Electricity market clearing mode evaluation method and device

Abstract

The invention relates to the technical field of power planning, and provides a power market clearing mode evaluation method and device. The method comprises the following steps: constructing clearing mode evaluation indexes and carrying out standardization processing; a fuzzy analytic hierarchy process (FAHP) is adopted to calculate the subjective weight of the index, a CRITIC method is adopted to calculate the objective weight of the index, and the subjective weight and the objective weight of the index are integrated to obtain a comprehensive weight; and evaluating the power market clearing mode based on an improved matter-element extension model by using the obtained comprehensive weight. According to the method, a comprehensive weight is obtained by adopting a comprehensive subjective weighting method and an objective weighting method, and evaluation is carried out by utilizing an improved matter-element extendable power market clearing mode, so that evaluation of the power market clearing mode is realized, a scientific basis is provided for optimization and policy making of the power market, and more efficient and fair power resource allocation is facilitated to be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power planning, and in particular relates to a method and device for evaluating power market clearing methods. Background Art

[0002] The trading mechanism of the power market has a direct impact on the market operation effect, among which the market clearing method is the core issue and needs to be discussed in depth. The current clearing methods of the power market mainly include marginal uniform clearing and high-low matching clearing. Different clearing methods will affect the market structure and the bidding strategies of market participants. Previous studies have not compared the advantages and disadvantages of various clearing methods in various aspects, and the evaluation of clearing methods is not perfect.

[0003] In summary, there is an urgent need for an electricity market clearing mode evaluation method to evaluate various clearing modes. Summary of the invention

[0004] To solve the above problems, the present disclosure provides a method and device for evaluating the clearing mode of electricity market, which adopts the weights of comprehensive subjective weighting and objective weighting method to obtain the comprehensive weight, and uses the improved matter-element extension method to evaluate the clearing mode of electricity market, thereby realizing the evaluation of the clearing mode of electricity market.

[0005] In a first aspect, a method for evaluating a power market clearing method is provided, the method comprising: Construct evaluation indicators for electricity market clearing methods and standardize the constructed evaluation indicators; The fuzzy analytic hierarchy process (FAHP) is used to calculate the subjective weight of the evaluation index, and the CRITIC method is used to calculate the objective weight of the evaluation index. The subjective weight and objective weight of the evaluation index are combined to obtain the comprehensive weight. Using the obtained comprehensive weights, the electricity market clearing mode is evaluated based on the improved matter-element extension model.

[0006] Furthermore, the subjective weight and objective weight of the evaluation index are combined to obtain the comprehensive weight, including: The Lagrange multiplier method is used to combine the subjective weight and objective weight of the evaluation index to obtain the comprehensive weight.

[0007] Furthermore, the evaluation indicators of the clearing method are constructed, including: Construct an evaluation index system for electricity market clearing methods; Based on web crawler technology, the word frequency of the content describing each power market clearing method is captured, and the first Q high-frequency words with high occurrence frequency and related to power market clearing are selected. The selected high-frequency words are used as evaluation indicators of the evaluation index system, where Q is greater than or equal to the number of evaluation aspects included in the evaluation index system.

[0008] Furthermore, the constructed evaluation index system for electricity market clearing methods consists of market competitiveness, market reliability, market activity, social welfare and renewable energy.

[0009] Furthermore, market competitiveness includes some or all evaluation indicators of market concentration, barrier index, surplus supply rate and enterprise winning bid rate; Market reliability includes: some or all evaluation indicators of price volatility, power supply reliability and system availability; Market activity includes: market transaction amount, market transaction frequency and some or all evaluation indicators in the bid-ask spread; Social welfare includes: some or all evaluation indicators of social welfare of power purchasing enterprises and social welfare of power generation enterprises; Renewable energy includes: the proportion of renewable energy trading electricity and some or all evaluation indicators in the renewable energy power generation trading price.

[0010] Furthermore, FAHP is used to calculate the subjective weights of the evaluation indicators, including: The hierarchical structure is constructed with the comprehensive evaluation of the electricity market clearing mode as the target layer, market competitiveness, market reliability, market activity, social welfare and renewable energy as the criterion layer, and the evaluation indicators under each criterion in the criterion layer as the decision layer. The fuzzy complementary matrix is ​​constructed using the evaluation index under each criterion, and the fuzzy complementary matrix is ​​transformed into a fuzzy consistent matrix; The subjective weight matrix of evaluation indexes is calculated using fuzzy consistent matrix.

[0011] Furthermore, the CRITIC method is used to calculate the objective weights of the evaluation indicators, including: The original evaluation matrix is ​​constructed by using the number of evaluation samples and the number of evaluation indicators in the electricity market clearing method; Based on the correlation between the evaluation index and the evaluation result, the original evaluation matrix is ​​processed accordingly to obtain a standardized matrix; when the evaluation index is positively correlated with the evaluation result, the positively correlated evaluation index is processed positively; when the evaluation index is negatively correlated with the evaluation result, the negatively correlated evaluation index is processed inversely; Use the correlation coefficients between evaluation indicators to determine the conflict between evaluation indicators; Calculate the information content of each evaluation index; The information amount of each evaluation index is normalized to obtain the objective weight matrix of the evaluation index.

[0012] Furthermore, the obtained comprehensive weights are used to evaluate the electricity market clearing methods based on the improved matter-element extension model, including: The matter-element extension model is adopted to establish the classical domain and the matter-element to be evaluated of the power market clearing evaluation index, and construct the corresponding matter-element matrix. The value range of the evaluation index at each evaluation index level in the classical domain is obtained by dividing the historical clearing data of the power market using the time series average segmentation method. Calculate the closeness between the various evaluation indicators of the entity to be evaluated and the evaluation indicator level, and determine the evaluation level to which the electricity market clearing method belongs.

[0013] In a second aspect, a device for evaluating a power market clearing method is provided, the device comprising a construction unit, a comprehensive weight determination unit and an evaluation unit; wherein: A construction unit is used to construct evaluation indicators of power market clearing methods and standardize the evaluation indicators; A comprehensive weight determination unit is used to calculate the subjective weight of the evaluation index by using the fuzzy analytic hierarchy process FAHP, calculate the objective weight of the evaluation index by using the CRITIC method, and synthesize the subjective weight and the objective weight of the evaluation index to obtain the comprehensive weight; The evaluation unit is used to evaluate the electricity market clearing mode by using the obtained comprehensive weight and adopting the improved matter-element extension model.

[0014] Furthermore, the comprehensive weight determination unit is specifically used to use the Lagrange multiplier method to integrate the subjective weight and the objective weight of the evaluation index to obtain the comprehensive weight.

[0015] Furthermore, a construction unit is specifically used to capture the word frequency of the content describing each power market clearing method based on the web crawler technology, select the first Q high-frequency words with high occurrence frequency and related to the power market clearing, and use the selected high-frequency words as evaluation indicators of the evaluation indicator system, wherein Q is greater than or equal to the number of evaluation aspects included in the evaluation indicator system.

[0016] Furthermore, the constructed evaluation index system for electricity market clearing methods consists of market competitiveness, market reliability, market activity, social welfare and renewable energy.

[0017] Furthermore, market competitiveness includes some or all evaluation indicators of market concentration, barrier index, surplus supply rate and enterprise winning bid rate; Market reliability includes: some or all evaluation indicators of price volatility, power supply reliability and system availability; Market activity includes: market transaction amount, market transaction frequency and some or all evaluation indicators in the bid-ask spread; Social welfare includes: some or all evaluation indicators of social welfare of power purchasing enterprises and social welfare of power generation enterprises; Renewable energy includes: the proportion of renewable energy trading electricity and some or all evaluation indicators in the renewable energy power generation trading price.

[0018] Furthermore, the comprehensive weight determination unit is specifically used to construct a hierarchical structure with the comprehensive evaluation of the electricity market clearing mode as the target layer, market competitiveness, market reliability, market activity, social welfare and renewable energy as the criterion layer, and the evaluation indicators under each criterion in the criterion layer as the decision-making layer; a fuzzy complementary matrix is ​​constructed using the evaluation indicators under each criterion, and the fuzzy complementary matrix is ​​converted into a fuzzy consistent matrix; and the fuzzy consistent matrix is ​​used to calculate the subjective weight matrix of the evaluation indicators.

[0019] Further, the comprehensive weight determination unit is specifically used to construct the original evaluation matrix by evaluating the number of samples and the number of evaluation indicators using the power market clearing method; Based on the correlation between the evaluation indicators and the evaluation results, the original evaluation matrix is ​​processed accordingly to obtain a standardized matrix; the correlation coefficients between the evaluation indicators are used to determine the conflict of the evaluation indicators; the amount of information of each evaluation indicator is calculated; the amount of information of each evaluation indicator is normalized to obtain an objective weight matrix of the evaluation indicators, wherein, when the evaluation indicator is positively correlated with the evaluation result, the positively correlated evaluation indicator is positively processed; when the evaluation indicator is negatively correlated with the evaluation result, the negatively correlated evaluation indicator is reversely processed.

[0020] Furthermore, the evaluation unit is specifically used to adopt the matter-element extension model to establish the classical domain of the electricity market clearing evaluation index and the matter-element to be evaluated, and to construct the corresponding matter-element matrix, calculate the closeness between each evaluation index of the matter-element to be evaluated and the evaluation index level, and determine the evaluation level to which the electricity market clearing method belongs, wherein the value range of the evaluation index at each evaluation index level in the classical domain is based on the historical clearing data of the electricity market and is obtained by dividing it using the time series average segmentation method.

[0021] In a third aspect, an electronic device is provided, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; The processor is used to implement the steps of the above method when executing the program stored in the memory.

[0022] In a fourth aspect, a computer storage medium is provided, wherein the computer storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0023] Compared with the prior art, the present invention has the following advantages: 1. The present invention provides an evaluation method for power market clearing methods, which combines the subjective weights obtained by the FAHP method and the objective weights obtained by the CRITIC method. According to the relevant contents of power market clearing methods, various indicators of various aspects of power market evaluation are given, ensuring the comprehensiveness and systematicness of the evaluation process, providing a scientific basis for power market optimization and policy formulation, and helping to achieve more efficient and fair allocation of power resources.

[0024] 2. This disclosure evaluates the electricity market clearing method by using the Lagrange multiplier method and the improved matter-element extension method, so that the distribution of the comprehensive weight in space is close to the distribution of the subjective weight and the objective weight in space, ensuring the effectiveness and accuracy of the evaluation method.

[0025] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present disclosure 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 some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 A flow chart of a method for evaluating a power market clearing mode according to an embodiment of the present disclosure is shown; Figure 2 A block diagram of a power market clearing method evaluation device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0029] Figure 1 A schematic diagram of a method for evaluating a power market clearing method according to an embodiment of the present disclosure is shown. Figure 1 As shown, a method for evaluating a power market clearing mode according to an embodiment of the present disclosure includes the following steps: Step 101: construct evaluation indicators for electricity market clearing methods; Specifically, a system of evaluation indicators for clearing methods can be constructed based on the web crawler method.

[0030] Firstly, combined with the actual situation of the power market, an evaluation index system for power market clearing methods is constructed. Taking into account the characteristics of the two clearing methods, marginal unified clearing (MUC) and high-low matching clearing (HLC) and the current power market rules, this method constructs an evaluation index system for power market clearing methods from five aspects: market competitiveness, market reliability, market activity, social welfare and renewable energy.

[0031] Web crawler technology is an important tool for collecting network data. This technology can selectively crawl relevant pages for word frequency statistics and has been widely used in news aggregation, public opinion monitoring, industry data and other fields. This method is based on web crawler technology to capture the word frequency of the content of unified / high-low matching power market clearing, including literature, policies and articles, etc., and focuses on selecting the top Q. The embodiment of the present invention selects 14 high-frequency words with high frequency of occurrence and related to power market clearing, which provides data support for the construction of the evaluation system of this method. The final evaluation indicators include: 1) Market competitiveness: some or all of market concentration, barrier index, remaining supply rate and enterprise winning rate; 2) Market reliability: some or all of price volatility, power supply reliability, and system availability; 3) Market activity: part or all of market transaction amount, market transaction frequency and bid-ask spread; 4) Social welfare: part or all of the social welfare of electricity purchasing enterprises and the social welfare of power generation enterprises; 5) Renewable energy: the proportion of renewable energy electricity trading and part or all of the renewable energy power generation trading price.

[0032] Step 102: Standardize the constructed evaluation index.

[0033] Since the order of magnitude and dimension of each evaluation index vary greatly, there is no comparability when they are directly used for evaluation models, so the evaluation indexes need to be processed.

[0034] The evaluation indexes of the clearing method are generally divided into extremely large and extremely small. The first method of clearing The evaluation index is , where i=1, 2, 3..., , .

[0035] The maximum normalization formula for a certain evaluation index , its normalized formula is:

[0036] Minimization normalization formula for a certain evaluation index The normalized formula is:

[0037] in, is the normalized value, is the jth evaluation index under the i-th clearing method, and its range is usually [0, 1].

[0038] Step 103: FAHP is used to calculate the subjective weight of the evaluation index, CRITIC method is used to calculate the objective weight of the index, and the subjective weight and objective weight of the index are combined to obtain the comprehensive weight; Specifically, the subjective weight and objective weight of the evaluation index may be integrated using the Lagrange multiplier method to obtain a comprehensive weight.

[0039] Among them, the use of FAHP to calculate the subjective weight of the evaluation index includes the following steps 1) to 4): 1) Construct a hierarchical structure. Combined with specific research issues, the hierarchical structure can be divided into many layers, but in the evaluation of the electricity market clearing method, a three-layer structure of target layer, criterion layer, and decision layer is generally adopted. The target layer is a comprehensive evaluation of the electricity market clearing method, the criterion layer is market competitiveness, market reliability, market activity, social welfare and renewable energy, and the decision layer is the evaluation index under each criterion. Here, market competitiveness is 1 criterion, market reliability is 1 criterion, market activity is 1 criterion, social welfare is 1 criterion, and renewable energy is 1 criterion. There are a total of 5 criteria in the criterion layer.

[0040] 2) Construct the fuzzy complementary matrix. Let the fuzzy complementary matrix ,in represents the matrix rows, Represents the matrix column. 0≤ ≤1, and satisfies + =1 (where ; ), n is the number of evaluation indicators of electricity market clearing mode. Since the number of evaluation indicators of electricity market clearing mode proposed by this method is 14, n=14. Indicates The evaluation index of electricity market clearing mode is The relative importance of the evaluation indicators of the power market clearing method is calculated. Then C is called the fuzzy complementary matrix. According to the definition of the fuzzy complementary matrix, the "0.1~0.9 five-scale method" is selected as the quantitative standard. This method compares the importance of each pair of indicators and quantifies them, as shown in Table 1.

[0041] Table 1 Five-scale method

[0042] 3) Construct the fuzzy consistency matrix R. According to the following steps, the matrix C can be converted into a fuzzy consistency matrix , if satisfied , then R is the fuzzy consistent matrix.

[0043] Solve the fuzzy consistent matrix corresponding to the fuzzy complementary matrix C The steps are as follows: ① Sum the fuzzy complementary matrix C row by row and record it as , ; ② Calculate the fuzzy consistent matrix Each element in .

[0044] 4) Calculate weights. Using the fuzzy consistent matrix R to calculate weights, we can get the weight of the j-th power market clearing mode evaluation index: , The calculation formula is: , where: , is the order of R; For R Based on this, we can get the subjective weight matrix , j is the serial number of the evaluation index, and n is the number of evaluation indicators.

[0045] Since FAHP is a subjective weighting method, there is subjectivity and uncertainty in the weighting process. Therefore, this method introduces the CRITIC objective weighting method and combines it with it to perform comprehensive weighting of indicators.

[0046] Among them, the CRITIC method is used to calculate the objective weight of the evaluation index, including the following steps 1) to 5): Step 1) Construct the original evaluation matrix; Assume that the number of samples for evaluating the electricity market clearing method is , the number of evaluation indicators is , No. The first method of clearing The evaluation index is expressed as , then the original evaluation matrix is ​​constructed: ; Step 2) When an evaluation indicator that is positively correlated with the evaluation result appears in each evaluation indicator, the positively correlated evaluation indicator is positively processed; when an evaluation indicator that is negatively correlated with the evaluation result appears in each evaluation indicator, the negatively correlated evaluation indicator is reversely processed to obtain a standardized matrix; Step 3) Determine the index conflict and measure it by the correlation coefficient between the indicators; Step 4) Calculate the information content of each indicator; Step 5) Normalize the information of each indicator to obtain the objective weight matrix , j is the serial number of the evaluation index, and n is the number of evaluation indicators.

[0047] The Lagrange multiplier method is used to combine the subjective weight and objective weight of the evaluation index to obtain the comprehensive weight.

[0048] The use of a single subjective or objective weight determination method is somewhat one-sided. Therefore, this method uses the Lagrange multiplier method to determine the comprehensive weight. The weights obtained by FAHP and the weights obtained by the CRITIC method are combined and weighted, which can reflect the importance of the indicators and make the weight value of each indicator more objective and reasonable, and more accurately reflect the weight coefficient of each indicator in the clearing method evaluation index.

[0049] The comprehensive weight determination method based on Lagrange multiplier method optimization specifically includes: First, calculate the minimum information entropy according to the principle of minimum information entropy F calculate:

[0050] The subjective weight of each evaluation index and objective weight Solving for comprehensive weight ,Require Distribution in space and and The distribution in space should be as close as possible, so the principle of minimum information entropy can be used. of which, is the j-th evaluation index weight obtained by FAHP; is the j-th evaluation index weight obtained by the CRITIC method.

[0051] Then, the Lagrange multiplier method is used to solve and optimize the above minimum information entropy F Here, the formula of Lagrange multiplier method is: , is the Lagrange multiplier, through and Calculate the partial derivatives separately and set them to zero to find the optimal solution.

[0052] At this point, the comprehensive weight optimized by the Lagrange multiplier method is obtained.

[0053] Step 104: Using the obtained combined weights, the electricity market clearing method is evaluated using the improved matter-element extension model.

[0054] The evaluation method of clearing mode based on the improved matter-element extension model is as follows: The traditional matter-element extension model represents the matter-element as an ordered triple r=(N, C, V), that is, the object to be evaluated N, with the feature C and its feature value V. This model establishes a matter-element evaluation model with the same features. This method first improves the classical domain, using the time series average segmentation method to divide the range of the evaluation index under the evaluation level, that is, the value domain, so as to determine the classical domain of the index. Secondly, the proximity principle is used instead of the maximum membership principle for improvement. The specific steps of the model are as follows: 1) Establish the classical domain matter-element matrix, recorded as:

[0055] In the above formula: C is the index set of the ith clearing method in step 102 X = (X j )n×1; is the kth evaluation level; represents the evaluation index X at the kth (k=1, ..., K) evaluation level j range.

[0056] Since there is no clear classification standard for evaluation indicators, this method collects the data of the evaluation indicator system of the market to be evaluated in the previous month. The classification standard can be divided into value ranges according to the method of average segmentation of time series, so as to determine the classical domain of the indicator. That is to say, the value range of the evaluation indicator at each evaluation indicator level in the classical domain is based on the historical clearing data of the power market and is obtained by using the average segmentation method of time series.

[0057] Assume that a certain evaluation index after standardization The time series is X1, X2, …X f , the evaluation level N of the evaluation index can be determined as follows k The value range is: First, determine the time series length L=max -min ; Average interval between time and column interval: G= (max -min ) / K; Then, the range of the evaluation level can be modified to [max -KG, max -(K-1)G]= .

[0058] 2) Determination of the section domain matter element. Based on the classical domain, the section domain matter element matrix is ​​further constructed as follows:

[0059] In the above formula: Indicates the totality of the evaluation levels of the clearing method; for About evaluation index X j The value range of .

[0060] 3) Determination of the matter-element to be evaluated.

[0061] In the above formula: Indicates the evaluation level of the clearing method; express About X j The value of the factor that affects the level of clearing method is the measured value of the parameter.

[0062] 4) Normalize the value data of the classical domain matter-element matrix, and divide both ends of the classical domain value by the node domain The value on the right , and obtain a new matter-element classical domain matrix :

[0063] 5) The matrix of matter-element to be evaluated Divide both ends of the value by the right end of the section The value on the right , and obtain the new matter-element matrix . Calculate the new object element to be evaluated About the New Classical Domain Matrix The distance of the measurement range is expressed in express: , In the above formula , , They are the results after normalization.

[0064] 7) Calculate the closeness value. Introduce asymmetric closeness to replace the maximum membership principle for evaluation. The asymmetric closeness formula is: , Where: D is the distance; is the comprehensive weight of the jth evaluation index; n is the number of evaluation indicators.

[0065] Applying the above formula to the matter-element extension model, the closeness of each level corresponding to the matter-element to be evaluated can be calculated:

[0066] 8) Determine the evaluation level.

[0067]

[0068] The final evaluation object, that is, the electricity market clearing method, is rated at level k.

[0069] 9) Calculate the characteristic value of the evaluation grade variable. In order to obtain the degree of bias of the grade, the variable characteristic value of the evaluation grade can be used for calculation. The formula is: ,

[0070] In the above formula is the grade variable characteristic value of the electricity market clearing mode N, which can reflect the degree of bias of the electricity market clearing mode N in the evaluation grade.

[0071] Among them, the levels are set as: excellent (value range: 90-100), relatively excellent (value range: 80-90), average (value range: 60-80), and unqualified (value range: 0-60).

[0072] The beneficial effects of the above method are described below through specific cases: This case uses a data sample of cross-regional electricity trading in which 1,000 generators and 30 large electricity users participated in market bidding. The evaluation results of different evaluation methods on clearing methods are shown in Table 2.

[0073] Table 2 Evaluation results of clearing methods

[0074] It can be seen from Table 2 that the evaluation conclusions obtained by different evaluation methods are different. According to the actual ratings, the evaluation results of the evaluation method disclosed in the present invention are more accurate and more fair.

[0075] The evaluation method disclosed in the present invention combines a large amount of indicator data and conducts a comprehensive evaluation on the two current clearing methods of the electricity market, namely, marginal unified clearing and high-low matching clearing. The proposed evaluation method ensures the comprehensiveness and systematicness of the evaluation process, thereby providing a scientific basis for the optimization of the electricity market and policy formulation, and helping to achieve more efficient and fair allocation of electricity resources.

[0076] Based on the same inventive concept as the above disclosed content, the present disclosure also provides a power market clearing mode evaluation device, such as Figure 2 As shown, it includes a construction unit 21, a comprehensive weight determination unit 22 and an evaluation unit 23; wherein: A construction unit 21 is used to construct an evaluation index of the power market clearing mode and standardize the evaluation index; A comprehensive weight determination unit 22 is used to calculate the subjective weight of the evaluation index by using the fuzzy analytic hierarchy process FAHP, calculate the objective weight of the evaluation index by using the CRITIC method, and synthesize the subjective weight and the objective weight of the evaluation index to obtain a comprehensive weight; The evaluation unit 23 is used to evaluate the electricity market clearing mode based on the improved matter-element extension model by using the obtained comprehensive weight.

[0077] Preferably, the comprehensive weight determination unit is specifically used to use the Lagrange multiplier method to combine the subjective weight and the objective weight of the evaluation index to obtain the comprehensive weight.

[0078] Preferably, construction unit 21 is specifically used to crawl the word frequency of the content describing each power market clearing method based on web crawler technology, select the first Q high-frequency words with high occurrence frequency and related to power market clearing, and use the selected high-frequency words as evaluation indicators of the evaluation indicator system, wherein Q is greater than or equal to the number of evaluation aspects included in the evaluation indicator system.

[0079] Preferably, the evaluation index system of electricity market clearing methods is composed of market competitiveness, market reliability, market activity, social welfare and renewable energy.

[0080] For better ones, market competitiveness includes some or all evaluation indicators among market concentration, barrier index, surplus supply rate and enterprise winning bid rate; Market reliability includes: some or all evaluation indicators of price volatility, power supply reliability and system availability; Market activity includes: market transaction amount, market transaction frequency and some or all evaluation indicators in the bid-ask spread; Social welfare includes: some or all evaluation indicators of social welfare of power purchasing enterprises and social welfare of power generation enterprises; Renewable energy includes: the proportion of renewable energy trading electricity and some or all evaluation indicators in the renewable energy power generation trading price.

[0081] Preferably, the comprehensive weight determination unit 22 is specifically used to construct a hierarchical structure with the comprehensive evaluation of the electricity market clearing mode as the target layer, market competitiveness, market reliability, market activity, social welfare and renewable energy as the criterion layer, and the evaluation indicators under each criterion in the criterion layer as the decision-making layer; construct a fuzzy complementary matrix using the evaluation indicators under each criterion, and convert the fuzzy complementary matrix into a fuzzy consistent matrix; and use the fuzzy consistent matrix to calculate the subjective weight matrix of the evaluation indicators.

[0082] Preferably, the comprehensive weight determination unit 22 is specifically used to construct an original evaluation matrix by evaluating the number of samples and the number of evaluation indicators using the electricity market clearing method; based on the correlation between the evaluation indicators and the evaluation results, the original evaluation matrix is ​​processed accordingly to obtain a standardized matrix; the conflict of the evaluation indicators is determined using the correlation coefficients between the evaluation indicators; the amount of information of each evaluation indicator is calculated; the amount of information of each evaluation indicator is normalized to obtain an objective weight matrix of the evaluation indicators, wherein when the evaluation indicator is positively correlated with the evaluation result, the positively correlated evaluation indicator is positively processed; when the evaluation indicator is negatively correlated with the evaluation result, the negatively correlated evaluation indicator is reversely processed.

[0083] Preferably, the evaluation unit 23 is specifically used to adopt the matter-element extension model to establish the classical domain of the electricity market clearing evaluation index and the matter-element to be evaluated, and to construct the corresponding matter-element matrix, calculate the closeness between each evaluation index of the matter-element to be evaluated and the evaluation index level, and determine the evaluation level to which the electricity market clearing method belongs, wherein the value range of the evaluation index at each evaluation index level in the classical domain is based on the historical clearing data of the electricity market and is obtained by dividing it using the time series average segmentation method.

[0084] Based on the same inventive concept as the above disclosed content, the present disclosure also provides an electronic device accordingly. The electronic device of the present disclosure embodiment includes at least one processor and at least one memory electrically connected, the memory is electrically connected to the processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the method as described above.

[0085] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between lines. An indirect connection method can be applied to the embodiments of the present disclosure as long as the purpose of the present disclosure is achieved.

[0086] Based on the same inventive concept, the present disclosure also provides a computer storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0087] Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for evaluating power market clearing methods, characterized in that: The method comprises, Construct evaluation indicators for electricity market clearing methods and standardize the constructed evaluation indicators; The fuzzy analytic hierarchy process (FAHP) is used to calculate the subjective weight of the evaluation index, and the CRITIC method is used to calculate the objective weight of the evaluation index. The subjective weight and objective weight of the evaluation index are combined to obtain the comprehensive weight. Using the obtained comprehensive weights, the electricity market clearing mode is evaluated based on the improved matter-element extension model.

2. The method according to claim 1, characterized in that The subjective weight and objective weight of the evaluation index are combined to obtain the comprehensive weight, including: The Lagrange multiplier method is used to combine the subjective weight and objective weight of the evaluation index to obtain the comprehensive weight.

3. The method according to claim 1 or 2, characterized in that: Construct evaluation indicators for clearing methods, including: Construct an evaluation index system for electricity market clearing methods; Based on web crawler technology, the word frequency of the content describing each power market clearing method is captured, and the first Q high-frequency words with high occurrence frequency and related to power market clearing are selected. The selected high-frequency words are used as evaluation indicators of the evaluation index system, where Q is greater than or equal to the number of evaluation aspects included in the evaluation index system.

4. The method according to claim 3, characterized in that The constructed evaluation index system for electricity market clearing methods consists of market competitiveness, market reliability, market activity, social welfare and renewable energy.

5. The method according to claim 4, characterized in that Market competitiveness includes: market concentration, barrier index, surplus supply rate and some or all evaluation indicators of enterprise winning bid rate; Market reliability includes: some or all evaluation indicators of price volatility, power supply reliability and system availability; Market activity includes: market transaction amount, market transaction frequency and some or all evaluation indicators in the bid-ask spread; Social welfare includes: some or all evaluation indicators of social welfare of power purchasing enterprises and social welfare of power generation enterprises; Renewable energy includes: the proportion of renewable energy trading electricity and some or all evaluation indicators in the renewable energy power generation trading price.

6. The method according to claim 5, characterized in that FAHP is used to calculate the subjective weights of the evaluation indicators, including: The hierarchical structure is constructed with the comprehensive evaluation of the electricity market clearing mode as the target layer, market competitiveness, market reliability, market activity, social welfare and renewable energy as the criterion layer, and the evaluation indicators under each criterion in the criterion layer as the decision layer. The fuzzy complementary matrix is ​​constructed using the evaluation index under each criterion, and the fuzzy complementary matrix is ​​transformed into a fuzzy consistent matrix; The subjective weight matrix of evaluation indexes is calculated using fuzzy consistent matrix.

7. The method according to claim 5, characterized in that The objective weights of evaluation indicators are calculated using the CRITIC method, including: The original evaluation matrix is ​​constructed by using the number of evaluation samples and the number of evaluation indicators in the electricity market clearing method; Based on the correlation between the evaluation index and the evaluation result, the original evaluation matrix is ​​processed accordingly to obtain a standardized matrix; when the evaluation index is positively correlated with the evaluation result, the positively correlated evaluation index is processed positively; when the evaluation index is negatively correlated with the evaluation result, the negatively correlated evaluation index is processed inversely; Use the correlation coefficients between evaluation indicators to determine the conflict between evaluation indicators; Calculate the information content of each evaluation index; The information amount of each evaluation index is normalized to obtain the objective weight matrix of the evaluation index.

8. The method according to any one of claims 5 to 7, characterized in that: Using the obtained comprehensive weights, the electricity market clearing methods are evaluated based on the improved matter-element extension model, including: The matter-element extension model is adopted to establish the classical domain and the matter-element to be evaluated of the power market clearing evaluation index, and construct the corresponding matter-element matrix. The value range of the evaluation index at each evaluation index level in the classical domain is obtained by dividing the historical clearing data of the power market using the time series average segmentation method. Calculate the closeness between the various evaluation indicators of the entity to be evaluated and the evaluation indicator level, and determine the evaluation level to which the electricity market clearing method belongs.

9. An electricity market clearing mode evaluation device, characterized in that: The device comprises a construction unit, a comprehensive weight determination unit and an evaluation unit; wherein: A construction unit is used to construct evaluation indicators of power market clearing methods and standardize the evaluation indicators; A comprehensive weight determination unit is used to calculate the subjective weight of the evaluation index by using the fuzzy analytic hierarchy process FAHP, calculate the objective weight of the evaluation index by using the CRITIC method, and synthesize the subjective weight and the objective weight of the evaluation index to obtain the comprehensive weight; The evaluation unit is used to evaluate the electricity market clearing mode by using the obtained comprehensive weight and adopting the improved matter-element extension model.

10. The device according to claim 9, characterized in that The comprehensive weight determination unit is specifically used to use the Lagrange multiplier method to integrate the subjective weight and the objective weight of the evaluation index to obtain the comprehensive weight.

11. The device according to claim 9 or 10, characterized in that The construction unit is specifically used to crawl the word frequency of the content describing each power market clearing method based on the web crawler technology, select the first Q high-frequency words with high occurrence frequency and related to the power market clearing, and use the selected high-frequency words as evaluation indicators of the evaluation indicator system, wherein Q is greater than or equal to the number of evaluation aspects included in the evaluation indicator system.

12. The device according to claim 11, characterized in that The constructed evaluation index system for electricity market clearing methods consists of market competitiveness, market reliability, market activity, social welfare and renewable energy.

13. The device according to claim 12, characterized in that Market competitiveness includes: market concentration, barrier index, surplus supply rate and some or all evaluation indicators of enterprise winning bid rate; Market reliability includes: some or all evaluation indicators of price volatility, power supply reliability and system availability; Market activity includes: market transaction amount, market transaction frequency and some or all evaluation indicators in the bid-ask spread; Social welfare includes: some or all evaluation indicators of social welfare of power purchasing enterprises and social welfare of power generation enterprises; Renewable energy includes: the proportion of renewable energy trading electricity and some or all evaluation indicators in the renewable energy power generation trading price.

14. The device according to claim 13, characterized in that The comprehensive weight determination unit is specifically used to construct a hierarchical structure with the comprehensive evaluation of the electricity market clearing mode as the target layer, market competitiveness, market reliability, market activity, social welfare and renewable energy as the criterion layer, and the evaluation index under each criterion in the criterion layer as the decision layer; construct a fuzzy complementary matrix using the evaluation index under each criterion, and transform the fuzzy complementary matrix into a fuzzy consistent matrix; The subjective weight matrix of evaluation indexes is calculated using fuzzy consistent matrix.

15. The device according to claim 13, characterized in that A comprehensive weight determination unit, specifically used to construct an original evaluation matrix using the power market clearing method to evaluate the number of samples and the number of evaluation indicators; Based on the correlation between the evaluation indicators and the evaluation results, the original evaluation matrix is ​​processed accordingly to obtain a standardized matrix; the conflict of evaluation indicators is determined using the correlation coefficient between the evaluation indicators; the information content of each evaluation indicator is calculated; The information amount of each evaluation index is normalized to obtain the objective weight matrix of the evaluation index. When the evaluation index is positively correlated with the evaluation result, the positively correlated evaluation index is positively processed; when the evaluation index is negatively correlated with the evaluation result, the negatively correlated evaluation index is reversely processed.

16. The device according to any one of claims 13 to 15, characterized in that: The evaluation unit is specifically used to adopt the matter-element extension model to establish the classical domain of the electricity market clearing evaluation index and the matter-element to be evaluated, and to construct the corresponding matter-element matrix, calculate the closeness between each evaluation index of the matter-element to be evaluated and the evaluation index level, and determine the evaluation level to which the electricity market clearing method belongs, wherein the value range of the evaluation index at each evaluation index level in the classical domain is based on the historical clearing data of the electricity market and is obtained by dividing it using the time series average segmentation method.

17. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; A processor, for implementing the steps of any method described in claims 1-8 when executing a program stored in a memory.

18. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the methods of claims 1-8 are implemented.

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