Electric power market subject credit risk assessment method and system

Abstract

The invention discloses an electricity market subject credit risk assessment method and system, and relates to the field of electricity market operation and risk management, and the method comprises the steps: constructing a first assessment system based on an SCP framework, and carrying out the corresponding data collection; determining an index weight based on the first evaluation system and the collected data; establishing a fuzzy comprehensive evaluation model based on the first evaluation system and the index weight; evaluating the evaluation object by using the fuzzy comprehensive evaluation model; the method is an effective tool for evaluating and managing the risk of a market subject, provides a decision basis for market participants, helps to realize effective configuration of resources, stable operation of the market and sustainable development of the new energy industry, promotes healthy development of the new energy industry, improves the operation efficiency of the market, and reduces systematic risks.

Description

Technical Field

[0001] The present invention relates to the field of power market operation and risk management, and particularly to a method and system for evaluating the credit risk of power market entities. Background Art

[0002] With the acceleration of China's power market reform, various reform measures have been promoted in an orderly manner, achieving remarkable results and progress in many fields. Market compliance is a key link to ensure power market transactions, while market default is a common risk in power market operation.

[0003] Under such circumstances, it is crucial to accurately evaluate the credit risk of power market entities. However, the current research has obvious limitations. First, most of the existing research only involves a single market entity, such as only focusing on the credit risk of power generation enterprises or power users, and it is impossible to comprehensively and effectively measure the credit risk of all participants in the power market. This makes the evaluation results unable to reflect the credit status of the entire power market and is difficult to meet the needs of market refined operation and risk control. Second, in terms of index weighting, the existing research only uses a single weighting method. For example, the subjective weighting method (such as the analytic hierarchy process) is greatly affected by experts' subjective judgments, while the objective weighting method (such as the entropy weight method) overly relies on the distribution characteristics of data, resulting in the weights of indicators being greatly affected by human factors or data distribution, and the scientificity and reliability of the evaluation results are questioned. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: how to comprehensively, scientifically and accurately evaluate the credit risk status of power market entities and provide effective support for power market refined operation and risk control.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for evaluating the credit risk of power market entities, including:

[0008] Constructing a first evaluation system based on the SCP framework and collecting corresponding data;

[0009] Determining index weights based on the first evaluation system and the collected data;

[0010] Establishing a fuzzy comprehensive evaluation model based on the first evaluation system and the index weights;

[0011] Evaluating the evaluation object using the fuzzy comprehensive evaluation model.

[0012] As an optimal solution for the credit risk assessment method of power market entities, where:

[0013] Determining the index weights based on the first evaluation system and the collected data includes:

[0014] Conduct objective weighting and subjective weighting respectively to obtain the final objective weight and the final subjective weight, and determine the index weights based on the final objective weight and the final subjective weight.

[0015] As an optimal solution for the credit risk assessment method of power market entities, where:

[0016] The objective weighting includes:

[0017] For each evaluation index, calculate the standard deviation and information entropy based on the sample data;

[0018] Measure the conflict between each index by calculating the correlation coefficient between the indexes;

[0019] Combining the calculated standard deviation, information entropy and conflict, calculate the amount of information contained in each index, and then determine the final objective weight of each index.

[0020] As an optimal solution for the credit risk assessment method of power market entities, where:

[0021] The subjective weighting includes:

[0022] Determine the subjective weight vector according to the expert opinions, measure the similarity degree between any two experts' subjective weight vectors by calculating the Euclidean distance, and then obtain the similarity degree between each expert and other experts;

[0023] Determine the decision weight coefficient according to the similarity degree between the expert and other experts, and perform weighted calculation on the expert decision weight coefficient and each weight vector to obtain the final subjective weight.

[0024] As an optimal solution for the credit risk assessment method of power market entities, where:

[0025] Establishing a fuzzy comprehensive evaluation model based on the first evaluation system and the index weights includes:

[0026] Based on the first evaluation system, establish a credit risk assessment factor set for power trading entities, including the first-level and second-level factor sets; comprehensively consider the risk occurrence probability and impact consequences, and divide the evaluation levels to determine the comment set;

[0027] Construct an expert fuzzy evaluation table, calculate the membership degree of each single factor to each comment set, and then form a membership degree matrix.

[0028] As an optimal solution for the credit risk assessment method of power market entities, where:

[0029] Based on the first evaluation system and index weights, establishing the fuzzy comprehensive evaluation model further includes:

[0030] Based on the membership degree matrix, obtain the secondary index fuzzy evaluation matrix according to the membership degree of the secondary index, calculate the membership degree of the primary index based on the secondary index fuzzy evaluation matrix, and obtain the fuzzy evaluation matrix of the primary index; Multiply the fuzzy matrix of the primary index by the weight of the corresponding target layer to obtain the membership degree of the target layer, and after normalizing it, determine the final fuzzy comprehensive evaluation result according to the principle of maximum membership degree.

[0031] As an optimal solution of the credit risk assessment method for power market entities, wherein:

[0032] Determining the index weights based on the final objective weight and the final subjective weight includes:

[0033] Define the index weight as a linear combination of the two weights, expressed as:

[0034]

[0035] Wherein, W i represents the weight of the i-th index, represents the final objective weight of the i-th index, represents the final subjective weight of the i-th index, and α is the proportion of the final objective weight.

[0036] On the second aspect, an embodiment of the present invention provides a credit risk assessment system for power market entities, including:

[0037] An evaluation system construction module, configured to construct a first evaluation system based on the SCP framework and perform corresponding data collection;

[0038] An index weight determination module, configured to determine index weights based on the first evaluation system and the collected data;

[0039] A comprehensive evaluation model establishment module, configured to establish a fuzzy comprehensive evaluation model based on the first evaluation system and index weights;

[0040] An evaluation module, configured to evaluate an evaluation object using the fuzzy comprehensive evaluation model.

[0041] On the third aspect, an embodiment of the present invention provides a computing device, including:

[0042] A memory and a processor;

[0043] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the one or more programs are executed by the one or more processors, the one or more processors implement the power market entity credit risk assessment method as described in any embodiment of the present invention.

[0044] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, which when executed by a processor implement the power market entity credit risk assessment method described above.

[0045] Advantages of the present invention: The present invention is not only an effective tool for market entities to assess and manage their own risks, but also provides a decision-making basis for market participants, helping to achieve effective allocation of resources, smooth operation of the market and sustainable development of the new energy industry, promoting the healthy development of the new energy industry, improving market operation efficiency and reducing systemic risks. Description of the Drawings

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is the overall flowchart of the power market entity credit risk assessment method described in the present invention. Detailed Embodiments

[0048] To make the above objects, features and advantages of the present invention more obvious and understandable, the detailed embodiments of the present invention will be described below with reference to the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0049] Embodiment 1

[0050] Refer to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a power market entity credit risk assessment method, including:

[0051] S1: Construct a first evaluation system based on the SCP framework and collect corresponding data;

[0052] S2: Determine the index weights based on the first evaluation system and the collected data;

[0053] S3: Based on the first evaluation system and index weights, establish a fuzzy comprehensive evaluation model;

[0054] S4: Use the fuzzy comprehensive evaluation model to evaluate the evaluation object.

[0055] It should be noted that through steps S1 - S4, a scientific, comprehensive, and effective credit risk assessment system for power trading entities can be constructed to reasonably and accurately evaluate the credit risk of the evaluation object (power trading entity); in step S1, based on the SCP (Structure - Conduct - Performance) framework, the first evaluation system is constructed, which can systematically analyze the credit risk of power trading entities from multiple dimensions, comprehensively covering the key elements of power trading entities in aspects such as market structure, market conduct, and market performance. At the same time, corresponding data collection is carried out, providing a rich and reliable data basis for subsequent evaluation work; in step S2, the index weights are determined based on the first evaluation system and the collected data. By comprehensively considering the objective data characteristics and expert subjective experience, an improved CRITIC method is used for objective weighting and the GAHP method is used for subjective weighting in combination, which can more reasonably reflect the importance of each index in the evaluation, reduce the limitations of a single weighting method, and make the evaluation more scientific and reliable; in step S3, based on the first evaluation system and the determined index weights, a fuzzy comprehensive evaluation model is established. This model takes into account the complex relationships between the indexes and the fuzziness of the evaluation, and can better handle the uncertainty and subjectivity problems in the credit risk assessment of power trading entities, providing an effective tool for accurate evaluation; in step S4, the constructed fuzzy comprehensive evaluation model is used to evaluate the evaluation object (power trading entity). The model processes and analyzes the collected data, and based on the principle of maximum membership degree, the final evaluation result is obtained, which can clearly reflect the level of the credit risk of power trading entities, providing a strong decision - making basis for the operation and risk management of the power market. For example, it helps the regulatory department formulate reasonable regulatory policies and also helps market participants better identify and manage the credit risk of trading counterparts.

[0056] Embodiment 2

[0057] Refer to Figure 1 and Table 1, which is an embodiment of the present invention. Based on the previous embodiment, a method for evaluating the credit risk of power market entities is provided, including:

[0058] In the embodiment of the present application, in the above step S1, constructing the first evaluation system based on the SCP framework and performing corresponding data collection includes:

[0059] At the structural level, construct indicators such as the scale index C11, asset-liability ratio C12, market share C13, current ratio C14, and the proportion of renewable energy installed capacity C15. At the behavioral level, construct indicators such as the declaration accuracy C21, contract electricity fulfillment rate C22, cost control rate C23, credit history C24, the market expansion ability of participants C25, and the proportion of technology innovation and R & D investment C26. At the performance level, construct indicators such as the revenue growth rate C31, net profit rate C32, return on assets C33, return on investment C34, customer satisfaction index C35, and subsidy dependence C36. After constructing the indicator system, collect data for the corresponding indicators.

[0060] In another possible implementation, when constructing the first evaluation system, it can also be constructed from five levels: resource endowment, operation management, market competition, social responsibility, and innovation development.

[0061] Specifically, at the resource endowment level: Own energy reserve D11: refers to the reserve of fossil energy such as coal and natural gas owned by the power generation enterprise itself, or the amount of clean energy resources such as exploitable hydropower, wind energy, and solar energy. For thermal power enterprises, this indicator reflects their autonomy and stability in fuel supply; for new energy enterprises, it reflects their potential for sustainable development.

[0062] Land resource suitability D12: refers to the suitability of the land used for the enterprise's power generation project for surrounding energy resources and load centers, including whether the land area meets the project requirements and whether the geographical location is conducive to energy transmission. Good land resource suitability helps to reduce construction and operation costs.

[0063] At the operation management level: Equipment operation and maintenance efficiency D21: measured by the ratio of the actual operation time of the equipment to the planned operation time, reflecting the enterprise's maintenance and management level of power generation equipment. Higher equipment operation and maintenance efficiency means fewer equipment failures and shorter downtime, ensuring stable power supply.

[0064] Material procurement cost control rate D22: calculates the ratio of the actual material procurement cost of the enterprise to the budgeted procurement cost, reflecting the enterprise's cost control ability in the material procurement link. The lower the cost control rate, the more effectively the enterprise can save costs in the procurement process.

[0065] Employee professional skill compliance rate D23: counts the proportion of employees with corresponding professional skills and qualifications in the total number of employees in the enterprise, reflecting the overall quality and professional ability of the enterprise's employee team.

[0066] At the market competition level: Price competitiveness index D31: expressed as the ratio of the enterprise's electricity sales price to the market average price, reflecting the enterprise's competitive advantage in terms of price. The lower the ratio, the more competitive the enterprise's electricity price.

[0067] Customer retention rate D32: It is the ratio of the number of old customers retained by an enterprise within a certain period to the number of customers at the beginning of the period, reflecting the market attractiveness and customer loyalty of the enterprise. The higher the customer retention rate, the more popular the enterprise is in the market.

[0068] Market share growth rate D33: Calculate the growth ratio of an enterprise's market share within a certain period, reflecting the enterprise's expansion ability and competitive situation in the market. The higher the market share growth rate, the more the enterprise's market position is continuously improving.

[0069] Social responsibility aspect: Pollutant emission reduction compliance rate D41: It is the ratio of the actual emission reduction amount of various pollutants (such as sulfur dioxide, nitrogen oxides, soot, etc.) of an enterprise to the emission reduction target amount, reflecting the enterprise's efforts and achievements in environmental protection.

[0070] Power emergency response time D42: It refers to the time interval from receiving a notice to taking effective emergency measures when an enterprise faces a sudden power accident or emergency situation, reflecting the enterprise's social responsibility and emergency response ability.

[0071] Community contribution investment ratio D43: Calculate the ratio of the investment funds used by an enterprise for community construction, public welfare activities, etc. to the total operating income of the enterprise, reflecting the degree of the enterprise's feedback and support to the community where it is located.

[0072] Innovation and development aspect: Degree of application of smart grid technology D51: Evaluate the breadth and depth of the application of smart grid technology (such as automatic control, smart metering, distributed energy management, etc.) by an enterprise in power generation, power transmission, power distribution and other links, reflecting the enterprise's technological innovation and modernization level.

[0073] Number of new energy technology R & D projects D52: Count the number of new energy technology R & D projects being carried out by an enterprise, reflecting the enterprise's innovation investment and development potential in the new energy field.

[0074] Quantity of intellectual property rights owned D53: It includes the number of intellectual property rights such as patents, trademarks, software copyrights, etc. owned by an enterprise, reflecting the enterprise's innovation achievements and technological strength.

[0075] In the embodiment of the present application, determining the index weights based on the first evaluation system and the collected data in the above step S2 includes:

[0076] Conducting objective weighting and subjective weighting respectively;

[0077] Objective weighting includes:

[0078] Calculating the standard deviation and information entropy of each index:

[0079] Suppose there are p evaluation indicators, where the standard deviation S of the i-th indicator i and information entropy Ei The calculation formula is expressed as:

[0080]

[0081] where x ki represents the value corresponding to the i-th index in the k-th sample; is the mean value of all samples corresponding to the i-th index.

[0082] It should be noted that when calculating the information entropy, logarithmic operations are required. In this method, the change amount of the evaluation index is normalized between -1 and 1. Therefore, the absolute value of the sample value needs to be taken.

[0083] Calculate the conflict of each index:

[0084] Express the conflict of each index as:

[0085]

[0086] where r ij represents the correlation coefficient between the i-th and j-th indices, which is calculated using the Pearson correlation coefficient. The calculation formula is expressed as:

[0087]

[0088] It should be noted that considering the possible negative correlation between indices, the absolute value needs to be added to the correlation coefficient.

[0089] Calculate the weight of each index:

[0090] From the standard deviation S i and the information entropy E i and the conflict R i calculate the amount of information C i contained in each index and the objective weight The calculation formula is expressed as:

[0091] C i =(1 - E i + S i )R i

[0092]

[0093] Obtain the final objective weight

[0094] It should be noted that CRITIC is an objective weighting method that comprehensively determines weights based on the volatility of each index sample value and the conflict between indices. To more comprehensively evaluate the volatility and dispersion degree of indices, this embodiment uses an improved CRITIC method to calculate weights, introducing information entropy into CRITIC; the traditional method is relatively one-sided in evaluating the volatility and dispersion degree of indices. After improvement, it combines the standard deviation and information entropy to capture data information from multiple aspects, more comprehensively reflecting the situation of indices; reducing the influence of a single weighting method by humans and data distribution, comprehensively considering index volatility, conflict, and information entropy, taking into account the complex relationships between indices, making the determination of weights more reasonable; comprehensively reflecting index information, reasonably allocating weights, and enhancing the scientific nature of the model; reducing the impact of data volatility on the evaluation results, enhancing the reliability of the model, and providing strong support for the operation and risk control of the power market.

[0095] Subjective weighting includes:

[0096] Calculate the similarity of each expert:

[0097] Determine each subjective weight vector according to the opinions of each expert. Let the subjective weight W determined by the i-th expert (i) and the subjective weight W determined by the j-th expert (j) The similarity degree is d ij , which is calculated using the Euclidean distance, and the calculation formula is expressed as:

[0098]

[0099] where d ij satisfies d ij =1, i = j; d ij =d ji ≥0; The similarity degree between the l-th expert and other experts among L experts is

[0100] Calculate the decision weights of each expert:

[0101] The decision weight coefficient λ of the l-th expert l The calculation formula is expressed as:

[0102]

[0103] where d l The larger it is, the greater the difference between this expert and other experts, and the smaller the decision weight coefficient; conversely, the more similar the opinions of this expert and other experts are, the larger the decision weight coefficient. When d l =0, the opinions of L experts are the same. Therefore, the decision weights are also the same, which is 1 / L.

[0104] Calculate the subjective weight:

[0105] From the expert decision weight coefficient λl and weighted by each weight vector to obtain the final subjective weight The calculation formula is expressed as:

[0106]

[0107] It should be noted that AHP determines the subjective weights of each index according to expert opinions. However, when integrating the opinions of multiple experts, the traditional AHP method does not consider the degree of difference in expert opinions. The GAHP adopted in this embodiment first uses AHP to determine multiple subjective weight vectors according to the opinions of each expert, then determines the decision weights of different experts according to the similarity between the subjective weight vectors, and finally obtains the comprehensive subjective weight through weighted synthesis. The traditional AHP simply averages expert opinions, while GAHP calculates the similarity of expert opinions to determine decision weights, avoiding the excessive influence of individual opinions with large deviations on the results; uses an objective method to determine decision weights, synthesizes the expert's own judgment and opinion consistency, making the weight determination more scientific and reliable; the result reflects the consensus of the expert group and is easy to be accepted; it is applicable to complex decision-making scenarios, provides more accurate information for decision-making, and improves the rationality of decision-making.

[0108] Determine the index weight based on the final objective weight and the final subjective weight; specifically, define the index weight as a linear combination of the two weights, which is expressed as:

[0109]

[0110] where, where, W i represents the weight of the i-th index, represents the final objective weight of the i-th index, represents the final subjective weight of the i-th index, and α is the proportion of the final objective weight. The specific proportional value is comprehensively determined by senior experts in the field according to the actual business experience of the power market, the analysis results of historical data, and the current market development trend.

[0111] Exemplarily, in the initial stage of market development, due to limited data accumulation, experts may appropriately increase the proportion of subjective weight, that is, reduce the value of α, to refer more to the professional judgment of experts; as the market data continues to be enriched and improved, experts may increase the value of α and increase the proportion of objective weight in the combined weight, so that the evaluation result can better reflect the characteristics of the actual data.

[0112] In another possible implementation, the specific proportional value is determined by the quality and stability of the data. If the collected data is highly accurate, complete, and has strong stability, then the value of α can be appropriately increased to make the objective weight account for a larger proportion in the combined weight, because at this time the objective data can reliably reflect the importance of the indicators. On the contrary, if there is a lot of noise, missing values, or large fluctuations in the data, in order to avoid the objective weight being affected by the bad data, it is necessary to reduce the value of α and increase the proportion of the subjective weight, and rely on the experience of experts to balance the evaluation results.

[0113] In another possible implementation, the specific proportional value is determined by the evaluation objective and the requirement for the result accuracy. When the evaluation objective focuses on quickly obtaining a rough result and the requirement for accuracy is relatively low, the value of α can be appropriately reduced to increase the influence of the subjective weight, so that the judgment can be quickly made using the experience of experts. When the evaluation objective is to make a precise decision and the requirement for the result accuracy is high, then the value of α needs to be increased to give full play to the objective weighting advantage of the improved CRITIC method based on data, so as to improve the accuracy and reliability of the evaluation results.

[0114] In the embodiment of the present application, establishing a fuzzy comprehensive evaluation model based on the first evaluation system and the index weight in step S3 above includes:

[0115] Determine the factor set:

[0116] Based on the first evaluation system, establish a factor set U for the credit risk assessment of power trading entities.

[0117] Specifically, the first-level factor set: U = {U1, U2, U3} = {structure, behavior, performance}.

[0118] The second-level factor set: U1 = {C11, C12, C13, C14, C15}; U2 = {C21, C22, C23, C24, C25, C26}; U3 = {C31, C32, C33, C34, C35, C26}.

[0119] Determine the comment set:

[0120] Denote the comment set of the credit risk assessment index system of power trading entities as V. Considering the probability of risk occurrence and the impact consequences, divide the evaluation levels into V = {V1, V2, V3, V4, V5} = {small, relatively small, medium, relatively large, large}.

[0121] Determine the weight set:

[0122] According to the method in S2, determine the index weight based on the final objective weight and the final subjective weight, expressed as W = (W1, W2,..., W p )

[0123] Establish a fuzzy evaluation matrix:

[0124] Construct an expert fuzzy evaluation form and invite relevant experts to judge the risk comments of each index. Through the recovered risk fuzzy evaluation form, calculate the membership degree of a single factor to each comment set V. The judgment horizontal vector of a single factor U is M i =(m n1 , m n2 ,..., m n5 ), m iv represents the frequency distribution of the nth factor on the vth comment. n is the number of indicators in each factor set, and v is the comment set level. According to the membership degree of the indicators, construct the membership degree matrix S B .

[0125] Construct a fuzzy comprehensive evaluation model:

[0126] Through matrix multiplication and addition, obtain the fuzzy comprehensive evaluation model. The specific calculation steps are as follows:

[0127] Based on the membership degree matrix S B , according to the membership degree of the secondary indicators, obtain the secondary indicator fuzzy evaluation matrix as shown below:

[0128]

[0129] where the number of indicators included in the factor set UB is.

[0130] According to the calculated secondary indicator fuzzy evaluation matrix, calculate the membership degree of the primary indicator, expressed as:

[0131] X B =W B *S B

[0132] where, W B is the weight vector of the secondary indicator corresponding to the primary indicator, S B is the secondary fuzzy matrix, and X B is the membership degree of the primary indicator. Synthesize the membership degree of the primary indicator to obtain the fuzzy evaluation matrix of the primary indicator.

[0133] Perform the comprehensive operation of the target layer to obtain the final evaluation result:

[0134] Multiply the fuzzy matrix S A of the primary indicator by the weight X B of the primary indicator corresponding to the target layer to obtain the membership degree X A of the target layer, expressed as:

[0135] X A =W B·S A

[0136] Normalize the membership degree X of the target layer. According to the principle of maximum membership degree, that is, the evaluation object belongs to the v-th level, and the evaluation level is the final fuzzy comprehensive evaluation result. A It should be noted that this step determines the factor set based on the evaluation system, comprehensively covering multiple aspects such as structure, behavior, and performance, and constructs a complete evaluation framework; clarifies the evaluation set, divides the risk levels in detail, and makes the evaluation results intuitive and easy to understand; through matrix operations and normalization processing, the results are obtained according to the principle of maximum membership degree, which is scientific, reasonable, and persuasive.

[0137] In the embodiment of the present application, the use of the fuzzy comprehensive evaluation model to evaluate the evaluation object in the above step S4 includes:

[0138] Exemplarily, through collecting the trading entities in a local power market, an example analysis is carried out. For the convenience of comparison, scores are assigned to the evaluation set: {small, relatively small, medium, relatively large, large} = {10, 8, 6, 4, 2}, and the analysis results are shown in Table 1:

[0139] Table 1 Comparison of risk assessment results of multiple methods for power trading entities

[0140] As can be seen from the results, there are differences in the risk assessments of different methods. In this embodiment, by combining the advantages of GAHP and the improved CRITIC, the weights are corrected, and more practical evaluation results can be obtained.

[0141]

[0142]

[0143]

[0144] Example 3

[0145] The above is a schematic solution of the power market entity credit risk assessment method in this embodiment. It should be noted that the technical solution of the power market entity credit risk assessment system belongs to the same concept as the technical solution of the above power market entity credit risk assessment method. For the details not described in detail in the technical solution of the power market entity credit risk assessment system in this embodiment, reference can be made to the description of the technical solution of the above power market entity credit risk assessment method.

[0146] This embodiment also provides a system based on the power market entity credit risk assessment method, including:

[0147] An evaluation system construction module, used to construct the first evaluation system based on the SCP framework and collect corresponding data;

[0148] ​An index weight determination module, configured to determine index weights based on the first evaluation system and the collected data;

[0149] A comprehensive evaluation model establishment module, configured to establish a fuzzy comprehensive evaluation model based on the first evaluation system and the index weights;

[0150] An evaluation module, configured to evaluate an evaluation object using the fuzzy comprehensive evaluation model.

[0151] This embodiment also provides a computing device, applicable to the case of the credit risk assessment method for power market entities, including:

[0152] A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the credit risk assessment method for power market entities as proposed in the above embodiment.

[0153] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the credit risk assessment method for power market entities as proposed in the above embodiment.

[0154] The storage medium proposed in this embodiment and the credit risk assessment method for power market entities proposed in the above embodiment belong to the same inventive concept. Technical details not described in detail in this embodiment can be referred to in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0155] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for evaluating the credit risk of electricity market entities, characterized in that, Including: Construct the first evaluation system based on the SCP framework and collect corresponding data; Determine the index weights based on the first evaluation system and the collected data; Establish a fuzzy comprehensive evaluation model based on the first evaluation system and the index weights; Use the fuzzy comprehensive evaluation model to evaluate the evaluation object.

2. The credit risk assessment method for power market entities according to claim 1, wherein, The determination of the index weights based on the first evaluation system and the collected data includes: Conduct objective weighting and subjective weighting respectively to obtain the final objective weight and the final subjective weight, and determine the index weights based on the final objective weight and the final subjective weight.

3. The credit risk assessment method for power market entities as described in claim 2, wherein The objective weighting includes: For each evaluation index, calculate the standard deviation and information entropy based on the sample data; Measure the conflict between each index by calculating the correlation coefficient between the indexes; Combining the calculated standard deviation, information entropy and conflict, calculate the information content of each index, and then determine the final objective weight of each index.

4. The credit risk assessment method for power market entities according to claim 3, wherein The subjective weighting includes: Determine the subjective weight vector according to the expert opinions, measure the similarity degree of the subjective weight vectors of any two experts by calculating the Euclidean distance, and then obtain the similarity degree of each expert with other experts; Determine the decision weight coefficient according to the similarity degree of the expert with other experts, and perform weighted calculation on the expert decision weight coefficient and each weight vector to obtain the final subjective weight.

5. The credit risk assessment method for power market entities according to claim 4, wherein The establishment of the fuzzy comprehensive evaluation model based on the first evaluation system and the index weights includes: Based on the first evaluation system, establish a credit risk assessment factor set for power trading entities, including the first-level and second-level factor sets; comprehensively consider the probability of risk occurrence and the impact consequences, and divide the evaluation levels to determine the comment set; Construct an expert fuzzy evaluation form, calculate the membership degree of a single factor to each comment set, and then form a membership degree matrix.

6. The credit risk assessment method for power market entities as claimed in claim 5, wherein, The establishment of the fuzzy comprehensive evaluation model based on the first evaluation system and the index weights also includes: Based on the membership degree matrix, obtain the secondary index fuzzy evaluation matrix according to the membership degree of the secondary index, calculate the membership degree of the primary index based on the secondary index fuzzy evaluation matrix, and obtain the fuzzy evaluation matrix of the primary index; multiply the fuzzy matrix of the primary index by the weight of the corresponding target layer to obtain the membership degree of the target layer, and after normalizing it, determine the final fuzzy comprehensive evaluation result according to the principle of the maximum membership degree.

7. The credit risk assessment method for power market entities according to claim 6, characterized in that, The determination of the index weights based on the final objective weight and the final subjective weight includes: Define the index weight as a linear combination of the two weights, expressed as: Among them, W i represents the weight of the i-th index, represents the final objective weight of the i-th index, represents the final subjective weight of the i-th index, and α is the proportion of the final objective weight.

8. A system adopting the power market entity credit risk assessment method as described in any one of claims 1 to 7, characterized in that, Including: An evaluation system construction module, which is used to construct the first evaluation system based on the SCP framework and collect corresponding data; An index weight determination module, which is used to determine the index weights based on the first evaluation system and the collected data; A comprehensive evaluation model establishment module, which is used to establish a fuzzy comprehensive evaluation model based on the first evaluation system and the index weights; An evaluation module, which is used to evaluate the evaluation object using the fuzzy comprehensive evaluation model.

9. A computing device, including: A memory and a processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the power market entity credit risk assessment method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps of the credit risk assessment method for a power market entity described in any one of claims 1 to 7 are implemented.

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