Stability comprehensive evaluation method for source network load storage system

Abstract

The invention relates to the technical field of power systems, in particular to a comprehensive stability evaluation method for a source network load storage system. Establishing a comprehensive stability evaluation index system of the source-network-load-storage integrated system, obtaining system evaluation indexes, performing stability grade division, and determining scoring intervals corresponding to the evaluation indexes under different stability grades; using a COWA operator weighting method to obtain the subjective weight of the system evaluation index; obtaining objective weights of system evaluation indexes by using a CRITIC weight method; obtaining a combined weight of the system evaluation indexes according to the subjective weight and the objective weight based on a game theory method; establishing an extension cloud model, and improving the extension cloud model through an optimal cloud entropy algorithm to obtain a stability comprehensive evaluation model of the source network load storage system; and inputting a system evaluation index into the evaluation model to obtain a stability evaluation result of the source network load storage system. According to the method, based on the logic analysis of the actual data of the indexes, the indexes are accurately assigned, and the accuracy of the evaluation result is high.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a comprehensive stability evaluation method for a source-grid-load-storage system. Background Art

[0002] At present, my country's new energy development has entered a new stage. The installed capacity of solar energy has reached about 350GW, and the installed capacity of wind energy has exceeded 300GW. However, renewable energy such as photovoltaic and wind power has large volatility and randomness in the power generation process, which leads to challenges in maintaining voltage stability and power balance in the power system. This makes it difficult for the power grid to effectively match power generation and load demand, especially when large-scale photovoltaic and wind power are connected to the grid, wind and light abandonment may still occur. In order to cope with the current challenges of intensified environmental pollution and high dependence on fossil energy, it is of great significance to build a new power system with new energy as the core. With the continuous promotion of new energy and the continuous emergence of flexible loads, while increasing the uncertainty of the distribution network, it also has more abundant controllable resources. Therefore, how to reasonably utilize and dispatch flexible and controllable resources to improve the operating status of the distribution network and reduce operating costs is worthy of research. When flexible resources appear on both the power supply side and the user side of the distribution network, and energy storage technology is promoted, source-grid-load-storage coordinated optimization is an effective way to maximize the utilization of flexible resources.

[0003] There are significant differences between the traditional power system and the source-grid-load-storage integrated system in terms of energy management and utilization. The traditional power system usually relies on a single energy form, such as coal, oil or. Unlike the traditional power system, the source-grid-load-storage integrated system has higher flexibility, intelligence and environmental protection, and can better adapt to and lead the development trend of the modern energy system. It can realize multi-energy complementary integration and integrate multiple energy forms, including grid electricity, distributed renewable energy, electric vehicle charging and energy storage equipment, to achieve diversified energy utilization; the use of advanced intelligent algorithms and data analysis technologies can improve the stability and power supply quality of the power grid; the source-grid-load-storage integrated system supports renewable energy, can effectively manage the intermittent and uncontrollable nature of large-scale renewable energy, solve the problems of its integration and utilization, and effectively reduce carbon emissions and promote environmental protection and emission reduction. At present, my country has not yet formed a unified evaluation standard for the source-grid-load-storage integrated system, so it is necessary to improve the evaluation system and methods to provide a theoretical basis and data support for the formulation of mature evaluation standards in the future.

[0004] With the continuous improvement of the penetration rate of renewable energy in the power system, the operation mode of the power system is constantly changing. At present, the power system mostly adopts the source system. However, due to the uncertainty of the integrated renewable energy of grid-load-storage, when it is connected in large quantities, it will have a certain impact on the power system. In order to ensure the economic applicability and safety of the source side, grid side, load and energy storage, it is necessary to design a complete stability assessment model of the integrated source-grid-load-storage system, among which the establishment of the assessment method and the multi-objective evaluation index set is particularly important; the access of the integrated source-grid-load-storage system also has an impact on the operation of the power grid. The connection of the surplus power to the grid causes the problem of absorption, and the extreme situation brings Supply guarantee pressure and operation efficiency affect the economy of grid-connected equipment, and the interactive demand increases the difficulty of grid regulation. Therefore, the reliability evaluation of the integrated source-grid-load-storage system in the power grid is also an indispensable part of the comprehensive evaluation. Currently, the stability evaluation of the integrated source-grid-load-storage system is mostly achieved by traditional evaluation methods. The evaluation results of this type of evaluation method are relatively one-sided and fail to capture the differences between the integrated source-grid-load-storage system and other systems. The evaluation results are of low accuracy. Traditional evaluation methods only analyze from a single objective or subjective perspective, such as TOPSIS method, AHP-hierarchy analysis method, etc., resulting in large differences in evaluation results. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention provides a comprehensive evaluation method for the stability of a source-grid-load-storage system. The method includes: establishing a comprehensive evaluation index system for the stability of a source-grid-load-storage integrated system, obtaining system evaluation indicators for stability level classification, and determining the scoring intervals of the corresponding evaluation indicators under different stability levels; obtaining the subjective weights of the system evaluation indicators using the COWA operator weighting method; obtaining the objective weights of the system evaluation indicators using the CRITIC weighting method; obtaining the combined weights of the system evaluation indicators based on the subjective weights and the objective weights based on the game theory method; establishing a topological cloud model, and improving it through the optimal cloud entropy algorithm to obtain a comprehensive evaluation model for the stability of the source-grid-load-storage system; inputting the system evaluation indicators into the evaluation model to obtain the stability evaluation results of the source-grid-load-storage system. The present invention is based on the logical analysis of the actual data of the indicators, and the scoring of the indicators is relatively accurate, and the evaluation results are highly accurate.

[0006] The present invention adopts the following technical solution, a comprehensive stability evaluation method of a source-grid-load-storage system, comprising:

[0007] Establish a comprehensive evaluation index system for the stability of the integrated power-grid-load-storage system and obtain system evaluation indicators;

[0008] The obtained system evaluation indicators are divided into stability levels, and the scoring ranges of the corresponding evaluation indicators under different stability levels are determined;

[0009] The COWA operator weighting method is used to modify the expert scoring results to obtain the subjective weights of the system evaluation indicators; the CRITIC weighting method is used to obtain the objective weights of the system evaluation indicators;

[0010] Obtaining a combined weight of a system evaluation index according to the subjective weight and the objective weight based on a game theory method;

[0011] Establishing a topological cloud model, and improving the topological cloud model through an optimal cloud entropy algorithm, and obtaining a comprehensive stability evaluation model of a source-grid-load-storage system;

[0012] The system evaluation index is input into the evaluation model to obtain the stability evaluation result of the source-grid-load-storage system.

[0013] Furthermore, a comprehensive evaluation index system for the stability of the integrated source-grid-load-storage system is established to obtain system evaluation indicators, including:

[0014] The system evaluation indicators include economic indicators, technical indicators, reliability indicators and environmental protection indicators;

[0015] The economic indicators include initial investment cost, operating cost, system life cycle and investment payback period;

[0016] The technical indicators include equipment maintainability, renewable energy utilization rate, voltage stability rate and system energy supply reliability rate;

[0017] The reliability indicators include system complementarity, average release depth, controllable load proportion, and wind and photovoltaic abandonment rates;

[0018] The environmental protection indicators include carbon emissions, land resource occupation, noise pollution and renewable energy contribution rate.

[0019] Furthermore, the COWA operator weighting method is used to modify the expert scoring results to obtain the subjective weights of the system evaluation indicators, including:

[0020] The system evaluation indicators are normalized, and the experts' scores on the importance of indicators at the same level are arranged in descending order to construct the decision data set A of indicator factors. i That is (b1,b2,…,b n );

[0021] Among them, i represents the number of evaluation indicators, and n represents the number of expert scores;

[0022] Calculate the decision data set b j The weight vector of :

[0023] Among them, j is the expert scoring result in the decision data set of the evaluation index, It is the number of combinations of taking j data out of n-1 data;

[0024] Using the weight vector ω j+1 For decision data (b1, b2, ..., b n ) to perform weighted aggregation and calculate the indicator decision data set A i The absolute weight of

[0025] Decision making based on indicators Data set A i The absolute weight value of A is calculated i The relative weight value w i ':

[0026] Furthermore, the CRITIC weight method is used to obtain the objective weights of the system evaluation indicators, including:

[0027] Standardizing the system evaluation indicators;

[0028] Calculate the difference and conflict of system evaluation indicators respectively to determine the contrast strength S j , conflict correlation coefficient β ij :

[0029]

[0030]

[0031] Among them: In the contrast intensity formula, x is the number of options to be selected, a' ij is the initial value of the i-th indicator of the j-th evaluation object, is the mean value of the jth indicator; i and j represent two sets of data in the conflict correlation coefficient formula; are the means of the indicators respectively;

[0032] Determine the comprehensive information volume C of each indicator j :

[0033] Calculate objective weights:

[0034] Furthermore, based on the game theory method, the combined weight of the system evaluation index is obtained according to the subjective weight and the objective weight, including:

[0035] Assuming n kinds of weighting methods, the combined weight corresponding to the i-th weighting method is:

[0036] Among them, u i is the combination coefficient, the purpose is to make the combination weight ω and the basic weight ω iMinimize the deviation of

[0037] The combination coefficients are normalized to construct a combination weight vector:

[0038] Furthermore, the extension cloud model is improved by using an optimal cloud entropy algorithm, including:

[0039] Get the cloud parameters corresponding to each evaluation index in the extension cloud model

[0040] Based on Normal 3E n A regular cloud entropy calculation method is used to obtain a first cloud entropy set;

[0041] Obtaining a second cloud entropy set based on a cloud entropy calculation method of a 50% association rule;

[0042] Combined with Normal 3E n A cloud entropy calculation method for the rule and the 50% correlation rule is used to obtain a third cloud entropy set;

[0043] Obtain the first, second, and third cloud model correlations corresponding to the first, second, and third cloud entropy sets respectively, and calculate the maximum correlation deviation;

[0044] A nonlinear decision optimization model is established based on the first, second, and third cloud entropy sets and the maximum correlation deviation;

[0045] The nonlinear decision optimization model is solved to obtain the optimal cloud entropy value of each system evaluation index in the extension cloud model.

[0046] Furthermore, the system evaluation index is input into the evaluation model to obtain the stability evaluation result of the source-grid-load-storage system, including:

[0047] Gaussian cloud is used to determine the classification limit of the extension cloud model, and the cloud correlation of each system evaluation index under the corresponding classification limit is calculated;

[0048] An evaluation matrix is ​​constructed according to the cloud correlation of each system evaluation indicator, and a stability evaluation score is obtained according to the evaluation matrix and the combined weight of the system indicators.

[0049] The beneficial effects of the present invention are as follows: the present invention combines subjective and objective evaluation results and combines game theory to optimize weights to evaluate the source-grid-load-storage integrated system. Starting from the four characteristics of economy, technology, reliability and environmental protection, suitable and comprehensive evaluation indicators are selected to form a comprehensive evaluation indicator set for evaluating the stability of the source-grid-load-storage integrated system, and a stability ranking model for the improved extension cloud model is constructed. At the same time, the COWA operator method and the CRITIC method are combined, and the game theory combination optimization weight method is used to couple the subjective and objective weights; then the matter-element extension model is introduced to improve the traditional cloud model, and the various factors are comprehensively analyzed. The relationship between the randomness and fuzziness of the elements; finally, the optimal cloud entropy is used to improve the extension cloud, and the optimal cloud entropy value set of each evaluation indicator at their respective evaluation level is solved, and the acceptable value range of the evaluation level is obtained. Different cloud entropy determination methods in the traditional extension cloud model may lead to conflicting judgment results. The extension cloud model is optimized by the optimal cloud entropy. Compared with the traditional matter-element model, the calculation result is more accurate and effectively applicable to fuzzy and uncertain problems. Moreover, this model is based on the logical analysis of the actual data of the indicators, with absolute objectivity. At the same time, the scoring of the indicators is relatively accurate, and the evaluation results have a certain accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 A schematic flow chart of a comprehensive stability evaluation method for a source-grid-load-storage system according to an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of the structure of a source-grid-load-storage integrated system according to an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of an evaluation index system structure of a source-grid-load-storage integrated system according to an embodiment of the present invention;

[0054] Figure 4 A schematic diagram of a process of calculating subjective weights using a COWA operator weighting method according to an embodiment of the present invention;

[0055] Figure 5 A schematic diagram of a flow chart of calculating objective weights using the CRITIC method according to an embodiment of the present invention;

[0056] Figure 6 The figure is a schematic diagram of comparing indicator weights according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] A schematic flow chart of a comprehensive stability evaluation method for a source-grid-load-storage system according to an embodiment of the present invention is shown in FIG. Figure 1 As shown, including:

[0059] Establish a comprehensive evaluation index system for the stability of the integrated power-grid-load-storage system and obtain system evaluation indicators;

[0060] like Figure 2 As shown, the present invention first starts with the basic framework of the source-grid-load-storage integrated system, and seeks representative indicators from multiple aspects; the disorderly access of the source-grid-load-storage integrated system will cause problems in the operation of the power grid, so the reliability of the power grid can be evaluated by selecting indicators from different dimensions, and with the increasing flexibility of the current supply side, demand side and energy storage side, the application of new technologies and new equipment is becoming more and more extensive, and each link of the source-grid-load-storage system not only has a large amount of resources but also faces many challenges. For example, the power generation of new energy power generation equipment on the supply side continues to increase, effectively reducing carbon emissions, but the control difficulty is high, and the large-scale grid connection of equipment brings pressure to the reliable operation of the system; electric vehicles on the demand side can be interconnected to the grid to buffer the impact of renewable energy grid connection on the grid, but the access of a large number of vehicles will reduce the stability of the grid;

[0061] The embodiment of the present invention mainly performs stability analysis on the integrated power grid, load and storage system. On the one hand, stability analysis can evaluate the safety of the power system under different operating conditions, including considering factors such as fluctuations in power load, fluctuations in renewable energy, and reliability of power generation equipment. By analyzing the stability of the system, risks that may cause system collapse or failure can be predicted and prevented, so that necessary measures can be taken to enhance the stability and robustness of the system; on the other hand, stability analysis helps to determine the stable control strategy of frequency and voltage in the power system and predict future changes in power load. Based on this, a performance evaluation index set for the integrated power grid, load and storage system is established from multiple dimensions.

[0062] Source side, grid side, load side and energy storage are descriptions of different functional parts in the source-grid-load-storage system, which work together to achieve intelligent management, efficient energy utilization and sustainable development of the power system. Source side refers to the energy generation or supply end. It mainly involves the generation and supply of electricity, including traditional power plants, distributed renewable energy (such as solar energy, wind energy) and other energy generation facilities connected to the power grid. The function of the source side is to generate electricity through various energy forms and inject it into the power grid; the grid side refers to one side of the power grid, that is, the power grid itself and its related facilities and management systems. It is mainly responsible for the transmission, distribution and management of electricity to ensure the stable supply and operation of electricity. The grid-side management system usually includes functions such as monitoring, dispatching, and safety protection of the power grid to maintain the stable operation of the power system; the load side refers to the load side or consumption side of the power grid; the load side includes all electricity consumers, such as households, industrial enterprises, commercial buildings, etc. The load side plays the role of power demand in the source-grid-load-storage system, that is, the actual power consumption and load. Load-side management involves the analysis, prediction and regulation of electricity demand so that the time and amount of electricity consumption can be adjusted when necessary to achieve more efficient energy utilization; energy storage refers to the storage and release of energy. Energy storage systems include various types of energy storage facilities, such as battery energy storage systems, supercapacitors, compressed air energy storage, etc. Energy storage equipment can store electricity when there is an excess of energy supply, and release the stored electricity when demand peaks or power supply is insufficient. Energy storage plays an important role in balancing the load of the power grid and improving the stability and flexibility of the power system. Therefore, an evaluation index system for the source-grid-load-storage integrated system has been established, such as Figure 3 As shown in Table 1, a specific indicator analysis table is given.

[0063] Table 1 Evaluation index system of source-grid-load-storage integrated system

[0064]

[0065]

[0066] The obtained system evaluation indicators are divided into stability levels, and the scoring ranges of the corresponding evaluation indicators under different stability levels are determined;

[0067] In the embodiment of the present invention, in order to effectively evaluate the operating stability of the integrated source-grid-load-storage system, it is necessary to classify its operating stability level. The present invention divides the system into five instability risk levels according to the possibility of frequent accidents caused by various factors during the operation of the integrated source-grid-load-storage system and the losses caused, namely, U=(low risk, medium-low risk, medium risk, medium-high risk, high risk), as shown in Table 2.

[0068] Table 2 Risk level of instability in operation of source-grid-load-storage integrated system

[0069]

[0070] The COWA operator weighting method is used to obtain the subjective weights of the system evaluation indicators; the CRITIC weighting method is used to obtain the objective weights of the system evaluation indicators. Specifically:

[0071] Methods for obtaining subjective weights of system evaluation indicators include:

[0072] Normalize the system evaluation indicators, arrange the experts' scores on the importance of the indicators at the same level in descending order, and construct the decision data set Ai of the indicator factors, i.e. (b1, b2, ..., bn);

[0073] Among them, i represents the number of evaluation indicators, and n represents the number of expert scores;

[0074] Calculate the weighted vector of bj in the decision data set:

[0075] Among them, j is the expert scoring result in the decision data set of the evaluation index, It is the number of combinations of taking j data out of n-1 data;

[0076] Use the weighted vector ωj+1 to weight the decision data (b1, b2, ..., bn) and calculate the absolute weight value of the indicator decision data set Ai

[0077] Calculate the relative weight value w′ of Ai according to the absolute weight value of the indicator decision data set Ai i :

[0078] Methods for obtaining objective weights of system evaluation indicators using the CRITIC weight method include:

[0079] Standardizing the system evaluation indicators;

[0080] Calculate the difference and conflict of system evaluation indicators respectively to determine the contrast strength S j , conflict correlation coefficient β ij :

[0081]

[0082] Among them: In the contrast intensity formula, x is the number of options to be selected, a' ij is the initial value of the i-th indicator of the j-th evaluation object, is the mean value of the jth indicator; i and j represent two sets of data in the conflict correlation coefficient formula; are the means of the indicators respectively;

[0083] Determine the comprehensive information volume C of each indicator j :

[0084] Calculate objective weights:

[0085] In the embodiment of the present invention, the idea of ​​dynamic combination evaluation is used to combine the subjective and objective weights of each indicator based on the COWA-CRITIC method to effectively reduce subjectivity and reduce weight fluctuations caused by data changes; among them, the COWA operator weighting method requires multiple experts to compare and evaluate the importance of evaluation indicators based on academic research and work experience. The evaluation process is as follows: Figure 4 The CRITIC method is an improvement on the entropy weight method. It objectively evaluates the indicators, taking into account the differences between the indicators while taking into account the correlation between the indicators. The evaluation flow chart is shown in Figure 5 After obtaining the subjective and objective weights respectively, the embodiment of the present invention adopts the game theory optimization combination weighting method to combine and weight the decision indicators.

[0086] Obtaining a combined weight of a system evaluation index according to the subjective weight and the objective weight based on a game theory method;

[0087] The COWA operator weighting method can avoid the influence of extreme values ​​on the weighting results, and the CRITIC method can effectively solve the problem of low description of differences and correlations between indicators in other methods. In order to ensure the scientificity and rationality of the evaluation of the working stability of the source-grid-load-storage integrated system, the present invention introduces the idea of ​​game theory and combines the COWA operator weighting method and the CRITIC method to perform subjective and objective comprehensive weighting; compared with the traditional evaluation system, it can be seen that the excellence of each indicator and the advantages and disadvantages of different systems cannot be evaluated based on the combined weights alone. Therefore, the idea of ​​game theory is introduced into the evaluation work, and the subjective and objective weights are taken into consideration by using the game theory combined optimization weights to achieve the optimal weights, as follows:

[0088] Suppose there are n weighting methods, then the weight vector combination of one of the methods is

[0089] Based on the combined weight ω and the basic weight ω i The deviation is minimized to construct a model, and the combination coefficient u of subjective and objective weights is obtained i :

[0090]

[0091] Right now

[0092]

[0093] By normalizing the combination coefficient u i ,Right now The combined weight vector is established as follows:

[0094] Establishing a topological cloud model, and improving the topological cloud model through an optimal cloud entropy algorithm, and obtaining a comprehensive stability evaluation model of a source-grid-load-storage system;

[0095] The embodiment of the present invention combines the traditional game theory combination optimization weight model with the extension cloud model, and combines the combination weight value to construct a comprehensive evaluation method suitable for the stability evaluation of the source-grid-load-storage integrated system; wherein, the traditional cloud model realizes the qualitative and quantitative conversion through mathematical methods, combines the fuzzy set with the uncertainty of the indicator, the fuzziness of the level correlation and other issues, has a high degree of visualization, and is very suitable for dealing with uncertain problems, but it still has certain shortcomings, such as the traditional cloud model can only calculate the level correlation and judge the level of the object to be evaluated, but cannot judge its level of favoritism between two adjacent levels, and different cloud entropy determination methods may lead to conflicting judgment results; by improving the extension cloud model through the optimal cloud entropy, the uncertain reasoning characteristics of the cloud model and the qualitative and quantitative analysis advantages of the matter-element theory can be effectively integrated, specifically:

[0096] The matter-element extension model is recorded as R = (N, C, V) (where N represents the name of the thing; C represents a certain feature of the thing; V represents the value range corresponding to a certain feature of the thing); the extension cloud model introduces the cloud model into the matter-element theory, and replaces the fixed characteristic value V in the matter-element extension theory with (E x ,E n ,H e ), the extension cloud model can be expressed as:

[0097]

[0098] Where R is the level set for evaluating the operation stability of the source-grid-load-storage integrated system; C i is the i-th evaluation index (i=1,2…m); is the corresponding cloud parameter, where the expected value E x Refers to the point value of the level concept of operation stability evaluation, entropy E n The measurable granularity of the qualitative concept can reflect the randomness and fuzziness of the data sample, the super entropy H e The uncertainty measure representing entropy, namely the entropy of entropy, can reflect the discrete degree of evaluation data samples and reveal the correlation between the randomness and fuzziness of various factors.

[0099] There are two commonly used cloud entropy calculation methods: cloud entropy calculation method based on the normal "3En" rule and cloud entropy calculation method based on the "50% correlation" rule. The calculation methods are:

[0100]

[0101] Where, E' n and E” n represents the cloud entropy obtained by different calculation methods, c max and c mix The two methods define cloud entropy values ​​from different perspectives of level division, but the level correlations calculated based on different cloud entropies will be contradictory. Therefore, the embodiment of the present invention introduces the concept of optimal cloud entropy and combines the first two methods, taking into account both the clarity and fuzziness of level division.

[0102] When combining the concept of optimal cloud entropy with the first two methods, assuming that the data of a certain indicator is x i , based on “3E n The first cloud entropy set and the second cloud entropy set calculated by the "50% correlation degree" rule are respectively expressed as (E n_d )' 1*q and (E n_d )” 1*q , the optimal cloud entropy set obtained after combined optimization is expressed as (E n_d ) 1*q , where d = 1, 2, ..., q, represents a certain level; through 3E n The cloud model correlation calculated by this method is the smallest, denoted as k' min_d ; The cloud model correlation calculated by the 50% correlation method has the largest correlation, which is denoted as k” max_d . Use k exp_d represents the cloud model correlation calculated by the optimal cloud entropy method, then the maximum correlation deviation k of the three at level d max The calculation method is:

[0103] Δk max =(k" max_d -k exp_d ) 2 +(k exp_d -k' min_d ) 2

[0104] The principle of solving the optimal cloud entropy set is to make the evaluation value x i For the q groups of standard-level clouds, the sum of the correlation deviation values ​​is the smallest. The present invention constructs Δk max The nonlinear decision optimization model is implemented. By solving the model, the optimal cloud entropy value set of each evaluation index at each evaluation level can be obtained. The model expression is:

[0105]

[0106] The system evaluation index is input into the evaluation model to obtain the stability evaluation result of the source-grid-load-storage system.

[0107] In an embodiment of the present invention, obtaining the stability evaluation result includes the following steps:

[0108] Determine the evaluation standard cloud: Usually, the classification boundary of the cloud theory index is a double constraint space [c min , c max ], the embodiment of the present invention expands it into a Gaussian cloud based on the consideration of boundary uncertainty, and takes the determined dual-constraint space as the calculation basis. Three key parameters need to be determined, namely, cloud expectation E x 、Cloud Entropy E n Heyun Super Entropy H e According to the concept definition of cloud expectation, the calculation formula is E x =(c max +c min ) / 2, and the cloud super entropy is a constant, usually taken as 0.01, which can be adjusted appropriately according to the actual experience and uncertainty of the evaluation indicators.

[0109] Calculate cloud relevance:

[0110] k ij =exp[-(x i -E x ) 2 / 2(E ni ) 2 ]

[0111] In the formula, x i is the value of the i-th evaluation indicator; E x ,E n ,H e E is the mathematical eigenvalue of the index corresponding to a certain standard level of extension cloud; ni is an expected value of E n , standard deviation is H e Normal random numbers.

[0112] On the basis of determining the stability level of the source-grid-load-storage integrated system, the bias degree between two adjacent levels is judged by the level characteristic value. The comprehensive evaluation matrix Z composed of all cloud correlations is combined with the comprehensive weight matrix W of the evaluation index to calculate the comprehensive evaluation vector X of the evaluation object; then the components of Z and X calculated are used to calculate the comprehensive evaluation scores r of the four first-level indicators. i and the evaluation score r of a certain evaluation index at the i-th level i (c); Finally, calculate the level characteristic value. The calculation formula is as follows:

[0113] X=WZ

[0114]

[0115] Where W is the comprehensive weight vector; Z is the comprehensive evaluation matrix composed of all cloud correlations; x i is the component of the comprehensive evaluation vector X; f i is the score value of level i; j is the level characteristic value; r i (c) is the evaluation score of a certain evaluation index at the i-th level, maxr i (c) is the highest score of a certain evaluation indicator among all evaluation levels, minr i (c) is the lowest score of a certain evaluation indicator among all evaluation levels.

[0116] In another specific embodiment of the present invention, basic data of a source-grid-load-storage integrated system project is calculated through example analysis, and the methods in the two data schemes are compared to analyze the sustainable development capabilities of the two system schemes, as shown in Table 3:

[0117] Table 3 Index data

[0118]

[0119]

[0120] Further, according to the constructed comprehensive evaluation index system of the stability of the source-grid-load-storage integrated system, for each indicator in the table, the COWA operator method is first used to subjectively assign weights to each secondary indicator; then, after the indicators are standardized, the CRITIC method is used to calculate the objective weight value of each indicator. In order to effectively evaluate the operational stability of the source-grid-load-storage integrated system, it is necessary to classify its operational stability levels. Through literature research, the present invention divides the instability risk levels into 5 levels according to the possibility of frequent accidents caused by various factors during the operation of the source-grid-load-storage integrated system and the losses caused, namely U = (low risk, medium-low risk, medium risk, medium-high risk, high risk), which has been given, as shown in Table 2.

[0121] In order to avoid the strong subjectivity of the weight results calculated by the COWA operator method and the strong objectivity of the calculation results of the CRITIC method, the embodiment of the present invention adopts the game theory optimization combination weighting method to dynamically adjust the results of the COWA operator method and the CRITIC method, and obtains the combination coefficients of subjective and objective weights u1=0.461, u2=0.539, and the final combination weight w * , thus determining the cloud matter elements of each level of the indicator, and calculating the optimal cloud entropy of the indicator at each evaluation level, as shown in Table 4:

[0122] Table 3 Index combination weights and their levels Cloud matter element

[0123]

[0124]

[0125] The table shows the specific values ​​of the comprehensive weights of the stability evaluation system of the source-grid-load-storage integrated system. From the results, it can be seen that carbon emissions, average release depth, wind curtailment, photovoltaic rate, voltage stability rate and operating cost are the indicators with the highest weights in the evaluation system. Among them, the weights of the secondary indicators under the first-level indicator reliability are generally higher than the secondary indicators under other first-level indicators. The weight calculation results show that reliability and technical factors have the greatest impact on the operational stability of the source-grid-load-storage integrated system.

[0126] The present invention adopts subjective and objective evaluation methods combined with weighting and matches with the game theory combined weight optimization method to calculate the combined weight of the evaluation index. Compared with the traditional single evaluation method, the evaluation result of the comprehensive evaluation method is more accurate. Figure 6 As shown in the figure, series 1 represents the combined weights of indicators calculated by the game theory combined weight method, series 2 refers to the subjective weights of indicators calculated by the COWA operator method, and series 3 refers to the objective weights of indicators calculated by the CRITIC method. It can be seen from the figure that the combined weights are mostly between the subjective weights and the objective weights. The combined weighting method better integrates the two weight values, and the calculation results are more accurate and scientific.

[0127] Data from different time periods of system operation are selected for calculation. Combined with the combined weights, the correlation and eigenvalues ​​of various indicators are calculated. In order to avoid the adverse effects of the randomness of random data on the evaluation results, the results of different evaluation objects can be repeatedly calculated 1000 times, and finally Tables 5 and 6 are obtained.

[0128] Table 5 Correlation degree and eigenvalue of primary indicators

[0129]

[0130] Table 6 Target layer correlation and eigenvalues

[0131]

[0132] It can be seen from Table 6 that according to the maximum correlation principle, the instability level of the source-grid-load-storage integrated system during operation is judged to be level II, which is a medium-low risk. j = 1.70, indicating that the construction risk level of the project is between level I and level II, and tends to be level II. The overall instability level is a medium-low risk. Necessary monitoring of the instability source should be carried out to prevent the risk from expanding.

[0133] It can be seen from Table 5 that the instability level of reliability factors and technical factors is the highest, and necessary measures should be taken to deal with it to avoid major accidents. It can be seen that the present invention, based on the source-grid-load-storage integrated system, establishes a stability evaluation index system and a comprehensive evaluation model that conforms to the system characteristics, evaluates the operating stability of a source-grid-load-storage integrated system, and verifies the effectiveness of the evaluation model, which can help workers promptly discover instability hazards in the system.

[0134] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A comprehensive stability evaluation method for a source-grid-load-storage system, characterized in that: include: Establish a comprehensive evaluation index system for the stability of the integrated power-grid-load-storage system and obtain system evaluation indicators; The obtained system evaluation indicators are divided into stability levels, and the scoring ranges of the corresponding evaluation indicators under different stability levels are determined; The COWA operator weighting method is used to obtain the subjective weights of system evaluation indicators; Use the CRITIC weight method to obtain the objective weights of system evaluation indicators; Obtaining a combined weight of a system evaluation index according to the subjective weight and the objective weight based on a game theory method; Establishing a topological cloud model, and improving the topological cloud model through an optimal cloud entropy algorithm, and obtaining a comprehensive stability evaluation model of a source-grid-load-storage system; The system evaluation index is input into the evaluation model to obtain the stability evaluation result of the source-grid-load-storage system.

2. The comprehensive stability evaluation method of a source-grid-load-storage system according to claim 1 is characterized by: Establish a comprehensive evaluation index system for the stability of the integrated power grid, load and storage system and obtain system evaluation indicators, including: The system evaluation indicators include economic indicators, technical indicators, reliability indicators and environmental protection indicators; The economic indicators include initial investment cost, operating cost, system life cycle and investment payback period; The technical indicators include equipment maintainability, renewable energy utilization rate, voltage stability rate and system energy supply reliability rate; The reliability indicators include system complementarity, average release depth, controllable load ratio, and wind and photovoltaic abandonment rates; the environmental protection indicators include carbon emissions, land resource occupation, noise pollution, and renewable energy contribution rate.

3. The comprehensive stability evaluation method of a source-grid-load-storage system according to claim 1 is characterized by: The COWA operator weighting method is used to obtain the subjective weights of system evaluation indicators, including: The system evaluation indicators are normalized, and the experts' scores on the importance of indicators at the same level are arranged in descending order to construct the decision data set A of indicator factors. i That is (b1,b2,…,b n ); Among them, i represents the number of evaluation indicators, and n represents the number of expert scores; Calculate the decision data set b j The weight vector of : Among them, j is the expert scoring result in the decision data set of the evaluation index, It is the number of combinations of taking j data out of n-1 data; Using the weight vector ω j+1 For decision data (b1, b2, ..., b n ) to perform weighted aggregation and calculate the indicator decision data set A i The absolute weight of Decision making based on indicators Data set A i The absolute weight value of A is calculated i The relative weight of 4. The comprehensive stability evaluation method of a source-grid-load-storage system according to claim 1 is characterized by: The CRITIC weight method is used to obtain the objective weights of system evaluation indicators, including: Standardizing the system evaluation indicators; Calculate the difference and conflict of system evaluation indicators respectively to determine the contrast strength S j , conflict correlation coefficient β ij : Among them: In the contrast intensity formula, x is the number of options to be selected, a' ij is the initial value of the i-th indicator of the j-th evaluation object, is the mean value of the jth indicator; in the conflict correlation coefficient formula, i and j represent two sets of data; are the means of the indicators respectively; Determine the comprehensive information content of each indicator Calculate objective weights:

5. The comprehensive stability evaluation method of a source-grid-load-storage system according to claim 1 is characterized by: Obtaining the combined weight of the system evaluation index according to the subjective weight and the objective weight based on the game theory method includes: Assuming n kinds of weighting methods, the combined weight corresponding to the i-th weighting method is: Among them, u i is the combination coefficient, the purpose is to make the combination weight ω and the basic weight ω i Minimize the deviation of The combination coefficients are normalized to construct a combination weight vector:

6. The comprehensive stability evaluation method of a source-grid-load-storage system according to claim 1 is characterized by: The extension cloud model is improved by using the optimal cloud entropy algorithm, including: Get the cloud parameters corresponding to each evaluation index in the extension cloud model Based on Normal 3E n A regular cloud entropy calculation method is used to obtain a first cloud entropy set; Obtaining a second cloud entropy set based on a cloud entropy calculation method of a 50% association rule; Combined with Normal 3E n A cloud entropy calculation method for the rule and the 50% correlation rule is used to obtain a third cloud entropy set; Obtain the first, second, and third cloud model correlations corresponding to the first, second, and third cloud entropy sets respectively, and calculate the maximum correlation deviation; A nonlinear decision optimization model is established based on the first, second, and third cloud entropy sets and the maximum correlation deviation; The nonlinear decision optimization model is solved to obtain the optimal cloud entropy value of each system evaluation index in the extension cloud model.

7. The comprehensive stability evaluation method of a source-grid-load-storage system according to claim 1 is characterized by: Inputting the system evaluation index into the evaluation model to obtain the stability evaluation result of the source-grid-load-storage system includes: Gaussian cloud is used to determine the classification limit of the extension cloud model, and the cloud correlation of each system evaluation index under the corresponding classification limit is calculated; An evaluation matrix is ​​constructed according to the cloud correlation of each system evaluation indicator, and a stability evaluation score is obtained according to the evaluation matrix and the combined weight of the system indicators.

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