Renewable energy dynamic binding green certificate time-sharing and step-by-step issuing method

Abstract

The invention provides a renewable energy dynamic binding green certificate time-sharing and step-by-step issuing method, and belongs to the technical field of green certificate issuing. Firstly, active power and timestamps of power generation equipment are collected in a fixed period to form collected data, independent windows are divided according to energy types, and real-time generating capacity is calculated based on the collected data; time-sharing and step-by-step issuing is carried out by comparing the real-time generating capacity with the preset reference electric quantity; a certificate is issued immediately when the electric quantity reaches the standard; if not, the electric quantity is stored in the scattered electric quantity pool. In the process, by calculating the window power generation characteristics of the independent window, the green certificate and the window power generation characteristics are bound to generate an irreplaceable digital green certificate, and the irreplaceable digital green certificate is written into the block chain. When the user cancels after verification, the system verifies the similarity between the data integrity and the feature vector, and the user can cancel after verification if the similarity reaches the standard. Through the above design, the green certificate is accurately checked and issued, the association between the green certificate and the power generation characteristics is enhanced, and the data is ensured to be safe and traceable.

Description

Technical Field

[0001] The present invention relates to a method for dynamically binding green certificates and issuing them in a time - divided and step - by - step manner for renewable energy, belonging to the technical field of green certificate issuance. Background Art

[0002] As the core electronic certificate for measuring the environmental value of green electricity, its issuance mechanism directly affects market fairness and optimal resource allocation. Currently, with the rise of new application scenarios such as time - of - use electricity price trading and accurate carbon footprint tracing for enterprises, the market urgently needs the green certificate issuance process to have higher timeliness and granularity. It is required that green certificates can not only accurately reflect the total power generation, but also be bound to specific power generation time periods and output characteristics to ensure the authenticity and traceability of environmental attributes. Achieving precise matching of green electricity production and consumption on the time scale has become a key foundation for promoting the high - quality development of the green electricity market.

[0003] For example, the Chinese patent application with the publication number CN115965385A discloses a method for issuing green certificates for distributed green electricity. By obtaining the first power generation amount of distributed green electricity of a target object in a first statistical period; determining the partial power generation amount that is not enough to exchange for one green certificate in the first power generation amount as scattered power generation; predicting the second power generation amount of distributed green electricity of the target object in a second statistical period; and in response to the sum of the second power generation amount and the scattered power generation amount being enough to exchange for one green certificate, pre - issuing green certificates for the scattered power generation amount when settling green certificates in the first statistical period. The above - mentioned patent aims at the partial power generation amount in the first power generation amount of a target object in one statistical period that is not enough to exchange for one green certificate. By predicting the second power generation amount of the target object in the second statistical period, it is seen whether the target object can generate enough distributed green electricity in the second statistical period, and then green certificates can be pre - issued for the scattered power generation amount in advance. Although the existing green certificate issuance methods attempt to optimize the utilization rate of scattered electricity, they rely on the framework of fixed - length period (such as monthly / yearly) settlement. The issuance of green certificates is disconnected from the time point when the power generation behavior occurs, and it is impossible to represent the environmental value differences of electricity at different time periods within a day. Moreover, in order to integrate the residual electricity that is not enough to exchange for a whole green certificate, the above - mentioned patent needs to predict future power generation across periods. However, meteorological modeling errors and equipment loss estimation deviations will lead to insufficient accuracy of pre - issued green certificates, and the pre - issued green certificates are completely decoupled from the original power generation curve. In addition, abnormal power generation fluctuations in the above - mentioned patent are not effectively identified and isolated, the authenticity of green certificates is insufficient, and there is a lack of matching verification of the physical characteristics bound to green certificates and the user's electricity consumption curve in the cancellation link, resulting in the risk of "separation of certificates and electricity".

[0004] The above problems make it difficult for green certificates to support high - precision green electricity traceability and trading, restricting market vitality. Therefore, there is an urgent need for a green certificate issuance method that can break through the fixed - cycle limit, achieve precise real - time association between green certificates and power generation behavior, and ensure the uniqueness, verifiability, and security of their environmental attributes. Summary of the Invention

[0005] To solve the problems existing in the above-mentioned prior art, the present invention proposes a method for dynamically binding renewable energy certificates and issuing them in a time-sharing and step-by-step manner.

[0006] The technical solution of the present invention is as follows: On the one hand, the present invention provides a method for dynamically binding renewable energy certificates and issuing them in a time-sharing and step-by-step manner, and the method includes: Collect the active power of the renewable energy power generation equipment and the corresponding timestamps at a fixed period, and store the fixed period, active power and the corresponding timestamps as acquisition data; Divide the continuous time into multiple independent windows with different fixed durations according to the type of renewable energy, and calculate the real-time power generation amount corresponding to each independent window based on the acquisition data; Based on the comparison result between the real-time power generation amount corresponding to each independent window and the preset reference power amount, implement a time-sharing and distributed green certificate issuance strategy; Calculate the window power generation characteristics of the independent window, bind the issued green certificate with the window power generation characteristics of its corresponding independent window to generate an irreplaceable digital green certificate, and synchronize it to the green certificate issuance trading system for automatic cancellation.

[0007] Preferably, the time-sharing and distributed green certificate issuance strategy is specifically: When the real-time power generation amount corresponding to the independent window reaches or exceeds the preset reference power amount, immediately generate a green certificate; When the real-time power generation amount corresponding to the independent window is lower than the preset reference power amount, deposit the real-time power generation amount into the scattered power pool; When the accumulated power amount in the scattered power pool reaches or exceeds the preset reference power amount, deduct the power amount consistent with the preset reference power amount from the scattered power pool.

[0008] Preferably, the method further includes performing anomaly detection on the independent window for the acquisition data, specifically: Calculate the standard deviation of the active power sequence within the independent window. If the standard deviation is greater than the preset anomaly threshold, determine that the independent window is an abnormal window, otherwise it is a normal window; Adopt a differentiated anomaly processing method for the abnormal window according to the type of renewable energy corresponding to the power generation equipment, including marking the abnormal window as an invalid data source or reconstructing the output curve of the abnormal window using an interpolation algorithm based on the data of adjacent normal windows.

[0009] Preferably, calculating the real-time power generation amount corresponding to each independent window based on the acquisition data is specifically: Divide the fixed duration of the normal window and the abnormal window of the reconstructed output curve by the fixed period in the collected data to obtain the total number of times of active power collection of the renewable energy generation equipment within the normal window and the abnormal window of the reconstructed output curve; starting from the starting moment of the corresponding timestamps of the active power within the normal window and the abnormal window of the reconstructed output curve, collect the active power in sequence according to the fixed period until the collection operation corresponding to the pre-calculated total number of collections is completed; at each collection, multiply the collected active power of the renewable energy generation equipment by the value after converting the fixed period into hours, and accumulate the products to obtain the real-time power generation of the normal window and the abnormal window of the reconstructed output curve; Estimate the power generation of the abnormal window marked as an invalid data source using the mean estimation method and use it as the real-time power generation of the abnormal window marked as an invalid data source.

[0010] Preferably, the specific calculation of the window power generation characteristics of the independent window is as follows: When the independent window is a normal window and the real-time power generation meets the standard, the window power generation characteristics are the weighted output characteristic vector; when the independent window is a normal window but is issued through the scattered power pool, the window power generation characteristics are the mean value of the weighted output characteristic vectors corresponding to the normal windows participating in splicing; when the independent window is an abnormal window, the window power generation characteristics are the real-time power generation and the marked characteristic status is invalid; Among them, the calculation steps of the weighted output characteristic vector include: Obtain the active power sequence and its corresponding timestamp sequence of all fixed-period collection points within the normal window; Multiply the difference between the end moment of the timestamp of the normal window where the collection point is located and the current collection moment by the preset adjustable attenuation coefficient, and take the negative exponent of the product to obtain the time weight of the collection point; Take the difference between the active power of the current collection point and the active power of the next collection point as the output change amount, calculate the output change amount corresponding to the collection points in the normal window, and divide the output change amount of each collection point by the difference between the maximum value and the minimum value of the output change amount to obtain the output change rate weight of the collection point; Take the product of the time weight of the collection point and the output change rate weight as the comprehensive weight vector; Obtain the rated power of the renewable energy generation equipment, perform weighted averaging on the active power sequence of the normal window collection points and the comprehensive weight vector, and perform normalization processing using the rated power to obtain the weighted output characteristic vector of the normal window.

[0011] Preferably, bind the issued green certificate to the window power generation characteristics of its corresponding independent window to generate an irreplaceable digital green certificate, specifically: Encapsulate the green certificate unique identifier, issuance time, corresponding renewable energy power generation equipment identifier, real-time power generation amount, preset reference power amount, and window power generation characteristics into a data block; Calculate the hash value of the data block using a cryptographic hash function; write the data block, the corresponding hash value, and the hash value of the previous green certificate data block into the blockchain distributed ledger; Generate the non-fungible digital green certificate by combining the address, transaction ID, and database information recorded when writing to the blockchain distributed ledger.

[0012] Preferably, the synchronization to the green certificate issuance trading system for automatic cancellation is specifically as follows: When the user holds a non-fungible digital green certificate and initiates a cancellation request, the green certificate issuance trading system retrieves the data block stored on the blockchain through the address or transaction ID in the non-fungible digital green certificate, and verifies the integrity of the data block by recalculating the hash value of the stored data block and comparing it with the hash value stored on the blockchain; If the verification passes, perform a similarity match between the window power generation characteristics bound to the non-fungible digital green certificate to be cancelled and the window power generation characteristics calculated from the user's actual electricity load curve during the corresponding time period. If the similarity reaches the preset matching threshold, it is determined that the cancellation is valid; Record the cancellation operation in the blockchain and the green certificate issuance trading system database, and mark the status of the non-fungible digital green certificate as cancelled.

[0013] Preferably, the operation of the scattered power pool follows the first-in, first-out principle, and the independent window time ranges corresponding to the deducted power are continuously spliced in chronological order. When splicing, select continuous windows with an output mode similarity higher than the preset similarity threshold for combination, and the output mode similarity is determined by calculating the cosine similarity between the window power generation characteristics of the independent windows to be spliced.

[0014] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements a method for dynamically binding green certificates and issuing them in a time-sharing and step-by-step manner as described in the present invention.

[0015] On the other hand, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements a method for dynamically binding green certificates and issuing them in a time-sharing and step-by-step manner as described in the present invention.

[0016] The present invention has the following beneficial effects: 1. The present invention relates to a method for dynamically binding renewable energy certificates and issuing them in a time-sharing and step-by-step manner. Independent windows are divided according to different types of renewable energy. By comparing the real-time power generation with the preset reference power, a time-sharing and distributed certificate issuance strategy is implemented. When the real-time power generation meets the standard, the green certificate is immediately issued. When it does not meet the standard, the power is stored in a scattered power pool and processed after the cumulative standard is met. Through the above dynamic adjustment method, the actual power generation of renewable energy can be more accurately reflected, improving the accuracy and rationality of green certificate issuance; 2. The present invention relates to a method for dynamically binding renewable energy certificates and issuing them in a time-sharing and step-by-step manner. By calculating the window power generation characteristics of independent windows and binding them with the issued green certificates to generate irreplaceable digital green certificates, the green certificates can more comprehensively reflect the characteristics and quality of renewable energy power generation. At the same time, in the verification link, the present invention ensures the effective association between the green certificate and the actual electricity load by performing similarity matching on the window power generation characteristics, improving the use value and credibility of the green certificate; 3. The present invention relates to a method for dynamically binding renewable energy certificates and issuing them in a time-sharing and step-by-step manner. The blockchain technology is used to write the green certificate-related data into a distributed ledger, and the hash value is used to verify the data integrity, making the green certificate data tamper-proof and traceable. Moreover, in the process of green certificate verification, the present invention accurately verifies the data by virtue of the characteristics of the blockchain, improving the security and reliability of the green certificate issuance trading system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention.

[0019] It should be understood that the step numbers used in the text are only for convenience of description and do not limit the execution order of the steps.

[0020] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0021] The terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0022] The term "and / or" refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0023] Example 1: See Figure 1 , this embodiment provides a method for dynamically binding renewable energy certificates and issuing them in a time - divided and step - by - step manner. The method includes: S1. Collect the active power of the renewable energy generation equipment and the corresponding timestamps at a fixed period, and store the fixed period, active power and the corresponding timestamps as acquisition data. Specifically, use a high - precision data acquisition device to accurately measure the active power of the renewable energy generation equipment at a preset fixed period, such as every 5 minutes, and synchronously record the corresponding timestamp at each measurement. The collected data will be stored in a dedicated data storage module in a timely and accurate manner to form acquisition data for subsequent analysis and processing, so as to ensure the integrity and traceability of the data.

[0024] S2. Divide the continuous time into multiple independent windows with different fixed durations according to the type of renewable energy. The fixed durations of the independent windows for different types of renewable energy are different. In this implementation, the renewable energy includes wind power and photovoltaic power. Because the power generation characteristics of wind power are relatively stable but there are certain fluctuations, a 20 - minute window can better reflect the power generation situation of wind power. Therefore, the window duration of wind power in this embodiment is fixed at 20 minutes; since photovoltaic power generation depends on sunlight and basically does not generate electricity during periods when the sunlight radiation intensity is zero, in this embodiment, only the periods with sunlight are selected for window division, that is, only the periods with sunlight radiation intensity greater than zero are included, which can more accurately reflect the actual power generation situation of photovoltaic power. The window duration of photovoltaic power is fixed at 10 minutes. Different types of renewable energy have their own different power generation characteristics, and the variation laws of their power generation power within a day or in different seasons are different. By dividing independent windows with different fixed durations in this embodiment, it is equivalent to a distributed division of the entire time axis, enabling independent monitoring and calculation of the power generation amount in different time periods.

[0025] S3. Before calculating the real - time power generation amount, it also includes an abnormal data detection and processing step. Specifically: Calculate the standard deviation of the active power sequence within the independent window. If the standard deviation is greater than the preset abnormal threshold, that is , then determine that this independent window is an abnormal window, otherwise it is a normal window, where is the anomaly coefficient. In this embodiment, is 0.2, is the rated power of the renewable energy power generation equipment; Wind power fluctuates frequently due to natural phenomena such as turbulence, while photovoltaic power is mainly affected by instantaneous clouds. This differential power generation characteristic requires different anomaly handling strategies. Therefore, different anomaly handling methods are adopted for the anomaly window according to the type of renewable energy. Specifically: S31. Wind power scenario handling strategy: Mark the anomaly window as an invalid data source, which does not participate in subsequent green certificate issuance and feature vector calculation. Through this processing method, the adverse impact of the abnormal fluctuations in the anomaly window on the overall green certificate issuance strategy is avoided. Its generated electricity is obtained by the mean estimation method and stored in the scattered electricity pool, enabling these scattered electricity to have the opportunity to participate in subsequent green certificate issuance; S32. Photovoltaic scenario handling strategy: Photovoltaic power is affected by instantaneous clouds, and interpolation reconstruction can retain the true power generation trend. Therefore, in this embodiment, an interpolation algorithm based on the data of adjacent normal windows is used to reconstruct the output curve of the anomaly window, and then the real-time generated electricity is calculated based on the reconstructed curve; Preferably, the above strategy selection needs to be solidified during system deployment, and the strategy mapping is realized through configuration parameters.

[0026] For the convenience of understanding, in this embodiment, two interpolation reconstruction algorithms, linear interpolation and cubic spline interpolation, are provided as examples. In actual applications, appropriate algorithms can be selected according to specific situations: S321. Linear interpolation: Assume that the two adjacent normal windows of the anomaly window are respectively and , and at a certain time point within the anomaly window, its corresponding active power can be calculated by linear interpolation. Let The end time of is [[ID=3l]] , and the corresponding active power is ; The start time of is [[ID=۳۸]]The corresponding active power is , then The calculation formula of is: ; It should be noted that linear interpolation is suitable for situations where the data changes relatively smoothly, and the calculation speed is fast. However, for situations where the data changes violently, it may not be able to fit the actual curve well; Interpolate each time point in chronological order within the abnormal window to obtain the active power value corresponding to that time point. Combine these active power values with the corresponding time points to form the reconstructed output curve of the abnormal window. S322. Cubic spline interpolation: Cubic spline interpolation fits the data by constructing cubic polynomials on each sub-interval, ensuring the continuity of the first and second derivatives of the curve at the nodes, thus obtaining a smoother interpolation curve. In specific implementation, it is necessary to first determine the coefficients of the cubic polynomials for each sub-interval, which usually requires solving a system of linear equations: Assume that the time range of the abnormal window is from to , and the two adjacent normal windows are respectively: The time range of the previous window is from to , and the data points are , , where is the number of data points in the previous normal window; is the first acquisition time and its corresponding active power within the previous normal window, and so on, is the th acquisition time (i.e., the last acquisition time ) and its corresponding active power within the previous normal window; The time range of the subsequent window is from to , and the data points are , , where is the number of data points in the subsequent normal window, is the first acquisition time (i.e., ) and its corresponding active power within the subsequent normal window, and so on, is the th acquisition time and its corresponding active power within the subsequent normal window; Combine the last data points of the previous normal window and the first data points of the subsequent normal window to form a node set for cubic spline interpolation. Assume there are nodes after combination, and it is expressed by the formula: ; where , forming sub-intervals; to are partial time points of the previous normal window, to are partial time points of the subsequent normal window; Simplify the expressions of the above nodes into . According to the conditions of cubic spline interpolation, a linear equation system about the second derivative coefficients can be obtained, which is expressed by the formula as: ; where: ; ; ; ; In the formula, is the second derivative coefficient at node ; is the second derivative coefficient at node ; is the second derivative coefficient at node ; is the second derivative coefficient at node ; is the second derivative coefficient at node ; is the second derivative coefficient at node ; is the second derivative coefficient at node ; is the node index corresponding to the merged nodes; is the weight coefficient related to the lengths of two adjacent subintervals of node ; is the weight coefficient related to the lengths of two adjacent subintervals of node ; represents the time interval between two adjacent nodes and , and are the acquisition times corresponding to nodes and respectively; represents the time interval between two adjacent nodes and ; represents the time interval between two adjacent nodes and ; is complementary to and jointly describes the relationship with node The weight coefficient of the relative relationship between the lengths of two adjacent subintervals adjusts the contributions of different subintervals to the second derivative coefficient under the condition of ensuring the continuity of the second derivative of the interpolation curve. ; To be complementary to and jointly describe the weight coefficient of the relative relationship between the lengths of two adjacent subintervals with respect to the node ; Is the slope change difference coefficient, used to measure the degree of difference in the average change rate on two adjacent subintervals with respect to the node ; Used to measure the degree of difference in the average change rate on two adjacent subintervals with respect to the node ; Used to measure the degree of difference in the average change rate on two adjacent subintervals with respect to the node ; , and Are the active powers corresponding to the nodes , and respectively; It should be noted that in the above formula, , , and The corresponding nodes and In fact, there is only one adjacent subinterval, and the other side is a hypothetically existing subinterval. The definition of the above formula is to make the equation form at the endpoints and the equation form of the internal nodes have a certain consistency, which is convenient to transform the entire cubic spline interpolation problem into a system of linear equations for solution. Under the natural boundary conditions, , simplify the above system of linear equations and use numerical methods (such as the chasing method) to solve the above system of linear equations to obtain the numerical value; Based on calculate to obtain the other coefficients of the cubic polynomial (constant term coefficient , first-order term coefficient , third-order term coefficient ): ; ; ; Within the time range of the abnormal window, there are nodes , among which, , is the node index corresponding to the nodes; find the acquisition moment The corresponding sub-interval , and calculate the active power using the cubic polynomial corresponding to the sub-interval , which is expressed by the formula: ; Based on the acquisition time and the calculated active power, the reconstructed output curve is obtained.

[0027] Cubic spline interpolation can better adapt to the complex changes of data, but the computational complexity is relatively high. In practical applications, users can select the algorithm according to their own computing resources and time costs. If high computing speed is required and the data changes relatively smoothly, then the linear interpolation algorithm is preferred, which can complete the reconstruction of the output curve of the abnormal window in a short time and meet the real-time requirements. When the data changes complexly, high interpolation accuracy is required, and sufficient computing power and time margin are available, then the cubic spline interpolation algorithm is selected, which can fit the data more accurately and make the reconstructed output curve closer to the actual situation.

[0028] After selecting the appropriate interpolation reconstruction algorithm to complete the reconstruction of the output curve of the abnormal window, the reconstructed abnormal window and the normal window are included in the real-time power generation calculation process together. Through the above processing of the abnormal window, the interference of abnormal data on subsequent calculations and green certificate issuance is excluded, ensuring the accuracy and reliability of the data. The reconstructed output curve will be used for power generation calculation, making the calculated real-time power generation more reflective of the actual power generation status.

[0029] At the same time, to ensure the authenticity and reliability of the feature vector, the reconstructed output curve does not participate in the calculation of the weighted output feature vector. This hierarchical processing mechanism not only retains the power generation value of the abnormal window but also ensures that the feature vector is completely based on verifiable real measurement data, improving the accuracy and rationality of green certificate issuance from the root.

[0030] S4. Calculate the real-time power generation corresponding to each independent window based on the collected data. Specifically: The independent windows participating in the real-time power generation calculation only include the normal windows and the abnormal windows of the reconstructed output curve. The abnormal windows marked as invalid data sources directly use the real-time power generation estimated in step S3.

[0031] Furthermore, the real-time power generation calculation is specifically as follows: Divide the fixed duration of the normal window and the abnormal window of the reconstructed output curve by the fixed period in the collected data to obtain the total number of times the active power of the renewable energy generation equipment is collected within the normal window and the abnormal window of the reconstructed output curve; starting from the starting moment of the time stamps corresponding to the active power within the normal window and the abnormal window of the reconstructed output curve, collect the active power sequentially at fixed intervals until the collection operation corresponding to the pre-calculated total number of collections is completed; at each collection, multiply the collected active power of the renewable energy generation equipment by the value obtained by converting the fixed period into hours, and accumulate the products to obtain the real-time power generation of the normal window and the abnormal window of the reconstructed output curve; Further, calculating the real-time power generation corresponding to each independent window based on the collected data is expressed by the formula: ; ; In the formula, is the real-time power generation corresponding to the th independent window; is the number of data points within the independent window, that is, the total number of collections; is the fixed duration; is the fixed period; is the time stamp corresponding to the th sampling; is the time stamp corresponding to the active power.

[0032] S5. Based on the comparison results between the real-time power generation corresponding to each independent window and the preset reference power consumption, implement a time-sharing distributed green certificate issuance strategy. Specifically: When the real-time power generation corresponding to the independent window reaches or exceeds the preset reference power consumption, immediately generate a green certificate; When the real-time power generation corresponding to the independent window is lower than the preset reference power consumption, deposit the real-time power generation into a scattered power pool. The scattered power pool is a virtual container used to collect and manage the real-time power generation of independent windows that do not reach the preset reference power consumption. Since the renewable energy generation has certain volatility and intermittency, the power generation of many independent windows may not directly meet the standard for issuing green certificates. The role of the scattered power pool is to accumulate these scattered power. When the accumulated power reaches or exceeds the preset reference power consumption, it can be regarded as a complete issuance unit, and thus a green certificate is generated.

[0033] To ensure the rationality and consistency of the power deduction operation, the sporadic power pool operation follows the first-in, first-out principle, and the independent window time ranges corresponding to the deducted power are continuously spliced in chronological order. When splicing, continuous windows with high similarity in output modes are preferentially selected for combination. The similarity of the output modes is determined by calculating the cosine similarity between the window power generation characteristics of the independent windows to be spliced. Splicing is performed when the similarity of the output modes is higher than the preset similarity threshold. Furthermore, when both independent windows are normal windows and the real-time power generation meets the standard, calculate the cosine similarity between their weighted output feature vectors; when both independent windows are normal windows but are issued through the sporadic power pool, calculate the cosine similarity between the means of the weighted output feature vectors corresponding to the normal windows participating in the splicing; when both independent windows are abnormal windows, compare the difference in their real-time power generations. If the difference is less than the preset difference threshold, the similarity is considered to meet the standard; if the difference is greater than or equal to the preset difference threshold, the similarity is considered not to meet the standard; when one independent window is a normal window (including when the real-time power generation meets the standard and is issued through the sporadic power pool) and the other is an abnormal window, their similarity is considered not to meet the standard and no combination is performed.

[0034] When the cumulative power in the sporadic power pool reaches or exceeds the preset reference power, deduct power equal to the preset reference power value from the sporadic power pool, generate a green certificate, and bind it to the weighted output feature vector of this independent window. Preferably, in this embodiment, the preset reference power is a unified issuance standard in the field of renewable energy power generation, that is, 1 megawatt-hour. Considering the differences in the power generation characteristics of different types of renewable energy, different issuance standards can also be considered for different energy types. For example, wind power generation has large fluctuations and relatively poor power generation stability; while hydropower generation has relatively good stability. For wind power projects, the issuance standard can be appropriately reduced to reflect the difficulty and characteristics of its power generation and encourage more wind power projects to participate in the green certificate issuance. In addition, for some small-scale distributed renewable energy power generation projects, considering their small scale and limited power generation capacity, special and relatively low issuance standards can also be formulated to support the development of such projects. This embodiment does not limit the specific value of the preset reference power, but only provides a common and general standard setting example. In actual applications, the preset reference power can be flexibly adjusted according to various factors such as the development needs of the energy market, policy guidance, and the actual development status of various renewable energy sources to achieve more accurate and effective incentives and management for the renewable energy power generation industry.

[0035] S6. On this basis, calculate the window power generation characteristics, specifically: When the independent window is a normal window and the real-time power generation reaches the standard, the window power generation feature is the weighted output feature vector. When the independent window is a normal window but is issued through the scattered power pool, the window power generation feature is the mean value of the weighted output feature vectors corresponding to the normal windows participating in splicing. When the independent window is an abnormal window, the window power generation feature records the real-time power generation but marks the feature status as invalid; Furthermore, the calculation steps of the weighted output feature vector are as follows: Obtain the active power sequence of all fixed-period collection points within the normal window and its corresponding timestamp sequence ; Subtract the current collection time from the end time of the timestamp of the normal window where the collection point is located and then multiply the result by the preset adjustable attenuation coefficient and take the negative exponent of the product to obtain the time weight of the collection point , which is expressed by the formula: ; Take the difference between the active power of the current collection point and the active power of the next collection point as the output change amount, and calculate the output change amount corresponding to the collection points in the normal window , divide the output change amount of each collection point by the difference between the maximum value and the minimum value of the output change amount to obtain the output change rate weight , which is expressed by the formula: ; ; Among them, is the set of all ; is the active power of the current collection point (the th sampling) in the normal window; is the active power of the next collection point (the th sampling) in the normal window; Take the product of the time weight of the collection point and the output change rate weight as the comprehensive weight vector , which is expressed by the formula: ; In the formula, is the comprehensive weight vector of the collection point corresponding to the th sampling; Obtain the rated power of the renewable energy power generation equipment, perform weighted averaging on the active power sequence of the normal window acquisition points and the comprehensive weight vector, and utilize the rated power to perform normalization processing to obtain the weighted output feature vector of the normal window , which is expressed by the formula: ; S7. Bind the issued green certificate to the window power generation characteristics of its corresponding independent window to generate an irreplaceable digital green certificate. Specifically: Encapsulate the green certificate unique identifier, issuance time, corresponding renewable energy power generation equipment identifier, real-time power generation amount, preset reference power amount, and window power generation characteristics (in this embodiment, JSON format is used for encapsulation) into a data block. Among them, the green certificate unique identifier is a unique identifier generated for each issued green certificate, and in this embodiment, the UUID (Universally Unique Identifier) algorithm is used to generate it; the issuance time is the specific time when the green certificate is accurately recorded, and in this embodiment, the ISO 8601 standard format (such as 2025 - 06 - 10T09:57:44.218Z) is adopted to facilitate unified time representation and subsequent data processing; the corresponding renewable energy power generation equipment identifier is unique information such as the serial number and model of the renewable energy power generation equipment, which is used to clarify the power generation equipment corresponding to the green certificate; Calculate the hash value of the data block using a cryptographic hash function (such as SHA-256); write the data block, the corresponding hash value, and the hash value of the previous green certificate data block into the blockchain distributed ledger (a chained structure composed of multiple data blocks). The write operation is completed by using the client tool or API of the blockchain. Different blockchain platforms have different implementation methods. For example, in the Ethereum blockchain, the data write operation can be implemented through a smart contract.

[0036] Combine the address, transaction ID, and database information recorded when writing into the blockchain distributed ledger to generate the irreplaceable digital green certificate. These information can uniquely identify the storage location and related transaction information of the green certificate on the blockchain, facilitating subsequent query and verification.

[0037] S8. Synchronize the irreplaceable digital green certificate to the green certificate issuance trading system for automatic cancellation. Specifically: When the user holds an irreplaceable digital green certificate and wishes to perform cancellation, initiate a cancellation request through the user interface or API of the green certificate issuance trading system. The request contains relevant information of the irreplaceable digital green certificate, such as the address or transaction ID; After the green certificate issuance and trading system receives the cancellation request, it communicates with the blockchain node. Through the address or transaction ID in the non-fungible digital green certificate, it uses the query interface provided by the blockchain to retrieve the data blocks stored on the blockchain; Recalculate the hash value of the stored data block and compare it with the hash value stored on the blockchain. If the two hash values are the same, it indicates that the data block has not been tampered with during storage and transmission, and the integrity of the data is guaranteed; If the verification passes, obtain the data of the user's actual electricity load curve during the corresponding time period, and calculate the window power generation characteristics according to the steps with the same principle as steps S3 - S7; Perform a similarity match between the window power generation characteristics bound to the non-fungible digital green certificate to be cancelled and the window power generation characteristics calculated from the user's actual electricity load curve during the corresponding time period. In this embodiment, cosine similarity is used to calculate the similarity of the two characteristics. When the similarity reaches the preset matching threshold, it is determined that the cancellation is valid; If both the verification and the match pass, record the cancellation operation in the blockchain and the green certificate issuance and trading system database, and mark the status of this non-fungible digital green certificate as cancelled. Specifically, by updating the status of the data block on the blockchain and the relevant records in the database, ensure that the cancellation information of the green certificate is accurately recorded and traceable.

[0038] Embodiment 2: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements a method for dynamically binding green certificates and issuing them in a time-sharing and step-by-step manner for renewable energy as described in any embodiment of the present invention.

[0039] Embodiment 3: This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements a method for dynamically binding green certificates and issuing them in a time-sharing and step-by-step manner for renewable energy as described in any embodiment of the present invention.

[0040] It should be noted that the electronic device and the computer-readable storage medium described in the present invention are both based on the same inventive concept as the method described in Embodiment 1 of the present invention, and will not be elaborated here.

[0041] In the embodiments of the present invention, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent the cases of A existing alone, A and B existing simultaneously, and B existing alone. Wherein A and B may be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c may represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c may be single or multiple.

[0042] Those of ordinary skill in the art can realize that the various units and algorithm steps described in the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0043] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0044] In several embodiments provided by the present invention, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (hereinafter referred to as ROM), random access memories (hereinafter referred to as RAM), magnetic disks, or optical discs that can store program codes.

[0045] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A method for dynamically binding green certificates and issuing them in a time-sharing and step-by-step manner for renewable energy, characterized in that, The method includes: Collecting the active power of the renewable energy power generation equipment and the corresponding timestamps at fixed intervals, and storing the fixed intervals, active power, and the corresponding timestamps as the collected data; Dividing the continuous time into multiple independent windows with different fixed durations according to the type of renewable energy, and calculating the real-time power generation of each independent window based on the collected data; Implementing a time-sharing distributed green certificate issuance strategy based on the comparison results between the real-time power generation of each independent window and the preset benchmark power; Calculating the window power generation characteristics of the independent window, binding the issued green certificate to the window power generation characteristics of its corresponding independent window to generate an irreplaceable digital green certificate, and synchronizing it to the green certificate issuance trading system for automatic cancellation.

2. A method for dynamically binding green certificates and issuing them in a time-sharing and step-by-step manner for renewable energy according to claim 1, characterized in that The time-sharing distributed green certificate issuance strategy is specifically as follows: When the real-time power generation of the independent window reaches or exceeds the preset benchmark power, a green certificate is immediately generated; When the real-time power generation of the independent window is lower than the preset benchmark power, the real-time power generation is deposited into the scattered power pool; When the cumulative power in the scattered power pool reaches or exceeds the preset benchmark power, the power equal to the preset benchmark power value is deducted from the scattered power pool.

3. A method for dynamically binding green certificates and issuing them in a time-sharing and step-by-step manner for renewable energy according to claim 2, characterized in that The method further includes performing anomaly detection on the collected data for independent windows, specifically: Calculating the standard deviation of the active power sequence within the independent window. If the standard deviation is greater than the preset anomaly threshold, it is determined that the independent window is an abnormal window, otherwise it is a normal window; Adopting a differentiated anomaly handling method for the abnormal window according to the type of renewable energy corresponding to the power generation equipment, including marking the abnormal window as an invalid data source or reconstructing the output curve of the abnormal window using an interpolation algorithm based on the data of adjacent normal windows.

4. A method for dynamically binding green certificates and issuing them in a time - divided and step - by - step manner for renewable energy according to claim 3, wherein Calculating the real-time power generation of each independent window based on the collected data is specifically as follows: Dividing the fixed duration of the normal window and the abnormal window with the reconstructed output curve by the fixed interval in the collected data to obtain the total number of acquisitions of the active power of the renewable energy power generation equipment within the normal window and the abnormal window with the reconstructed output curve; Starting from the starting moment of the timestamps corresponding to the active power within the normal window and the abnormal window with the reconstructed output curve, collecting the active power in sequence according to the fixed interval until the acquisition operation corresponding to the pre-calculated total number of acquisitions is completed; During each acquisition, multiplying the collected active power of the renewable energy power generation equipment by the value obtained by converting the fixed interval into hours, and accumulating the products to obtain the real-time power generation of the normal window and the abnormal window with the reconstructed output curve; Estimating the power generation of the abnormal window marked as an invalid data source using the mean estimation method and taking it as the real-time power generation of the abnormal window marked as an invalid data source.

5. A method for dynamically binding green certificates and issuing them step by step at different times for renewable energy according to claim 4, characterized in that, Calculating the window power generation characteristics of the independent window is specifically as follows: When the independent window is a normal window and the real-time power generation meets the standard, the window power generation characteristic is the weighted output characteristic vector. When the independent window is a normal window but is issued through the scattered power pool, the window power generation characteristic is the mean value of the weighted output characteristic vectors corresponding to the normal windows participating in the splicing. When the independent window is an abnormal window, the window power generation characteristic is the real-time power generation and the marked characteristic status is invalid; Among them, the calculation steps of the weighted output characteristic vector include: Obtain the active power sequence of all fixed - period collection points within the normal window and its corresponding timestamp sequence; Subtract the current collection time from the end time of the timestamp of the normal window where the collection point is located, multiply the result by the preset adjustable attenuation coefficient, and take the negative exponent of the product to obtain the time weight of the collection point; Take the difference between the active power of the current collection point and the active power of the next collection point as the output change amount, calculate the output change amount corresponding to the collection points in the normal window, and divide the output change amount of each collection point by the difference between the maximum value and the minimum value of the output change amount to obtain the output change rate weight of the collection point; Take the product of the time weight of the collection point and the output change rate weight as the comprehensive weight vector; Obtain the rated power of the renewable energy power generation equipment, perform weighted averaging on the active power sequence of the collection points in the normal window and the comprehensive weight vector, and perform normalization processing using the rated power to obtain the weighted output feature vector of the normal window.

6. A method for dynamically binding green certificates and issuing them in a time-sharing and step-by-step manner for renewable energy according to claim 5, characterized in that Bind the issued green certificate to the window power generation feature of its corresponding independent window to generate an irreplaceable digital green certificate, specifically: Package the green certificate unique identifier, issuance time, corresponding renewable energy power generation equipment identifier, real - time power generation amount, preset reference power amount, and window power generation feature into a data block; Use a cryptographic hash function to calculate the hash value of the data block; write the data block, the corresponding hash value, and the hash value of the previous green certificate data block into the blockchain distributed ledger; Combine the address, transaction ID, and database information recorded when writing into the blockchain distributed ledger to generate the irreplaceable digital green certificate.

7. A method for dynamically binding green certificates and issuing them in a time-sharing and step-by-step manner for renewable energy according to claim 6, characterized in that, The synchronization to the green certificate issuance trading system for automatic cancellation is specifically as follows: When the user holds an irreplaceable digital green certificate and initiates a cancellation request, the green certificate issuance trading system retrieves the data block stored on the blockchain through the address or transaction ID in the irreplaceable digital green certificate, and verifies the integrity of the data block by recalculating the hash value of the stored data block and comparing it with the hash value stored on the blockchain; If the verification passes, perform a similarity match between the window power generation feature bound to the irreplaceable digital green certificate to be cancelled and the window power generation feature calculated from the user's actual electricity consumption load curve within the corresponding time period. If the similarity reaches the preset matching threshold, it is determined that the cancellation is valid; Record the cancellation operation in the blockchain and the green certificate issuance trading system database, and mark the status of the irreplaceable digital green certificate as cancelled.

8. A method for dynamically binding green certificates and issuing them in a time - divided and step - by - step manner for renewable energy according to claim 5, characterized in that The operation of the scattered power pool follows the first - in - first - out principle, and the time ranges of the independent windows corresponding to the deducted power are continuously spliced in chronological order. When splicing, select continuous windows with an output mode similarity higher than the preset similarity threshold for combination, and the output mode similarity is determined by calculating the cosine similarity between the window power generation features of the independent windows to be spliced.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that When the processor executes the program, it implements a method for time - divided and step - by - step issuance of renewable energy dynamic binding green certificates as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a method for time - divided and step - by - step issuance of renewable energy dynamic binding green certificates as described in any one of claims 1 to 8.

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