A time-sharing and step-by-step issuance method for renewable energy dynamic binding green certificates

Abstract

The present invention proposes a method for issuing green certificates in a time-sharing and step-by-step manner by dynamically binding renewable energy, which belongs to the technical field of green certificate issuance. First, the present invention collects the active power and timestamp of power generation equipment in a fixed period to form collected data, divides independent windows according to energy type, and calculates real-time power generation based on the collected data; and carries out time-sharing and step-by-step issuance by comparing real-time power generation with preset benchmark power: if the power meets the standard, the certificate will be issued immediately; if the standard is not met, the power will be stored in a scattered power pool. In this process, the present invention calculates the window power generation characteristics of the independent window, binds the green certificate with the window power generation characteristics to generate an irreplaceable digital green certificate and writes it into the blockchain. When the user cancels, the system verifies the data integrity and feature vector similarity, and it can be cancelled if it meets the standard. The present invention accurately issues green certificates through the above design, strengthens the association between green certificates and power generation characteristics, and ensures data security and traceability.

Description

Technical Field

[0001] The present invention relates to a method for issuing renewable energy green certificates in a dynamic and time-sharing manner, 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, the issuance mechanism of green certificates (Green Certificates) directly impacts market fairness and optimal resource allocation. Currently, with the rise of new application scenarios such as time-of-use electricity pricing and precise carbon footprint tracing for enterprises, the market urgently needs a green certificate issuance process with greater timeliness and granularity. These certificates must not only accurately reflect the total amount of electricity generated, but also be tied to specific power generation periods and output characteristics to ensure the authenticity and traceability of environmental attributes. Achieving precise temporal matching between green electricity production and consumption has become a key foundation for promoting high-quality development of the green electricity market.

[0003] For example, the Chinese invention patent application with publication number CN115965385A discloses a method for issuing green certificates for distributed green electricity, which obtains the first power generation of distributed green electricity of the target object in the first statistical period; determines the portion of the first power generation that is insufficient to exchange for a green certificate as zero-emission power; predicts the second power generation of the distributed green electricity of the target object in the second statistical period; and in response to the sum of the second power generation and the power generation of zero-emission power being sufficient to exchange for a green certificate, pre-issues a green certificate for the zero-emission power at the time of the green certificate settlement in the first statistical period. The above patent targets the portion of the first power generation of the target object in a statistical period that is insufficient to exchange for a green certificate, and by predicting the second power generation of the target object in the second statistical period, determines whether the target object can generate enough distributed green electricity in the second statistical period, and thus can pre-issue a green certificate for the zero-emission power. Although the existing green certificate issuance method attempts to optimize the utilization rate of scattered electricity, it relies on a fixed long-term (such as monthly / annual) settlement framework. The issuance of green certificates is disconnected from the time of power generation, and cannot represent the difference in environmental value of electricity at different times of the day. In addition, the above-mentioned patent integrates the residual electricity that is not enough to redeem the entire green certificate, and it is necessary to predict future power generation across cycles. 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 patents are not effectively identified and isolated, the authenticity of the green certificates is insufficient, and the cancellation process lacks matching verification of the physical characteristics bound to the green certificates and the user's electricity consumption curve, which poses a risk of "certificate-electricity separation."

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

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

[0006] The technical solutions of the present invention are as follows:

[0007] In one aspect, the present invention provides a method for issuing renewable energy green certificates in a dynamic, time-sharing and step-by-step manner, the method comprising:

[0008] Collecting active power and corresponding timestamps of renewable energy power generation equipment at a fixed period, and storing the fixed period, active power and corresponding timestamps as collected data;

[0009] 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 corresponding to each independent window based on the collected data;

[0010] Based on the comparison results of the real-time power generation corresponding to each independent window and the preset benchmark power, a time-sharing distributed green certificate issuance strategy is implemented;

[0011] 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, generate an irreplaceable digital green certificate, and synchronize it to the green certificate issuance and trading system for automatic cancellation.

[0012] Preferably, the time-sharing distributed green certificate issuance strategy is specifically as follows:

[0013] When the real-time power generation corresponding to the independent window reaches or exceeds the preset benchmark power, a green certificate will be generated immediately;

[0014] When the real-time power generation corresponding to the independent window is lower than the preset benchmark power, the real-time power generation will be stored in the scattered power pool;

[0015] When the accumulated power of the scattered power pool reaches or exceeds the preset reference power, power consistent with the preset reference power value is deducted from the scattered power pool.

[0016] Preferably, the method further comprises performing anomaly detection on the collected data in an independent window, specifically:

[0017] Calculate the standard deviation of the active power sequence in the independent window. If the standard deviation is greater than the preset abnormal threshold, the independent window is determined to be an abnormal window, otherwise it is a normal window.

[0018] Differentiated exception handling methods are adopted for abnormal windows according to the renewable energy type corresponding to the power generation equipment, including marking the abnormal window as an invalid data source or using an interpolation algorithm based on adjacent normal window data to reconstruct the output curve of the abnormal window.

[0019] Preferably, the real-time power generation corresponding to each independent window is calculated based on the collected data as follows:

[0020] The fixed duration of the normal window and the abnormal window of the reconstructed output curve is divided by the fixed period in the collected data to obtain the total number of times the active power of the renewable energy power generation equipment is collected within the normal window and the abnormal window of the reconstructed output curve; starting from the starting time of the timestamp corresponding to the active power within the normal window and the abnormal window of the reconstructed output curve, the active power is collected in sequence according to the fixed period until the collection operation corresponding to the pre-calculated total number of collections is completed; during each collection, the collected active power of the renewable energy power generation equipment is multiplied by the value after the fixed period is converted into hours, and the products are accumulated to obtain the real-time power generation of the normal window and the abnormal window of the reconstructed output curve;

[0021] The power generation of the abnormal window marked as an invalid data source is estimated by using the mean estimation method and then used as the real-time power generation of the abnormal window marked as an invalid data source.

[0022] Preferably, the window power generation characteristics of the independent window are calculated as follows:

[0023] When the independent window is a normal window and the real-time power generation meets 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 of the weighted output feature vectors corresponding to the normal windows involved in the splicing. When the independent window is an abnormal window, the window power generation feature is the real-time power generation and the mark feature status is invalid.

[0024] The calculation steps of the weighted output eigenvector include:

[0025] Obtain the active power sequence and its corresponding timestamp sequence of all fixed-period acquisition points within the normal window;

[0026] Subtract the current acquisition time from the end time of the normal window timestamp of the acquisition point, multiply it by the preset adjustable attenuation coefficient, and take the negative exponent of the product to obtain the time weight of the acquisition point;

[0027] The difference between the active power of the current collection point and the active power of the next collection point is used as the output change. The output change corresponding to the collection point in the normal window is calculated. The output change of each collection point is divided by the difference between the maximum output change and the minimum output change to obtain the output change rate weight of the collection point.

[0028] The product of the time weight of the acquisition point and the output change rate weight is used as the comprehensive weight vector;

[0029] The rated power of renewable energy power generation equipment is obtained, the active power sequence of the normal window collection point is weighted averaged with the comprehensive weight vector, and the rated power is used for normalization to obtain the weighted output feature vector of the normal window.

[0030] Preferably, the issued green certificate is bound to the window power generation characteristics of its corresponding independent window to generate an irreplaceable digital green certificate, specifically:

[0031] The green certificate unique identifier, issuance time, corresponding renewable energy power generation equipment identifier, real-time power generation, preset benchmark power and window power generation characteristics are encapsulated into a data block;

[0032] Calculate the hash value of the data block using a cryptographic hash function; write the data block and the corresponding hash value and the hash value of the previous green certificate data block into the blockchain distributed ledger;

[0033] The address, transaction ID and database information recorded when writing into the blockchain distributed ledger are combined to generate the non-fungible digital green certificate.

[0034] Preferably, the synchronization to the green certificate issuance and trading system for automatic cancellation is specifically as follows:

[0035] When a user holds a non-fungible digital green certificate and initiates a cancellation request, the green certificate issuance and 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;

[0036] If the verification is successful, the window power generation characteristics bound to the irreplaceable digital green certificate to be cancelled will be matched with 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, the cancellation is deemed valid.

[0037] The cancellation operation is recorded in the blockchain and the green certificate issuance and transaction system database, marking the status of the irreplaceable digital green certificate as cancelled.

[0038] Preferably, the operation of the scattered electricity pool follows the first-in-first-out principle, and the independent window time ranges corresponding to the deducted electricity are continuously spliced ​​in chronological order. When splicing, continuous windows with output mode similarity higher than a preset similarity threshold are selected 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.

[0039] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements a method for dynamically binding green certificates for renewable energy and issuing them in a time-sharing and step-by-step manner as described in the present invention.

[0040] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for dynamically binding green certificates for renewable energy and issuing them in a time-sharing and step-by-step manner as described in the present invention.

[0041] The present invention has the following beneficial effects:

[0042] 1. This invention provides a method for dynamically binding renewable energy to green certificates and issuing them in a time-sharing and step-by-step manner. It divides independent windows according to different types of renewable energy and implements a time-sharing distributed green certificate issuance strategy by comparing real-time power generation with a preset benchmark power. When real-time power generation meets the standard, the green certificate is immediately issued. If it does not meet the standard, the power is stored in a scattered power pool and processed again after the cumulative power meets the standard. This dynamic adjustment method more accurately reflects the actual power generation situation of renewable energy, improving the accuracy and rationality of green certificate issuance.

[0043] 2. This invention provides a method for dynamically binding renewable energy to green certificates for time-sharing and step-by-step issuance. By calculating the power generation characteristics of independent windows and binding them to the issued green certificates, an irreplaceable digital green certificate is generated, enabling the green certificate to more comprehensively reflect the characteristics and quality of renewable energy power generation. Furthermore, during the verification phase, this invention ensures the effective association between the green certificate and actual electricity load by performing similarity matching on the window power generation characteristics, thereby improving the use value and credibility of the green certificate.

[0044] 3. The present invention is a method for issuing green certificates for renewable energy in a dynamic and step-by-step manner. Blockchain technology is used to write green certificate-related data into a distributed ledger, and hash values ​​are used to verify data integrity, so that the green certificate data has the characteristics of being tamper-proof and traceable. In the process of green certificate cancellation, the present invention uses the characteristics of blockchain to accurately verify data, thereby improving the security and reliability of the green certificate issuance and transaction system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0047] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0048] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0049] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0050] The term "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items.

[0051] Example 1:

[0052] See also Figure 1 This embodiment provides a method for dynamically binding renewable energy green certificates to issue them in a time-sharing and step-by-step manner, the method comprising:

[0053] S1. Collect the active power of renewable energy generation equipment and the corresponding timestamps at a fixed period. The fixed period, active power, and corresponding timestamps are stored as collected data. Specifically, a high-precision data acquisition device is used to accurately measure the active power of renewable energy generation equipment at a pre-set fixed period, such as every 5 minutes, and the corresponding timestamp of each measurement is simultaneously recorded. The collected data is promptly and accurately stored in a dedicated data storage module, forming collected data that can be subsequently analyzed and processed to ensure data integrity and traceability.

[0054] S2. Divide the continuous time into multiple independent windows with different fixed durations based on the type of renewable energy. The fixed durations of the independent windows vary for different types of renewable energy. In this embodiment, the renewable energy sources include wind power and photovoltaic power. Because wind power generation characteristics are relatively stable but subject to certain fluctuations, a 20-minute window can better reflect wind power generation. Therefore, in this embodiment, the wind power window duration is fixed at 20 minutes. Since photovoltaic power generation depends on sunlight and generates virtually no electricity during periods of zero solar radiation intensity, in this embodiment, only periods with sunlight are selected for window division, i.e., only periods with solar radiation intensity greater than zero are included. This more accurately reflects the actual photovoltaic power generation situation. The photovoltaic window duration is fixed at 10 minutes. Different types of renewable energy have different power generation characteristics, and their power generation varies differently throughout the day or across seasons. In this embodiment, by dividing the independent windows into different fixed durations, the entire timeline is distributed, allowing for independent monitoring and calculation of power generation during different time periods.

[0055] S3. Before calculating the real-time power generation, it also includes abnormal data detection and processing steps, specifically:

[0056] Calculate the standard deviation of the active power sequence in the independent window. If the standard deviation Greater than the preset abnormal threshold, that is, , then the independent window is determined to be an abnormal window, otherwise it is a normal window, where is the abnormal coefficient. In this embodiment, is 0.2, The rated power of the renewable energy power generation equipment;

[0057] Wind power fluctuates frequently due to natural phenomena such as turbulence, while photovoltaic power is mainly affected by transient cloud cover. These differentiated power generation characteristics require different anomaly handling strategies. Therefore, differentiated anomaly handling methods are adopted for abnormal windows based on the type of renewable energy. Specifically:

[0058] S31. Wind power scenario processing strategy: The abnormal window is marked as an invalid data source and does not participate in the subsequent green certificate issuance and feature vector calculation. Through this processing method, the abnormal fluctuation of the abnormal window is avoided. The adverse impact of the abnormal fluctuation on the overall green certificate issuance strategy is avoided. The power generation is obtained by the mean estimation method and stored in the scattered power pool, so that these scattered power flows have the opportunity to participate in the subsequent green certificate issuance;

[0059] S32. Photovoltaic scenario processing strategy: Photovoltaic is affected by transient cloud cover, and interpolation reconstruction can preserve the true power generation trend. Therefore, in this embodiment, an interpolation algorithm based on adjacent normal window data is used to reconstruct the output curve of the abnormal window, and then the real-time power generation is calculated based on the reconstructed curve;

[0060] Preferably, the above policy selection needs to be solidified during system deployment, and policy mapping is achieved through configuration parameters.

[0061] For ease of understanding, in this embodiment, two interpolation and reconstruction algorithms, linear interpolation and cubic spline interpolation, are provided as examples. In actual applications, a suitable algorithm can be selected according to specific circumstances:

[0062] S321, linear interpolation:

[0063] Assume that the two normal windows adjacent to the abnormal window are and , at a certain point in time within the abnormal window , the corresponding active power It can be calculated by linear interpolation. The end time is , the corresponding active power is ; Start time is , the corresponding active power is ,but The calculation formula is:

[0064] ;

[0065] It is worth noting that linear interpolation is suitable for situations where data changes relatively slowly and has a fast calculation speed, but it may not fit the actual curve well when data changes drastically;

[0066] According to the time sequence within the abnormal window, interpolation calculation is performed on each time point in turn to obtain the active power value corresponding to the time point. These active power values ​​are combined with the corresponding time points to form the reconstructed abnormal window output curve;

[0067] S322, cubic spline interpolation:

[0068] Cubic spline interpolation fits the data by constructing a cubic polynomial on each subinterval, ensuring the continuity of the first and second derivatives of the curve at the nodes, thereby obtaining a smoother interpolation curve. In practice, the coefficients of the cubic polynomial for each subinterval must be determined first, which usually requires solving a system of linear equations:

[0069] Assume that the time range of the abnormal window is from arrive , the two adjacent normal windows are:

[0070] The previous window time range is from arrive , the data point is , ,in, is the number of data points in the previous normal window; is the first acquisition moment in the previous normal window and its corresponding active power, and so on. The first Collection time (i.e. the last collection time ) and its corresponding active power;

[0071] The next window time range is from arrive , the data point is , ,in, is the number of data points in the next normal window, is the first acquisition moment in the next normal window (i.e. ) and its corresponding active power, and so on, The first The acquisition moments and their corresponding active power;

[0072] The last data points and the first and last normal windows The data points are merged to form a node set for cubic spline interpolation. Assume that after the merger, nodes, expressed as:

[0073] ;

[0074] in, ,form subintervals; arrive is a partial time point of the previous normal window, arrive It is a partial time point of the latter normal window;

[0075] The above The expression of the nodes is simplified to ,According to the conditions of cubic spline interpolation, we can get a second-order derivative coefficient The linear equations of , can be expressed as:

[0076] ;

[0077] in:

[0078] ;

[0079] ;

[0080] ;

[0081] ;

[0082] Where, For nodes The second derivative coefficient at ; For nodes The second derivative coefficient at ; For nodes The second derivative coefficient at ; For nodes The second derivative coefficient at ; For nodes The second derivative coefficient at ; For nodes The second derivative coefficient at ; For nodes The second derivative coefficient at ; After the merger The node index corresponding to the node; For the node The weight coefficient related to the length of two adjacent subintervals; For the node The weight coefficient related to the length of two adjacent subintervals; Represents two adjacent nodes and The time interval between and Node and The corresponding collection time; Represents two adjacent nodes and the time interval between Represents two adjacent nodes and the time interval between For Complement each other and jointly describe and connect nodes The weight coefficient of the relative relationship between the lengths of two adjacent subintervals is used to adjust the second-order derivative coefficients of different subintervals while ensuring the continuity of the second-order derivative of the interpolation curve. contribution; For Complement each other and jointly describe and connect nodes The weight coefficient of the relative relationship between the lengths of two adjacent subintervals; is the slope change coefficient, which is used to measure the difference between the slope and the node The degree of difference between the average change rates of two adjacent subintervals; Used to measure and node The degree of difference between the average change rates of two adjacent subintervals; Used to measure and node The degree of difference between the average change rates of two adjacent subintervals; 、 and Node 、 and Corresponding active power;

[0083] It is worth noting that in the above formula, 、 、 and Corresponding nodes and In reality, there is only one adjacent subinterval, and the other side is an imaginary subinterval. The definition of the above formula is to make the equation form at the endpoint and the equation form of the internal node consistent to a certain extent, so as to facilitate the transformation of the entire cubic spline interpolation problem into a linear equation system for solution. Under natural boundary conditions, , simplify the above linear equations and use numerical methods (such as the pursuit method) to solve the above linear equations, and we get The value of

[0084] based on Calculate the other coefficients of the cubic polynomial (constant term coefficient , linear coefficient , cubic coefficient ):

[0085] ;

[0086] ;

[0087] ;

[0088] In the time range of the abnormal window Inner existence nodes ,in, , for The node index corresponding to the node; find the collection time The corresponding subinterval , and use the cubic polynomial corresponding to the subinterval to calculate the active power , expressed as:

[0089] ;

[0090] The reconstructed output curve is obtained based on the acquisition time and the calculated active power.

[0091] Cubic spline interpolation can better adapt to complex data changes, but the computational complexity is relatively high. In actual applications, users can choose an algorithm based on their own computing resources and time costs. If high computing speed is required and data changes are relatively gradual, then the linear interpolation algorithm is preferred. It can reconstruct the output curve in the abnormal window in a short time, meeting real-time requirements. If the data changes are complex, high interpolation accuracy is required, and sufficient computing power and time margin are available, then the cubic spline interpolation algorithm is selected. It can fit the data more accurately, making the reconstructed output curve closer to the actual situation.

[0092] After selecting an appropriate interpolation reconstruction algorithm to reconstruct the output curve for the abnormal window, the reconstructed abnormal window is incorporated into the real-time power generation calculation process along with the normal window. This abnormal window processing eliminates the interference of abnormal data on subsequent calculations and the issuance of green certificates, ensuring data accuracy and reliability. The reconstructed output curve is then used in power generation calculations, ensuring that the calculated real-time power generation better reflects the actual power generation status.

[0093] To ensure the authenticity and reliability of the eigenvectors, the reconstructed output curve is not included in the calculation of the weighted output eigenvectors. This hierarchical processing mechanism not only preserves the power generation value of abnormal windows, but also ensures that the eigenvectors are based entirely on verifiable real-world measurement data, fundamentally improving the accuracy and rationality of green certificate issuance.

[0094] S4. Calculate the real-time power generation corresponding to each independent window based on the collected data. Specifically:

[0095] The independent windows involved in the real-time power generation calculation only include normal windows and 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.

[0096] Furthermore, the real-time power generation calculation is as follows:

[0097] The fixed duration of the normal window and the abnormal window of the reconstructed output curve is divided by the fixed period in the collected data to obtain the total number of times the active power of the renewable energy power generation equipment is collected within the normal window and the abnormal window of the reconstructed output curve; starting from the starting time of the timestamp corresponding to the active power within the normal window and the abnormal window of the reconstructed output curve, the active power is collected in sequence according to the fixed period until the collection operation corresponding to the pre-calculated total number of collections is completed; during each collection, the collected active power of the renewable energy power generation equipment is multiplied by the value after the fixed period is converted into hours, and the products are accumulated to obtain the real-time power generation of the normal window and the abnormal window of the reconstructed output curve;

[0098] Furthermore, the real-time power generation corresponding to each independent window is calculated based on the collected data and expressed as follows:

[0099] ;

[0100] ;

[0101] Where, For the Real-time power generation corresponding to each independent window; is the number of data points in the independent window, that is, the total number of acquisitions; For a fixed duration; is a fixed period; For the The timestamp corresponding to the subsampling; Timestamp The corresponding active power.

[0102] S5. Based on the comparison results of the real-time power generation corresponding to each independent window and the preset benchmark power, a time-sharing distributed green certificate issuance strategy is implemented. Specifically:

[0103] When the real-time power generation corresponding to the independent window reaches or exceeds the preset benchmark power, a green certificate will be generated immediately;

[0104] When the real-time power generation corresponding to an independent window is lower than the preset benchmark power, the real-time power generation will be stored in the scattered power pool. The scattered power pool is a virtual container for collecting and managing the real-time power generation of independent windows that have not reached the preset benchmark power. Due to the volatility and intermittency of renewable energy generation, the power generation of many independent windows may not directly meet the standards 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 benchmark power, it can be regarded as a complete issuance unit, thereby generating a green certificate.

[0105] To ensure the rationality and consistency of the power deduction operation, the scattered power pool operation follows the first-in-first-out principle, and the independent window time ranges corresponding to the power deduction are continuously spliced ​​in chronological order. When splicing, continuous windows with high output mode similarity are preferentially selected for combination. The output mode similarity 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 output mode similarity exceeds a preset similarity threshold.

[0106] Furthermore, when both independent windows are normal windows and their real-time power generation meets the standards, the cosine similarity between their weighted output feature vectors is calculated; when both independent windows are normal windows but are issued through the scattered power pool, the cosine similarity between the means of the corresponding weighted output feature vectors of the normal windows in which they participate in the splicing is calculated; when both independent windows are abnormal windows, the difference in their real-time power generation is compared. If the difference is less than the preset difference threshold, the similarity is considered to meet the standards; if the difference is greater than or equal to the preset difference threshold, the similarity is considered to meet the standards; when one independent window is a normal window (including the real-time power generation meeting the standards and being issued through the scattered power pool) and the other is an abnormal window, their similarity is considered to meet the standards and they are not combined.

[0107] When the accumulated power in the scattered power pool reaches or exceeds the preset benchmark power, the power that is consistent with the preset benchmark power value is deducted from the scattered power pool to generate a green certificate and bind the weighted output feature vector of the independent window;

[0108] Preferably, in this embodiment, the preset benchmark electricity quantity is the unified issuance standard for renewable energy generation, namely, 1 megawatt-hour. Given the varying characteristics of renewable energy generation, it is also possible to consider setting different issuance standards for different energy types. For example, wind power generation exhibits significant volatility and relatively poor power generation stability, while hydropower generation offers relatively good stability. Therefore, the issuance standard for wind power projects could be appropriately lowered to reflect the difficulty and characteristics of these projects and encourage more wind power projects to participate in green certificate issuance. Furthermore, for small, distributed renewable energy generation projects, given their smaller scale and limited power generation capacity, specialized, relatively low issuance standards could be established to support their development. This embodiment does not limit the specific value of the preset benchmark electricity quantity; it merely provides a common, universal standard setting example. In actual applications, the preset benchmark electricity quantity can be flexibly adjusted based on various factors, such as energy market development needs, policy guidance, and the actual development status of various renewable energy sources, to achieve more precise and effective incentives and management for the renewable energy generation industry.

[0109] S6. Based on the above, calculate the window power generation characteristics, specifically:

[0110] When the independent window is a normal window and the real-time power generation meets the standard, the window power generation feature is a weighted output feature vector. When the independent window is a normal window but is issued through a scattered power pool, the window power generation feature is the mean of the weighted output feature vectors corresponding to the normal windows involved in the 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.

[0111] Furthermore, the calculation steps of the weighted output feature vector are:

[0112] Get the active power sequence of all fixed period acquisition points within the normal window and its corresponding timestamp sequence ;

[0113] The end time of the normal window timestamp of the collection point Subtract the current collection time Adjustable attenuation coefficient with preset Perform multiplication and take the negative exponent of the product to obtain the time weight of the acquisition point , expressed as:

[0114] ;

[0115] The difference between the active power of the current acquisition point and the active power of the next acquisition point is used as the output change, and the output change corresponding to the acquisition point in the normal window is calculated. , divide the output change of each acquisition point by the maximum output change Minimum output change The difference between the two values ​​is used to obtain the output change rate weight of the acquisition point. , expressed as:

[0116] ;

[0117] ;

[0118] in, For all A collection of is the current acquisition point in the normal window ( subsampling) active power; is the next acquisition point in the normal window ( subsampling) active power;

[0119] The product of the time weight of the acquisition point and the output change rate weight is used as the comprehensive weight vector , expressed as:

[0120] ;

[0121] Where, For the The comprehensive weight vector of the acquisition points corresponding to the subsampling;

[0122] Get the rated power of renewable energy power generation equipment, perform weighted average of the active power sequence of the normal window collection point and the comprehensive weight vector, and use the rated power Perform normalization to obtain the weighted output feature vector of the normal window , expressed as:

[0123] ;

[0124] 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:

[0125] The green certificate unique identifier, issuance time, corresponding renewable energy power generation equipment identifier, real-time power generation, preset benchmark power, and window power generation characteristics are encapsulated (JSON format is used for encapsulation in this embodiment) into a data block, wherein the green certificate unique identifier is a unique identifier generated for each issued green certificate, which is generated using the UUID (Universally Unique Identifier) ​​algorithm in this embodiment; the issuance time is the specific time of issuance of the green certificate that is accurately recorded, and this embodiment adopts the ISO 8601 standard format (such as 2025-06-10T09:57:44.218Z) to facilitate unified time representation and subsequent data processing; the corresponding renewable energy power generation equipment identifier is the serial number, model, and other unique information of the renewable energy power generation equipment, which is used to clarify the power generation equipment corresponding to the green certificate;

[0126] Calculate the hash value of the data block using a cryptographic hash function (such as SHA-256); write the data block, its corresponding hash value, and the hash value of the previous green certificate data block to the blockchain distributed ledger (a chain structure consisting of multiple data blocks). This writing operation is completed using the blockchain's client tools or API. Different blockchain platforms have different implementation methods. For example, in the Ethereum blockchain, data writing operations can be implemented through smart contracts.

[0127] The address, transaction ID and database information recorded when writing into the blockchain distributed ledger are combined to generate the non-fungible digital green certificate. This information can uniquely identify the storage location and related transaction information of the green certificate on the blockchain, facilitating subsequent queries and verifications.

[0128] S8. Synchronize the irreplaceable digital green certificate to the green certificate issuance and trading system for automatic cancellation. Specifically:

[0129] When a user holds a non-fungible digital green certificate and wishes to cancel it, he / she shall initiate a cancellation request through the user interface or API of the green certificate issuance and trading system. The request shall include relevant information of the non-fungible digital green certificate, such as the address or transaction ID;

[0130] After receiving the cancellation request, the green certificate issuance and trading system communicates with the blockchain node and uses the query interface provided by the blockchain to retrieve the data block stored on the blockchain through the address or transaction ID in the irreplaceable digital green certificate;

[0131] 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 means that the data block has not been tampered with during storage and transmission, and the integrity of the data is guaranteed;

[0132] If the verification is successful, the data of the user's actual power load curve in the corresponding time period is obtained, and the window power generation characteristics are calculated according to the same principles as steps S3-S7;

[0133] The window power generation characteristics bound to the irreplaceable digital green certificate to be cancelled are matched with the window power generation characteristics calculated from the user's actual power 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 a preset matching threshold, the cancellation is determined to be valid.

[0134] If both the verification and matching are passed, the cancellation operation will be recorded in the blockchain and the green certificate issuance and transaction system database, and the status of the irreplaceable digital green certificate will be marked as cancelled. Specifically, by updating the data block status on the blockchain and the relevant records in the database, the cancellation information of the green certificate will be accurately recorded and traced.

[0135] Example 2:

[0136] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, a method for dynamically binding green certificates for renewable energy to be issued in a time-sharing and step-by-step manner is implemented as described in any embodiment of the present invention.

[0137] Example 3:

[0138] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, a method for dynamically binding renewable energy green certificates to time-sharing and step-by-step issuance as described in any embodiment of the present invention is implemented.

[0139] It is worth noting that the electronic device and computer-readable storage medium described in the present invention are based on the same inventive concept as the method described in Example 1 of the present invention, and will not be described in detail here.

[0140] In the embodiment of the present invention, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a, b and c, where a, b, c can be single or multiple.

[0141] Those skilled in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented using a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0142] Those skilled in the art will 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 aforementioned method embodiments and will not be repeated here.

[0143] In the 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 this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), magnetic disk or optical disk, and other media that can store program code.

[0144] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for issuing renewable energy green certificates in a dynamic, time-sharing and step-by-step manner, characterized in that: The method comprises: Collecting active power and corresponding timestamps of renewable energy power generation equipment at a fixed period, and storing the fixed period, active power and corresponding timestamps as collected data; According to the type of renewable energy, the continuous time is divided into multiple independent windows with different fixed durations, and anomaly detection is performed on the collected data in the independent windows. Specifically: Calculate the standard deviation of the active power sequence in the independent window. If the standard deviation is greater than the preset abnormal threshold, the independent window is determined to be an abnormal window, otherwise it is a normal window. Differentiated abnormality handling methods are adopted for abnormal windows based on the renewable energy type 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; Calculate the real-time power generation corresponding to each independent window based on the collected data; Based on the comparison results of the real-time power generation corresponding to each independent window and the preset benchmark power, a time-sharing distributed green certificate issuance strategy is implemented; 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, generate an irreplaceable digital green certificate, and synchronize it to the green certificate issuance and trading system for automatic cancellation.

2. A method for issuing renewable energy green certificates dynamically bound to renewable energy in a time-sharing and step-by-step manner according to claim 1, characterized in that: The specific time-sharing distributed green certificate issuance strategy is as follows: When the real-time power generation corresponding to the independent window reaches or exceeds the preset benchmark power, a green certificate will be generated immediately; When the real-time power generation corresponding to the independent window is lower than the preset benchmark power, the real-time power generation will be stored in the scattered power pool; When the accumulated power of the scattered power pool reaches or exceeds the preset reference power, power consistent with the preset reference power value is deducted from the scattered power pool.

3. The method for issuing renewable energy green certificates dynamically bound to renewable energy in a time-sharing and step-by-step manner according to claim 2 is characterized in that: The real-time power generation corresponding to each independent window is calculated based on the collected data 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 power generation equipment is collected within the normal window and the abnormal window of the reconstructed output curve; Starting from the start time of the timestamp corresponding to the active power in the normal window and the abnormal window of the reconstructed output curve, the active power is collected in sequence according to a fixed period until the collection operation corresponding to the pre-calculated total number of collections is completed; At each collection, the collected active power of the renewable energy generation equipment is multiplied by the value after the fixed period is converted into hours, and the products are accumulated to obtain the real-time power generation in the normal window and the abnormal window of the reconstructed output curve; The power generation of the abnormal window marked as an invalid data source is estimated by using the mean estimation method and then used as the real-time power generation of the abnormal window marked as an invalid data source.

4. The method for issuing renewable energy green certificates dynamically bound to renewable energy according to claim 3 is characterized in that: The window power generation characteristics of the independent window are specifically calculated as follows: When the independent window is a normal window and the real-time power generation meets 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 of the weighted output feature vectors corresponding to the normal windows involved in the splicing. When the independent window is an abnormal window, the window power generation feature is the real-time power generation and the mark feature status is invalid. The calculation steps of the weighted output eigenvector include: Obtain the active power sequence and its corresponding timestamp sequence of all fixed-period acquisition points within the normal window; Subtract the current acquisition time from the end time of the normal window timestamp of the acquisition point, multiply it by the preset adjustable attenuation coefficient, and take the negative exponent of the product to obtain the time weight of the acquisition point; The difference between the active power of the current collection point and the active power of the next collection point is used as the output change. The output change corresponding to the collection point in the normal window is calculated. The output change of each collection point is divided by the difference between the maximum output change and the minimum output change to obtain the output change rate weight of the collection point. The product of the time weight of the acquisition point and the output change rate weight is used as the comprehensive weight vector; The rated power of renewable energy power generation equipment is obtained, the active power sequence of the normal window collection point is weighted averaged with the comprehensive weight vector, and the rated power is used for normalization to obtain the weighted output feature vector of the normal window.

5. The method for issuing renewable energy green certificates dynamically bound to renewable energy in a time-sharing and step-by-step manner according to claim 4 is characterized in that: The issued green certificate is bound to the window power generation characteristics of its corresponding independent window to generate an irreplaceable digital green certificate, specifically: The green certificate unique identifier, issuance time, corresponding renewable energy power generation equipment identifier, real-time power generation, preset benchmark power and window power generation characteristics are encapsulated into a data block; Calculate the hash value of the data block using a cryptographic hash function; write the data block and the corresponding hash value and the hash value of the previous green certificate data block into the blockchain distributed ledger; The address, transaction ID and database information recorded when writing into the blockchain distributed ledger are combined to generate the non-replaceable digital green certificate.

6. A method for issuing renewable energy green certificates dynamically bound to renewable energy in a time-sharing and step-by-step manner according to claim 5, characterized in that: The specific steps of synchronizing to the Green Certificate Issuance and Trading System for automatic cancellation are: When a user holds a non-fungible digital green certificate and initiates a cancellation request, the green certificate issuance and 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 is successful, the window power generation characteristics bound to the irreplaceable digital green certificate to be cancelled will be matched with 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, the cancellation is deemed valid. The cancellation operation is recorded in the blockchain and the green certificate issuance and transaction system database, marking the status of the irreplaceable digital green certificate as cancelled.

7. The method for issuing renewable energy green certificates dynamically bound to renewable energy in a time-sharing and step-by-step manner according to claim 4 is characterized in that: The scattered electricity pool operation follows the first-in-first-out principle, and the independent window time ranges corresponding to the deducted electricity are continuously spliced ​​in chronological order. When splicing, continuous windows with output mode similarity higher than a preset similarity threshold are selected for combination. The output mode similarity is determined by calculating the cosine similarity between the window power generation characteristics of the independent windows to be spliced.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements a method for issuing renewable energy dynamically bound green certificates in a time-sharing and step-by-step manner as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements a method for issuing renewable energy dynamic binding green certificates in a time-sharing and step-by-step manner as described in any one of claims 1 to 7.

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