Hourly-level green electricity transaction matching method and system considering green certificate transaction block chain

Abstract

The invention relates to the technical field of electricity transaction, in particular to an hour-level green electricity transaction matching method and system considering a green certificate transaction blockchain, and the method comprises the following steps: S1, data collection and preprocessing: collecting the power generation operation data of a green electricity supplier and the power utilization behavior data of a user, preprocessing the collected power generation operation data and power consumption behavior data; s2, constructing a green certificate block chain: constructing a green certificate transaction platform based on a block chain technology, and recording green certificate transaction information; s3, establishing a matching model: constructing a multi-objective optimization model, and integrating multiple factors to carry out transaction matching; s4, transaction execution monitoring: after successful matching, executing the transaction and performing real-time monitoring; s5, transaction settlement evaluation: performing settlement and evaluation after the transaction is completed; according to the invention, the efficiency of green electricity transaction and the satisfaction degree of users and suppliers are effectively improved, and efficient utilization of green electricity resources and vitality improvement of the energy market are realized.

Description

Technical Field

[0001] The present invention relates to the field of power trading technology, and in particular to an hourly green electricity trading matching method and system taking into account a green certificate trading blockchain. Background Art

[0002] As the world pays more and more attention to environmental protection and sustainable development, green electricity, as a clean and renewable energy form, has become increasingly prominent in the energy field. However, in the field of green electricity trading, it is still facing a series of technical and management challenges that need to be solved urgently. Traditional green electricity trading models often have problems such as information asymmetry, low transaction efficiency and lack of transparency in the transaction process. Due to the lack of effective data collection and processing methods, it is difficult to accurately match the supply and demand information between green electricity suppliers and users, resulting in the waste of some green electricity resources, and the needs of some users cannot be met in a timely manner.

[0003] In terms of the time scale of transactions, existing green electricity transactions are usually based on longer time periods, such as days, months, or even years. This coarse-grained trading method cannot flexibly adapt to real-time changes in green electricity supply and demand. For some green electricity supply and demand with hourly fluctuation characteristics, the existing trading model is difficult to achieve accurate matching, which affects the effective utilization of green electricity and the vitality of the market. In addition, as an important part of green electricity transactions, green certificate transactions also have many imperfections in their management and trading mechanisms. The issuance, trading, and circulation of green certificates lack reliable records and traceability methods, which can easily lead to a crisis of trust and also bring difficulties to supervision.

[0004] Against the backdrop of energy transformation, the reform of the electricity market continues to deepen, putting forward higher requirements for the efficiency, fairness and security of green electricity trading. How to use advanced technical means, such as blockchain technology, to build a more efficient, transparent and accurate hourly green electricity trading matching method and system has become a key issue that needs to be broken through in the current green electricity trading field. This will not only help optimize the allocation of energy resources and promote the widespread application of green electricity, but also have important strategic significance for achieving sustainable energy development and responding to global climate change. Summary of the invention

[0005] The present invention provides an hourly green electricity transaction matching method and system taking into account the green certificate transaction blockchain.

[0006] An hourly green electricity transaction matching method taking into account the green certificate transaction blockchain comprises the following steps:

[0007] S1, data collection and preprocessing: collect the power generation operation data of green electricity suppliers and the electricity consumption behavior data of users, and preprocess the collected power generation operation data and electricity consumption behavior data;

[0008] S2, build green certificate blockchain: build a green certificate trading platform based on blockchain technology to record green certificate transaction information;

[0009] S3, establish matching model: build a multi-objective optimization model and conduct transaction matching based on multiple factors;

[0010] S4, transaction execution monitoring: after successful matching, the transaction is executed and monitored in real time;

[0011] S5, Transaction Settlement Evaluation: Settlement and evaluation are carried out after the transaction is completed.

[0012] Optionally, the data collection and preprocessing in S1 includes:

[0013] S11, Power generation operation data collection: Collect power generation operation data of green power suppliers, including the type and specifications of power generation equipment, power generation power P gen (t), power generation cost C gen (t), maintenance plan and historical records, number of green certificates held N cert (t), predicted power generation capacity P pred (t), power generation efficiency η gen (t), equipment failure rate F fail (t) and the availability of renewable energy resources R available (t);

[0014] S12, electricity consumption behavior data collection: collect users' electricity consumption behavior data, including electricity demand D load (t), power consumption time distribution T load (t), Ability to bear electricity prices P load (t), historical electricity consumption data, potential electricity consumption growth trend, electricity quality requirements Q load (t) and power outage tolerance T tolerance (t);

[0015] S13, data preprocessing: preprocessing the collected power generation operation data and electricity consumption behavior data, including cleaning, statistical calculation, data smoothing and standardization, specifically including:

[0016] Cleaning: Clean the collected data to remove outliers and erroneous data;

[0017] Statistical calculation: Evaluate the quality and stability of data by calculating the mean and variance of the data;

[0018] Data smoothing: Use moving average method to deal with data fluctuations;

[0019] Standardization: Data standardization converts data of different dimensions into a unified standard format.

[0020] Optionally, the construction of the green certificate blockchain in S2 includes:

[0021] S21, transaction information encryption processing: encrypting the transaction information using a hash function H(M), where M is the transaction information data;

[0022] S22, Smart Contract Automated Trading: The automatic issuance, trading and circulation of green certificates are realized through smart contracts. The execution conditions and logic of smart contracts are expressed as follows:

[0023] If(condition1Λcondition2…Λcondition n )Thenaction;

[0024] S23, distributed ledger application: Distributed ledger technology is used to ensure consistent storage of transaction data on multiple nodes. Data consistency verification is expressed as:

[0025] Data node1 =Data node2 =…=Data noden ;

[0026] S24, Identity and information security assurance: Use asymmetric encryption algorithm for digital signature, expressed as:

[0027] Signature=Sign(privateKey,message);

[0028] Verify(publicKey,message,Signatuer)=True / False;

[0029] S25, node consistency guarantee: Use the Practical Byzantine Fault Tolerance (PBFT) algorithm to ensure that multiple nodes have consistent recognition of transactions. In the Practical Byzantine Fault Tolerance (PBFT) algorithm, nodes need to conduct multiple rounds of voting to reach a consensus.

[0030] Optionally, establishing a matching model in S3 includes:

[0031] S31, multi-factor input: comprehensively consider the power generation capacity of green power suppliers, the power demand of users, the holding of green certificates, market prices and the transmission capacity limit of the power grid as the basic input conditions;

[0032] S32, constructing a multi-objective optimization model: by constructing a multi-objective optimization model and combining multiple input factors, the optimal matching of green electricity transactions can be achieved;

[0033] S33, setting constraints: defining constraints of the multi-objective optimization model;

[0034] S34, solving the optimal solution: using linear programming algorithm to solve the multi-objective optimization model and obtain the optimal green electricity trading matching solution.

[0035] Optionally, the objective function of the multi-objective optimization model in S32 is expressed as:

[0036] MaximizeZ=ω1∑ t (P gen (t)-C gen (t))+ω2∑ t N cert (t)-ω3∑ t Cost transmisssion (t);

[0037] Among them, ω1, ω2, and v3 are weight coefficients, Cost transmisssion (t) is the transmission cost in hours.

[0038] Optionally, the constraint condition in S33 is expressed as:

[0039] P gen (t)≤Capacity gen ;

[0040] D load (t)≤P trams (t)≤Capacity gen .

[0041] Optionally, the transaction execution monitoring in S4 includes:

[0042] S41, real-time monitoring of key indicators: real-time monitoring of key indicators of the transaction process through the blockchain platform, including the real-time power P of green electricity transmission trams (t), voltage stability index V stab (t) and the turnover amount of transaction funds F trans (t);

[0043] S42, monitoring condition check and alarm: ensure that the monitoring conditions are met during the transaction process. If the monitoring conditions are not met, an alarm is automatically issued and corresponding measures are taken, including adjusting the power generation or reallocating the power supply;

[0044] S43, real-time data processing and prediction: The Kalman filter algorithm is used to process and predict the monitoring data, which is expressed as:

[0045]

[0046] in, is the state estimate, A, B, C are system matrices, u t is the control input, yt is the measured value, K is the Kalman gain;

[0047] S44, integration with external systems and dynamic adjustment: integration and data exchange with power dispatching systems and financial payment systems, real-time acquisition of power grid operation status and power market prices, and dynamic adjustment of transaction execution strategies;

[0048] S45, Fault detection and diagnosis: Discover and handle abnormal situations in the transaction execution process based on fault tree analysis, expressed as:

[0049]

[0050] Optionally, the monitoring condition in S42 is expressed as:

[0051] P trans (t)≥αD load (t);

[0052] F trans (t) = P load (t)D load (t);

[0053] Among them, α is the coefficient.

[0054] Optionally, the transaction settlement evaluation in S5 includes:

[0055] S51, Fee Settlement: Fee settlement for transactions, including maintenance costs and service costs, expressed as:

[0056] Fee supplier =∑ t (P load (t)-C gen (t))D load (t)-∑ t Cost maintenance (t);

[0057] Fee user =∑ t P load (t)D load (t)+∑ t Cost service (t);

[0058] S52, multi-dimensional transaction effect evaluation: multi-dimensional evaluation of transaction matching effect, expressed as:

[0059] It is expressed as:

[0060]

[0061] Among them, S matchis the success rate, E trans is transaction efficiency, S user For user satisfaction, S supplier For supplier satisfaction;

[0062] S53, Generate transaction settlement report and visualization: Automatically generate transaction settlement report and use visualization technology to display evaluation results, including using bar charts and line charts to present the changing trends of matching success rate and transaction efficiency;

[0063] S54, Data feedback mechanism optimization: Use the data feedback mechanism to provide feedback on evaluation results to optimize performance and decision-making, including adjusting transaction matching strategies based on user satisfaction feedback and optimizing supplier management and incentive mechanisms based on supplier satisfaction feedback.

[0064] An hourly green electricity transaction matching system taking into account the green certificate transaction blockchain is used to implement the above-mentioned hourly green electricity transaction matching method taking into account the green certificate transaction blockchain, including the following modules:

[0065] Data collection and preprocessing module: collects power generation operation data from green power suppliers and electricity consumption behavior data from users, and preprocesses the power generation operation data and electricity consumption behavior data;

[0066] Green certificate blockchain building module: build a green certificate trading platform based on blockchain technology to record and encrypt green certificate transaction information;

[0067] Transaction matching module: Based on a multi-objective optimization model, it integrates multiple factors to achieve optimal transaction matching;

[0068] Transaction execution monitoring module: executes the transaction after successful matching and monitors key indicators in the transaction process in real time;

[0069] Transaction settlement evaluation module: settlement and multi-dimensional evaluation after the transaction is completed, and display of transaction evaluation results through visualization technology.

[0070] Beneficial effects of the present invention:

[0071] The present invention effectively solves many problems existing in traditional green electricity transactions by combining blockchain technology with a multi-objective optimization model. First, through the data acquisition and preprocessing module, the accurate acquisition and processing of the power generation operation data of green electricity suppliers and the electricity consumption behavior data of users are realized, ensuring the symmetry and accuracy of information between the supply and demand sides, thereby improving the matching efficiency of green electricity transactions. At the same time, the green certificate trading platform built on blockchain technology not only ensures the data security and non-tamperability during the issuance, trading and circulation of green certificates, but also provides a reliable traceability mechanism for green electricity transactions, increases the transparency and trust of the transaction process, and reduces potential disputes and regulatory difficulties.

[0072] The present invention, by constructing a multi-objective optimization model, combining multiple factors such as power generation capacity, user demand, market price, etc., adopts a linear programming algorithm to achieve optimal matching, thereby solving the problem that the existing trading model is difficult to accurately adapt to the hourly green electricity supply and demand fluctuations. The method also ensures the efficiency and stability of transaction execution through technical means such as real-time monitoring, fault detection, and Kalman filtering algorithm. In addition, through automated transaction settlement evaluation and data feedback mechanism, it is possible to conduct multi-dimensional effect evaluation of the transaction process, dynamically adjust the transaction strategy, and optimize system performance, thereby effectively improving the efficiency of green electricity transactions and the satisfaction of users and suppliers, and achieving efficient utilization of green power resources and enhanced vitality of the energy market. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0074] Figure 1 A schematic diagram of the data collection and preprocessing process of an embodiment of the present invention;

[0075] Figure 2 A schematic diagram of the process of constructing a green certificate blockchain according to an embodiment of the present invention;

[0076] Figure 3 A schematic diagram of a matching model establishment process according to an embodiment of the present invention;

[0077] Figure 4 A schematic diagram of a configuration method flow chart of an embodiment of the present invention;

[0078] Figure 5 Schematic diagram of system function modules according to an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0080] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0081] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0082] Assume that there are three green electricity suppliers (A, B, C) and two users (D, E) participating in the green electricity trading market.

[0083] like Figure 1-Figure 4 As shown, a method for matching hourly green electricity transactions taking into account the green certificate transaction blockchain includes the following steps:

[0084] 1. Data collection and preprocessing:

[0085] (1) Data collection:

[0086] 1. Data of green electricity supplier A:

[0087] Power generation equipment: wind farm with an installed capacity of 100 megawatts;

[0088] Power generation P gen (t): 80 MW at t1 and 70 MW at t2;

[0089] Power generation cost C gen (t): 40 yuan / MWh at t1 and 42 yuan / MWh at t2;

[0090] Maintenance plan and history records: Maintenance is performed every six months, and the last maintenance was one month ago;

[0091] Number of green certificates held n cert (t): 50 photos at time t1, 50 photos at time t2;

[0092] Predicted power generation capacity P pred (t): According to the weather forecast, tomorrow's power generation capacity is expected to be 75 megawatts;

[0093] Power generation efficiency η gen (t): 85% at t1 and 82% at t2;

[0094] Equipment failure rate F fail (t): Average failure rate is 3%;

[0095] Availability of renewable energy resources R available (t): The current availability of wind resources is good.

[0096] 2. Data of green electricity supplier B:

[0097] Power generation equipment: Solar power station with an installed capacity of 80 megawatts;

[0098] Power generation P gen (t): 60 MW at t1 and 55 MW at t2;

[0099] Power generation cost C gen (t): 45 yuan / MWh at t1, 46 yuan / MWh at t2; Maintenance plan and historical records: Maintenance is performed once a quarter, and the last maintenance was two weeks ago; Number of green certificates held N cert (t): 40 photos at time t1 and 40 photos at time t2;

[0100] Predicted power generation capacity P pred (t): The power generation capacity tomorrow is expected to be 60 MW;

[0101] Power generation efficiency η gen (t): 90% at t1 and 88% at t2;

[0102] Equipment failure rate F fail (t): average failure rate is 2%;

[0103] Availability of renewable energy resources R available (t): Currently, solar energy resources are sufficient.

[0104] 3. Data of green electricity supplier C:

[0105] Power generation equipment: hydroelectric power station with an installed capacity of 60 megawatts;

[0106] Power generation P gen (t): 50 MW at t1 and 48 MW at t2;

[0107] Power generation cost C gen (t): 35 yuan / MWh at t1, 36 yuan / MWh at t2; Maintenance plan and historical records: Maintenance is performed once a year, and the last maintenance time was three months ago; Number of green certificates held Ncert (t): 30 photos at time t1 and 30 photos at time t2;

[0108] Predicted power generation capacity P pred (t): Tomorrow's power generation capacity is expected to be 55 MW;

[0109] Power generation efficiency η gen (t): 88% at t1 and 86% at t2;

[0110] Equipment failure rate F fail (t): average failure rate is 1%;

[0111] Availability of renewable energy resources R available (t): Water resources are stable.

[0112] 4. User D’s data:

[0113] Electricity demand D load (t): 40 MW at t1 and 35 MW at t2;

[0114] Electricity consumption time distribution T load (t): The main electricity consumption period is daytime;

[0115] Ability to bear electricity prices load (t): The highest price it can bear is 60 yuan per megawatt-hour;

[0116] Historical electricity consumption data: Average monthly electricity consumption over the past year was 350 MWh;

[0117] Potential electricity demand growth trend: Electricity demand is expected to increase by 5% in the next year;

[0118] Power quality requirements load (t): The voltage is required to be stable within ±3%;

[0119] Power outage tolerance T tolerance (t): The maximum power outage tolerance is 30 minutes.

[0120] 5. User E’s data:

[0121] Electricity demand D load (t): 30 MW at t1 and 28 MW at t2;

[0122] Electricity consumption time distribution T load (t): The electricity consumption period is relatively uniform;

[0123] Ability to bear electricity prices load (t): The highest price it can bear is 55 yuan per megawatt-hour;

[0124] Historical electricity consumption data: Average monthly electricity consumption over the past year was 280 MWh;

[0125] Potential electricity demand growth trend: Electricity demand is expected to increase by 3% in the next year;

[0126] Power quality requirements load (t): The voltage is required to be stable within ±4%;

[0127] Power outage tolerance T tolerance (t): The maximum power outage tolerance is 20 minutes.

[0128] (2) Data preprocessing:

[0129] Preprocess the collected data, including cleaning, statistics calculation, data smoothing and standardization, including:

[0130] Cleaning: Clean the collected data to remove outliers and erroneous data;

[0131] Statistical calculation: Evaluate the quality and stability of data by calculating the mean and variance of the data;

[0132] Data smoothing: Use moving average method to deal with short-term fluctuations in data;

[0133] Standardization: Data standardization converts data of different dimensions into a unified standard format.

[0134] 2. Building a green certificate blockchain:

[0135] 1. Use the hash function H(M) to encrypt the transaction information to ensure that the information cannot be tampered with and is traceable. For example, when A and D conduct a green certificate transaction, the transaction information M includes the identities of A and D, the transaction time t1, the transaction quantity 20 green certificates, etc. After the hash function is processed, the hash value H1 is obtained. The transaction information is recorded on the blockchain and can be viewed by any participant but cannot be tampered with.

[0136] 2. Automatic issuance, trading and circulation of green certificates are realized through smart contracts. For example, when B's power generation reaches 55 MW and E's demand is confirmed, the smart contract automatically executes the green certificate transaction. The execution conditions and logic of the smart contract are expressed by the following formula:

[0137] If (B power generation ≥ 50MW∧E demand confirmation) Then execute green certificate transaction.

[0138] 3. Use distributed ledger technology to ensure consistent storage of transaction data on multiple nodes. For example, verify the data consistency on nodes 1, 2, and 3 to ensure that Data node1 =Data node2 =Datanode3 ;

[0139] If data inconsistency is found, the system will automatically trigger an alarm and synchronize the data.

[0140] 4. Use digital signature technology and encryption algorithms to ensure the authenticity of the identities of transaction participants and the confidentiality and integrity of transaction information. For example, using an asymmetric encryption algorithm for digital signature, A generates a digital signature, which is expressed as:

[0141] SignatureA=Sign(privateKeyA, messageA);

[0142] Other participants can

[0143] Verify(publicKeyA,messageA,SignatuerA);

[0144] To verify A's identity and the integrity of transaction information.

[0145] 5. Use blockchain consensus mechanisms, such as the Practical Byzantine Fault Tolerance (PBFT) algorithm, to ensure that multiple nodes agree on transactions. In the PBFT algorithm, nodes need to conduct multiple rounds of voting to reach a consensus and ensure the legitimacy and consistency of transactions.

[0146] 3. Establish a matching model:

[0147] 1. Comprehensively consider many factors such as the power generation capacity of green electricity suppliers, electricity demand of users, green certificate holdings, market price (assuming 50 yuan per megawatt-hour) and transmission capacity limit of the power grid (assuming 150 megawatts).

[0148] 2. The optimal matching is achieved by establishing a multi-objective optimization model, and the objective function is

[0149] MaximizeZ=ω1∑ t (P gen (t)-C gen (t))+ω2∑ t N cert (t)-ω3∑ t Cost transmisssion (t);

[0150] Among them, ω1, ω2, ω3 are weight coefficients, ω1 = 0.6, ω2 = 0.3, ω3 = 0.1, Cost transmisssion (t) is the hourly transmission cost (assumed to be RMB 3 per MWh).

[0151] 3. Constraints include but are not limited to: gen (t)≤Capacitygen (The power generation capacity does not exceed the maximum capacity of the equipment)D load (t)≤P trans (t)≤Capacity gen (The transmission power meets user demand and does not exceed the generation capacity).

[0152] 4. Use the linear programming algorithm to solve the above model and obtain the optimal green electricity trading matching solution. For example, at time t1, A supplies 30 MW of electricity to D, and B supplies 25 MW of electricity to E; at time t2, A supplies 25 MW of electricity to D, B supplies 20 MW of electricity to E, and C supplies 10 MW of electricity to D; at time t3, A supplies 35 MW of electricity to D, B supplies 25 MW of electricity to E, and C supplies 7 MW of electricity to D.

[0153] 4. Transaction Execution Monitoring:

[0154] 1. Strictly execute the successfully matched green electricity transactions through the blockchain platform and monitor the real-time power P of green electricity transmission in real time trans (t), voltage stability index V stab (t) The amount of transaction funds F trans (t) and other key indicators. For example, at time t1, P trans (t) is 55 MW, V stab (t)) is stable within ±2%, F trans (t) Calculated based on electricity consumption and price, it is 55×50=2750 yuan.

[0155] 2. Ensure the following conditions are met: trans (t)≥αD load (t)(α is the coefficient, assumed to be 0.8)F trans (t) = P load (t)D load (t). If these conditions are not met, the system will automatically issue an alarm and take appropriate measures, such as adjusting the power generation or reallocating the power supply.

[0156] 3. Use real-time data processing and analysis technology, such as the Kalman filter algorithm, to process and predict the monitoring data. For example, the Kalman filter algorithm can be used to predict the power generation and electricity demand at time t4, providing a reference for the next round of transaction matching.

[0157] The Kalman filter algorithm is expressed as:

[0158]

[0159] in, is the state estimate, A, B, C are system matrices, u t is the control input, yt is the measured value, and K is the Kalman gain.

[0160] 4. Closely integrate and interact with external systems such as the power dispatching system and the financial payment system to obtain real-time information such as the power grid's operating status and power market prices, and dynamically adjust transaction execution strategies based on this information. For example, if the power grid is overloaded, the system will automatically reduce the power supply of certain transactions to ensure the safe and stable operation of the power grid.

[0161] 5. Use fault detection and diagnosis technology to timely discover and handle abnormal situations during transaction execution. For example, based on fault tree analysis, when a fault event occurs,

[0162]

[0163] Determine the probability of a failure and take appropriate measures. If the probability of a failure is high, the system will notify relevant personnel in advance to perform maintenance or take alternative measures.

[0164] 5. Transaction Settlement Assessment:

[0165] 1. Fees are settled according to the following detailed formula:

[0166] Fee supplier =∑ t (P load (t)-C gen (t))D load (t)-∑ t Cost maintenance (t);

[0167] Assume maintenance cost is 2 yuan per MWh.

[0168] Fee user =∑ t P load (t)D load (t)+∑ t Cost service (t);

[0169] The service cost is 1 yuan per megawatt-hour.

[0170] 2. Conduct in-depth and multi-dimensional evaluation of transaction matching results, using a series of evaluation indicators such as matching success rate S match (Assuming that the matching success rate of this transaction is 100%), transaction efficiency E trans (Transaction efficiency is 2 transactions per hour), user satisfaction S user (User D satisfaction is 90%, User E satisfaction is 85%), Supplier satisfaction S supplierSupplier A's satisfaction is 88%, Supplier B's satisfaction is 90%, Supplier C's satisfaction is 85%, etc., expressed as:

[0171]

[0172] 3. Automatically generate detailed and accurate transaction settlement reports, and use visualization technology to display evaluation results, such as using bar charts and line charts to intuitively present the changing trends of indicators such as matching success rate and transaction efficiency. Through these charts, participants can clearly understand the situation and trend of the transaction, providing a basis for decision-making.

[0173] 4. Use the data feedback mechanism to feed back the evaluation results to other modules to optimize the system's performance and decision-making. For example, based on user satisfaction feedback, adjust the transaction matching strategy to improve user satisfaction; based on supplier satisfaction feedback, optimize the supplier's management and incentive mechanism.

[0174] Through the above specific implementation cases, it can be seen that the hourly green electricity transaction matching method and system taking into account the green certificate trading blockchain can effectively improve the efficiency, transparency and security of green electricity transactions, achieve more accurate green electricity supply and demand matching, promote the development of green electricity and optimize the energy structure.

[0175] like Figure 5 As shown, an hourly green electricity transaction matching system taking into account the green certificate transaction blockchain is used to implement the above-mentioned hourly green electricity transaction matching method taking into account the green certificate transaction blockchain, including the following modules:

[0176] Data collection and preprocessing module: collects power generation operation data from green power suppliers and electricity consumption behavior data from users, and preprocesses the power generation operation data and electricity consumption behavior data;

[0177] Green certificate transaction blockchain building module: build a green certificate transaction platform based on blockchain technology to record and encrypt green certificate transaction information;

[0178] Hourly green electricity transaction matching module: Based on a multi-objective optimization model, it integrates multiple factors to achieve optimal transaction matching;

[0179] Transaction execution and monitoring module: executes transactions after successful matching and monitors key indicators during the transaction in real time;

[0180] Transaction settlement and evaluation module: settlement and multi-dimensional evaluation after the transaction is completed, and display of transaction evaluation results through visualization technology.

[0181] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0182] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An hourly green electricity transaction matching method taking into account the green certificate transaction blockchain, characterized in that: The following steps are involved: S1, data collection and preprocessing: collect the power generation operation data of green electricity suppliers and the electricity consumption behavior data of users, and preprocess the collected power generation operation data and electricity consumption behavior data; S2, build green certificate blockchain: build a green certificate trading platform based on blockchain technology to record green certificate transaction information; S3, establish matching model: build a multi-objective optimization model and conduct transaction matching based on multiple factors; S4, transaction execution monitoring: after successful matching, the transaction is executed and monitored in real time; S5, Transaction Settlement Evaluation: Settlement and evaluation are carried out after the transaction is completed.

2. According to claim 1, a method for matching hourly green electricity transactions taking into account the green certificate transaction blockchain is characterized in that: The data collection and preprocessing in S1 include: S11, Power generation operation data collection: Collect power generation operation data of green power suppliers, including the type and specifications of power generation equipment, power generation power P gen (t), power generation cost C gen (t), maintenance plan and historical records, number of green certificates held N cert (t), predicted power generation capacity P pred (t), power generation efficiency η gen (t), equipment failure rate F fail (t) and the availability of renewable energy resources R available (t); S12, electricity consumption behavior data collection: collect users' electricity consumption behavior data, including electricity demand D load (t), power consumption time distribution T load (t), Ability to bear electricity prices P load (t), historical electricity consumption data, potential electricity consumption growth trend, electricity quality requirements Q load (t) and power outage tolerance T tolerance (t); S13, data preprocessing: preprocessing the collected power generation operation data and electricity consumption behavior data, including cleaning, statistical calculation, data smoothing and standardization, specifically including: Cleaning: Clean the collected data to remove outliers and erroneous data; Statistical calculation: Evaluate the quality and stability of data by calculating the mean and variance of the data; Data smoothing: Use moving average method to deal with data fluctuations; Standardization: Data standardization converts data of different dimensions into a unified standard format.

3. According to claim 2, a method for matching hourly green electricity transactions taking into account the green certificate transaction blockchain is characterized in that: The construction of the green certificate blockchain in S2 includes: S21, transaction information encryption processing: encrypting the transaction information using a hash function H(M), where M is the transaction information data; S22, Smart Contract Automated Trading: The automatic issuance, trading and circulation of green certificates are realized through smart contracts. The execution conditions and logic of smart contracts are expressed as follows: If(condition1∧condition2…∧condition n )Thenaction; S23, distributed ledger application: Distributed ledger technology is used to ensure consistent storage of transaction data on multiple nodes. Data consistency verification is expressed as: Date node1 =Date node2 =…=Date noden ; S24, Identity and information security assurance: Use asymmetric encryption algorithm for digital signature, expressed as: Signature=Sign(privateKey,message); Verify(publicKey,message,Signatuer)=True / False; S25, node consistency guarantee: Use the practical Byzantine fault tolerance algorithm to ensure that multiple nodes have consistent recognition of transactions. In the practical Byzantine fault tolerance algorithm, nodes need to conduct multiple rounds of voting to reach a consensus.

4. According to claim 3, a method for matching hourly green electricity transactions taking into account the green certificate transaction blockchain is characterized in that: The establishment of the matching model in S3 includes: S31, multi-factor input: comprehensively consider the power generation capacity of green power suppliers, the power demand of users, the holding of green certificates, market prices and the transmission capacity limit of the power grid as the basic input conditions; S32, constructing a multi-objective optimization model: by constructing a multi-objective optimization model and combining multiple input factors, the optimal matching of green electricity transactions can be achieved; S33, setting constraints: defining constraints of the multi-objective optimization model; S34, solving the optimal solution: using linear programming algorithm to solve the multi-objective optimization model and obtain the optimal green electricity trading matching solution.

5. According to claim 4, a method for matching hourly green electricity transactions taking into account the green certificate transaction blockchain is characterized in that: The objective function of the multi-objective optimization model in S32 is expressed as: MaximizeZ=ω1∑ t (P gen (t)-C gen (t))+ω2∑ t N cert (t)-ω3∑ t Cost transmisssion (t); Among them, ω1, ω2, ω3 are weight coefficients, Cost transmisssion (t) is the transmission cost in hours.

6. According to claim 5, a method for matching hourly green electricity transactions taking into account the green certificate transaction blockchain is characterized in that: The constraint condition in S33 is expressed as: P gen (t)≤Capacity gen ; D load (t)≤P trans (t)≤Capacity gen 。 7. According to claim 6, a method for matching hourly green electricity transactions taking into account the green certificate transaction blockchain is characterized in that: The transaction execution monitoring in S4 includes: S41, real-time monitoring of key indicators: real-time monitoring of key indicators of the transaction process through the blockchain platform, including the real-time power P of green electricity transmission trans (t), voltage stability index V stab (t) and the turnover amount of transaction funds F trans (t); S42, monitoring condition check and alarm: ensure that the monitoring conditions are met during the transaction process. If the monitoring conditions are not met, an alarm is automatically issued and corresponding measures are taken, including adjusting the power generation or reallocating the power supply; S43, real-time data processing and prediction: The Kalman filter algorithm is used to process and predict the monitoring data, which is expressed as: in, is the state estimate, A, B, C are system matrices, u t is the control input, y t is the measured value, K is the Kalman gain; S44, integration with external systems and dynamic adjustment: integration and data exchange with power dispatching systems and financial payment systems, real-time acquisition of power grid operation status and power market prices, and dynamic adjustment of transaction execution strategies; S45, Fault detection and diagnosis: Discover and handle abnormal situations in the transaction execution process based on fault tree analysis, expressed as:

8. The hourly green electricity transaction matching method taking into account the green certificate transaction blockchain according to claim 7 is characterized in that: The monitoring condition in S42 is expressed as: P trans (t)≥αD load (t); F trans (t)=P load (t)D load (t); Among them, α is the coefficient.

9. The hourly green electricity transaction matching method taking into account the green certificate transaction blockchain according to claim 8 is characterized in that: The transaction settlement evaluation in S5 includes: S51, Fee Settlement: Fee settlement for transactions, including maintenance costs and service costs, expressed as: Fee supplier =∑ t (P load (t)-C gen (t))D load (t)-∑ t Cost maintenance (t); Fee user =∑ t P load (t)D load (t)+∑ t Cost service (t); S52, multi-dimensional transaction effect evaluation: multi-dimensional evaluation of transaction matching effect, expressed as: It is expressed as: Among them, S match is the success rate, E trans is transaction efficiency, S user For user satisfaction, S supplier For supplier satisfaction; S53, Generate transaction settlement report and visualization: Automatically generate transaction settlement report and use visualization technology to display evaluation results, including using bar charts and line charts to present the changing trends of matching success rate and transaction efficiency; S54, Data feedback mechanism optimization: Use the data feedback mechanism to provide feedback on evaluation results to optimize performance and decision-making, including adjusting transaction matching strategies based on user satisfaction feedback and optimizing supplier management and incentive mechanisms based on supplier satisfaction feedback.

10. An hourly green electricity transaction matching system taking into account the green certificate transaction blockchain, used to implement an hourly green electricity transaction matching method taking into account the green certificate transaction blockchain as described in any one of claims 1 to 9, characterized in that: Includes the following modules: Data collection and preprocessing module: collects power generation operation data from green power suppliers and electricity consumption behavior data from users, and preprocesses the power generation operation data and electricity consumption behavior data; Green certificate blockchain building module: build a green certificate trading platform based on blockchain technology to record and encrypt green certificate transaction information; Transaction matching module: Based on a multi-objective optimization model, it integrates multiple factors to achieve optimal transaction matching; Transaction execution monitoring module: executes the transaction after successful matching and monitors key indicators in the transaction process in real time; Transaction settlement evaluation module: settlement and multi-dimensional evaluation after the transaction is completed, and display of transaction evaluation results through visualization technology.

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