A multi-microgrid p2p energy transaction method and system for improving a reputation management model

Abstract

The application discloses a kind of multi-microgrid P2P energy transaction method and system for improving reputation management model, belong to energy transaction technical field, this method includes: according to multi-microgrid P2P energy transaction history, using improved reputation management model real-time calculation each microgrid's reputation score;The improvement of reputation management model includes: introduce the dynamic forgetting factor calculated according to reputation score change amount, adjust the weight of historical information and real-time information in reputation score calculation;According to reputation score, the priority score of each microgrid is calculated, and the coupling weight of the corresponding transaction is obtained through the priority score of the microgrid of transaction party;The priority of each microgrid transaction is adjusted by weighting distributed optimization algorithm through coupling weight, and multi-microgrid P2P energy transaction strategy is generated.The application can solve the problem that multi-microgrid P2P energy transaction method is not high in reliability, and energy waste is easily caused.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of energy transaction, and particularly relates to a multi-microgrid P2P energy transaction method and system for improving a reputation management model. BACKGROUND

[0002] Multi-microgrid P2P energy transaction refers to a mode of direct energy transaction between multiple microgrids, without the need for a traditional centralized transaction center. Each microgrid can act as an independent subject and conduct point-to-point transactions with other microgrids according to its own energy supply and demand. The multi-microgrid P2P energy transaction market is a decentralized transaction platform, and participants directly conduct transactions, lacking a central authority to ensure the fairness and trustworthiness of transactions. Therefore, the reputation management model is crucial, and a trust mechanism is established by evaluating the historical behavior of participants to facilitate smooth transactions.

[0003] However, the traditional reputation management model in multi-microgrid P2P energy transaction has several shortcomings. First, the static scoring mechanism cannot reflect the latest behavior of participants in real time, resulting in a disconnection between reputation scores and actual performance. Second, single-dimensional evaluation ignores key factors such as event severity, making the score lack comprehensiveness. In addition, the traditional reputation management model is easily manipulated, such as participants may improve their reputation scores through whitewashing behavior, which refers to a series of small, compliant transactions to quickly improve reputation and cover up past misdeeds. Finally, the poor adaptability of the traditional multi-microgrid P2P energy transaction method makes it difficult to respond to market changes and the dynamic nature of participant behavior, resulting in low energy transaction reliability and easy energy waste, limiting its application in complex energy transaction environments. SUMMARY

[0004] The purpose of the present application is to overcome the shortcomings in the prior art and provide a multi-microgrid P2P energy transaction method and system for improving a reputation management model, which solves the problem of low reliability of the multi-microgrid P2P energy transaction method and easy energy waste.

[0005] To solve the above technical problems, the present application is implemented by using the following technical solutions:

[0006] In a first aspect, the present application provides a multi-microgrid P2P energy transaction method for improving a reputation management model, comprising:

[0007] Based on the P2P energy trading history of the multi-microgrid, an improved reputation management model is used to calculate the reputation score of each microgrid in real time. The calculation of the reputation score of each microgrid includes: based on a pre-set dynamic forgetting factor calculated according to the change in reputation score and the P2P energy trading history of the multi-microgrid, the historical positive and negative feedback accumulation and the real-time global positive and negative feedback increment are weighted and fused respectively to calculate the real-time positive and negative feedback accumulation of each microgrid, and the reputation score of each microgrid is updated according to the real-time positive and negative feedback accumulation.

[0008] The priority score of each micronetwork is calculated based on the reputation score, and the coupling weight of the corresponding transaction is calculated through the priority scores of the micronetworks of both parties to the transaction.

[0009] By weighting the distributed optimization algorithm with the coupling weights, the priority of each microgrid transaction is adjusted, and a multi-microgrid P2P energy trading strategy is generated.

[0010] The aforementioned improved reputation management model for multi-microgrid P2P energy trading, wherein the method calculates the real-time positive and negative feedback accumulation of each microgrid based on a pre-set dynamic forgetting factor calculated according to reputation score changes and the history of multi-microgrid P2P energy trading, weighted and fused with the historical positive and negative feedback accumulation and the real-time global positive and negative feedback increment, and updates the reputation score of each microgrid according to the real-time positive and negative feedback accumulation, includes:

[0011] Based on the P2P energy trading history of multi-microgrid, a dynamic forgetting factor calculated based on the change in reputation score is introduced in the Beta reputation model reputation score calculation. The cumulative positive and negative feedback at the previous moment and the global positive and negative feedback increment at the current moment are weighted and fused to calculate the cumulative positive and negative feedback of each microgrid at the current moment.

[0012] Micronet i Current Moment Positive feedback accumulation The calculation formula is:

[0013] ,

[0014] In the formula, Indicates the current moment of microgrid i The dynamic forgetting factor; Indicates the previous moment of the microgrid i The cumulative amount of positive feedback; Indicates the current moment of microgrid i The global positive feedback increment;

[0015] Micronet i Current Moment negative feedback accumulation The calculation formula is:

[0016] ,

[0017] wherein, represents the negative feedback cumulative amount of micro-grid i at the previous time point ; represents the global negative feedback increment of micro-grid i at the current time point ;

[0018] According to the positive feedback cumulative amount and the negative feedback cumulative amount at the current time point, the reputation score of each micro-grid at the next time point is updated;

[0019] The calculation formula of the reputation score of micro-grid i at the next time point is as follows:

[0020] ,

[0021] wherein, represents the positive feedback cumulative amount of micro-grid i at the current time point ; represents the negative feedback cumulative amount of micro-grid i at the current time point ; and represents a preset smoothing term coefficient.

[0022] The multi-micro-grid P2P energy transaction method of the improved reputation management model described above calculates a dynamic forgetting factor according to the reputation score change amount, comprising:

[0023] According to the multi-micro-grid P2P energy transaction history, the short-term reputation fluctuation and the long-term reputation trend of each micro-grid are calculated;

[0024] The calculation formula of the short-term reputation fluctuation of micro-grid i at the current time point is as follows:

[0025] ,

[0026] wherein, represents the reputation score of micro-grid i at the current time point ; represents the reputation score of micro-grid i at the previous time point ;

[0027] The calculation formula of the long-term reputation trend of micro-grid i at the current time point is as follows:

[0028] ,

[0029] wherein, represents the reputation score of micro-grid i at the previous seven time points ; ​​​

[0030] The dynamic forgetting factor for each micronet at the current moment is calculated based on short-term reputation fluctuations and long-term reputation trends; micronet i at the current moment... Dynamic forgetting factor The calculation formula is:

[0031] , ,

[0032] In the formula, This represents the preset balance factor that adjusts short-term weights; This represents the preset balance factor that adjusts the long-term weights; This indicates the preset sensitivity to short-term fluctuations. ; This indicates the preset sensitivity to long-term fluctuations. .

[0033] The aforementioned improved reputation management model for multi-microgrid P2P energy trading methods further includes the calculation of reputation scores for each microgrid, specifically:

[0034] The system employs a comprehensive transaction weight calculated based on transaction size and risk. It then uses an asymmetric positive and negative feedback increment calculation method to calculate the positive and negative feedback increments separately. Finally, it calculates the global positive and negative feedback increments for each microgrid based on these positive and negative feedback increments.

[0035] The aforementioned improved reputation management model for multi-microgrid P2P energy trading calculates a comprehensive transaction weight based on transaction size and risk, including:

[0036] Based on the P2P energy trading history of Duowei.com, the normalized weight of the corresponding transaction size is calculated to measure the transaction size; at the current moment... microgrid With micro-network Normalized weights of transaction size The calculation formula is:

[0037] ,

[0038] ,

[0039] In the formula, Indicates the current time microgrid peer-to-peer microgrid The promised delivery volume; Indicates to microgrid A collection of micro-networks for transactions; Represents any time from the initial time to time t; microgrid Largest transaction commitment in history;

[0040] According to the reputation score of the peer microgrid, a microgrid reputation risk weight of the corresponding transaction is calculated to measure the transaction risk; the current time microgrid with the peer microgrid The microgrid reputation risk weight of the transaction The calculation formula is:

[0041] ,

[0042] In the formula, represents a preset risk amplification coefficient; represents the microgrid The peer microgrid of the transaction The global default risk degree of the current time ; represents the reputation score of the peer microgrid The current time ;

[0043] According to the size normalization weight based on the transaction commitment amount and the microgrid reputation risk weight based on the reputation score of the peer microgrid, the transaction comprehensive weight of the corresponding transaction is calculated; the current time microgrid with the peer microgrid The transaction comprehensive weight of the transaction The calculation formula is:

[0044] .

[0045] The multi-microgrid P2P energy transaction method of the improved reputation management model described above, the asymmetric positive and negative feedback increment calculation method, comprises:

[0046] The current time The positive feedback increment of the microgrid i and the peer microgrid j transaction The calculation formula is:

[0047] ,

[0048] In the formula, represents a preset positive feedback index; represents the microgrid The performance rate of the peer microgrid at the current time The performance rate of the microgrid The peer microgrid at the current time The performance rate of the microgrid The peer microgrid at the current time The calculation formula of the performance rate of the microgrid The calculation formula is:

[0049] ,

[0050] wherein, denotes the current time microgrid the opposite microgrid the promised delivery amount of the opposite microgrid, denotes the current time microgrid the opposite microgrid the actual delivery amount of the opposite microgrid;

[0051] the current time the calculation formula of the negative feedback increment of the microgrid i and the opposite microgrid j in the transaction is:

[0052] ,

[0053] wherein, denotes the preset negative feedback index.

[0054] The multi-microgrid P2P energy transaction method of the improved reputation management model, characterized in that the calculation of the global positive and negative feedback increments of each microgrid comprises:

[0055] respectively summing up the positive feedback increment and the negative feedback increment of each microgrid at the current time in the global transaction, and correspondingly obtaining the global positive feedback increment and the global negative feedback increment of each microgrid at the current time;

[0056] the calculation formula of the global positive feedback increment of the microgrid i at the current time ,

[0057] the calculation formula of the global negative feedback increment of the microgrid i at the current time .

[0058] The multi-microgrid P2P energy transaction method of the improved reputation management model, wherein the priority score of each microgrid is calculated according to the reputation score, and the coupling weight of the corresponding transaction is calculated through the priority scores of the microgrids of the transaction parties, comprising:

[0059] The priority score of each microgrid at the corresponding time is calculated according to the reputation score of each microgrid, and the calculation formula of the priority score of the microgrid i at the current time

[0060] ,

[0061] wherein, denotes the preset reputation amplification coefficient;

[0062] ​​​​​​​The coupling weight corresponding to the transaction is obtained by multiplying the priority scores of the microgrids of the two parties of the transaction, the microgrid i and the opposite microgrid j being the two parties of the transaction, and the coupling weight corresponding to the transaction of the microgrid i and the opposite microgrid j The calculation formula is as follows:

[0063] ,

[0064] In the formula, indicates the priority score of the opposite microgrid j at the current time.

[0065] The multi-microgrid P2P energy transaction method has the characteristics that the priority of the transaction of each microgrid is adjusted by weighting the distributed optimization algorithm by the coupling weight, and a multi-microgrid P2P energy transaction strategy is generated, and the method comprises the following steps:

[0066] The distributed optimization algorithm is a C-ADMM algorithm, the consensus penalty term of the C-ADMM algorithm is weighted by the coupling weight corresponding to the transaction, wherein the coupling weight is positively correlated with the priority of the corresponding microgrid transaction, and the priority of the transaction of each microgrid is adjusted to generate a multi-microgrid P2P energy transaction strategy;

[0067] The local variable of the microgrid i of the C-ADMM algorithm is In the iteration formula, the consensus penalty term weighted by the coupling weight is as follows:

[0068] ,

[0069] In the formula, indicates the original consensus penalty term of the C-ADMM algorithm; indicates a penalty factor; indicates the common variable of the transaction of the microgrid i and the opposite microgrid j in the kth iteration; indicates the dual variable of the transaction of the microgrid i and the opposite microgrid j in the kth iteration.

[0070] In the second aspect, the application provides a multi-microgrid P2P energy transaction system for improving a reputation management model, which comprises a reputation calculation module, a weight generation module and a strategy generation module.

[0071] The reputation calculation module is used for calculating the reputation scores of the microgrids in real time according to the history of multi-microgrid P2P energy transactions by using an improved reputation management model; the improvement of the reputation management model comprises introducing a dynamic forgetting factor calculated according to the change amount of the reputation score, and adjusting the weights of the historical information and real-time information in the calculation of the reputation score, so as to improve the timeliness of the reputation management.

[0072] ​The weight generation module is configured to calculate a priority score of each micro-grid according to the reputation score, and calculate a coupling weight of a corresponding transaction through the priority scores of the micro-grids of the two parties of the transaction.

[0073] The strategy generation module is configured to weight a distributed optimization algorithm through the coupling weight, adjust the priority of the transaction of each micro-grid, and generate a multi-micro-grid P2P energy transaction strategy.

[0074] Compared with the prior art, the improved reputation management model multi-micro-grid P2P energy transaction method has the following beneficial effects:

[0075] The improved reputation management model multi-micro-grid P2P energy transaction method can keep the memory of the historical energy transaction behaviors of each micro-grid and evaluate the current energy transaction behavior of the micro-grid in real time, improve the timeliness of reputation management, and further improve the reliability of the evaluation of the energy transaction behaviors of each micro-grid, thereby solving the problem of low reliability of the multi-micro-grid P2P energy transaction method and easy energy waste.

[0076] The improved reputation management model multi-micro-grid P2P energy transaction method retains the bottom mathematical framework of the reputation mean in the Beta reputation model, introduces a dynamic forgetting mechanism based on the P2P energy transaction history, improves the timeliness of reputation management, introduces a transaction comprehensive weight based on the energy transaction scale and transaction risk, measures the importance of each energy transaction, and introduces an asymmetric positive and negative feedback increment calculation based on the transaction comprehensive weight, and improves the identification of the energy transaction risk.

[0077] The reputation evaluation has better timeliness and accuracy, the evaluation angle is more comprehensive, the malicious behavior of reputation whitewashing is better resisted, the entire system is more efficient, stable and reliable compared with other P2P energy transaction systems. BRIEF DESCRIPTION OF DRAWINGS

[0078] Figure 1 is a flowchart of an improved reputation management model multi-micro-grid P2P energy transaction method of embodiment 1 of the present application;

[0079] Figure 2 is a reputation score and performance rate diagram of five micro-grids under different fault scenarios of embodiment 1 of the present application;

[0080] Figure 3 is an energy transaction method based on a traditional reputation model of embodiment 1 of the present application facing the 24h energy transaction of micro-grid 5 whitewashing behavior;

[0081] Figure 4 ​This is a schematic diagram of 24-hour energy trading in response to microgrid P2P energy trading methods using an improved reputation management model according to Embodiment 1 of the present invention, which addresses the whitewashing behavior of microgrid 5. Detailed Implementation

[0082] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0083] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0084] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0085] Example 1:

[0086] Figure 1 This is a flowchart of the multi-microgrid P2P energy trading method of the improved reputation management model in Embodiment 1 of the present invention. This flowchart only illustrates the logical sequence of the method described in this embodiment. Provided there are no conflicts, different methods may be used in other possible embodiments of the present invention. Figure 1 Complete the steps shown or described in the order indicated.

[0087] The methods include:

[0088] S1: Based on the P2P energy trading history of the multi-microgrid, an improved reputation management model is used to calculate the reputation score of each microgrid in real time. The calculation of the reputation score of each microgrid includes: based on a pre-set dynamic forgetting factor calculated according to the change in reputation score and the P2P energy trading history of the multi-microgrid, the historical positive and negative feedback accumulation and the real-time global positive and negative feedback increment are weighted and fused respectively to calculate the real-time positive and negative feedback accumulation of each microgrid, and the reputation score of each microgrid is updated according to the real-time positive and negative feedback accumulation.

[0089] S2: Calculate the priority score of each micro-network based on the reputation score, and calculate the coupling weight of the corresponding transaction through the priority scores of the micro-networks of both parties to the transaction;

[0090] S3: Adjust the priority of each micro-grid transaction by weighting the distributed optimization algorithm by the coupling weight, and generate a multi-micro-grid P2P energy transaction strategy.

[0091] The specific implementation of each step is introduced as follows:

[0092] The reputation model is a Beta reputation model, which associates the interaction behavior between entities with the number of positive feedback and the number of negative feedback through the statistics of Beta distribution, and quantifies the reputation of entities; the statistics of Beta distribution include the reputation mean, which is the most commonly used reputation indicator, representing the average credibility of entities, and the calculation formula of the reputation mean R is: ;

[0093] In the Beta reputation model, the increment calculation of positive and negative feedback is symmetrical, for example, adding 1 to when a new positive feedback record is added; correspondingly, adding 1 to when a new negative feedback record is added. The asymmetrical positive and negative feedback increment calculation refers to that the positive and negative feedback increments are calculated in different ways.

[0094] The reputation model is improved in this embodiment: The reputation model is improved in this embodiment:

[0095] This embodiment retains the bottom mathematical framework of the reputation mean in the Beta reputation model, introduces a dynamic forgetting mechanism based on the P2P energy transaction history (formulas 1 and 2, formulas 5-8), improves the timeliness of reputation management; introduces a transaction comprehensive weight based on the transaction scale and transaction risk to measure the importance of the transaction (formulas 9-12); introduces an asymmetrical positive and negative feedback increment calculation based on the transaction comprehensive weight to improve the recognition of transaction risk in reputation management (formulas 13 and 15).

[0096] Step S1 includes:

[0097] According to the multi-micro-grid P2P energy transaction history, a dynamic forgetting factor calculated according to the reputation score change is introduced in the reputation score calculation of the Beta reputation model, and the global positive and negative feedback increments at the current time are weighted and fused to calculate the positive feedback cumulative amount and the negative feedback cumulative amount of each micro-grid at the current time.

[0098] The calculation formula of the positive feedback cumulative amount of the micro-grid i at the current time is:

[0099] , (1),

[0100] In the formula, Indicates the current moment of microgrid i The dynamic forgetting factor; Indicates the previous moment of the microgrid i The cumulative amount of positive feedback; Indicates the current moment of microgrid i The global positive feedback increment;

[0101] Micronet i Current Moment negative feedback accumulation The calculation formula is:

[0102] (2),

[0103] In the formula, Indicates the previous moment of the microgrid i The cumulative amount of negative feedback; Indicates the current moment of microgrid i The global negative feedback increment;

[0104] Positive feedback accumulation microgrid The cumulative amount of positive feedback at the current time t, the cumulative amount of positive feedback The larger the microgrid, the more likely it is to be a microgrid. There are more reliable and well-performing interaction records; the cumulative amount of negative feedback. microgrid The cumulative amount of negative feedback at the current time t, the cumulative amount of negative feedback The larger the microgrid, the more likely it is to be a microgrid. There are more high-risk or dishonest interaction records.

[0105] To achieve a smooth transition between preserving history and incorporating new evidence, the cumulative positive feedback is updated in each iteration. With negative feedback accumulation Afterwards, micro-network Positive feedback accumulation With negative feedback accumulation All will be used to update the next moment of the micro-network. reputation score .

[0106] Update the reputation score of each micronet for the next time step based on the current cumulative positive and negative feedback. (Micronet i next time step) reputation score The calculation formula is:

[0107] (3)

[0108] In the formula, represents the positive feedback cumulative amount of the micro-grid i at the current time represents the negative feedback cumulative amount of the micro-grid i at the current time and represents the preset smoothing term coefficient, in the embodiment, the value is 1, the value is 2, and the reputation score of the micro-grid i at the next time The calculation formula is:

[0109] , (4),

[0110] In the formula, the smoothing term of +1 in the numerator and the smoothing term of +2 in the denominator are used to prevent excessive fluctuation of the reputation score when the positive feedback cumulative amount and the negative feedback cumulative amount are small at the initial stage.

[0111] The smoothing term of +1 in the numerator and the smoothing term of +2 in the denominator are used to prevent excessive fluctuation of the reputation score when the positive feedback cumulative amount and the negative feedback cumulative amount are small at the initial stage.

[0112] The reputation score of the micro-grid i represents the comprehensive quantification of the performance and the credit risk of the node at the historical and current time transaction feedback, the value range of which is ; when , that is, the positive feedback is much more than the negative, then , the micro-grid i is considered to be highly reliable; when , that is, the negative feedback is more, then , the micro-grid i is considered to be high-risk or untrustworthy.

[0113] In the improvement of the reputation management model, the dynamic forgetting factor is calculated according to the reputation score change amount, including:

[0114] According to the history of multi-micro-grid P2P energy transaction, the short-term reputation fluctuation and the long-term reputation trend of each micro-grid are calculated;

[0115] The calculation formula of the short-term reputation fluctuation of the micro-grid i at the current time The calculation formula is:

[0116] , (5),

[0117] ​​​​​​In the formula, represents the reputation score of the micro-grid i at the current time; represents the reputation score of the micro-grid i at the previous time; represents the basic time period length for distinguishing between short-term and long-term; short-term reputation fluctuation represents the short-term reputation fluctuation within 1 long-term reputation trend represents the long-term reputation trend average within 7

[0118] The long-term reputation trend of the micro-grid i at the current time is calculated according to the following formula:

[0119] , (6),

[0120] In the formula, represents the reputation score of the micro-grid i at the previous seven times;

[0121] The dynamic forgetting factor is calculated according to the fluctuation of the short-term reputation fluctuation and the long-term reputation trend. The greater the fluctuation, the smaller the dynamic forgetting factor calculated by formula (7), that is, the weight of the historical data in formula (1) and formula (2) is reduced, that is, the forgetting of the historical data is more;

[0122] The dynamic forgetting factor of the micro-grid i at the current time is calculated according to the following formula:

[0123] , (7),

[0124] , (8)

[0125] In the formula, by dynamically monitoring the short-term and long-term reputation change rate, the dynamic forgetting factor is adjusted in real time ; represents a preset balance factor for adjusting the short-term weight; represents a preset balance factor for adjusting the long-term weight;

[0126] The value of is set to a large value, which can improve the short-term response speed when the short-term fluctuation is obvious: represents the short-term response, and when the short-term reputation fluctuation becomes larger, the value becomes smaller; The role of ​​​​​​Setting a preset value to a large value is equivalent to setting a strategy that prioritizes short-term changes. Any subtle change will affect the final result. This will have a significant impact, thereby achieving the design goal of improving short-term response speed;

[0127] Will Setting it to a larger value can enhance long-term trust when the long-term trend is significant. Indicating long-term trust, it reflects the stability of a node's long-term performance, and its long-term reputation trend. Very small The value will be close to 1, indicating that it is trustworthy. Its function is to measure long-term trust in the dynamic forgetting factor. The components in, if Setting a preset value to a large value is equivalent to formulating a strategy that prioritizes long-term trends. Any subtle change will affect the final result. This will have a significant impact, thereby achieving the design goal of enhancing long-term trust.

[0128] This indicates the preset sensitivity to short-term fluctuations. ; This indicates the preset sensitivity to long-term fluctuations. If there are large short-term fluctuations, that is, short-term reputation fluctuations... If it gets bigger, then The dynamic forgetting factor is reduced and calculated. A decrease in size indicates accelerated forgetting; if the performance fluctuates over a long period, it indicates a long-term reputation trend. If it gets bigger, then The dynamic forgetting factor is reduced and calculated. A smaller value indicates accelerated forgetting; if there are no significant short-term fluctuations and the long-term trend remains stable, that is... and To become smaller, accordingly and If it increases, the dynamic forgetting factor... An increase in size indicates a slower rate of forgetting.

[0129] The dynamic forgetting factor is used to balance new and old data: when there are large fluctuations, the new data is used as the standard, and when the data is stable, the old data is retained.

[0130] In this embodiment, the improvement to the reputation management model, besides introducing a dynamic forgetting factor calculated based on changes in reputation score to adjust the weights of historical and real-time information in reputation score calculation to improve the timeliness of reputation management, also includes:

[0131] A comprehensive transaction weight calculated based on transaction size and risk is introduced as a parameter for calculating positive and negative feedback increments, and an asymmetric method for calculating positive and negative feedback increments is designed.

[0132] In the improvement of the reputation management model, a comprehensive weighting of transactions is calculated based on transaction size and risk to measure the importance of a transaction, including:

[0133] Based on the historical P2P energy transactions of Duowei.com, the normalized weight of the corresponding transaction size is calculated to measure the transaction size:

[0134] Measure the current moment microgrid peer-to-peer microgrid Transaction commitment volume This represents the largest transaction commitment in its entire history of foreign transactions. The proportion is the size normalized weight. Scale normalized weights The transaction volume of each micro-network is normalized and the weight of large transaction commitments is increased.

[0135] Current moment microgrid With the peer microgrid Normalized weights of transaction size The calculation formula is:

[0136] (9)

[0137] (10)

[0138] In the formula, Indicates the current time microgrid peer-to-peer microgrid The promised delivery volume; Indicates to microgrid A collection of micro-networks for transactions; Represents any time from the initial time to time t; microgrid Largest transaction commitment in history; The denominator is such that formula (9) calculates ;

[0139] Based on the reputation score of the peer micro-network, the reputation risk weight of the corresponding transaction is calculated to measure the transaction risk; that is, the global default risk of the peer micro-network is adjusted to obtain the micro-network reputation risk weight, wherein the global default risk of the peer micro-network is calculated based on the reputation score of the peer micro-network.

[0140] Current moment microgrid With the peer microgrid Micro-network reputation risk weighting of transactions The calculation formula is:

[0141] (11)

[0142] In the formula, This represents the preset risk amplification factor. This indicates that the counterparty in the transaction with Weinet is Weinet. Current moment The overall level of default risk. The other end microgrid The higher the reputation score, the better the peer micro-network. The lower the risk of default, the better; for end-microgrids Current moment reputation score The lower the value, the higher the reputation risk weight of the microgrid. The larger.

[0143] The overall transaction weight is calculated based on the normalized weight of the transaction commitment amount and the microgrid reputation risk weight based on the peer microgrid reputation score:

[0144] Microgrid reputation risk weight With size normalized weights Perform a product operation to obtain the overall transaction weight. At present microgrid With the peer microgrid Overall weight of transactions The calculation formula is:

[0145] (12)

[0146] Overall trading weight By combining transaction volume with peer microgrid risk, large-scale default transactions and transactions with high-risk peer microgrids are penalized.

[0147] In improving the reputation management model, an asymmetric positive and negative feedback incremental calculation method is designed, including:

[0148] Using the overall transaction weight as a parameter for calculating the positive and negative feedback increments, and combining it with nonlinear processing, positive feedback increments based on the performance rate and negative feedback increments based on the global default risk are generated respectively. The nonlinear processing in the calculation of the positive feedback increment refers to the use of a positive feedback index. This involves nonlinear suppression to smooth out performance rewards and prevent excessive reputation gains from a single perfect performance. (Current moment) Positive feedback increment of transactions between microgrid i and its counterpart microgrid j The calculation formula is:

[0149] (13)

[0150] In the formula, This represents the positive feedback exponent, used to suppress extreme increments. ; Indicates the current time micronet peer-to-peer microgrid The contract fulfillment rate;

[0151] Positive feedback incremental calculation emphasizes real-time collaboration and strengthens immediate incentives: real-time fulfillment rate High, then positive feedback increment Large scale can instantly reward good cooperative behavior from both parties in a transaction, incentivize micro-networks to maintain a high level of performance in real-time interactions, and promote the stability of short-term cooperation.

[0152] The current time micronet peer-to-peer microgrid fulfillment rate The calculation formula is:

[0153] (14)

[0154] In the formula, Indicates the current time microgrid peer-to-peer microgrid Committed delivery volume Indicates the current time microgrid peer-to-peer microgrid The actual delivery volume; The fulfillment rate is used to quantify the degree of partial fulfillment in real time. This indicates a serious breach of contract in the transaction. The smaller the value, the more significant the impact of default.

[0155] Nonlinear processing in the negative feedback increment calculation process refers to the use of negative feedback exponents. This is achieved through nonlinear amplification, which amplifies the risk penalty; at the current moment... Negative feedback increment of transactions between microgrid i and its counterpart microgrid j The calculation formula is:

[0156] (15)

[0157] In the formula, Indicates the negative feedback index. ; This indicates the global default risk level of the peer micronetwork j;

[0158] Negative feedback increment calculation is based on global risk assessment, improving the comprehensiveness of risk identification: negative feedback increment The calculation is based on the current time of the peer micronet j. Global default risk The calculation, rather than a symmetrical one, is based on the real-time default rate between microgrid i and its peer microgrid j. The calculation shows that this negative feedback incremental calculation can: avoid the random deviations of local real-time transactions and integrate overall transaction risk information;

[0159] To avoid the accidental deviations in local real-time transactions, the following measures should be taken: a single real-time default by microgrid i and its counterpart microgrid j may be an accidental event caused by a temporary technical failure, but the overall default risk of counterpart microgrid j reflects its historical comprehensive performance in all energy trading cooperation and can better reflect the essential trading risk level of counterpart microgrid j.

[0160] The comprehensive transaction risk information includes: the global default risk level integrates the transaction history of peer microgrid j with other microgrids, rather than just the local data between microgrid i and peer microgrid j. This allows it to capture the potential risks of peer microgrid j within the entire energy trading network. When peer microgrid j frequently defaults on other microgrids, even if peer microgrid i temporarily fulfills its obligations, the actual transaction risk of peer microgrid j remains high. This integrates comprehensive transaction risk information, and the real-time default rate between microgrid i and its counterpart microgrid j. Trading risks that do not reflect the overall picture.

[0161] Current moment microgrid peer-to-peer microgrid fulfillment rate The higher, that is At that time, positive feedback increment The larger the value, the higher the confidence level of the counterpart micronetwork j in trading with micronetwork i; at the current moment... peer microgrid Reputation Score Low, meaning the overall risk of default is relatively low. When it is large, the current time Negative feedback increment of transactions between microgrid i and its counterpart microgrid j The larger the value, the greater the penalty for micro-network i, in order to incentivize micro-network i to actively avoid transacting with high-risk micro-networks.

[0162] The asymmetric positive and negative feedback increment calculation balances the rewards of reputation management for real-time transactions and the penalties for transaction risks. It uses different calculation methods: the positive feedback increment is calculated based on the real-time fulfillment rate of micronet i and peer micronet j, and the negative feedback increment is calculated based on the global default risk of peer micronet j. The global default risk is calculated based on the reputation score of peer micronets, which reflects the overall historical situation, rather than the negative feedback increment calculated based on the real-time default rate corresponding to the real-time fulfillment rate of micronet i and peer micronet j. The real-time default rate = 1 - real-time fulfillment rate.

[0163] Positive feedback incremental computation focuses on real-time interaction, incentivizing good real-time collaboration; negative feedback incremental computation is based on global history, improving the comprehensiveness and stability of risk assessment; asymmetric positive and negative feedback incremental computation avoids the limitations of local real-time data, reduces misjudgments, and can combine real-time and historical data to balance collaboration incentives and risk prevention.

[0164] In formulas (1) and (2), the calculation of the global positive and negative feedback increments of each microgrid includes:

[0165] By summing the positive and negative feedback increments of each microgrid at the current moment in the global transaction, we can obtain the global positive and negative feedback increments of each microgrid at the current moment:

[0166] microgrid Current moment The global positive feedback increment of microgrid i is obtained by summing the positive feedback increments received from all transactions. Micronet i at the current moment Global positive feedback increment The calculation formula is:

[0167] (16)

[0168] microgrid Current moment The global negative feedback increment of microgrid i is obtained by summing the negative feedback increments received from all transactions. Micronet i at the current moment Global negative feedback increment The calculation formula is:

[0169] , (17).

[0170] Step S2 includes:

[0171] For each microgrid Based on its reputation score Computational microgrids At any moment Priority score , Define the reputation score as The power amplification of the priority score of the microgrid i at the current time The calculation formula is:

[0172] , (18),

[0173] In the formula, represents a preset reputation amplification coefficient, The greater the reputation score, the higher the priority score of the microgrid;

[0174] The priority scores of the microgrids of the two parties are multiplied to obtain the coupling weight corresponding to the transaction, and the microgrid i and the opposite microgrid j are the two parties of the transaction, and the coupling weight corresponding to the transaction of the microgrid i and the opposite microgrid j The calculation formula is:

[0175] , (19),

[0176] In the formula, represents the priority score of the opposite microgrid j at the current time .

[0177] The microgrids of the two parties form a transaction object pair , and the coupling weight corresponding to the transaction is calculated according to the priority scores of the microgrids of the two parties.

[0178] Step S3 comprises:

[0179] In one specific embodiment, the distributed optimization algorithm is a C-ADMM algorithm, the consensus penalty term of the C-ADMM algorithm is weighted by the coupling weight corresponding to the transaction, and the influence of each microgrid transaction in the global transaction optimization is dynamically adjusted; wherein the coupling weight is positively correlated with the priority of the corresponding microgrid transaction; by adjusting the priority of each microgrid transaction, a multi-microgrid P2P energy transaction strategy is generated;

[0180] The coupling weight is used to adjust the closeness of the consensus reached by the two parties of the transaction. The greater the coupling weight, the closer the coupling, and the more important the consensus. The coupling weight calculated by two microgrids with high reputation scores is large, which means that the two microgrids with high reputation scores are encouraged and facilitated to reach a transaction between them, and the two microgrids with high reputation scores are more likely to obtain energy transactions.

[0181] The C-ADMM algorithm gives the local variable of the microgrid i, the public variable , the dual variable , and the penalty parameter , and iterates in turn for each transaction object pair .​​​

[0182] Local variables of microgrid i The iterative formula is:

[0183] (20)

[0184] In the formula, This represents the original consensus penalty term of the C-ADMM algorithm;

[0185] This represents the consensus penalty term after weighting by coupling weights;

[0186] Indicates the penalty factor; Indicates to microgrid A collection of micro-networks for transactions; Let represent the common variables in the transaction between microgrid i and its counterpart microgrid j during the k-th iteration; Let represent the dual variable of the transaction between microgrid i and its counterpart microgrid j in the k-th iteration.

[0187] Public variables The iterative formula is:

[0188] ,(twenty one),

[0189] Dual variables The iterative formula is:

[0190] ,(twenty two),

[0191] In the formula, k represents the iteration round; microgrid Objective function; local variables This indicates the planned energy trading volume of microgrid i; This represents the planned energy trading volume of microgrid i in the (k+1)th iteration; Represents the planned energy trading volume of the peer microgrid j in the (k+1)th iteration; common variables This represents the consensus transaction volume between micronet i and its peer micronet j; Represents the consensus transaction volume between micronet i and its peer micronet j in the (k+1)th iteration; dual variable Represents local variables and common variables Deviation penalties; Represents the local variables of the (k+1)th iteration. Common variables of the (k+1)th iteration Deviation penalties; Let represent the dual variable of the k-th iteration;

[0192] With the self-optimizing tendency, the C-ADMM algorithm is combined with the design of the consensus mechanism, so that each microgrid considers the collaboration with the transaction object when optimizing the self-object function, and finally realizes the dynamic balance between individual interests and group consensus.

[0193] In order to verify the implementation effect of the multi-microgrid P2P energy transaction method of the improved reputation management model of the application in the multi-microgrid P2P energy transaction, the following simulation test is carried out:

[0194] Figure 2 The figure shows the change trend of the reputation scores and the performance rates of the five microgrids under different fault scenarios, wherein, Figure 2 The three subgraphs on the left side from top to bottom represent the reputation score curves of each microgrid within 28 days under the normal operation scenario, the occasional fault scenario and the continuous fault scenario, reflecting the reputation score change of different microgrids within 28 days; Figure 2 The three subgraphs on the right side from top to bottom correspond to the performance rate curves of each microgrid within 28 days under the normal operation scenario, the occasional fault scenario and the continuous fault scenario, showing the success rate fluctuation of each microgrid in the transaction process, and the starting value of the horizontal coordinate is 1, 1 to 2 represents the first day, the occasional fault scenario is that an occasional fault is set in a microgrid on the 15th day, and the continuous fault scenario is that a continuous fault is set in a microgrid from the 15th day to the 28th day. Figure 2 As shown in the subgraph on the right side, under the occasional fault scenario on the 15th day, the performance rate of the orange curve microgrid decreases in a short time on the 15th day and the 16th day, corresponding to Figure 2 As shown in the subgraph on the left side, the reputation score of the orange curve microgrid corresponds to a short-term decrease on the 15th day and a rapid recovery. However, Figure 2 As shown in the subgraph on the right side, under the continuous fault scenario from the 15th day to the 28th day, the performance rate of the orange curve microgrid decreases for a long time, corresponding to Figure 2 As shown in the subgraph on the left side, the reputation score of the orange curve microgrid decreases obviously from the 15th day and remains at a low level, reflecting the long-term impact of the long-time fault on the reputation score of the microgrid. Figure 2 It is shown that under the fault condition, the multi-microgrid P2P energy transaction method of the improved reputation management model of the application can identify and adapt to the change of the microgrid behavior.

[0195] In a multi-microgrid P2P energy transaction simulation experiment, microgrid 5 is a malicious microgrid with a low reputation score, Figure 3 The 24h energy transaction schematic diagram of the energy transaction method based on the traditional reputation model in the face of the whitewashing behavior of microgrid 5, Figure 4 The 24h energy transaction schematic diagram of the multi-microgrid P2P energy transaction method of the improved reputation management model of the application in the face of the whitewashing behavior of microgrid 5, Figure 3 andFigure 4 the bar chart of each time point is the share of each micro-grid participating in energy transaction, Figure 3 and Figure 4 Only the energy transaction amount of micro-grids 1, 2, 3, 4 and 5 is counted, the micro-grids participating in energy transaction are not only these five, and the energy transaction does not only occur between micro-grids 1, 2, 3, 4 and 5, so Figure 3 and Figure 4 The energy transaction amount shown each time does not always keep balance between micro-grids 1, 2, 3, 4 and 5, for example, at the 1st hour, only micro-grids 2 and 3 participate in energy transaction among the 5 micro-grids, and the energy transaction amount of micro-grid 3 is greater than that of micro-grid 2. According to Figure 3 and Figure 4 The comparison of the energy transaction amount verifies the effectiveness of the multi-micro-grid P2P energy transaction method of the improved reputation management model of the application in resisting reputation whitewashing malicious attacks.

[0196] In the multi-micro-grid P2P energy transaction, the reputation score of each micro-grid is positively correlated with its energy transaction priority, and reputation whitewashing is a malicious micro-grid with a low reputation score due to historical bad behavior, which quickly and improperly improves its reputation score by performing a large number of small and compliant energy transactions to cover up its historical bad record.

[0197] The conventional The reputation model has no historical decay, lacks effective historical decay and event weight mechanism, Figure 3 As shown, from the 1st hour to the 9th hour, micro-grid 5 does not participate in energy transaction, and at the 10th hour, micro-grid 5 participates in the initial energy transaction, micro-grids 2, 3, 4 and 5 participate in energy transaction, and at this time, the energy transaction amount of the four micro-grids 2, 3, 4 and 5 has little difference, because the energy transaction method based on the conventional The reputation model does not remember the historical malicious behavior of micro-grid 5; Figure 4 As shown, at the 10th hour, micro-grid 5 participates in the initial energy transaction, at this time, the energy transaction amount of micro-grids 2 and 3 is the same as that of micro-grids 2 and 3 of the Figure 3 The energy transaction amount of micro-grids 4 and 5 is obviously smaller than that of micro-grids 2 and 3, because the multi-micro-grid P2P energy transaction method of the improved reputation management model of the application remembers the historical malicious behavior of micro-grid 5, when micro-grids 4 and 5 transact, because the reputation score of micro-grid 5 is low, only a small energy transaction amount is allowed, and micro-grid 4 transacting with micro-grid 5 can only obtain a small energy transaction amount.

[0198] Figure 3 As shown, at the 11th hour, micro-grid 5 participates in the second energy transaction, micro-grids 1, 4 and 5 participate in energy transaction, and the energy transaction amount of micro-grid 4 and micro-grid 5 is obviously greater than that of micro-grid 1, because the energy transaction method based on the conventional The reputation model's energy trading methodology recognizes the compliant energy trading of Microgrid 5 in the 10th hour, allowing for a larger energy trading volume for Microgrid 5; Figure 4 As shown, the energy trading volume of microgrids 4 and 5 in the 11th hour was similar to that of microgrid 1. This is because the multi-microgrid P2P energy trading method of the improved reputation management model of this invention retains the memory of microgrid 5's past malicious behavior and did not allow microgrid 5 to have a large energy trading volume due to a compliant energy transaction in the 10th hour. However, compared to... Figure 4 The energy trading volumes of Microgrid 5 in the 10th and 11th hours show that after the compliant energy trading in the 10th hour, Microgrid 5 was allowed a slightly larger energy trading volume in the 11th hour. This means that the multi-microgrid P2P energy trading method of the improved reputation management model of this invention retains the memory of the historical malicious behavior of Microgrid 5 while taking into account the positive impact of compliant energy trading of Microgrid 5.

[0199] Figure 3 As shown, in the six compliant energy transactions from hour 12 to hour 17, only Microgrid 4 and Microgrid 5 participated, compared to... Figure 4 , Figure 3 The energy trading volume of both Microgrid 4 and Microgrid 5 was significantly higher than that of the same period last year. Figure 4 This is because it is based on conventional The reputation model's energy trading method continuously recognizes the compliant energy trading of Microgrid 5 in the 10th hour, while the energy trading method of this invention keeps a memory of Microgrid 5's historical malicious behavior and takes into account the positive impact of Microgrid 5's compliant energy trading.

[0200] Figure 3 As shown, in the 9th energy transaction involving microgrid 5 in the 18th hour, microgrids 1 through 5 all participated in energy trading. Microgrid 5 had the largest energy trading volume, which was significantly greater than the other four microgrids. Figure 3 The example shown is based on conventional In the reputation model-based energy trading method, Microgrid 5 successfully cleaned its name, while in contrast... Figure 4 As shown, the energy trading volume of microgrid 5 in the 18th hour was also the largest, but not significantly greater than the other four microgrids. This is because the energy trading method of the present invention retains a memory of the historical malicious behavior of microgrid 5 while also taking into account the positive impact of compliant energy trading of microgrid 5.

[0201] Depend on Figure 3 Analysis shows that Microgrid 5, relying on compliant transactions from hour 10 to hour 17, presented a trustworthy facade in the energy trading market. Other microgrids believed it was safe to trade with this malicious microgrid, and therefore signed large, high-value energy contracts with it in hour 18, indirectly reflecting that its reputation was successfully whitewashed. From Figure 3The energy trading volume clearly shows that malicious microgrid 5 successfully participated in multiple energy transactions between the 10th and 18th hours, and by the 18th hour, microgrid 5's energy trading volume dominated the market. This indicates that based on... Traditional energy trading methods based on reputation models fail to identify such attacks, leading to malicious microgrids being wrongly given excessive trading opportunities and increasing the overall risk in the energy trading market. Figure 4 Analysis shows that the multi-microgrid P2P energy trading method of this invention, which improves reputation management models, significantly reduces the energy trading share of malicious microgrid 5 when faced with the same multiple compliant energy trading operations. This indirectly reflects that microgrid 5's reputation score only slightly increases, almost maintaining the penalty for early negative records. Furthermore, other microgrids recognize the high risk of microgrid 5 and actively choose to abandon transactions with this malicious microgrid, resulting in a decrease in the total market trading volume compared to the previous method. Figure 3 There has been a decline. Meanwhile, even if micronet 4 is not a malicious micronet, its excessive transactions with malicious micronet 5 pose a risk of association. Figure 4 The energy trading data for the 18th hour shows that Microgrid 4 also had its energy trading share reduced. Therefore, according to... Figure 3 and Figure 4 Analysis shows that the multi-microgrid P2P energy trading method of the improved reputation management model of this invention can effectively suppress malicious behavior that uses low-risk transactions to quickly improve reputation scores.

[0202] In this specific application scenario, a standard IEEE 33-node peer-to-peer (P2P) energy trading network is adopted. Each node is connected to a distributed energy provider (DER), and a certain degree of power prediction error exists in actual operation. Transactions between nodes are limited to direct energy purchase and sale contracts, and participants remain anonymous, simulating the complexity and trust challenges of a real decentralized market. The simulation period is 28 days, and the system collects the fulfillment rate and reputation score of each node daily for the previous 24 hours. To differentiate between good and bad behavior, the fulfillment rate is divided into three levels: This indicates a good contract fulfillment rate. This indicates that the compliance rate is tolerable. This indicates that the microgrid's compliance rate has deteriorated to the point where it is prohibited from participating in energy trading; subsequent failure scenarios will only affect the compliance rate. The micro-network unfolds.

[0203] The simulation uses the following five metrics to measure system performance: average reputation score (arithmetic mean of daily reputation scores over 28 days); successful transaction volume over 28 days (total energy transactions successfully completed by all nodes over 28 days); successful transaction rate (proportion of successful transactions to total transactions); and participant retention rate (proportion of active microgrids at the end of the simulation to the initial number of microgrids). Active microgrids refer to the fulfillment rate. The micro-network, when the fulfillment rate At this time, the microgrid is prohibited from participating in energy transactions and becomes an inactive microgrid.

[0204] The following four scenarios are used in the simulation: no reputation model, reputation model, reputation model + time forgetting factor, improved reputation management model of the present application.

[0205] Table 1 Comparison of indicators under the standard 33-node system

[0206]

[0207] According to Table 1, under the no reputation model, the successful transaction volume is 482.56 MWh, the successful transaction rate is 84.43%, and the participant retention rate is 67.86%. After introducing the reputation model, the successful transaction volume increases to 530.20 MWh, the successful transaction rate increases to 93.96%, and the participant retention rate increases to 93.21%, which shows that the introduction of the reputation management model is the key to improving the efficiency and safety of the energy transaction market. In the reputation management model, a fixed value is added as a fixed time forgetting factor to weight the historical and current behavior, which considers the time factor and can balance the historical and current behavior. The average reputation score increases from 0.81 to 0.86; the successful transaction rate increases to 94.16%, but the participant retention rate slightly decreases to 92.86%, which shows that reasonable time decay can refine the reputation score distribution. The improved reputation management model of the present application improves the dynamic reputation model and the multi-dimensional forgetting factor, which not only considers time, but also dynamically adjusts the forgetting speed according to the fluctuation of reputation itself, the severity of events and other multiple dimensions, and the simulation results are the best: the successful transaction volume is 538.52 MWh, the successful transaction rate is 96.24%, and the participant retention rate is 98.93%, which shows that the improved reputation management model of the present application can effectively optimize the market of multi-microgrid P2P energy transaction.

[0208] Table 2 Dynamic comparison of reputation scores of the improved reputation management model of the present application under different fault scenarios

[0209] Failure scenario Average reputation score Lowest reputation score Starting reputation score Reputation score recovery Drop in reputation Normal situation 0.92 0.90 0.92 / / Incidental failure 0.90 0.85 0.92 ≈ 3 days 0.07 Persistent failure 0.86 0.78 0.92 / 0.14

[0210] ​As shown in Table 2, under normal circumstances, the microgrid maintains a high fulfillment rate, with an average reputation score of 0.92 over 28 days, and a minimum of no less than 0.90. The reputation score curve only oscillates slightly. During occasional failures, the fulfillment rate drops rapidly, and the reputation score falls from 0.92 to a minimum of 0.85, before quickly rebounding to its original stable level within approximately 3 days. During persistent failures, the microgrid maintains a low fulfillment rate from the onset, with the reputation score continuously declining to a minimum of 0.78, and an average reputation score of only 0.86 over 28 days, showing no significant recovery. This comparison verifies the ability of the multi-microgrid P2P energy trading method of this invention's improved reputation management model to smoothly punish and quickly recover from short-term fluctuations, as well as its sustained effectiveness in combating long-term misconduct.

[0211] Table 3 Comparison of Reputation Whitewashing Attack Resistance

[0212] ;

[0213] To test whitewashing attacks, 100 successful small-scale energy sales were simulated in a case with an initial reputation score of 0.50: based on Traditional energy trading methods based on reputation models can easily boost a reputation score from an initial 0.50 to 0.92 through small transactions, an increase of 0.42. The improved reputation management model of this invention, using a multi-microgrid P2P energy trading method, only slightly increases the reputation score from 0.50 to 0.53 for the same small transactions, almost maintaining the penalty for early negative records. The comparison results in Table 3 demonstrate that the improved reputation management model of this invention effectively suppresses malicious behavior that uses low-risk transactions to quickly boost reputation scores, effectively resisting reputation whitewashing attacks.

[0214] The above three comparisons demonstrate that the multi-microgrid P2P energy trading method of the present invention, which improves the reputation management model, not only increases the successful transaction volume and success rate of the multi-microgrid P2P energy trading market and improves user retention rate, but also exhibits differentiated response capabilities to occasional and continuous failures, and strong resistance to reputation whitewashing attacks. This fully proves its practical value and robustness in the real decentralized energy market.

[0215] Example 2:

[0216] Based on the same inventive concept as Embodiment 1, this embodiment introduces an improved reputation management model for a multi-microgrid P2P energy trading system, including: a reputation calculation module, a weight generation module, and a strategy generation module;

[0217] The reputation calculation module is configured to calculate the reputation scores of the microgrids in real time according to the P2P energy transaction history of the multi-microgrid, by using an improved reputation management model; the improvement of the reputation management model includes: introducing a dynamic forgetting factor calculated according to the change amount of the reputation score, and adjusting the weights of the historical information and the real-time information in the reputation score calculation, so as to improve the timeliness of the reputation management.

[0218] The weight generation module is configured to calculate the priority scores of the microgrids according to the reputation scores, and to calculate the coupling weights of the corresponding transactions by using the priority scores of the microgrids of the transaction parties.

[0219] The strategy generation module is configured to adjust the priorities of the transactions of the microgrids by weighting the distributed optimization algorithm by using the coupling weights, and to generate the P2P energy transaction strategy of the multi-microgrid.

[0220] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0221] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 An apparatus for performing each flow or multiple flows and / or blocks Figure 1 The functions specified in each flow or multiple flows and / or blocks.

[0222] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 An apparatus for performing each flow or multiple flows and / or blocks Figure 1 The functions specified in each flow or multiple flows and / or blocks.

[0223] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate a computer-implemented process, so that the instructions executed on the computer or other programmable devices provide a process for implementing the flowchart Figure 1 one flow or multiple flows and / or the functions specified in the flow Figure 1 one flow or multiple flows and / or the functions specified in the flow

[0224] The above description is only the preferred embodiment of the present application, and it should be pointed out that for those skilled in the art, without departing from the technical principles of the present application, a number of improvements and modifications can be made, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims

1. A multi-microgrid P2P energy transaction method for improving a reputation management model, characterized in that, The method comprises the following steps: According to the multi-microgrid P2P energy transaction history, the reputation score of each microgrid is calculated in real time by using an improved reputation management model; The calculation of the reputation score of each microgrid comprises: based on the pre-set dynamic forgetting factor calculated according to the reputation score change amount and the multi-microgrid P2P energy transaction history, the historical positive and negative feedback cumulative amounts and the real-time global positive and negative feedback increments are respectively weighted and fused to calculate the real-time positive and negative feedback cumulative amounts of each microgrid, and the reputation score of each microgrid is updated according to the real-time positive and negative feedback cumulative amounts, which comprises: according to the multi-microgrid P2P energy transaction history, in the Beta reputation model reputation score calculation, the dynamic forgetting factor calculated according to the reputation score change amount is introduced, and the positive and negative feedback cumulative amounts at the last moment and the global positive and negative feedback increments at the current moment are respectively weighted and fused to calculate the positive feedback cumulative amount and the negative feedback cumulative amount of each microgrid at the current moment; The microgrid i current time The positive feedback cumulative amount The calculation formula is: ， wherein denotes the dynamic forgetting factor of the microgrid i at the current time instant ; denotes the positive feedback accumulation of the microgrid i at the previous time instant ; denotes the global positive feedback increment of the microgrid i at the current time instant ; The microgrid i current time The negative feedback cumulative amount The calculation formula is: ， wherein represents the negative feedback cumulative amount at a time instant for the microgrid i; represents the global negative feedback increment at a current time instant for the microgrid i; According to the positive feedback cumulative amount and the negative feedback cumulative amount at the current moment, the reputation score of each microgrid at the next moment is updated; Microgrid i next time Reputation score of The calculation formula is: ， In the formula, represents the positive feedback cumulative amount of the microgrid i at the current time ; represents the negative feedback cumulative amount of the microgrid i at the current time ; and represents a preset smoothing term coefficient; According to the reputation score, the priority score of each microgrid is calculated, and the coupling weight of the corresponding transaction is calculated through the priority score of the microgrid of the transaction party; The priority of each microgrid transaction is adjusted by weighting the distributed optimization algorithm with the coupling weight, and a multi-microgrid P2P energy transaction strategy is generated.

2. The method for P2P energy transaction of multiple microgrids for improving reputation management model according to claim 1, characterized in that, The dynamic forgetting factor is calculated according to the reputation score change amount, which comprises: According to the multi-microgrid P2P energy transaction history, the short-term reputation fluctuation and the long-term reputation trend of each microgrid are calculated; Microgrid i current time Short-term reputation fluctuations The formula is: ， wherein represents the reputation score of the microgrid i at the current time instant ; represents the reputation score of the microgrid i at the previous time instant ; Microgrid i current time Long-term reputation trends The calculation formula is: ， wherein represents the reputation score of the microgrid i at the first seven time points ; The dynamic forgetting factor of each micro-grid at the current time is calculated according to short-term reputation fluctuation and long-term reputation trend; the calculation formula of the dynamic forgetting factor of the micro-grid i at the current time is as follows: The dynamic forgetting factor of each micro-grid at the current time is calculated according to short-term reputation fluctuation and long-term reputation trend; the calculation formula of the dynamic forgetting factor of the micro-grid i at the current time is as follows: The dynamic forgetting factor of each micro-grid at the current time is calculated according to ， ， wherein represents a preset balancing factor that regulates the short-term weight; represents a preset balancing factor that regulates the long-term weight; represents a preset sensitivity that controls the short-term volatility, represents a preset sensitivity that controls the long-term volatility, .​ 3. The method for multi-microgrid P2P energy transaction to improve reputation management model according to claim 1, characterized in that, The calculation of the reputation score of each microgrid further comprises: The transaction comprehensive weight is calculated according to the transaction scale and the transaction risk, and the positive and negative feedback increments are respectively calculated by using an asymmetric positive and negative feedback increment calculation method, and the global positive and negative feedback increments of each microgrid are respectively calculated according to the positive and negative feedback increments.

4. The method for multi-microgrid P2P energy transaction to improve reputation management model according to claim 3, characterized in that, The transaction comprehensive weight is calculated according to the transaction scale and the transaction risk, which comprises: According to the multi-microgrid P2P energy transaction history, the size normalization weight corresponding to the transaction is calculated to measure the transaction size; the current time microgrid the opposite microgrid the size normalization weight of the transaction The calculation formula is: ， ， In the formula, denotes the current time microgrid the opposite microgrid the committed delivery amount of the microgrid; denotes the microgrid the microgrid set of transaction; denotes any time from the initial time to the time t; denotes the microgrid the historical maximum transaction commitment amount; According to the micro-network reputation score of the opposite end, a micro-network reputation risk weight of the corresponding transaction is calculated to measure the transaction risk; the current time micro-network with the opposite end micro-network micro-network reputation risk weight of the transaction The calculation formula is: ， In the formula, represents a preset risk amplification coefficient; represents a global default risk degree of the microgrid a peer microgrid of the transaction a current time a current time represents a reputation score of the peer microgrid a current time a current time According to the size normalization weight based on the transaction commitment amount and the microgrid reputation risk weight based on the reputation score of the opposite microgrid, a transaction comprehensive weight of the corresponding transaction is calculated; the current time Microgrid With the opposite microgrid Transaction comprehensive weight of the transaction The calculation formula is: 。 5. The method for multi-microgrid P2P energy transaction to improve reputation management model according to claim 4, characterized in that, The asymmetric positive and negative feedback increment calculation method comprises: Current time Positive feedback increment of micro network i and opposite micro network j transaction The calculation formula is: ， In the formula, represents a preset positive feedback index; represents a current time of a microgrid of a peer microgrid of a peer microgrid of a peer microgrid of a peer microgrid of a peer microgrid The calculation formula is: ， wherein denotes the current time microgrid to the peer microgrid the committed delivery quantity, denotes the current time microgrid to the peer microgrid the actual delivery quantity; Current time The negative feedback increment of the microgrid i and the opposite microgrid j in the transaction The calculation formula is: ， In the formula, represents a preset negative feedback index.

6. The method for multi-microgrid P2P energy transaction to improve reputation management model according to claim 5, characterized in that, The calculation of the global positive and negative feedback increments of each microgrid comprises: The global positive feedback increment and the global negative feedback increment of each microgrid at the current moment are respectively obtained by respectively collecting the positive feedback increment and the negative feedback increment of each microgrid at the current moment in the global transaction; The microgrid i current time The global positive feedback increment The calculation formula is: , The global negative feedback increment of the microgrid i at the current time The calculation formula is: .​ 7. The method for multi-microgrid P2P energy transaction to improve reputation management model according to claim 1, characterized in that, The priority score of each microgrid is calculated according to the reputation score, and the coupling weight of the corresponding transaction is calculated through the priority score of the microgrid of the transaction party, which comprises: According to the respective microgrid reputation scores, the priority score of each microgrid at the corresponding time is calculated, and the priority score of the microgrid i at the current time is calculated according to the following formula: The calculation formula of the priority score of the microgrid i at the current time is as follows:​ ， In the formula, represents a preset reputation amplification coefficient; The coupling weight corresponding to the transaction is obtained by multiplying the priority scores of the microgrids of the two parties of the transaction, and the microgrid i and the opposite microgrid j are the two parties of the transaction, and the coupling weight corresponding to the transaction of the microgrid i and the opposite microgrid j The calculation formula is as follows: ， wherein represents the priority score of the peer microgrid j at the current time t.

8. The multi-microgrid P2P energy transaction method according to claim 7, characterized in that, The priority of each microgrid transaction is adjusted by weighting the distributed optimization algorithm with the coupling weight, and a multi-microgrid P2P energy transaction strategy is generated. The distributed optimization algorithm is a C-ADMM algorithm; the consensus penalty term of the C-ADMM algorithm is weighted by the coupling weight of the corresponding transaction, wherein the coupling weight is positively correlated with the priority of the corresponding microgrid transaction; by adjusting the priority of each microgrid transaction, a multi-microgrid P2P energy transaction strategy is generated; Microgrid i local variables of the C-ADMM algorithm In the iteration formula, the consensus penalty term weighted by the coupling weight is: ， In the formula, represents the original consensus penalty term of the C-ADMM algorithm; represents a penalty factor; represents the public variable of the transaction between the microgrid i and the opposite microgrid j in the kth iteration; represents the dual variable of the transaction between the microgrid i and the opposite microgrid j in the kth iteration.

9. An improved multi-microgrid P2P energy trading system with reputation management model, characterized in that, It comprises: A reputation calculation module, a weight generation module and a strategy generation module; The reputation calculation module is used to calculate the reputation score of each microgrid in real time by using an improved reputation management model according to the multi-microgrid P2P energy transaction history; The reputation score of each microgrid is calculated by: based on the pre-set dynamic forgetting factor calculated according to the reputation score change amount and the multi-microgrid P2P energy transaction history, the historical positive and negative feedback cumulative amount and the real-time global positive and negative feedback increment are weighted and fused respectively, the real-time positive and negative feedback cumulative amount of each microgrid is calculated, the reputation score of each microgrid is updated according to the real-time positive and negative feedback cumulative amount, including: according to the multi-microgrid P2P energy transaction history, introducing the dynamic forgetting factor calculated according to the reputation score change amount in the Beta reputation model reputation score calculation, the positive and negative feedback cumulative amount of the last moment and the global positive and negative feedback increment of the current moment are weighted and fused respectively, the positive feedback cumulative amount and the negative feedback cumulative amount of each microgrid at the current moment are calculated; The microgrid i current time The positive feedback cumulative amount The calculation formula is: ， wherein denotes the dynamic forgetting factor of the microgrid i at the current time instant ; denotes the positive feedback accumulation of the microgrid i at the previous time instant ; denotes the global positive feedback increment of the microgrid i at the current time instant ; The microgrid i current time The negative feedback cumulative amount The calculation formula is: ， wherein represents the negative feedback cumulative amount at a time instant of the microgrid i; represents the global negative feedback increment at a current time instant of the microgrid i; According to the positive feedback cumulative amount and the negative feedback cumulative amount at the current moment, the reputation score of each microgrid at the next moment is updated; Microgrid i next time Reputation score of The calculation formula is: ， In the formula, represents the positive feedback cumulative amount of the microgrid i at the current time; represents the negative feedback cumulative amount of the microgrid i at the current time; and represents a preset smoothing term coefficient;​​ The weight generation module is used to calculate the priority score of each microgrid according to the reputation score, and to calculate the coupling weight of the corresponding transaction through the priority score of the transaction parties microgrid; The strategy generation module is used to weight the distributed optimization algorithm through the coupling weight, adjust the priority of each microgrid transaction, and generate a multi-microgrid P2P energy transaction strategy.

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