Novel electricity transaction integrated clearing system evaluation method based on fuzzy comprehensive evaluation

Abstract

The invention belongs to the field of electricity transaction in the electricity market. Aiming at the problem of how to evaluate a novel electricity transaction integrated clearing system, the invention provides a novel electricity transaction integrated clearing system evaluation method based on fuzzy comprehensive evaluation. The method comprises the steps of firstly constructing a novel power transaction integrated clearing system evaluation index system, then determining weights between a first-level index and a second-level index and between the second-level index and a third-level index based on an analytic hierarchy process, then establishing an evaluation set, determining a score of the third-level index, and finally converting the evaluation set into a score. And the evaluation result of the novel electricity transaction integrated clearing system can be obtained.

Description

Technical Field

[0001] The present invention belongs to the field of power trading in the power market. In view of the problem of how to evaluate a new integrated power trading and clearing system, the present invention proposes an evaluation method for a new integrated power trading and clearing system based on fuzzy comprehensive evaluation. First, an evaluation index system for a new integrated power trading and clearing system is constructed, and then the weights between the primary index and the secondary index, and between the secondary index and the tertiary index are determined based on the hierarchical analysis method. Subsequently, an evaluation set is established, and the tertiary index score is determined. Finally, the evaluation set is converted into a score, and the evaluation result of the new integrated power trading and clearing system can be obtained. Background Art

[0002] With the gradual advancement of spot electricity trading in my country, the time granularity of electricity trading has been further subdivided, and the subject of trading is electricity within a certain period of time; medium- and long-term trading and spot trading complement each other, but the rules of the two markets are quite different, and the data coupling between trading products is high, which increasingly presents a situation of massive participating entities, curved transaction clearing and diversified market models.

[0003] The transaction clearing business has experienced exponential growth in terms of data volume and rule complexity. Currently, there is still a lack of a unified and standardized market clearing mechanism. The business generally adopts manual processing and case-by-case discussion. The market clearing system platform is in the state of development and testing, but has not yet been fully promoted, which makes it difficult to support the rapid development of the clearing business and the market. Therefore, it is necessary to build a new integrated clearing system for power trading and evaluate the system to determine whether it can further enter the market.

[0004] In summary, there is an urgent need for an evaluation method for the new integrated clearing system for power trading.

[0005] The present invention proposes an evaluation method for a new integrated clearing system for power trading based on fuzzy comprehensive evaluation. First, an evaluation index system for the new integrated clearing system for power trading is constructed. Then, the weights between the primary index and the secondary index, and between the secondary index and the tertiary index are determined based on the analytic hierarchy process. Then, an evaluation set is established, and the tertiary index score is determined. Finally, the evaluation set is converted into a score, and the evaluation result of the new integrated clearing system for power trading can be obtained.

[0006] Two technologies similar to the present invention:

[0007] (1) Guo Junhong et al. of State Grid Jibei Electric Power Co., Ltd. published a credit evaluation method for power sales companies applicable to power trading centers (CN201910050913.7). The method mainly involves the following modules:

[0008] First, calculate the initial credit rating of the power sales company before entering the power market transaction;

[0009] Second, the overall transaction performance of the power sales company within an evaluation period is evaluated and the credit rating is adjusted;

[0010] Third, identify suspected dishonest behavior;

[0011] Fourth, verify the dishonest behavior and adjust the credit rating again

[0012] Although this method improves the contract fulfillment rate of power sales companies and maintains the fairness and standardization of market operations to a certain extent, it is unable to make adjustments to the new situation of the current electricity spot market.

[0013] (2) Li Chunzhe et al. from State Grid Corporation of China proposed a comprehensive evaluation method for power customer credit based on grey correlation (CN201410642117.X). This method mainly involves the following modules:

[0014] First, collect data on electricity customers' electricity payment credit, electricity regulations credit, electricity cooperation credit, business ability credit, social interaction credit, mortgage credit guarantee, and development prospect credit for comprehensive credit evaluation of electricity customers;

[0015] Second, a comprehensive evaluation model of power customer credit based on grey correlation is constructed, that is, an ideal indicator set is constructed, and the resolution coefficient is determined by analyzing the discrete degree of data in the first step, thereby constructing a correlation coefficient matrix of the evaluation object and generating a correlation vector;

[0016] Third, the hierarchical analysis method of subjective weighting, the entropy weight method of objective weighting, and the neural network method of intelligent weighting are used comprehensively to determine the linear combination coefficient by minimizing the deviation between each "single evaluation weight" and the "combination weight" to obtain the target weight vector;

[0017] Fourth, based on steps two and three, the correlation of electricity customers is calculated. The larger the correlation value, the better the credit of the electricity customer. The smaller the correlation value, the worse the credit of the electricity customer. The electricity customers are ranked according to the correlation to obtain a comprehensive evaluation report on the credit of the electricity customers.

[0018] The first method uses cloud model and fuzzy petri net method to identify dishonest behavior of power sales companies in the transaction process, which has large errors and is not suitable for evaluation and prediction in the current power spot market environment. The second method is based on grey correlation to conduct a comprehensive evaluation of power customer credit. Both schemes use evaluation methods, but the research object is not the new integrated clearing system for power trading. Summary of the invention

[0019] The present invention proposes a new type of integrated power trading and clearing system evaluation method based on fuzzy comprehensive evaluation. This method is conducive to accurately judging the overall situation of the developed new type of integrated power trading and clearing system, thereby creating conditions for promoting the new type of integrated power trading and clearing system in the spot development area and establishing a unified and fair integrated power trading and clearing system. This evaluation method is conducive to the developer of the clearing system to have a comprehensive understanding of the situation of the clearing system.

[0020] The present invention proposes a new type of integrated clearing system evaluation method for power trading based on fuzzy comprehensive evaluation. First, the evaluation index system of the new type of integrated clearing system for power trading is constructed, and then the weights between the first-level index and the second-level index, and between the second-level index and the third-level index are determined based on the hierarchical analysis method. Then, an evaluation set is established, and the third-level index score is determined. Finally, the evaluation set is converted into a score, and the evaluation result of the new type of integrated clearing system for power trading can be obtained.

[0021] To achieve the above object, the present invention adopts the following technical solution:

[0022] 1. Constructing an evaluation index system for a new integrated power trading and clearing system

[0023] The evaluation dimensions of the new integrated clearing system for power trading include trigger condition risk, serious loss error, platform system design, and clearing algorithm, which reflects the development of the integrated clearing system for power trading. Figure 1 shown.

[0024] 1.1 Triggering Conditions Risk

[0025] (1) Monthly measurement / collection error rate

[0026] The probability of errors in monthly meter reading, measurement, calculation, etc. should be as low as possible.

[0027] (2) Policy adjustment risk rate

[0028] It refers to the risk rate of liquidation that needs to be adjusted due to the introduction of new policies and regulations by the country. The lower the better.

[0029] (3) System error rate

[0030] The probability of monthly data input errors and settlement data errors should be as low as possible.

[0031] 1.2 Serious loss error

[0032] (1) Major deviation error rate

[0033] The ratio of major deviations found during liquidation, such as errors that lead to serious consequences such as changes in scheduling and clearing results, to the total deviation items should be as low as possible.

[0034] (2) Expiration rate of retrospective period

[0035] No adjustments will be made to liquidations that exceed the retrospective period, but they will indeed result in losses for the liquidation. Therefore, the error rate that exceeds the retrospective period should be assessed, the lower the better.

[0036] 1.3 Platform system design

[0037] (1) Reasonableness of rules

[0038] The clearing platform system should have reasonable rules so that the clearing process complies with current clearing work regulations, truly reflects the clearing situation, and ensures fairness for all parties.

[0039] (2) Emergency disaster resistance index

[0040] The platform system’s ability to resist sudden accidents caused by human or force majeure.

[0041] (3) System recoverability

[0042] When faced with faults and errors, the platform system has the ability to recover work in a timely manner to ensure that the system resumes operations.

[0043] 1.4 Liquidation Algorithm

[0044] (1) Accuracy

[0045] The accuracy of the algorithm directly determines whether liquidation can proceed normally. It is the basis for the stable operation of the system and is a positive indicator.

[0046] (2) Fairness

[0047] The fairness of the liquidation algorithm requires that the liquidation algorithm should not be biased and should ensure fair distribution of interests among all parties involved in the liquidation, which is a positive indicator.

[0048] (3) Robustness

[0049] It is the algorithm's ability to resist noise and outliers in the input data and its resistance to attacks, and is a positive indicator.

[0050] 2. Determine the weights between primary and secondary indicators, and between secondary and tertiary indicators based on the analytic hierarchy process

[0051] Table 1 Evaluation index system of new integrated clearing system for power trading

[0052]

[0053] 1.1 Determine the control layer weight B = (B1, B2, B3, B4)

[0054] By comparing the importance of two different indicators in turn, the method is to construct a judgment matrix for each level, conduct a one-time test, and finally obtain the corresponding weight value by solving the weight.

[0055] (1) The matrix B formed by the pairwise comparison results = (b ij )n×n, called the judgment matrix.

[0056] Easy to see ij >0,b ii =1 and b ji =1 / b ij (i,j=1,2,3,4), that is, B is a positive reciprocal matrix.

[0057] The judgment matrix is ​​shown in Table 2:

[0058] Table 2 Control layer judgment matrix

[0059]

[0060] Where bij represents the importance of Bi compared to Bj. The scale definition for pairwise comparison of each factor is shown in Table 3:

[0061] Table 3 Judgment matrix scale definition table

[0062]

[0063] (2) Calculate the weight vector

[0064] According to the judgment matrix, the eigenvector W of the judgment matrix is ​​first calculated, and then normalized to satisfy w1+w2+…w i =1.

[0065]

[0066] (3)Consistency test

[0067] The consistency check can be determined by calculating the random consistency ratio CR:

[0068] CR=CI / RI (2)

[0069] In the formula, CI is called the consistency index, RI is called the average random consistency index, and CI is calculated by the following formula:

[0070]

[0071] Among them, λ max is the characteristic equation (BW = λ maxW), n is the order of the comparison judgment matrix B (that is, the number of factors contained in this level). The RI value selection rules are shown in Table 4:

[0072] Table 4 Average random consistency index

[0073]

[0074] If CR<0.1, it is considered that there is satisfactory consistency, and the control layer weight B=(B1, B2, B3, B4) can be determined, otherwise the element values ​​of bij need to be adjusted.

[0075] 1.2 Determine the index weight C = (C11, C12, C13; C21, C22; C31, C32; C41, C42, C43; C51, C52)

[0076] (1) Construct judgment matrices of indicator layer C1, indicator layer C2, indicator layer C3, indicator layer C4, and indicator layer C5 respectively;

[0077] Table 5 C1 judgment matrix table of indicator layer

[0078]

[0079] Table 6 C2 judgment matrix table of indicator layer

[0080]

[0081] Table 7 C3 judgment matrix table of indicator layer

[0082]

[0083]

[0084] Table 8 C4 judgment matrix table of indicator layer

[0085]

[0086] (2) The weights are calculated in the same way as the control layer process.

[0087] (3) The same process as the control layer is performed, and the consistency check is performed to obtain the indicator layer weight:

[0088] C=(C11,C12,C13;C21,C22;C31,C32,C33;C41,C42,C43)

[0089] 1.3 The weights of the control layer and the indicator layer are combined to obtain the overall weight W, which is divided into two layers. The upper layer is the control layer weight, and the lower layer is the indicator layer weight.

[0090] 3. Establish an evaluation set and determine the three-level indicator scores

[0091] Since the evaluation system proposed by the patent is mostly qualitative indicators, the idea of ​​fuzzy mathematics is used to quantify the qualitative indicators. A judgment set is established to grade each indicator. According to the evaluation set V = [very high, relatively high, medium, relatively low, low], each single factor judgment V = [V1, V2, V3, V4, V5] is obtained.

[0092] Through investigation and data statistics, experts can evaluate each factor separately and obtain the matrix:

[0093]

[0094] Among them, V1+V2+V3+V4+V5=1, V1~V5 values ​​are between 0 and 1, and the higher the value, the greater the possibility of belonging to the corresponding level.

[0095] 4. Obtain the comprehensive evaluation results of the new integrated power trading and clearing system

[0096] In order to study the overall situation of the new integrated clearing system for power trading, based on the calculations completed at the control layer and the indicator layer, combined with the weights of the indicators at the same level, the analytic hierarchy process is used to gradually calculate upwards until the comprehensive score of the new integrated clearing system for power trading is calculated.

[0097]

[0098] Where S represents the score of any non-bottom-level indicator in the indicator system; n (n>1) represents the number of bottom-level indicators of S; V j W represents the score of the lower-level indicator j (1<j<n); j Represents the weight of the lower-level indicator j.

[0099] (1) Calculate the fuzzy evaluation set of the C1 layer

[0100]

[0101] (2) Calculate the fuzzy evaluation set of the C2 layer

[0102]

[0103] (3) Calculate the C3 layer fuzzy evaluation set

[0104]

[0105] (4) Calculate the C4 layer fuzzy evaluation set

[0106]

[0107] (5) Calculate the B-layer fuzzy evaluation set

[0108]

[0109] (6) According to the fuzzy evaluation set of layer B, the evaluation is converted into a score

[0110] The object of evaluation is a qualitative indicator, and the weighted average method must quantify the evaluation set. Suppose the corresponding scores of the evaluation set are 5, 4, 3, 2, and 1 respectively.

[0111] S=M1×5+M2×4+M3×3+M2×2+M1×1 (11)

[0112] The evaluation result score is between 1 and 5 points. The higher the score, the higher the level of the new integrated power trading and clearing system, and vice versa.

[0113] Advantages: The existing evaluation methods are not targeted at the new integrated clearing system for power trading. The present invention is based on fuzzy comprehensive evaluation and can better evaluate the new integrated clearing system for power trading. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] Figure 1 It is an evaluation index system for the new integrated clearing system for power trading.

[0115] Figure 2 This is a flow chart for evaluating the new integrated clearing system for power trading. DETAILED DESCRIPTION

[0116] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0117] The present invention proposes a new type of integrated clearing system evaluation method for power trading based on fuzzy comprehensive evaluation. First, the evaluation index system of the new type of integrated clearing system for power trading is constructed, and then the weights between the first-level index and the second-level index, and between the second-level index and the third-level index are determined based on the hierarchical analysis method. Then, an evaluation set is established, and the third-level index score is determined. Finally, the evaluation set is converted into a score, and the evaluation result of the new type of integrated clearing system for power trading can be obtained.

[0118] To achieve the above object, the present invention adopts the following technical solution:

[0119] 1. Constructing an evaluation index system for a new integrated power trading and clearing system

[0120] The evaluation dimensions of the new integrated clearing system for power trading include four aspects: trigger condition risk, serious loss error, platform system design, and clearing algorithm, which reflect the development of the integrated clearing system for power trading.

[0121] 1.1 Triggering Conditions Risk

[0122] (1) Monthly measurement / collection error rate

[0123] The probability of errors in monthly meter reading, measurement, calculation, etc. should be as low as possible.

[0124] (2) Policy adjustment risk rate

[0125] It refers to the risk rate of liquidation that needs to be adjusted due to the introduction of new policies and regulations by the country. The lower the better.

[0126] (3) System error rate

[0127] The probability of monthly data input errors and settlement data errors should be as low as possible.

[0128] 1.2 Serious loss error

[0129] (1) Major deviation error rate

[0130] The ratio of major deviations found during liquidation, such as errors that lead to serious consequences such as changes in scheduling and clearing results, to the total deviation items should be as low as possible.

[0131] (2) Expiration rate of retrospective period

[0132] No adjustments will be made to liquidations that exceed the retrospective period, but they will indeed result in losses for the liquidation. Therefore, the error rate that exceeds the retrospective period should be assessed, the lower the better.

[0133] 1.3 Platform system design

[0134] (1) Reasonableness of rules

[0135] The clearing platform system should have reasonable rules so that the clearing process complies with current clearing work regulations, truly reflects the clearing situation, and ensures fairness for all parties.

[0136] (2) Emergency disaster resistance index

[0137] The platform system’s ability to resist sudden accidents caused by human or force majeure.

[0138] (3) System recoverability

[0139] When faced with faults and errors, the platform system has the ability to recover work in a timely manner to ensure that the system resumes operations.

[0140] 1.4 Liquidation Algorithm

[0141] (1) Accuracy

[0142] The accuracy of the algorithm directly determines whether liquidation can proceed normally. It is the basis for the stable operation of the system and is a positive indicator.

[0143] (2) Fairness

[0144] The fairness of the liquidation algorithm requires that the liquidation algorithm should not be biased and should ensure fair distribution of interests among all parties involved in the liquidation, which is a positive indicator.

[0145] (3) Robustness

[0146] It is the algorithm's ability to resist noise and outliers in the input data and its resistance to attacks, and is a positive indicator.

[0147] 2. Determine the weights between primary and secondary indicators, and between secondary and tertiary indicators based on the analytic hierarchy process

[0148] Table 1 Evaluation index system of new integrated clearing system for power trading

[0149]

[0150] 1.1 Determine the control layer weight B = (B1, B2, B3, B4)

[0151] By comparing the importance of two different indicators in turn, the method is to construct a judgment matrix for each level, conduct a one-time test, and finally obtain the corresponding weight value by solving the weight.

[0152] (1) The matrix B formed by the pairwise comparison results = (b ij )n×n, called the judgment matrix.

[0153] Easy to see ij >0,b ii =1 and b ji =1 / b ij (i,j=1,2,3,4), that is, B is a positive reciprocal matrix.

[0154] The judgment matrix is ​​shown in Table 2:

[0155] Table 2 Control layer judgment matrix

[0156]

[0157] Where bij represents the importance of Bi compared to Bj. The scale definition for pairwise comparison of each factor is shown in Table 3:

[0158] Table 3 Judgment matrix scale definition table

[0159]

[0160] (2) Calculate the weight vector

[0161] According to the judgment matrix, the eigenvector W of the judgment matrix is ​​first calculated, and then normalized to satisfy w1+w2+…w i =1.

[0162]

[0163] (3)Consistency test

[0164] The consistency check can be determined by calculating the random consistency ratio CR:

[0165] CR=CI / RI (2)

[0166] In the formula, CI is called the consistency index, RI is called the average random consistency index, and CI is calculated by the following formula:

[0167]

[0168] Among them, λ max is the characteristic equation (BW = λ max W), n is the order of the comparison judgment matrix B (that is, the number of factors contained in this level). The RI value selection rules are shown in Table 4:

[0169] Table 4 Average random consistency index

[0170]

[0171] If CR<0.1, it is considered that there is satisfactory consistency, and the control layer weight B=(B1, B2, B3, B4) can be determined, otherwise the element values ​​of bij need to be adjusted.

[0172] 1.2 Determine the index weight C = (C11, C12, C13; C21, C22; C31, C32; C41, C42, C43; C51, C52)

[0173] (1) Construct judgment matrices of indicator layer C1, indicator layer C2, indicator layer C3, indicator layer C4, and indicator layer C5 respectively;

[0174] Table 5 C1 judgment matrix table of indicator layer

[0175]

[0176] Table 6 C2 judgment matrix table of indicator layer

[0177]

[0178] Table 7 C3 judgment matrix table of indicator layer

[0179]

[0180] Table 8 C4 judgment matrix table of indicator layer

[0181]

[0182] (2) The weights are calculated in the same way as the control layer process.

[0183] (3) The same process as the control layer is performed, and the consistency check is performed to obtain the indicator layer weight:

[0184] C=(C11,C12,C13;C21,C22;C31,C32,C33;C41,C42,C43)

[0185] 1.3 The weights of the control layer and the indicator layer are combined to obtain the overall weight W, which is divided into two layers. The upper layer is the control layer weight, and the lower layer is the indicator layer weight.

[0186] 3. Establish an evaluation set and determine the three-level indicator scores

[0187] Since the evaluation system proposed by the patent is mostly qualitative indicators, the idea of ​​fuzzy mathematics is used to quantify the qualitative indicators. A judgment set is established to grade each indicator. According to the evaluation set V = [very high, relatively high, medium, relatively low, low], each single factor judgment V = [V1, V2, V3, V4, V5] is obtained.

[0188] Through investigation and data statistics, experts can evaluate each factor separately and obtain the matrix:

[0189]

[0190] Among them, V1+V2+V3+V4+V5=1, V1~V5 values ​​are between 0 and 1, and the higher the value, the greater the possibility of belonging to the corresponding level.

[0191] 4. Obtain the comprehensive evaluation results of the new integrated power trading and clearing system

[0192] In order to study the overall situation of the new integrated clearing system for power trading, based on the calculations completed at the control layer and the indicator layer, combined with the weights of the indicators at the same level, the analytic hierarchy process is used to gradually calculate upwards until the comprehensive score of the new integrated clearing system for power trading is calculated.

[0193]

[0194] Where S represents the score of any non-bottom-level indicator in the indicator system; n (n>1) represents the number of bottom-level indicators of S; V j W represents the score of the lower-level indicator j (1<j<n); j Represents the weight of the lower-level indicator j.

[0195] (1) Calculate the fuzzy evaluation set of the C1 layer

[0196]

[0197] (2) Calculate the fuzzy evaluation set of the C2 layer

[0198]

[0199] (3) Calculate the C3 layer fuzzy evaluation set

[0200]

[0201] (4) Calculate the C4 layer fuzzy evaluation set

[0202]

[0203] (5) Calculate the B-layer fuzzy evaluation set

[0204]

[0205] (6) According to the fuzzy evaluation set of layer B, the evaluation is converted into a score

[0206] The object of evaluation is a qualitative indicator, and the weighted average method must quantify the evaluation set. Suppose the corresponding scores of the evaluation set are 5, 4, 3, 2, and 1 respectively.

[0207] S=M1×5+M2×4+M3×3+M2×2+M1×1 (11)

[0208] The evaluation result score is between 1 and 5 points. The higher the score, the higher the level of the new integrated power trading and clearing system, and vice versa.

[0209] In summary, the evaluation process of the new integrated clearing system for power trading is as follows: Figure 2 shown.

[0210] The present invention belongs to the field of power trading in the power market. In view of the problem of how to evaluate a new integrated power trading and clearing system, the present invention proposes an evaluation method for a new integrated power trading and clearing system based on fuzzy comprehensive evaluation. First, an evaluation index system for a new integrated power trading and clearing system is constructed, and then the weights between the primary index and the secondary index, and between the secondary index and the tertiary index are determined based on the hierarchical analysis method. Subsequently, an evaluation set is established, and the tertiary index score is determined. Finally, the evaluation set is converted into a score, and the evaluation result of the new integrated power trading and clearing system can be obtained.

[0211] The key points and protection points of the present invention are:

[0212] 1) Evaluate the new integrated clearing system for power trading;

[0213] 2) Evaluation index system of the new integrated power trading and clearing system proposed in the present invention.

[0214] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A new type of power trading integrated clearing system evaluation method based on fuzzy comprehensive evaluation, characterized by: Firstly, an evaluation index system for the new integrated clearing system for power trading is constructed. Then, the weights between the primary and secondary indicators, and between the secondary and tertiary indicators are determined based on the analytic hierarchy process. Subsequently, an evaluation set is established and the scores of the tertiary indicators are determined. Finally, the evaluation set is converted into scores to obtain the evaluation results of the new integrated clearing system for power trading.

2. According to claim 1, a new type of power trading integrated clearing system evaluation method based on fuzzy comprehensive evaluation is characterized by:

1. Constructing an evaluation index system for a new integrated power trading and clearing system The evaluation dimensions of the new integrated clearing system for power trading include four aspects: trigger condition risk, serious loss error, platform system design, and clearing algorithm, which reflect the development of the integrated clearing system for power trading. 1.1 Triggering Conditions Risk (1) Monthly measurement / collection error rate The probability of errors in monthly meter reading, measurement, calculation, etc. should be as low as possible. (2) Policy adjustment risk rate It refers to the risk rate of liquidation that needs to be adjusted due to the introduction of new policies and regulations by the country. The lower the better. (3) System error rate The probability of monthly data input errors and settlement data errors should be as low as possible. 1.2 Serious loss error (1) Major deviation error rate The ratio of major deviations found during liquidation, such as errors that lead to serious consequences such as changes in scheduling and clearing results, to the total deviation items should be as low as possible. (2) Expiration rate of retrospective period No adjustments will be made to liquidations that exceed the retrospective period, but they will indeed result in losses for the liquidation. Therefore, the error rate that exceeds the retrospective period should be assessed, the lower the better. 1.3 Platform system design (1) Reasonableness of rules The clearing platform system should have reasonable rules so that the clearing process complies with current clearing work regulations, truly reflects the clearing situation, and ensures fairness for all parties. (2) Emergency disaster resistance index The platform system’s ability to resist sudden accidents caused by human or force majeure. (3) System recoverability When faced with faults and errors, the platform system has the ability to recover work in a timely manner to ensure that the system resumes operations. 1.4 Liquidation Algorithm (1) Accuracy The accuracy of the algorithm directly determines whether liquidation can proceed normally. It is the basis for the stable operation of the system and is a positive indicator. (2) Fairness The fairness of the liquidation algorithm requires that the liquidation algorithm should not be biased and should ensure fair distribution of interests among all parties involved in the liquidation, which is a positive indicator. (3) Robustness It is the algorithm's ability to resist noise and outliers in the input data and its resistance to attacks, and is a positive indicator.

3. According to the new type of power trading integrated clearing system evaluation method based on fuzzy comprehensive evaluation according to claim 1, it is characterized by:

2. Determine the weights between primary and secondary indicators, and between secondary and tertiary indicators based on the analytic hierarchy process Table 1 Evaluation index system of new integrated clearing system for power trading 1.1 Determine the control layer weight B = (B1, B2, B3, B4) By comparing the importance of two different indicators in turn, the method is to construct a judgment matrix for each level, conduct a one-time test, and finally obtain the corresponding weight value by solving the weight. (1) The matrix B formed by the pairwise comparison results = (b ij )n×n, called the judgment matrix. Easy to see ij >0,b ii =1 and b ji =1 / b ij (i,j=1,2,3,4), that is, B is a positive reciprocal matrix. The judgment matrix is ​​shown in Table 2: Table 2 Control layer judgment matrix Where bij represents the importance of Bi compared to Bj. The scale definition for pairwise comparison of each factor is shown in Table 3: Table 3 Judgment matrix scale definition table (2) Calculate the weight vector According to the judgment matrix, the eigenvector W of the judgment matrix is ​​first calculated, and then normalized to satisfy w1+w2+…w i =1. (3)Consistency test The consistency check can be determined by calculating the random consistency ratio CR: CR=CI / RI (2) In the formula, CI is called the consistency index, RI is called the average random consistency index, and CI is calculated by the following formula: in, λ max is the characteristic equation (BW = λ max W), n is the order of the comparison judgment matrix B (that is, the number of factors contained in this level). The RI value rules are shown in Table 4: Table 4 Average random consistency index If CR<0.1, it is considered that there is satisfactory consistency, and the control layer weight B=(B1, B2, B3, B4) can be determined, otherwise the element values ​​of bij need to be adjusted. 1.2 Determine the index weight C = (C11, C12, C13; C21, C22; C31, C32; C41, C42, C43; C51, C52) (1) Construct judgment matrices of indicator layer C1, indicator layer C2, indicator layer C3, indicator layer C4, and indicator layer C5 respectively; Table 5 C1 judgment matrix table of indicator layer Table 6 C2 judgment matrix table of indicator layer Table 7 C3 judgment matrix table of indicator layer Table 8 C4 judgment matrix table of indicator layer (2) The weights are calculated in the same way as the control layer process. (3) The same process as the control layer is performed, and the consistency check is performed to obtain the indicator layer weight: C=(C11,C12,C13;C21,C22;C31,C32,C33;C41,C42,C43) 1.3 The weights of the control layer and the indicator layer are combined to obtain the overall weight W, which is divided into two layers. The upper layer is the control layer weight, and the lower layer is the indicator layer weight.

4. According to claim 1, a new type of power trading integrated clearing system evaluation method based on fuzzy comprehensive evaluation is characterized by:

3. Establish an evaluation set and determine the three-level indicator scores Since the evaluation system proposed by the patent is mostly qualitative indicators, the idea of ​​fuzzy mathematics is used to quantify the qualitative indicators. A judgment set is established to grade each indicator. According to the evaluation set V = [very high, relatively high, medium, relatively low, low], each single factor judgment V = [V1, V2, V3, V4, V5] is obtained. Through investigation and data statistics, experts can evaluate each factor separately and obtain the matrix: Among them, V1+V2+V3+V4+V5=1, V1~V5 values ​​are between 0 and 1, and the higher the value, the greater the possibility of belonging to the corresponding level.

5. According to claim 1, a new type of power trading integrated clearing system evaluation method based on fuzzy comprehensive evaluation is characterized by: Obtained the comprehensive evaluation results of the new integrated power trading settlement system In order to study the overall situation of the new integrated clearing system for power trading, based on the calculations completed at the control layer and the indicator layer, combined with the weights of the indicators at the same level, the analytic hierarchy process is used to gradually calculate upwards until the comprehensive score of the new integrated clearing system for power trading is calculated. Where S represents the score of any non-bottom-level indicator in the indicator system; n (n>1) represents the number of bottom-level indicators of S; V j W represents the score of the lower-level indicator j (1<j<n); j Represents the weight of the lower-level indicator j. (1) Calculate the fuzzy evaluation set of the C1 layer (2) Calculate the fuzzy evaluation set of the C2 layer (3) Calculate the fuzzy evaluation set of the C3 layer (4) Calculate the C4 layer fuzzy evaluation set (5) Calculate the B-layer fuzzy evaluation set (6) According to the fuzzy evaluation set of layer B, the evaluation is converted into a score The object of evaluation is a qualitative indicator, and the weighted average method must quantify the evaluation set. Suppose the corresponding scores of the evaluation set are 5, 4, 3, 2, and 1 respectively. S=M1×5+M2×4+M3×3+M2×2+M1×1 (11) The evaluation result score is between 1 and 5 points. The higher the score, the higher the level of the new integrated power trading and clearing system, and vice versa.

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