted according to the weights, and the comprehensive complementarity scores between various adjustable resources in the target area power grid are determined by the weighted complementarity evaluation indicators and the probabilities to output as the comprehensive evaluation result of the source-load adjustable resources; wherein the comprehensive complementarity score is calculated by the following formula: In the formula, is the comprehensive score of complementarity between the adjustable resources on the source side of type a and the adjustable resources on the load side of type b; is the probability of the i-th typical operation scenario; is the value of the jth complementarity evaluation index under the i-th typical operation scenario.

7. A comprehensive evaluation system for adjustable power grid source and load resources, characterized in that: include: A database construction module is used to classify the source-load adjustable resources in the target area power grid according to the power supply and consumption characteristics, and obtain the historical power curves of various adjustable resources to build a power supply and consumption database of source-load adjustable resources; A scenario clustering module, used to cluster the historical operation scenarios in the source-load adjustable resource supply and consumption power database by using a density peak clustering algorithm based on a natural nearest neighbor, and generate a set of typical operation scenarios and their probabilities; An indicator construction module is used to construct a source-load adjustable resource complementarity evaluation index system of the target area power grid to quantify various complementarity evaluation indicators under the typical operation scenario set; the source-load adjustable resource complementarity evaluation index system includes supply-demand complementarity evaluation index, fluctuation complementarity evaluation index and energy complementarity evaluation index; A resource evaluation module, used to determine the weights of the complementary evaluation indicators by improving the CRITIC entropy weight combination weighting method, and quantify the comprehensive complementarity scores between various adjustable resources in the target area power grid based on the weighted complementary evaluation indicators and the probabilities; The supply-demand complementarity evaluation index is expressed by the following formula: In the formula, It is an evaluation indicator of supply and demand complementarity; is the power of the adjustable resource on the a-th source side at time t; is the power of the b-th load-side adjustable resource at time t; N is an integer; The volatility complementarity evaluation index is expressed by the following formula: In the formula, It is the evaluation index of volatility complementarity; is the power change rate of the adjustable resource on the a-th source side at time t; is the power change rate of the b-th load-side adjustable resource at time t; is the time interval; The energy complementarity evaluation index is expressed by the following formula: In the formula, It is an evaluation index of energy complementarity.

8. An electronic device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the comprehensive evaluation method for power grid source-load adjustable resources as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the comprehensive evaluation method for power grid source-load adjustable resources as described in any one of claims 1 to 6 is implemented.

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