Carbon reduction accounting and authentication method for green power consumption end facing carbon trading

Abstract

The present application relates to the technical field of carbon trading and green power, and specifically discloses a green power consumption end carbon emission reduction accounting and authentication method for carbon trading, which collects user electricity load and green electricity blending ratio, generates tamper-proof metadata through blockchain hash anchoring, establishes a dynamic marginal emission factor database, calculates baseline emission factors in combination with fixed base factors and renewable energy penetration rates, accounts for green electricity consumption and carbon emission reduction, introduces leakage correction factors and standardizes accounting boundaries, packages green electricity carbon certificates, establishes a cross-chain verification mechanism and sets value decay rules, and uses zero-knowledge proof to realize privacy protection and compliance verification. The present application realizes credible collection of green electricity consumption data and accurate accounting of emission reduction, solves problems such as static accounting bias and privacy leakage, realizes seamless connection with the carbon trading market, improves authentication efficiency and credibility, and promotes marketization of green electricity environmental value.

Description

Technical Field

[0001] This invention relates to the field of carbon trading and green electricity technology, specifically to a method for calculating and certifying carbon emission reductions at the green electricity consumption end for carbon trading. Background Technology

[0002] Driven by the dual carbon goals, the promotion of green electricity consumption has become a key path to reduce overall carbon emissions and optimize the energy structure. Accurate calculation and reliable certification of carbon emission reductions at the green electricity consumption end are crucial for facilitating the transformation of green electricity's environmental value and connecting it to the national carbon emissions trading market. Currently, my country still faces numerous technical bottlenecks in the calculation and certification of carbon emission reductions at the green electricity consumption end, severely hindering the realization of green electricity's environmental value and the efficient operation of the carbon trading market.

[0003] Current methods for calculating emission reductions from green electricity consumption mostly employ static average emission factors, failing to consider regional differences, time-based variations, and dynamic changes in renewable energy penetration rates. This leads to significant discrepancies between the calculated results and actual emission reduction effects, making it impossible to accurately reflect the true environmental contribution of green electricity consumption. Furthermore, the calculation process lacks effective monitoring of users' actual electricity consumption behavior. Some users engage in practices such as inflating green electricity consumption or falsifying electricity data, making it difficult to generate tamper-proof proof of green electricity consumption and impacting the credibility of emission reduction figures.

[0004] In the certification process, existing technologies mostly rely on manual verification by third-party organizations, which is not only inefficient and costly, but also suffers from problems such as inconsistent verification standards and the risk of human intervention. Furthermore, the certification process requires the public disclosure of users' original electricity load data, which can easily leak corporate business privacy and reduce users' enthusiasm for participating in green electricity consumption and carbon trading.

[0005] Furthermore, the existing mechanism for linking green electricity emission reductions with the carbon trading market is imperfect, lacking unified carbon asset certification standards and trading anchoring methods. This easily leads to problems such as duplicate reporting of emission reductions and unclear value decay, making it difficult to effectively convert green electricity emission reductions into tradable carbon assets, and the environmental value of green electricity cannot be fully reflected. At the same time, existing accounting and certification methods can no longer meet the needs of industry standardization and normalization. There is an urgent need for a precise, reliable, efficient, privacy-protecting method for calculating and certifying carbon emission reductions at the green electricity consumption end that is compatible with carbon trading. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing technologies, such as inaccurate carbon emission reduction calculation at the green electricity consumption end, unreliable certification, insufficient privacy protection, and poor integration with carbon trading. It provides a method for calculating and certifying carbon emission reductions at the green electricity consumption end for carbon trading. By accurately capturing green electricity consumption behavior, constructing a dynamic baseline emission factor, implementing spatiotemporal dual-dimensional emission reduction calculation, establishing a cross-chain certification and trading anchoring mechanism, and privacy-protected certification, it achieves accurate, reliable, and efficient calculation and certification of green electricity consumption emission reductions. This opens up a channel for converting the environmental value of green electricity into carbon assets, contributing to the standardized development of the carbon trading market and the achievement of "dual carbon" goals.

[0007] To achieve the above objectives, the technical solution provided by this invention is: a method for calculating and verifying carbon emission reductions at the green electricity consumption end for carbon trading, comprising the following steps: Step S1: Deploy smart meters and edge computing terminals at the consumer end to collect users' real electricity load curves at the minute-level granularity, and simultaneously obtain the real-time green electricity blending ratio signal of the distribution network node where the user is located. The green electricity blending ratio signal is calculated and generated by the distribution network dispatching system based on the real-time output of renewable energy units and the total power supply in the region. Through the smart contract layer of the blockchain, the user's average electricity consumption every 15 minutes is hashed and anchored with the green electricity blending ratio of the corresponding time period to generate tamper-proof green electricity consumption physical proof metadata. Step S2: Establish a dynamic database of regional marginal emission factors and update it hourly. The calculation method for the regional marginal emission factors in the dynamic database is as follows: ; in, for Always in the area The dynamic marginal emission factor for Always in the area All online fossil fuel generator sets of efforts, fossil fuel generator sets Measured carbon emission intensity; Set a fixed baseline emission factor and take the average emission factor of the power grid in the region in the previous year. Calculate the final baseline emission factor The final baseline emission factor is the weighted average of the dynamic marginal factor and the fixed base factor. The weighting coefficient is dynamically adjusted according to the renewable energy penetration rate of that hour. The higher the penetration rate, the greater the dynamic marginal weight. Step S3: For the user During the period Green electricity consumption within the country Its carbon emission reduction The calculation formula is as follows: ; in, Set to 0; The leakage correction factor is calculated by comparing the similarity between the typical daily load curve of users and the daily load curve of green electricity consumption. If the green electricity consumption in a certain period exceeds the user’s actual electricity consumption or exceeds the actual green electricity supply capacity of the distribution network node, the excess will not be counted. Step S4: Encapsulate the emission reduction data packet calculated in Step S3 into a green electricity carbon certificate, each containing a unique identifier; the green electricity carbon certificate establishes a one-way data channel with the registration system of the national carbon emission trading market through an oracle, and the carbon trading institution uses the hash value in the green electricity carbon certificate for cross-validation; an offsetting attenuation factor is introduced. Once green electricity carbon certificates are first traded on the carbon market, if they are not cancelled within 6 months, their value will be calculated as follows: Monthly linear decay; Step S5: When users submit carbon emission reductions to third parties or regulators, the original electricity load curve is not disclosed. Instead, a proof π is generated through a zero-knowledge proof algorithm to prove that there is green electricity consumption behavior of no less than the declared amount during the period and that the calculation process complies with the rules of steps S1-S4. The verifier verifies whether π matches the commitment value disclosed on the chain without needing to obtain detailed electricity consumption data from the user.

[0008] Furthermore, in step S1, the edge computing terminal has real-time data processing, encrypted transmission and local caching functions, and preprocesses the collected power load data to remove outliers.

[0009] Furthermore, in step S1, the green electricity blending ratio is calculated as follows: The green electricity blending ratio of a distribution network node during a certain period = the total real-time output of renewable energy units in the region during that period ÷ the total power supply of the distribution network node during that period × 100%; The hash anchoring uses the SHA-256 hash algorithm, and the generated hash value is written into the blockchain distributed ledger. At the same time, the edge computing terminal caches the relevant data locally for no less than 90 days.

[0010] Furthermore, in step S2, the renewable energy penetration rate The calculation method is as follows: = Time zone Total output of internal renewable energy units ÷ Time zone Total power supply × 100%; The weights of the dynamic marginal factors The weights of the fixed basic factors The formula for calculating the final baseline emission factor is as follows: .

[0011] Furthermore, in step S2, the regional marginal emission factor dynamic database covers all provincial and municipal distribution network nodes across the country, with dedicated personnel verifying its data daily, updating the fixed baseline emission factor annually, and connecting in real time with the regional power grid dispatch system and the data platform of the ecological and environmental departments.

[0012] Furthermore, in step S3, the user During the period Green electricity consumption within the country The calculation method is as follows: ; in For users During the period Average power consumption within the area For time period Duration For users During the period The average green electricity mixing ratio at the distribution network node within the area.

[0013] Further, in step S3, the leakage correction factor The calculation steps are as follows: S31: Collect normal electricity load data of users over the past 30 days, remove data from special periods, divide the data into 24-hour periods, calculate the average electricity load for each period, and construct a typical daily load curve for users. ; S32: Extract 24-hour electricity load data for each user's green electricity consumption day and construct a load curve for that day. ; S33: Calculate the similarity between two curves using the cosine similarity algorithm. ; S34: Based on similarity Determine the leakage correction factor: when hour, ; when hour, ; when hour, ; in .

[0014] Further, in step S4, the unique identifier of the green electricity carbon certificate includes: user blockchain account address, time period start and end timestamp, geographic coordinate hash value, baseline value, calculation result, leakage correction value, authentication identifier and hash verification value; the geographic coordinate hash value is obtained by processing the geographic coordinates of the distribution network node where the user is located using the SHA-256 hash algorithm, and the hash verification value is the SHA-256 hash value generated after packaging all the identifier information.

[0015] Furthermore, in step S4, the formula for calculating the decay of the value of the green electricity carbon certificate is as follows: ,in The number of months after the first trading of green electricity carbon certificates exceeds 6 months. When the value of the reduced emissions falls below 80% of the original value, the reduction will stop.

[0016] Furthermore, in step S5, the zero-knowledge proof algorithm adopts the Groth16 algorithm; the user terminal deploys a proof generation module, and the verifier deploys a proof verification module, with a verification time of no more than 10 seconds; at the same time, data anonymization technology is used to process user identification information, restrict the verifier's permissions, and all authentication process records are encrypted, stored, and written into the blockchain distributed ledger.

[0017] The advantages of this invention compared to the prior art are: This invention establishes a green electricity consumption behavior capture system based on physical electricity consumption curves to achieve real-time and accurate collection of user electricity load and green electricity blending ratio. Combined with blockchain hash anchoring technology, it ensures that the basic data is tamper-proof. It constructs a dynamic dual-benchmark emission factor model, combining the dynamic changes of regional marginal emission factors and the stability of fixed basic factors, to adapt to the dynamic adjustment of renewable energy penetration rate. This solves the problem of large deviations in the existing static emission factor calculation, making emission reduction calculation more in line with actual conditions, while meeting the requirements of the latest industry standards, and improving the standardization and acceptance of the calculation results.

[0018] This invention achieves credible certification of carbon emission reductions through mechanisms such as green electricity carbon certificate encapsulation, cross-chain data connection, and cross-verification, avoiding problems such as data tampering and duplicate reporting. The application of blockchain technology enables full traceability of green electricity consumption data, accounting process, certification records, and carbon certificate circulation, ensuring the fairness and credibility of certification results, while reducing the cost of third-party manual verification and the risk of human intervention, and improving certification efficiency.

[0019] This invention employs a zero-knowledge proof algorithm to achieve "usable but invisible" user's original electricity consumption data. The verifier can complete the authentication without obtaining sensitive data, effectively protecting the user's business privacy and electricity privacy, solving the pain point of privacy leakage in the existing authentication process, enhancing users' enthusiasm for participating in green electricity consumption and carbon trading, and meeting the industry's need for secure circulation of electricity data.

[0020] This invention establishes a cross-chain carbon asset certification and trading anchoring mechanism, transforming green electricity emission reductions into tradable carbon assets and achieving seamless integration with the national carbon emission trading market; it introduces a value decay mechanism to incentivize users to fulfill their obligations in a timely manner, avoid carbon asset idleness, promote the market-oriented transformation of the environmental value of green electricity, and contribute to the standardized development of the carbon trading market, while also addressing the problem of insufficient recognition of the environmental value of green electricity. Attached Figure Description

[0021] Figure 1 This is a flowchart of the carbon emission reduction accounting and certification method for green electricity consumption in carbon trading, as described in this invention. Detailed Implementation

[0022] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0023] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0024] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0025] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0026] The following detailed description, in conjunction with the accompanying drawings, provides a further explanation of the present invention's method for calculating and verifying carbon emission reductions at the green electricity consumption end in the context of carbon trading.

[0027] Combined with appendix Figure 1 The specific implementation process of the carbon emission reduction accounting and certification method for green electricity consumption in carbon trading, as described in this invention, is as follows: This invention provides a method for calculating and verifying carbon emission reductions at the green electricity consumption end for carbon trading, comprising the following steps: Step S1: Establish a green electricity consumption behavior capture system based on physical electricity consumption curves The core of this step is to achieve accurate and real-time capture of users' green electricity consumption behavior, generate tamper-proof physical proof metadata, provide real and reliable basic data support for subsequent emission reduction calculations, and solve the problems of easy tampering and low reliability of green electricity consumption data in existing technologies.

[0028] S1.1 Terminal Deployment: At the green electricity consumption end, including various electricity consumption scenarios such as industrial plants, commercial buildings, and residential communities, high-precision smart meters and edge computing terminals are deployed. The high-precision smart meters have a metering accuracy of no less than 0.5 class and a sampling frequency of 1 minute / time. They can accurately collect users' real-time electricity load, voltage, current and other electrical parameters to form a continuous and complete real-time electricity load curve for users. The edge computing terminals use industrial-grade embedded chips and have real-time data processing, encrypted transmission and local caching functions to avoid data transmission delay and loss, while reducing the data processing pressure on the cloud.

[0029] S1.2 Data Acquisition and Synchronization: High-precision smart meters collect user electricity load data every minute. The edge computing terminal simultaneously receives this data and preprocesses it, including outlier removal, data smoothing, and format standardization. This removes abnormal data caused by meter malfunctions or grid fluctuations, such as load spikes exceeding reasonable ranges, ensuring the authenticity and continuity of the electricity load curve. Simultaneously, the edge computing terminal, through a dedicated communication interface and using encrypted TCP / IP protocol, synchronously acquires the real-time green electricity blending ratio signal of the user's distribution network node. This signal is generated by the distribution network dispatching system based on the real-time output and total power supply of renewable energy units such as wind, solar, and hydropower within the region, calculated at a frequency of once per minute, ensuring the spatiotemporal synchronization of the green electricity blending ratio and the electricity load data. The calculation method for the green electricity blending ratio is as follows: The green electricity blending ratio of a distribution network node during a certain period = the total real-time output of renewable energy units in the region during that period ÷ the total power supply of the distribution network node during that period × 100%; The real-time output of renewable energy units is collected by the monitoring terminal on the unit itself and uploaded to the distribution network dispatch system, and the total power supply is statistically analyzed in real time by the metering device at the distribution network node.

[0030] S1.3 Data Hash Anchoring and Storage: Through the smart contract layer of the blockchain, the user's average electricity consumption every 15 minutes is hash-anchored with the corresponding green electricity blending ratio for that time period, generating immutable "green electricity consumption physical proof metadata". Specifically, the edge computing terminal averages the pre-processed electricity load data every 15 minutes to obtain the average electricity consumption for that time period, and simultaneously extracts the average green electricity blending ratio for the corresponding 15 minutes. These two core data are packaged with the user's unique identifier (such as the user's electricity account number and blockchain account address) and time period information (start and end timestamps) to form a data set. A unique hash value is generated using the SHA-256 hash algorithm, and then the smart contract automatically writes the hash value into the blockchain distributed ledger, achieving data immutability and traceability. The smart contract pre-sets hash anchoring rules and data verification logic. If the data is tampered with, missing, or mismatched in time and space, the smart contract will refuse to write it to the blockchain, ensuring the authenticity and integrity of the "green electricity consumption physical proof metadata". This process references the technical approach of storing data fingerprints on the blockchain in a trusted data space, thereby establishing a solid foundation for data trust from the source and preventing the forgery and tampering of green electricity consumption data.

[0031] S1.4 Data Backup and Redundancy: To further improve data reliability, the edge computing terminal locally caches the collected raw electricity consumption data, preprocessed data, and hash-anchored data (caching time is no less than 90 days). At the same time, the blockchain distributed ledger realizes multi-node backup to ensure that data is not lost in the event of terminal failure, network interruption, etc., and provides data support for subsequent verification and traceability.

[0032] Step S2: Construct a dynamic dual baseline emission factor model The core of this step is to address the problem of large deviations in existing static emission factor calculations. By constructing a dynamic dual-benchmark emission factor model, which combines the dynamic changes of regional marginal emission factors with the stability of fixed baseline emission factors, the baseline emission factors can be accurately calculated. This provides a scientific and reasonable reference standard for emission reduction calculations, while also meeting the official standardization requirements for green electricity carbon emission accounting.

[0033] S2.1 Establish a dynamic database of regional marginal emission factors: A dynamic database of regional marginal emission factors will be established, covering all provincial and municipal distribution network nodes nationwide. Data will be updated hourly to ensure the timeliness and regional relevance of emission factors. The calculation method for regional marginal emission factors is as follows: ;in, for Always in the area The dynamic marginal emission factor characterizes Always in the area Carbon emissions generated by newly added unit electricity load within the country; for Always in the area All online fossil fuel generator sets The power output includes non-renewable energy generating units such as thermal power and nuclear power, and the data is collected in real time by the regional power grid dispatch center and uploaded to the database; fossil fuel generator sets The measured carbon emission intensity is determined by a third-party testing agency on a regular basis, with a testing cycle of no more than one year. The determination is made after adjustments based on parameters such as the fuel type, combustion efficiency, and operation of desulfurization and denitrification facilities of the generator set, to ensure the accuracy of the measured data.

[0034] S2.2 Setting the fixed baseline emission factor: Fixed baseline emission factor Take this area The average emission factor of the power grid in the previous year is derived from the annual carbon emission reports released by the regional ecological and environmental departments and power grid companies, ensuring the data's authority and stability. Setting a fixed baseline emission factor avoids the impact of short-term fluctuations in regional marginal emission factors on the stability of emission reduction calculations, while also ensuring the continuity of the calculation method.

[0035] S2.3 Calculation of the final baseline emission factor: Final baseline emission factor Take dynamic marginal factor With fixed base factors The weighted average is calculated based on the renewable energy penetration rate of the hourly region z. The specific adjustment rule is as follows: the higher the penetration rate, the greater the weight of the dynamic marginal factor and the smaller the weight of the fixed basic factor; conversely, the lower the penetration rate, the greater the weight of the fixed basic factor and the smaller the weight of the dynamic marginal factor.

[0036] The weighting coefficients are calculated as follows: Let... Time zone The renewable energy penetration rate is: = Time zone Total output of internal renewable energy units ÷ Time zone Total power supply × 100%; The weight of the dynamic marginal factor Fixed base factor weights And satisfy The final baseline emission factor is calculated using the following formula: ; This setting ensures that the baseline emission factor can reflect the real-time grid carbon emission level while avoiding calculation bias caused by short-term fluctuations. It also aligns with the impact of increased renewable energy penetration on grid carbon emission levels, making the baseline emission factor more scientific and targeted. This effectively solves the problems of imperfect emission factor systems and low calculation accuracy in existing technologies.

[0037] S2.4 Database Maintenance and Updates: Assign dedicated personnel to maintain the regional marginal emission factor dynamic database, regularly verify the data in the database, and promptly correct any abnormal data; update the fixed baseline emission factor annually to ensure that the data is consistent with the actual carbon emissions of the regional power grid; at the same time, the database supports real-time connection with the regional power grid dispatch system and the data platform of the ecological and environmental department to achieve automatic data synchronization and updates, reducing manual maintenance costs.

[0038] Step S3: Implement emission reduction separation accounting based on spatiotemporal dual dimensions The core of this step is to implement spatiotemporal dual-dimensional emission reduction calculation based on the green electricity consumption data captured in step S1 and the baseline emission factor constructed in step S2. This involves deducting leakage corrections and eliminating invalid calculations to ensure the accuracy and rationality of emission reduction calculations, while avoiding the problem of double calculation of the environmental value of green electricity.

[0039] S3.1 Calculation of green electricity consumption: For user u's green electricity consumption during time period t The calculation method is as follows: ; in, For users During the period The average power consumption within the area is obtained by summing the average power consumption calculated by the edge computing terminal every 15 minutes in step S1. For time period Duration For users During the period The average green electricity blending ratio of the distribution network nodes within the area is obtained by averaging the green electricity blending ratio data acquired synchronously in step S1.

[0040] S3.2 Basic Calculation of Carbon Emission Reduction: User During the period The baseline value for carbon emission reduction within the region is calculated by subtracting the difference between green electricity consumption and the baseline emission factor, as well as the green electricity emission factor. The green electricity emission factor... Taking 0 (the green electricity emission factor at the consumption end is considered to be 0, which is in line with the current industry consensus on the environmental value of green electricity, i.e., no additional carbon emissions are generated during the consumption of green electricity, consistent with the green electricity carbon emission recognition standards already adopted in some regions), the basic formula for calculating carbon emission reduction is: .

[0041] S3.3 Leakage Correction Factor Calculation: Leakage Correction Factor This is used to deduct additional carbon emissions resulting from users adjusting their electricity consumption behavior to absorb green electricity (such as shifting high-energy-consuming production to non-green electricity periods, increasing electricity load during non-green electricity periods, etc.), thus avoiding inflated emission reduction figures. The calculation is based on comparing the similarity between a user's typical daily load curve and the daily load curve for green electricity consumption. The specific steps are as follows: S3.3.1 Constructing a typical daily load curve for users: Data collection from users Data on normal electricity load over the past 30 days, excluding data from holidays, equipment maintenance, extreme weather, and other special periods, is used to divide the data into 24-hour periods. The average electricity load for each period is calculated to construct a typical daily load curve for each user. ( For time period, =0-23).

[0042] S3.3.2 Extracting the daily load curve of green electricity consumption: Extracting users Construct a green electricity consumption day load curve based on 24-hour electricity load data from green electricity consumption days. ( For time period, =0-23).

[0043] S3.3.3 Calculate curve similarity: The cosine similarity algorithm is used to calculate the similarity between the two curves. , The value range of cosine similarity is [0,1]. Higher similarity indicates smaller adjustments to user electricity consumption behavior and lower leakage emissions; conversely, lower similarity indicates larger adjustments to user electricity consumption behavior and higher leakage emissions. The formula for calculating cosine similarity is: ; S3.3.4 Determine the leakage correction factor: based on similarity. The leakage correction factor is determined according to the following rules: when At that time, it was determined that the user's electricity consumption behavior did not show any significant adjustment. ; when hour, ; when hour, The maximum leakage correction ratio shall not exceed 30% to avoid excessive correction that could lead to inaccurate emission reduction calculations.

[0044] S3.4 Final Emission Reduction Calculation: User During the period Ultimate carbon emission reduction The calculation formula is as follows: .

[0045] S3.5 Invalid Calculations Eliminated: To ensure the reasonableness of the calculation results, an invalid calculation threshold is set. If the green electricity consumption within a certain period... Exceeding the user's actual electricity consumption ( If the amount exceeds the actual green electricity supply capacity of the distribution network node (total green electricity supply at time t ÷ total number of users at that node), the excess will not be calculated; that is, the emission reduction corresponding to the excess will not be included in the final calculation result. Meanwhile, if a user does not consume green electricity within a certain period ( Then the emission reduction during that period is... .

[0046] S3.6 Verification of Calculation Results: The calculation results of user emission reductions are verified every hour, and the reasonableness of core data such as green electricity consumption, baseline emission factor, and leakage correction factor is compared. If data abnormalities are found, such as negative emission reductions or leakage correction factors exceeding the reasonable range, a recalculation is automatically triggered, and the cause of the abnormality is recorded to ensure the accuracy of the calculation results.

[0047] Step S4: Establish a cross-chain carbon asset certification and trading anchoring mechanism The core of this step is to transform the calculated carbon emission reductions into tradable carbon assets, establish a connection mechanism with the national carbon emission trading market, realize credible certification, cross-chain traceability and orderly trading of carbon assets, solve existing problems such as poor connection between green electricity emission reductions and carbon trading, unclear value decay and easy duplicate declarations, and promote the market-oriented transformation of the environmental value of green electricity.

[0048] S4.1 Green Electricity Carbon Certificate Encapsulation: The user carbon emission reduction data package calculated in step S3 is encapsulated into green electricity carbon certificates. Each green electricity carbon certificate corresponds to a unique carbon asset unit, possessing the characteristics of being tamper-proof, traceable, and tradable. Each green electricity carbon certificate contains the following unique identification information to ensure its uniqueness and identifiability: (1) User blockchain account address: The unique account address registered by the user in the blockchain system, used to identify the owner of the green electricity carbon certificate; (2) Time period information: Start and end timestamps (accurate to the minute) corresponding to carbon emission reduction, clearly defining the valid time period of green electricity carbon certificates; (3) Geographic coordinate hash value: The hash value of the geographic coordinates (latitude and longitude) of the distribution network node where the user is located after being processed by the SHA-256 hash algorithm. It is used to identify the regional location of green electricity consumption and avoid regional confusion. (4) Baseline value: The baseline emission factor for this period. And weighting coefficients, used to verify the rationality of emission reduction calculations; (5) Calculation results: Green electricity consumption during this period Leakage correction factor Ultimate carbon emission reduction ; (6) Certification mark: A certification number generated by a third-party certification body with carbon emission reduction certification qualifications, used to confirm the legality and credibility of green electricity carbon certificates; (7) Hash verification value: The SHA-256 hash value generated after packaging all the above identification information is used for data verification in subsequent transactions and verification processes to prevent the voucher information from being tampered with.

[0049] S4.2 Cross-Chain Data Connection: Green electricity carbon certificates establish a one-way data channel with the national carbon emission trading market's registration system through an oracle, achieving secure and real-time data synchronization. The oracle acts as a bridge between the blockchain and external systems, responsible for encrypting and transmitting the core information of the green electricity carbon certificates (hash verification value, user identifier, emission reduction, and time period information) to the national carbon emission trading market's registration system. Simultaneously, it prohibits external systems from transmitting data back to the blockchain, ensuring the security of the blockchain data.

[0050] S4.3 Cross-validation and Prevention of Duplicate Declarations: When receiving green electricity carbon certificates for carbon trading and compliance offsetting, carbon trading institutions can cross-validate the hash verification value in the green electricity carbon certificate with data in the national carbon emission trading market registration system. Simultaneously, they can check whether the emission reduction has already been used for other carbon asset declarations. If duplicate declarations are found, the trading and compliance use of the green electricity carbon certificate will be immediately rejected, effectively solving the problem of dual calculation of the environmental value of green electricity and ensuring the uniqueness of carbon assets. Furthermore, the blockchain distributed ledger can record the entire lifecycle information of green electricity carbon certificates, from generation, authentication, trading to cancellation. Every step is immutable, enabling full traceability of carbon assets and facilitating verification by regulatory authorities.

[0051] S4.4 Value Decay Mechanism: Introducing a Counteracting Decay Factor This is intended to incentivize users to promptly use their green electricity carbon certificates for carbon trading compliance, thus preventing market disruptions caused by long-term idle carbon assets. The specific rules are as follows: Once green electricity carbon certificates are traded for the first time in the carbon market, if they are not cancelled within 6 months (cancellation means using them for carbon compliance offsetting), the value of the emission reductions available for compliance will be calculated as follows: Monthly linear decay, the decay calculation formula is: ; in, For months exceeding 6 months ( (The value is a positive integer). When the value of the decayed emission reductions falls below 80% of its original value, the decay stops, maintaining this minimum value to prevent excessive depreciation of carbon asset value. For example, a green electricity carbon certificate has an emission reduction of 100 tCO2. After being idle for 7 months following its initial transaction, the value of its decayed emission reductions is 100 × 0.98. 1 =98tCO2; after being idle for 12 months, the value of its emission reduction after decay is 100 × 0.98 6 The CO2 emissions are approximately 88.58 t, not below 80%, and will continue to decline. After being idle for 20 months, the value of the reduced emissions after decline is 100 × 0.98. 14 The CO2 content is approximately 75.36 tCO2. When it falls below 80%, the decay stops, and the value is maintained at 75.36 tCO2.

[0052] S4.5 Carbon Certificate Circulation Management: The circulation (trading, transfer, and cancellation) of green electricity carbon certificates is automatically executed through blockchain smart contracts. These smart contracts pre-set circulation rules, such as qualification verification of both parties, confirmation of transaction prices, and calculation of decay, eliminating the need for manual intervention, thus improving transaction efficiency and reducing transaction costs. Simultaneously, the smart contracts record every circulation transaction, ensuring traceability and verifiability, facilitating regulatory oversight of the carbon trading market.

[0053] Step S5: Privacy-preserving authentication based on zero-knowledge proof The core of this step is to protect users' business and electricity privacy while ensuring the credibility of carbon emission reduction certification, address the issue of easy leakage of users' original electricity data in the existing certification process, enhance users' enthusiasm for participating in green electricity consumption and carbon trading, and at the same time meet the industry's need for secure circulation of electricity data.

[0054] S5.1 Zero-Knowledge Proof Algorithm Selection and Deployment: An efficient and secure zero-knowledge proof algorithm (ZKP) is adopted, with the Groth16 algorithm being the preferred choice. This algorithm is deployed on both the user terminal and the verification system. The user terminal deploys a proof generation module, and the verification system deploys a proof verification module to ensure the algorithm's efficient operation.

[0055] S5.2 Proof Generation: When submitting a carbon emission reduction certification application to a third party or regulator, users do not disclose sensitive information such as the original electricity load curve and green electricity consumption data. Instead, they generate a zero-knowledge proof π through the proof generation module, based on the calculation rules of steps S1-S4 and their own green electricity consumption data. This proof π must meet the following conditions: (1) Prove that the user did indeed have green electricity consumption behavior of no less than the declared amount during the declaration period; (2) Prove that the calculation process of emission reduction strictly follows the technical rules of steps S1-S4, including green electricity consumption data collection, baseline emission factor calculation, leakage correction, and invalid part removal; (3) Prove that the carbon emission reduction data submitted by the user is consistent with the physical proof metadata of green electricity consumption stored on the chain.

[0056] During the proof generation process, the user terminal only uses locally cached green electricity consumption data and publicly available commitment values ​​on the blockchain, along with the hash value generated from the green electricity consumption physical proof metadata. No original sensitive data is transmitted to any third party, ensuring data privacy and security. Simultaneously, the proof generation module encrypts the data to further enhance privacy protection, referencing the technical concept of data being usable but invisible in a trusted data space, thus achieving a balance between privacy protection and trusted authentication.

[0057] S5.3 Proof Verification: After receiving the zero-knowledge proof π submitted by the user, the verifier verifies whether π matches the publicly disclosed commitment value on the chain through the proof verification module. This process does not require obtaining sensitive information such as the user's original electricity load curve or green electricity consumption data. The verification process only requires hash value comparison and algorithm logic verification, with a verification time of no more than 10 seconds, ensuring authentication efficiency. If the verification passes, the user's carbon emission reduction declaration is confirmed to be true and valid, and the legality of their carbon emission reduction and green electricity carbon certificates is recognized. If the verification fails, authentication is rejected, and the user is prompted to resubmit the application. The reason for the verification failure is recorded, such as a mismatch between the proof and the commitment value, or non-compliance with accounting rules.

[0058] S5.4 Enhanced Privacy Protection: To further strengthen privacy protection, data anonymization technology is used to process user identification information, such as hiding sensitive information like usernames and specific addresses, retaining only the blockchain account address and geographic coordinate hash value. Simultaneously, the verifier's permissions are restricted; the verifier can only obtain the proof π and the on-chain commitment value, and cannot access any original sensitive data. All records of the verification process are encrypted and stored to prevent data leakage. Furthermore, users can independently control the scope and permissions of authentication, submitting proof π only to designated verifiers to ensure the controllability of privacy data.

[0059] S5.5 Authentication Record Storage: All authentication processes, including proof generation time, verification time, verification results, and verifier information, are written into the blockchain distributed ledger through smart contracts, ensuring that authentication records are tamper-proof and traceable, facilitating subsequent regulatory verification and dispute resolution.

[0060] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for calculating and verifying carbon emission reductions at the green electricity consumption end of carbon trading, characterized in that, Includes the following steps: Step S1: Collect the user's actual electricity load curve and simultaneously obtain the real-time green electricity mixing ratio signal of the distribution network node where the user is located; Generate immutable physical proof metadata for green electricity consumption; Step S2: Establish a dynamic database of regional marginal emission factors; set a fixed baseline emission factor and take the average emission factor of the power grid in the previous year for the region; calculate the final baseline emission factor; Step S3: Calculate the user's green electricity consumption and carbon emission reduction within a certain period; Step S4: Encapsulate the carbon emission reduction data package calculated in Step S3 into green electricity carbon vouchers. Each green electricity carbon voucher contains a unique identifier. After the green electricity carbon voucher is traded for the first time, its value decreases linearly. Step S5: When a user submits carbon emission reductions to a third party or regulator, a proof π is generated using a zero-knowledge proof algorithm; the verifier verifies whether π matches the publicly disclosed commitment value on the blockchain.

2. The method for calculating and verifying carbon emission reductions at the green electricity consumption end for carbon trading as described in claim 1, characterized in that: The specific steps of S1 are as follows: Smart meters and edge computing terminals are deployed at the consumer end to collect users' real electricity load curves at the minute-level granularity and simultaneously acquire the real-time green electricity blending ratio signal of the distribution network node where the user is located. The green electricity blending ratio signal is calculated and generated by the distribution network dispatching system based on the real-time output of renewable energy units and the total power supply in the region. Through the smart contract layer of the blockchain, the user's average electricity consumption every 15 minutes is hashed and anchored with the corresponding green electricity blending ratio for the time period to generate tamper-proof green electricity consumption physical proof metadata. The edge computing terminal has real-time data processing, encrypted transmission and local caching functions to preprocess the collected electricity load data and remove outliers.

3. The method for calculating and verifying carbon emission reductions at the green electricity consumption end for carbon trading as described in claim 2, characterized in that: In step S1, the green electricity blending ratio is calculated as follows: The green electricity blending ratio of a distribution network node during a certain period = the total real-time output of renewable energy units in the region during that period ÷ the total power supply of the distribution network node during that period × 100%; The hash anchoring uses the SHA-256 hash algorithm, and the generated hash value is written into the blockchain distributed ledger. At the same time, the edge computing terminal caches the relevant data locally for no less than 90 days.

4. The method for calculating and verifying carbon emission reductions at the green electricity consumption end for carbon trading as described in claim 3, characterized in that: The specific details of step S2 are as follows: A dynamic database of regional marginal emission factors is established and updated hourly. The calculation method for the regional marginal emission factors in the dynamic database is as follows: ； in, for Always in the area The dynamic marginal emission factor for Always in the area All online fossil fuel generator sets of efforts, fossil fuel generator sets Measured carbon emission intensity; Set a fixed baseline emission factor and take the average emission factor of the power grid in the region in the previous year. Calculate the final baseline emission factor The final baseline emission factor is the weighted average of the dynamic marginal factor and the fixed base factor. The weighting coefficient is dynamically adjusted according to the renewable energy penetration rate of that hour. The higher the penetration rate, the greater the dynamic marginal weight. The renewable energy penetration rate The calculation method is as follows: = Time zone Total output of internal renewable energy units ÷ Time zone Total power supply × 100%; The weights of the dynamic marginal factors The weights of the fixed basic factors The formula for calculating the final baseline emission factor is as follows: 。 5. The method for calculating and verifying carbon emission reductions at the green electricity consumption end for carbon trading as described in claim 4, characterized in that: In step S2, the regional marginal emission factor dynamic database covers all provincial and municipal distribution network nodes across the country. Dedicated personnel are assigned to verify its data daily, update the fixed baseline emission factor once a year, and connect with the regional power grid dispatch system and the data platform of the ecological and environmental department in real time.

6. The method for calculating and verifying carbon emission reductions at the green electricity consumption end for carbon trading as described in claim 5, characterized in that: In step S3, the user During the period Green electricity consumption within the country The calculation method is as follows: ； in For users During the period Average power consumption within the area For time period Duration For users During the period The average green electricity mixing ratio of the distribution network nodes within the area; For users During the period Green electricity consumption within the country Its carbon emission reduction The calculation formula is as follows: ； in, Set to 0; The leakage correction factor is calculated by comparing the similarity between the typical daily load curve of users and the daily load curve of green electricity consumption.

7. The method for calculating and verifying carbon emission reductions at the green electricity consumption end for carbon trading as described in claim 6, characterized in that: In step S3, the leakage correction factor The calculation steps are as follows: S31: Collect normal electricity load data of users over the past 30 days, remove data from special periods, divide the data into 24-hour periods, calculate the average electricity load for each period, and construct a typical daily load curve for users. ; S32: Extract 24-hour electricity load data for each user's green electricity consumption day and construct a load curve for that day. ; S33: Calculate the similarity between two curves using the cosine similarity algorithm. ; S34: Based on similarity Determine the leakage correction factor: when hour, ; when hour, ; when hour, ; in .

8. The method for calculating and verifying carbon emission reductions at the green electricity consumption end for carbon trading as described in claim 7, characterized in that: In step S4, the green electricity carbon certificate establishes a one-way data channel with the registration system of the national carbon emission trading market through an oracle, and the carbon trading institution calls the hash value in the green electricity carbon certificate for cross-verification. The unique identifier of the green electricity carbon certificate includes: user blockchain account address, time period start and end timestamp, geographic coordinate hash value, baseline value, calculation result, leakage correction value, authentication identifier and hash verification value; The geographic coordinate hash value is obtained by processing the geographic coordinates of the user's distribution network node using the SHA-256 hash algorithm, and the hash verification value is the SHA-256 hash value generated after packaging all identification information.

9. The method for calculating and verifying carbon emission reductions at the green electricity consumption end for carbon trading as described in claim 8, characterized in that: In step S4, an attenuation cancellation factor is introduced. Once green electricity carbon certificates are first traded on the carbon market, if they are not cancelled within 6 months, their value will be calculated as follows: Monthly linear decay; The formula for calculating the depreciation of the value of the green electricity carbon certificate is as follows: ,in The number of months after the first trading of green electricity carbon certificates exceeds 6 months. When the value of the reduced emissions falls below 80% of the original value, the reduction will stop.

10. The method for calculating and verifying carbon emission reductions at the green electricity consumption end for carbon trading as described in claim 9, characterized in that: In step S5, the zero-knowledge proof algorithm adopts the Groth16 algorithm; the user terminal deploys a proof generation module, and the verifier deploys a proof verification module, with a verification time of no more than 10 seconds; at the same time, data anonymization technology is used to process user identification information, restrict the verifier's permissions, and all authentication process records are encrypted, stored, and written into the blockchain distributed ledger.

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