CN115545469A - Credit risk management and evaluation method and system based on electric power retail market - Google Patents

Credit risk management and evaluation method and system based on electric power retail market Download PDF

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CN115545469A
CN115545469A CN202211213575.2A CN202211213575A CN115545469A CN 115545469 A CN115545469 A CN 115545469A CN 202211213575 A CN202211213575 A CN 202211213575A CN 115545469 A CN115545469 A CN 115545469A
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赵尔敏
刘博�
陈建宇
戚翰德
刘睿
窦常永
胡卫东
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Gansu Electric Power Trading Center Co ltd
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Abstract

The invention provides a credit risk management evaluation method and system based on an electric power retail market, which comprises the following steps: the method comprises the steps of obtaining credit data of the electric power retail market, evaluating the credit data by adopting a multi-level credit evaluation system combined with credit lines, calculating the combined weight of each evaluation index by combining subjective and objective weight analysis, and calculating the guarantee fund line of a market main body by adopting a calculation and risk quantification model. The credit risk management evaluation method provided by the invention is suitable for a credit risk quantification mechanism of the electric power retail market and predicts the unknown risk component by adopting a price forecasting method, and compared with the traditional subsection corresponding method, the determined guaranteed amount can reflect the default risk of a main body and can reduce the economic burden of the main body of the market to a certain extent.

Description

Credit risk management evaluation method and system based on electric power retail market
Technical Field
The invention relates to the field of credit risk management of an electric power retail market, in particular to a credit risk management evaluation method and system based on the electric power retail market.
Background
The credit risk management and evaluation method for the electric power retail market comprises a credit evaluation system, a settlement credit calculation method and a market main body guaranteed amount calculation method.
The current credit risk management and evaluation method in the electric power retail market mainly has the following defects:
1. at present, the construction work of the credit management system of the electric power retail market in China is still in the initial stage, and the credit management system has a larger gap in the aspects of a credit rating mechanism, a risk quantification means and the like compared with the mature electric power market in China. The evaluation method of the electric power market credit is too single, and a domestic electric power market credit rating system needs to be built from aspects of market admission mechanism, credit rating mechanism, mutual credit evaluation among guiding bodies and the like.
2. The electric power retail market credit risk quantification quantifies and monitors the default risk of the market main body from transaction behaviors, financial conditions and the like. The traditional price measurement prediction method is insufficient in consideration of comprehensive risks, and calculation and evaluation of the current credit risk of the electric retail market need to be carried out by combining a subject-object weight analysis method.
3. The existing guarantee sum degree making methods of the domestic electric power retail market are simple inter-partition corresponding modes, and although the operation is easy and the efficiency is high, the size of default risks of market main bodies cannot be truly reflected. Therefore, a method for calculating the guaranteed amount of the main body suitable for the electric power retail market is required to be provided on the basis of quantification of default risks of market main bodies.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a credit risk management evaluation method and system based on an electric power retail market.
The invention provides a credit risk management evaluation method based on an electric power retail market, which comprises the following steps: the method comprises the steps of obtaining credit data of the electric power retail market, evaluating the credit data by adopting a multi-level credit evaluation system combined with credit lines, calculating the combined weight of each evaluation index by combining subjective and objective weight analysis, and calculating the guarantee fund line of a market main body by adopting a calculation and risk quantification model.
Preferably, the multi-level credit evaluation system combined with credit limit grades the credit data, and grades the credit data from basic condition, financial condition, credit management condition and information disclosure condition.
Preferably, the subjective and objective weight analysis calculates objective weights by using a G1 method to calculate subjective weights and an entropy weight method, and calculates combination weights by a multiplicative synthesis method.
Preferably, the G1 method for calculating the subjective weight of each index includes: the set of n indices of a certain level in the evaluation index system is X = { X = { [ X ] 1 ,x 2 ,...,x n In which x i The ith evaluation index is expressed, the importance of the n evaluation indexes is subjected to monotone non-increasing sequencing, and the relation is shown as a formula (1):
Figure BDA0003875835900000021
quantifying the ratio of the degree of importance between each adjacent indicator in the sequence, as described in equation (2):
Figure BDA0003875835900000022
in the formula, k i Is the ith importance ratio, r i Is the subjective weight value to be solved, k, of the ith index i The larger the degree of importance is;
calculating subjective weight r of each evaluation index i ：
Figure BDA0003875835900000023
r i ＝k i r i+1 (i＝1,2,...,n-1) (4)
In the formula, r i Is the subjective weight, k, of the ith index j Is the jth importance ratio.
Preferably, the calculating the objective weight of each index by the entropy weight method comprises:
n credit evaluation index score input matrix Y of m evaluated market participants m×n Represented by formula (5):
Figure BDA0003875835900000024
y ij for the jth credit rating index score of the ith market participant, the matrix Y m×n The matrix Z is obtained by the normalization and standardization m×n ：
Figure BDA0003875835900000025
Figure BDA0003875835900000031
Figure BDA0003875835900000032
In the formula, y jmax Is the maximum value of the j-th index,
Figure BDA0003875835900000033
the maximum score after the j evaluation index conversion of the ith evaluated market participant, z ij Scoring the corresponding normalized maxima;
will matrix Z m×n Normalizing by column to obtain probability matrix P m×n ：
Figure BDA0003875835900000034
Figure BDA0003875835900000035
p ij The j-th evaluation index of the ith evaluated market participant accounts for the proportion of the j-th evaluation index under the index;
calculating the information entropy e of each index j Information utility value d j Further obtain the final entropy weight omega of each index j As its objective weight:
Figure BDA0003875835900000036
d j ＝1-e j (12)
Figure BDA0003875835900000037
preferably, the multiplicative synthesis method combines the main and objective weights of each index as follows:
Figure BDA0003875835900000038
k j is the combined weight of the jth evaluation index.
Preferably, the calculating the guaranteed monetary degree of the market subject in consideration of the risk quantification model includes: selecting debt income risk quantification of three market types of medium and long-term markets, spot markets and retail markets in a transaction type dimension;
historical debt L to market subject in the time dimension h Debt L that has already issued a bill to the market entity but has not yet settled b Debt L not billed to market entity but confirming future payment ub Debt L resulting from the transfer of retail contracts in subsequent debts cost And wind that has completed electric power delivery but has not settledDanger L s Taking into account;
L h 、L b 、L ub with a known mean value, L cost The method is specified by a power market trading center, risk quantification is directly included, and a price measurement prediction method is adopted to measure the risk L which is in the medium-long term contract market and the spot market and has been subjected to power delivery but not settled s Quantization is performed.
Preferably, the calculating the guaranteed monetary degree of the market subject in consideration of the risk quantification model includes: analyzing the default risk of market members according to the dimensions of transaction types and time.
Preferably, the measuring price prediction method is as follows: predicting the trade amount A of each risk and the risk P corresponding to the trade amount of each unit, wherein the product of the trade amount A of each risk and the risk P is the total risk amount, and the risk of the week, middle and long term contract market which has been delivered but not settled
Figure BDA0003875835900000041
Obtained by the following formula:
Figure BDA0003875835900000042
Figure BDA0003875835900000043
wherein H is a market subject historical data set,
Figure BDA0003875835900000044
for weekly power consumption, P, of the maximum power consumption week in historical data w-a Is a unit risk, P i 1 The unit electricity loss in the ith week in the historical data is a coefficient k i Monotonically non-increasing sum sigma k from near to far in time i ＝1；
Risk of delivery completed but not yet settled in spot market
Figure BDA0003875835900000045
Is calculated by the following formulaCalculating to obtain:
Figure BDA0003875835900000046
wherein the content of the first and second substances,
Figure BDA0003875835900000047
for giving out the maximum week of the clearing amount in the historical data, P i 2 The unit electric quantity loss amount of the market before the ith week;
the sum of the quantified risk of default of the main body of the electric retail market is shown as a formula (18):
Figure BDA0003875835900000048
the market subject needs to pay the guarantee amount as the difference between the risk quantification total amount and the non-guarantee credit amount.
The invention provides a credit risk management evaluation system based on an electric power retail market, which comprises the following modules:
a module M1: acquiring credit data of the electric power retail market, and evaluating the credit data by adopting a multi-level credit evaluation system combined with credit lines;
a module M2: calculating the combination weight of each evaluation index by combining subjective and objective weight analysis;
a module M3: and calculating the guarantee fund amount of the market subject by adopting a risk quantification model.
Compared with the prior art, the invention has the following beneficial effects:
1. by summarizing the construction experience of the existing credit evaluation index system at home and abroad, taking both index practicability and data acquirability into consideration, integrating financial indexes and non-financial indexes, the multi-level market main body credit evaluation index system applicable to the electric power retail market is provided.
2. A credit evaluation method based on subjective and objective weight analysis is provided according to the main characteristics of the electric power retail market in China, and basis is provided for determining the non-guarantee credit limit.
3. The credit risk quantification mechanism suitable for the electric power retail market is provided, the unknown risk components are predicted by adopting a price forecasting method, and the determined guaranteed amount can reflect the default risk of the main body and reduce the economic burden of the main body of the market to a certain extent compared with the traditional subsection corresponding method.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of a credit risk management assessment method based on an electric retail market.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Aiming at the problems that the subjectivity of an evaluation method for the signal in the background technology is strong, the selection of evaluation indexes is lack of scientificity, and a suitable calculation method for the guaranteed sum of money is not provided in combination with the development status of the electric power retail market in China, the invention provides a credit risk management evaluation model based on the electric power retail market, and credit management and risk evaluation are comprehensively considered, so that the main body deposit of the market is calculated accurately to the maximum extent, and the determined guaranteed sum of money can better reflect the size of the default risk of the main body and can reduce the economic burden of the main body of the market to a certain extent compared with the traditional zoning corresponding method.
Example 1:
specifically, the credit risk management evaluation method based on the electric power retail market comprises the following steps: the method comprises the steps of obtaining credit data of the electric power retail market, evaluating the credit data by adopting a multi-level credit evaluation system combined with credit lines, calculating the combined weight of each evaluation index by combining subjective and objective weight analysis, and calculating the guarantee fund line of a market main body by adopting a calculation and risk quantification model.
The credit data is graded by combining a multi-level credit evaluation system of credit lines, the grading comprises 3 index grades of a first grade, a second grade and a third grade, and the credit level of a market main body is evaluated according to 23 specific evaluation indexes in 4 aspects of basic conditions, financial conditions, credit management conditions and information disclosure conditions, as shown in a table 1:
Figure BDA0003875835900000061
TABLE 1 multilevel Credit evaluation index System
The credit calculation method combined with the subjective and objective weight analysis theory endows each evaluation index with proper weight, and improves the effectiveness of the evaluation result by combining the discrimination size of each index in actual evaluation on the basis of the subjective preference of a maker, namely simultaneously considering the subjective weight and the objective weight and reasonably combining the subjective weight and the objective weight. The subjective and objective weight analysis adopts a G1 method to calculate subjective weight and an entropy weight method to calculate objective weight, and calculates combination weight through a multiplication synthesis method.
The subjective weight of each index calculated by the G1 method comprises the following steps: the set of n indices of a certain level in the evaluation index system is X = { X = { [ X ] 1 ,x 2 ,…,x n In which x i The ith evaluation index is expressed, and the experts perform monotone non-increasing sequencing on the importance of the n evaluation indexes, wherein the relation is shown as a formula (1):
Figure BDA0003875835900000062
quantifying the ratio of the degree of importance between each adjacent indicator in the sequence, as described in equation (2):
Figure BDA0003875835900000063
in the formula, k i Is the ith importance ratio, r i Is the subjective weight value to be solved, k, of the ith index i The larger the degree of importance is; k is a radical of i Specific values are shown in table 2.
Figure BDA0003875835900000071
TABLE 2 fold importance relationship
According to k i Calculating subjective weight r of each evaluation index i ：
Figure BDA0003875835900000072
r i ＝k i r i+1 (i＝1,2,…,n-1) (4)
In the formula, r i Is the subjective weight value of the ith index, k j Is the jth importance ratio.
The invention adopts an entropy weight method to calculate objective weight, and the first step is to define an input matrix Y m×n And subjected to normalization and normalization, and expressed as formula (5):
Figure BDA0003875835900000073
in the formula, Y m×n Input matrix for n credit rating index scores for the mth rated market participant, y ij The j-th credit rating index score for the i-th market participant being rated.
The index is normalized, namely the extremely small index is represented by the extremely large index so as to simplify the calculation process, and the standardization process can convert each index dimension into 1 so as to carry out comparison. The calculation method of forward normalization is shown in formula (6-8), and the matrix Z is obtained after processing mxn ：
Figure BDA0003875835900000074
Figure BDA0003875835900000075
Figure BDA0003875835900000081
In the formula, y jmax Is the maximum value of the j-th index,
Figure BDA0003875835900000082
the maximum score after the j evaluation index conversion of the ith evaluated market participant, z ij Scoring the corresponding normalized maxima;
will matrix Z m×n Normalizing by column to obtain probability matrix P m×n ：
Figure BDA0003875835900000083
Figure BDA0003875835900000084
p ij The j-th evaluation index of the ith evaluated market participant accounts for the proportion of the j-th evaluation index under the index;
calculating the information entropy e of each index j Information utility value d j Further obtain the final entropy weight omega of each index j As its objective weight:
Figure BDA0003875835900000085
d j ＝1-e j (12)
Figure BDA0003875835900000086
the index subjective and objective weights are combined by a multiplication synthesis method, and are shown as follows:
Figure BDA0003875835900000087
in the formula, k j Is the combined weight of the jth evaluation index.
The market subject guarantee amount calculation method considering the risk quantification model is used for analyzing the default risk of market members according to two dimensionalities of transaction types and time. In the domestic power market, the electricity selling company participates in the transactions of the wholesale market and the retail market at the same time, so in the transaction type dimension, the debt of the three market types of the medium-long term market, the spot market and the retail market is selected to be added into the risk quantification.
In the time dimension, as domestic electric marketization just starts, a relatively loose risk quantification mode is adopted to reduce the economic burden of market members, so that debts generated in the default restoration period are not considered, and only historical debts L of market subjects are treated h Debt L that has already issued a bill to the market entity but has not yet settled b Debt L not billed to market entity but confirming future payment ub Debt L resulting from the transfer of retail contracts in subsequent debts cost And risk L of completed power delivery but not yet settled s Taking into account;
L h 、L b 、L ub known average value, L cost The method is specified by a power market trading center, risk quantification is directly included, and a price measurement prediction method is adopted to measure the risk L which is in the medium-long term contract market and the spot market and has been subjected to power delivery but not settled s Quantization is performed.
The price forecasting method is to forecast the trade amount A of each risk and the risk P corresponding to the trade amount of each unit, the product of the two is the total risk amount, and the risk of the long-term contract market in the week, the trade is completed but the trade is not settled
Figure BDA0003875835900000091
Obtained by the following formula:
Figure BDA0003875835900000092
Figure BDA0003875835900000093
wherein H is a market subject historical data set,
Figure BDA0003875835900000094
(hundred million kilowatt hours) is the electricity consumption per week with the maximum electricity consumption week in the historical data, P w-a (Yuan/mega kilowatt-hour) as unit risk, P i 1 (yuan/mega kilowatt-hour) is the unit electricity loss of the ith week in the historical data and the coefficient k i Monotonically non-increasing sum sigma k from near to far in time i ＝1；
Risk of delivery completed but not yet settled in spot market
Figure BDA0003875835900000095
Calculated by the following formula:
Figure BDA0003875835900000096
wherein the content of the first and second substances,
Figure BDA0003875835900000097
for giving out the maximum week of the clearing amount in the historical data, P i 2 The unit electric quantity loss amount of the market before the ith week;
the sum of the quantified risk of default of the main body of the electric retail market is shown as a formula (18):
Figure BDA0003875835900000098
the market subject needs to pay the guarantee amount as the difference between the risk quantification total amount and the non-guarantee credit amount.
Example 2
The embodiment constructs a credit risk management and evaluation method of the electric power retail market, wherein the credit risk management and evaluation method comprises a multi-level credit evaluation system combined with credit limit, a credit calculation method combined with subjective and objective weight analysis theory and a market main body guaranteed amount calculation method considering a risk quantification model.
According to the G1 method calculation process, firstly, the importance ranking of each evaluation index is determined according to expert opinions, the importance ratio between every two indexes is quantified according to descending order, and the ranking result is shown in Table 3.
Figure BDA0003875835900000101
TABLE 3 ratio of importance between indices
Second, substituting a given importance ratio into equation (3-4) can calculate a subjective weight as shown in Table 4.
Figure BDA0003875835900000102
TABLE 4 subjective weighting of each index
The entropy weight method calculates objective weight, firstly determines an input matrix, selects four first-level indexes of A, B, C, D, E five market subjects to calculate, and scores are shown in table 5.
Figure BDA0003875835900000103
TABLE 5 score status of market subjects
Then, the input matrix is standardized according to a formula (6-8) to obtain a matrix Z, and the matrix Z is normalized to obtain a probability matrix P.
Figure BDA0003875835900000104
Figure BDA0003875835900000105
Finally, each element in the probability matrix P is substituted into a formula (11-13), and the information entropy, the utility value and the final entropy weight of each index can be calculated, as shown in Table 6.
Figure BDA0003875835900000106
Table 6 information entropy, utility value and final entropy weight of each index
Substituting the objective and objective weights into equation (14) can calculate the combination weight corresponding to each level of index, as shown in table 7.
Figure BDA0003875835900000111
TABLE 7 Combined weights of the indices
It can be seen from the table that the financial condition of the market subject in the evaluation system is large, the natural condition of the enterprise is investigated, and the index weights integrate the subjective and objective requirements, which is in accordance with the requirements of the credit evaluation method.
Calculating the margin of the market main body considering risk quantification:
it is assumed that historical transaction data in the long-term contract market and spot market of a certain power selling company in weeks 1 to 10 are shown in table 8. Now, the default risk of the power selling company is quantified in week 11, the data in the table is substituted into a formula (15-17), and a weight coefficient k is taken i =0.21-0.02i (i =1,2, …, 10), calculated
Figure BDA0003875835900000112
Figure BDA0003875835900000113
Figure BDA0003875835900000114
TABLE 8 historical transaction data
Through inspection, compared with the existing electric power retail market credit risk assessment method, even if other debts are taken into consideration, the guarantee fund amount calculated by the risk quantification method is still lower than the guarantee fund amount obtained according to a simple segmentation corresponding mode, so that the economic burden of market participants is favorably reduced, the method is suitable for the electric power retail market in China at the present stage, and the method is more reasonable.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.

Claims (10)

1. A credit risk management assessment method based on an electric power retail market is characterized by comprising the following steps: the method comprises the steps of obtaining credit data of the electric power retail market, evaluating the credit data by adopting a multi-level credit evaluation system combined with credit lines, calculating the combined weight of each evaluation index by combining subjective and objective weight analysis, and calculating the guarantee fund line of a market main body by adopting a calculation and risk quantification model.
2. The electric retail market-based credit risk management assessment method according to claim 1, characterized in that: the multi-level credit evaluation system combined with the credit limit grades the credit data and evaluates the credit data in a grading way from basic conditions, financial conditions, credit management conditions and information disclosure conditions.
3. The electric retail market-based credit risk management assessment method according to claim 1, characterized in that: the subjective and objective weight analysis adopts a G1 method to calculate subjective weight and an entropy weight method to calculate objective weight, and calculates combination weight through a multiplication synthesis method.
4. The electric retail market-based credit risk management assessment method according to claim 3, characterized in that: the step of calculating the subjective weight of each index by the G1 method comprises the following steps: the set of n indices of a certain level in the evaluation index system is X = { X = { [ X ] 1 ,x 2 ,...,x n In which x i The ith evaluation index is expressed, the importance of the n evaluation indexes is subjected to monotone non-increasing sequencing, and the relation is shown as a formula (1):
Figure FDA0003875835890000011
quantifying the ratio of the degree of importance between each adjacent indicator in the sequence, as described in equation (2):
Figure FDA0003875835890000012
in the formula, k i Is the ith importance ratio, r i Is the subjective weight value to be obtained, k, of the ith index i The greater the importance level isHigh;
calculating subjective weight r of each evaluation index i ：
Figure FDA0003875835890000013
r i ＝k i r i+1 (i＝1,2,...,n-1) (4)
In the formula, r i Is the subjective weight value of the ith index, k j Is the jth importance ratio.
5. The electric retail market-based credit risk management assessment method according to claim 3, characterized in that: the entropy weight method for calculating the objective weight of each index comprises the following steps:
n credit evaluation index score input matrix Y of m evaluated market participants m×n Represented by formula (5):
Figure FDA0003875835890000021
y ij for the jth credit rating index score of the ith market participant, the matrix Y m×n The matrix Z is obtained by the normalization and standardization m×n ：
Figure FDA0003875835890000022
Figure FDA0003875835890000023
Figure FDA0003875835890000024
In the formula, y jmax Is the maximum value of the j-th index,
Figure FDA0003875835890000025
the maximum score after the j evaluation index conversion of the ith evaluated market participant, z ij Scoring the corresponding normalized maxima;
will matrix Z m×n Normalizing by column to obtain probability matrix P m×n ：
Figure FDA0003875835890000026
Figure FDA0003875835890000027
p ij The j-th evaluation index of the ith evaluated market participant accounts for the proportion of the j-th evaluation index under the index;
calculating the information entropy e of each index j Information utility value d j Further obtain the final entropy weight omega of each index j As its objective weight:
Figure FDA0003875835890000028
d j ＝1-e j (12)
Figure FDA0003875835890000029
。
6. the electric retail market-based credit risk management assessment method according to claim 3, characterized in that: the multiplication and synthesis method combines the main and objective weights of each index as follows:
Figure FDA0003875835890000031
k j is the combined weight of the jth evaluation index.
7. The electric retail market-based credit risk management assessment method according to claim 1, characterized in that: the calculating of the guaranteed amount of money of the market subject by taking the risk quantification model into account comprises the following steps: selecting debt income risk quantification of three market types of medium and long-term markets, spot markets and retail markets in a transaction type dimension;
historical debt L to market subject in the time dimension h Debt L that has issued a bill to the market subject but has not completed the settlement b Debt L for which no bill is issued to the market entity but future payment is confirmed ub Debt L resulting from the transfer of retail contracts in subsequent debts cost And risk L of completed power delivery but not yet settled s Taking into account;
L h 、L b 、L ub known average value, L cost The risk L which is specified by a trading center of the power market, directly accounts for risk quantification, adopts a price measurement prediction method and is used for the risk L which is in the medium-long term contract market and the spot market and has been subjected to power delivery but not settled s Quantization is performed.
8. The electric retail market-based credit risk management assessment method according to claim 7, characterized in that: the calculating of the guaranteed amount of money of the market subject by taking the risk quantification model into account comprises the following steps: analyzing the default risk of market members according to the dimensions of transaction types and time.
9. The electric retail market-based credit risk management assessment method according to claim 8, characterized in that: the measuring price prediction method comprises the following steps: predicting the trade amount A of each risk and the risk P corresponding to the trade amount of each unit, wherein the product of the two is the total risk amount, and the risk of the long-term contract market in the week and the middle is the risk of the completed delivery but not yet settled
Figure FDA0003875835890000032
Obtained by the following formula:
Figure FDA0003875835890000033
Figure FDA0003875835890000034
wherein H is a market subject historical data set,
Figure FDA0003875835890000035
for weekly power consumption, P, of the maximum power consumption week in historical data w-a Is a unit risk, P i 1 The unit electricity deficit of the ith week in the historical data is a coefficient k i Monotonically non-increasing sum sigma k from near to far in time i ＝1；
Risk of delivery completed but not yet settled in spot market
Figure FDA0003875835890000036
Calculated by the following formula:
Figure FDA0003875835890000041
wherein the content of the first and second substances,
Figure FDA0003875835890000042
for giving out the maximum week of the clearing amount in the historical data, P i 2 The unit electric quantity loss of the market before the ith weekday;
the sum of the quantified risk of default of the main body of the electric retail market is shown as a formula (18):
Figure FDA0003875835890000043
the market subject needs to pay the guarantee amount which is the difference between the risk quantification total amount and the non-guarantee credit amount.
10. A credit risk management evaluation system based on an electric power retail market is characterized by comprising the following modules:
a module M1: acquiring credit data of the electric power retail market, and evaluating the credit data by adopting a multi-level credit evaluation system combined with credit lines;
a module M2: calculating the combination weight of each evaluation index by combining subjective and objective weight analysis;
a module M3: and calculating the guarantee fund amount of the market subject by adopting a risk quantification model.
CN202211213575.2A 2022-09-30 2022-09-30 Credit risk management and evaluation method and system based on electric power retail market Pending CN115545469A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116187768A (en) * 2023-04-26 2023-05-30 浙江电力交易中心有限公司 Risk assessment and protection method suitable for green electricity market

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116187768A (en) * 2023-04-26 2023-05-30 浙江电力交易中心有限公司 Risk assessment and protection method suitable for green electricity market
CN116187768B (en) * 2023-04-26 2023-07-18 浙江电力交易中心有限公司 Risk assessment and protection method suitable for green electricity market

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