A blockchain consensus-based power transaction method

Abstract

The application relates to the field of power transaction, in particular to a power transaction method based on blockchain consensus. The method comprises the following steps: acquiring real-time green power encryption data set, uploading the real-time green power encryption data set to a blockchain node, and generating an inactivated digital green certificate data set; based on the inactivated digital green certificate data set, generating a grid connection verification instruction, updating the inactivated digital green certificate data set to a tradable state, and generating a tradable digital green certificate data set; acquiring a power grid dynamic parameter set, based on the power grid dynamic parameter set and the tradable digital green certificate data set, generating a green certificate real-time pricing data set through a time-space right and interest dynamic pricing strategy; according to the green certificate real-time pricing data set, canceling the digital green certificate in the user power consumption process, and outputting a green power consumption traceability report. In the green power transaction process, the application promotes the fair and orderly development of the green power transaction market.

Description

Technical Field

[0001] This application relates to the field of electricity trading, and in particular to an electricity trading method based on blockchain consensus. Background Technology

[0002] The existing green electricity trading methods have a fundamental flaw in their green electricity value adjustment strategies. They cannot dynamically adjust based on the influencing factors generated during the trading process, resulting in a serious deviation between the price of green electricity resources and the true value of energy. Summary of the Invention

[0003] This application provides a blockchain consensus-based electricity trading method to solve the aforementioned technical problems. The method includes: acquiring a real-time encrypted green electricity dataset; uploading the real-time encrypted green electricity dataset to a blockchain node to generate an inactive digital green certificate dataset; generating a grid connection verification instruction based on the inactive digital green certificate dataset to update the inactive digital green certificate dataset to a tradable state, generating a tradable digital green certificate dataset; acquiring a set of dynamic grid parameters; generating a real-time pricing dataset for green certificates based on the set of dynamic grid parameters and the tradable digital green certificate dataset using a spatiotemporal rights dynamic pricing strategy; and verifying digital green certificates during user electricity consumption according to the real-time pricing dataset for green certificates, and outputting a green electricity consumption traceability report.

[0004] This solution first acquires encrypted real-time green electricity data from the intelligent monitoring equipment of distributed green power plants, uploads it to the blockchain node, and generates inactive digital green certificates containing basic green electricity information but without trading eligibility. Next, the power grid company's verification node generates grid connection instructions according to standards. After verification by the smart contract, the inactive green certificates are updated to tradable green certificates with compliance identifiers. Then, real-time grid parameters are acquired and combined with the tradable green certificates to generate a real-time pricing dataset through a spatiotemporal equity strategy. Finally, after a user purchases electricity, the smart contract verifies the green certificates, and the blockchain node integrates the entire process data to output a consumption traceability report, which is then sent to the user. Real-time encryption and blockchain storage ensure the authenticity and security of green electricity data, solving the problems of data fraud and privacy leaks in traditional transactions; automated grid connection verification improves transaction efficiency and compliance, unifies standards and shortens cycles, and ensures grid security; dynamic pricing of green electricity resources through time and space rights optimizes the allocation of green electricity resources, flexibly responding to market supply and demand to improve the renewable energy consumption rate; the green certificate verification mechanism and consumption traceability report enhance market trust and participation, eliminate fraud, and clearly present the entire green electricity process; ultimately, it helps the low-carbon transformation of the energy structure and promotes the fair and orderly development of the green electricity trading market.

[0005] Optionally, uploading the real-time green electricity encrypted dataset to the blockchain node to generate an inactive digital green certificate dataset includes: the real-time green electricity encrypted dataset includes a power generation data stream, millisecond-level precision timestamp data, and geographic coordinate data of the green electricity generator set; based on the real-time green electricity encrypted dataset, triggering a preset green certificate generation verification logic: based on the millisecond-level precision timestamp data, analyzing whether the fluctuation characteristics of the power generation data stream in the corresponding time interval conform to a preset green electricity output mode; based on the geographic coordinate data, verifying whether the green electricity generator set is located in a preset trusted green electricity area list; when the fluctuation characteristics of the power generation data stream conform to the green electricity output mode and the green electricity generator set is located in the trusted green electricity area list, the blockchain node determines that the verification is successful; after the blockchain node verifies the verification, the real-time green electricity encrypted dataset is encapsulated into a real-time green electricity encrypted data unit with a unique identifier, and the real-time green electricity encrypted data unit is recorded in the corresponding blockchain node on the blockchain to generate the inactive digital green certificate dataset.

[0006] This solution employs dual verification of power generation data flow fluctuations and geographic coordinates to filter out fraudulent green electricity data at the source. This ensures that the generated inactive digital green certificates correspond to the actual green electricity production process, providing a reliable credential basis for subsequent green electricity transactions and reducing transaction disputes caused by fraudulent data. Utilizing the distributed storage and immutability of blockchain nodes, verified green electricity data is permanently recorded in the ledger. Combined with millisecond-level timestamps and geographic coordinates, this achieves full traceability of green electricity from production to green certificate generation, meeting the regulatory authorities' verification requirements for green electricity compliance and providing users with transparent information on the source of green electricity.

[0007] Optionally, the step of generating a grid connection verification instruction based on the inactive digital green certificate dataset, updating the inactive digital green certificate dataset to a tradable state, and generating a tradable digital green certificate dataset includes: performing NFT encoding on each green certificate in the inactive digital green certificate dataset to generate a grid connection verification instruction containing the green certificate NFT encoding, the corresponding cumulative power generation value, geographical location information, and the planned grid connection time; analyzing whether the cumulative power generation value has physical transmission feasibility at the planned grid connection time based on the grid connection verification instruction; when the cumulative power generation value is transmittable, returning a verification pass signal to the blockchain node; the blockchain node responding to the verification pass signal by updating the state attribute of the corresponding green certificate NFT encoding from inactive to tradable; and generating the tradable digital green certificate dataset based on all the green certificate NFT encodings whose states have been updated to tradable.

[0008] This scheme verifies the feasibility of physical transmission, enabling tradable green certificates to have actual grid connection capabilities, avoiding "virtual green certificate" transactions, ensuring that the environmental value of green electricity obtained by buyers is supported by real power delivery, and enhancing the credibility of green electricity transactions. The combination of green certificate NFT encoding and blockchain technology fundamentally eliminates problems such as green certificate forgery and duplicate transactions, providing transparent and traceable digital credentials for the green electricity market and maintaining fair market competition. Based on the feasibility analysis of planned grid connection timelines and grid load, the scheme matches green electricity grid connection plans with grid dispatching needs, reducing power curtailment, increasing green electricity absorption rates, and promoting the efficient utilization of new energy sources.

[0009] Optionally, the step of generating a real-time pricing dataset for green certificates based on the power grid dynamic parameter set and the tradable digital green certificate dataset, through a spatiotemporal dynamic pricing strategy, includes: determining a base layer value based on the product of the cumulative power generation value and a preset fixed emission reduction per unit, and assigning the base layer value to each green certificate NFT code in the tradable digital green certificate dataset; analyzing the power grid dynamic parameter set to obtain a dynamic layer value adjustment factor, determining the dynamic layer value, and assigning the dynamic layer value to each green certificate NFT code in the tradable digital green certificate dataset; multiplying the base layer value of each green certificate NFT code with the corresponding dynamic layer value adjustment factor to generate a real-time equity price for the green certificate NFT code; and aggregating the real-time equity prices of all green certificate NFT codes to generate the real-time pricing dataset for green certificates.

[0010] This scheme transforms the environmental emission reduction contribution of green electricity into a tradable value indicator through the basic layer value, while the dynamic layer value adjustment factor reflects the actual utility of green electricity under different temporal and spatial conditions. The combination of the two allows the price of green certificates to reflect both environmental attributes and adapt to market dynamics, achieving a unity of "green value" and "market value." The temporal and spatial equity dynamic pricing strategy guides green electricity to be prioritized for consumption in key scenarios such as peak power periods and load centers through price signals, reducing the phenomenon of green electricity curtailment. At the same time, it lowers prices during off-peak power periods or in areas with ample supply and demand, encouraging users to consume more green electricity, balancing the grid load, and improving the stability of grid operation.

[0011] Optionally, the step of analyzing the power grid dynamic parameter set to obtain the dynamic layer value adjustment factor includes: the power grid dynamic parameter set including the real-time total output value of the regional power grid, the real-time power grid congestion level, and the real-time power grid load status; analyzing the real-time total output value of the regional power grid to determine the real-time environmental gain / loss coefficient of the regional power grid cleanliness corresponding to the green certificate NFT code on the base layer value; analyzing the real-time power grid congestion level to determine the spatial compensation coefficient required for the equivalent transmission loss from the geographical location of the corresponding green certificate NFT code to the target load center; analyzing the real-time power grid load status to determine the time gain / loss coefficient required when the current time is during the power grid peak or valley period; and multiplying the real-time environmental gain / loss coefficient, the spatial compensation coefficient, and the time gain / loss coefficient to generate the dynamic layer value adjustment factor for each green certificate NFT code.

[0012] This scheme comprehensively considers the real-time environment of the power grid, spatial transmission costs, and temporal load conditions, enabling green certificate prices to move beyond static environmental values ​​and reflect real-time market dynamics. This allows for a more accurate match between the actual value of green electricity and the actual value of green energy, avoiding resource misallocation caused by pricing discrepancies. The environmental coefficient dynamically responds to changes in the power grid's carbon intensity, increasing the premium rate of green certificates during high-pollution periods and truly reflecting the marginal benefits of emission reduction. The introduction of a spatial compensation coefficient balances the transmission costs of long-distance green electricity, enhances the market competitiveness of clean energy in remote areas, promotes the efficient flow of green electricity from the production end to load centers, and optimizes the allocation range of clean energy. The time coefficient makes green certificates more valuable during peak hours and more price-competitive during off-peak hours, incentivizing users to consume more green electricity during off-peak hours and rationally control electricity consumption during peak hours, thereby alleviating the peak-valley load difference in the power grid and improving the stability of power grid operation.

[0013] Optionally, the step of analyzing the real-time total output value of the regional power grid and determining the real-time environmental gain / loss coefficient of the regional power grid cleanliness corresponding to the green certificate NFT code on the base layer value includes: determining the real-time green power output value of the green power generating unit in the current time period based on the real-time total output value of the regional power grid and the power generation data stream; determining the real-time cleanliness percentage of the regional power grid based on the ratio of the real-time green power output value to the real-time total output value of the regional power grid; comparing the real-time cleanliness percentage of the regional power grid with the preset cleanliness benchmark threshold based on the preset cleanliness benchmark threshold, determining the deviation direction of the real-time cleanliness percentage of the regional power grid relative to the preset cleanliness benchmark threshold, and determining the deviation magnitude value; when the real-time cleanliness percentage of the regional power grid is higher than the preset cleanliness benchmark threshold, generating a real-time environmental gain coefficient with a value greater than 1, and dynamically increasing the real-time environmental gain coefficient according to the deviation magnitude value; when the real-time cleanliness percentage of the regional power grid is lower than the preset cleanliness benchmark threshold, generating a real-time environmental loss coefficient with a value less than 1, and dynamically decreasing the real-time environmental loss coefficient according to the deviation magnitude value.

[0014] This scheme uses real-time environmental gain / loss coefficients to dynamically link the value of green certificates with the cleanliness of the regional power grid, accurately reflecting the actual environmental contribution of green electricity in different power grid environments and avoiding value distortion. It guides green electricity generating units to be tilted towards areas with low cleanliness, maximizing the emission reduction benefits of green electricity, while avoiding excessive investment in green electricity in areas with high cleanliness, thereby improving the overall efficiency of energy resource utilization.

[0015] Optionally, the step of analyzing the real-time grid congestion level and determining the spatial compensation coefficient required for the equivalent transmission loss from the geographical location corresponding to the green certificate NFT code to the target load center includes: extracting the geographical location information of the target load center according to the grid connection verification command; analyzing the tolerance threshold of the current grid congestion state for power transmission distance based on the real-time grid congestion level, and dynamically generating the maximum effective transmission radius; determining the actual spatial distance from the green power generator to the target load center according to the geographical coordinate data of the green power generator and the geographical location information of the target load center; dynamically comparing the actual spatial distance with the maximum effective transmission radius: when the actual spatial distance is less than or equal to the maximum effective transmission radius, generating the spatial compensation coefficient with a base value of 1; when the actual spatial distance is greater than the maximum effective transmission radius, dynamically adjusting the spatial compensation coefficient according to the ratio of the excess distance to the maximum effective transmission radius, based on a preset nonlinear attenuation rule and with a base value of 1.

[0016] This scheme utilizes the dynamic adjustment of the spatial compensation coefficient to guide green electricity trading towards lower losses and higher efficiency: for green electricity located close to load centers and less affected by grid congestion, the spatial compensation coefficient is better, and the green certificate price is more competitive, thus being prioritized for consumption. For green electricity located far away but with reduced grid congestion, the compensation coefficient is increased, and reasonable pricing can also be obtained. This efficiency-first, dynamic balance mechanism can promote the precise matching of green electricity resources and load demand, reduce ineffective transmission losses, improve the overall green electricity consumption efficiency, and help the energy structure transformation.

[0017] Optionally, the step of analyzing the real-time grid load status to determine the time gain / loss coefficient required for the current moment to be in a peak or valley period of the grid includes: extracting the precise millisecond-level time point of the corresponding green power generation based on the millisecond-level timestamp data; determining the real-time load value corresponding to the precise millisecond-level time point based on the real-time grid load status and the precise millisecond-level time point; dynamically evaluating the changing trend of the real-time load value within a preset time window to determine the load change direction: when the load change direction indicates that the load is continuously rising, generating a time gain coefficient greater than 1, and dynamically adjusting and increasing the time gain coefficient according to the deviation of the number of continuously rising real-time load values ​​within the preset time window from a preset rise intensity threshold; when the load change direction indicates that the load is continuously falling, generating a time loss coefficient less than 1, and dynamically adjusting and decreasing the time loss coefficient according to the deviation of the number of continuously falling real-time load values ​​within the preset time window from a preset fall intensity threshold.

[0018] This solution, based on millisecond-level timestamps and real-time load status analysis, enables the time coefficient to match the instantaneous changes in grid load in real time, avoiding pricing deviations caused by coarse time granularity and ensuring that the value of green electricity is truly reflected at different points in time. By distinguishing the direction of load changes and combining the deviation magnitude adjustment coefficient, it ensures that the revenue of the power generator matches the actual contribution of green electricity to the grid, while also allowing the power consumer to obtain a reasonable green electricity price based on the actual load status, thus balancing the interests of both parties in the transaction.

[0019] Optionally, the step of redeeming digital green certificates during user electricity consumption based on the green certificate real-time pricing dataset and outputting a green electricity consumption traceability report includes: obtaining a redemption request from a target user for a specific green certificate NFT code in the green certificate real-time pricing dataset; analyzing the target redemption electricity value specified in the redemption request; verifying whether the current state of the specific green certificate NFT code is tradable; when the verification passes, marking the cumulative power generation value corresponding to the specific green certificate NFT code as redeemed on the blockchain node based on the target redemption electricity value; and collecting the real-time equity price, the corresponding cumulative power generation value, the consumption time point, and the geographical location information of all redeemed green certificate NFT codes to generate a green electricity consumption traceability report.

[0020] This solution ensures the integrity of the closed-loop green electricity trading system, creating a closed loop from green certificate generation and pricing to final user consumption. This guarantees that green electricity rights can be effectively transformed into users' actual electricity needs, promoting a virtuous cycle in the green electricity market. It also enhances the clarity and security of rights ownership. Through the state marking and immutability of the blockchain, it avoids the risks of duplicate trading and misappropriation of green electricity rights, clarifies the ownership of rights for each write-off, and reduces transaction disputes. Furthermore, it strengthens regulatory and compliance support. The green electricity consumption traceability report provides regulatory authorities with traceable and transparent green electricity consumption data, meeting the regulatory requirements of environmental policies and energy regulations for green electricity trading, and contributing to the achievement of clean energy development goals.

[0021] Optionally, the method further includes: based on the green electricity consumption traceability report, linking real-time exchange rate data of the carbon market when completing transaction settlement; dynamically calculating the number of carbon allowance tons that can be exchanged for each tradable digital green certificate represented by the green certificate NFT code according to the real-time exchange rate data of the carbon market; and on the blockchain node, according to a preset exchange ratio, transferring the green certificate rights corresponding to the completed green certificate NFT code to the corresponding number of carbon allowance tons in an equivalent value, and recording it in the trading party's account in the carbon market.

[0022] This plan converts green certificate rights into carbon allowances, directly reflecting the environmental value of green electricity into economic value. Enterprises that purchase green electricity can not only obtain electrical energy but also gain additional revenue through carbon allowance trading, thereby incentivizing more enterprises to participate in green electricity trading and expanding the scale of green electricity consumption. It breaks down the separation between the two markets, forming a complete value chain of "green electricity production - green certificate trading - carbon allowance conversion", promoting the cross-market flow of environmental rights and achieving a synergistic effect of "1+1>2". Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application; Figure 2 A flowchart illustrating a blockchain consensus-based power trading method provided in one embodiment of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0026] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0027] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0028] However, the existing green electricity trading methods have a fundamental flaw in their green electricity value adjustment strategies. They cannot dynamically adjust based on the influencing factors generated by green electricity resources during the trading process, resulting in a serious deviation between the price of green electricity resources and the true value of energy.

[0029] Based on this, this application provides a power trading method based on blockchain consensus. First, encrypted real-time green electricity data from intelligent monitoring equipment at distributed green power plants is acquired and uploaded to blockchain nodes, generating inactive digital green certificates containing basic green electricity information but lacking trading eligibility. Next, the power grid company's verification node generates grid connection instructions according to standards. After verification by a smart contract, the inactive green certificates are updated to tradable green certificates containing compliance identifiers. Then, real-time grid parameters are acquired and combined with the tradable green certificates to generate a real-time pricing dataset through a spatiotemporal equity strategy. Finally, after a user purchases electricity, the smart contract verifies the green certificates, and the blockchain nodes integrate the entire process data to output a consumption traceability report, which is then sent to the user. Real-time encryption and blockchain storage ensure the authenticity and security of green electricity data, solving the problems of data fraud and privacy leaks in traditional transactions; automated grid connection verification improves transaction efficiency and compliance, unifies standards and shortens cycles, and ensures grid security; dynamic pricing of green electricity resources through time and space rights optimizes the allocation of green electricity resources, flexibly responding to market supply and demand to improve the renewable energy consumption rate; the green certificate verification mechanism and consumption traceability report enhance market trust and participation, eliminate fraud, and clearly present the entire green electricity process; ultimately, it helps the low-carbon transformation of the energy structure and promotes the fair and orderly development of the green electricity trading market.

[0030] Figure 1 This is a schematic diagram illustrating an application scenario provided by this application. In the process of green electricity trading, the method provided in this application can dynamically adjust based on the influencing factors generated by green electricity resources during the trading process, accurately controlling the price of green electricity resources and the true value of energy.

[0031] Specifically, the method of this application is applied to any server that communicates with the new energy power plant monitoring system and the power dispatching system. Through this server, it obtains the real-time encrypted green electricity dataset provided by the new energy power plant monitoring system and the set of dynamic power grid parameters provided by the power dispatching system. First, it acquires the encrypted real-time green electricity data from the distributed green power plant's intelligent monitoring equipment and uploads it to the blockchain node, generating an inactive digital green certificate containing basic green electricity information but without trading eligibility. Next, the power grid company's verification node generates a grid connection instruction according to standards. After verification by the smart contract, the inactive green certificate is updated to a tradable green certificate containing a compliance identifier. Then, it acquires real-time power grid parameters and, combined with the tradable green certificate, generates a real-time pricing dataset through a spatiotemporal rights strategy. Finally, after the user purchases electricity, the smart contract verifies the green certificate, and the blockchain node integrates the entire process data to output a consumption traceability report, which is then sent to the user. Specific implementation details can be found in the following embodiments.

[0032] Figure 2 This is a flowchart illustrating a blockchain-based power trading method according to an embodiment of this application. The method of this embodiment can be applied to the server in the above scenario. For example... Figure 2 As shown, the method includes:

[0033] S201. Obtain the real-time encrypted green electricity dataset, upload the real-time encrypted green electricity dataset to the blockchain node, and generate an inactive digital green certificate dataset.

[0034] Real-time encrypted green electricity datasets refer to a collection of encrypted data related to green electricity (such as renewable energy generation from solar, wind, and hydropower) collected in real time. This includes power generation data streams, millisecond-level timestamp data, and geographic coordinates of green electricity generators. The data originates from a new energy power plant monitoring system. A blockchain node refers to a computer or server participating in the operation of a blockchain network, possessing functions for data storage, transaction verification, and consensus building; it is a fundamental building block of the blockchain network. Inactive digital green certificate datasets refer to a collection of digital green energy certificates generated based on real-time encrypted green electricity data that have not yet undergone grid connection compliance verification.

[0035] Specifically, in traditional green electricity trading, the lack of data credibility and traceability is a core bottleneck restricting the industry's development. On the one hand, key data such as green electricity generation and purity have long relied on manual recording or centralized storage methods, posing a serious risk of data tampering. This means that some companies may falsely report renewable energy generation or even disguise thermal power as green electricity to obtain subsidies, damaging the fairness of the green electricity market. On the other hand, the lack of encryption protection during data transmission makes it easy for power generation companies' trade secrets (such as equipment efficiency and power generation plans) to be stolen, affecting their enthusiasm for participating in green electricity trading. Furthermore, as a special commodity, green electricity requires full traceability, but in the traditional model, data is scattered among multiple entities such as power plants and grid companies, forming "green electricity data silos." Users find it difficult to verify the authenticity of the green electricity they purchase, leading to low consumer willingness to consume. This step involves real-time collection of data such as power generation and equipment operating status from intelligent monitoring devices at distributed green power plants. This data is then encrypted to form a real-time encrypted green power dataset. Power generation companies upload this dataset to their respective blockchain nodes via an interface. After the nodes verify the data's encryption integrity, they automatically generate an inactive digital green certificate dataset containing a unique identifier, which is then synchronized to multiple nodes on the blockchain network for storage. Generating this inactive digital green certificate dataset assigns a unique digital identity to green electricity, laying the foundation for subsequent compliance verification and transactions. This addresses the pain points of "unreliable data and lack of traceability" in traditional green power transactions, and is a prerequisite for building a transparent and trustworthy green power market. It plays a crucial role in promoting the large-scale development of green power transactions.

[0036] S202. Based on the inactive digital green certificate dataset, generate a grid connection verification instruction to update the inactive digital green certificate dataset to a tradable state and generate a tradable digital green certificate dataset.

[0037] Grid connection verification instructions can be used to verify whether the green electricity corresponding to an inactive digital green certificate complies with grid connection standards. These instructions include the green certificate NFT code, the corresponding cumulative power generation value, geographical location information, and the planned grid connection time. A tradable digital green certificate dataset can be a collection of digital green certificates that have undergone grid connection verification and are eligible for trading. This dataset adds verification pass indicators and compliance ratings to the inactive green certificates.

[0038] Specifically, green electricity must meet strict grid connection standards before being connected to the grid. This is a core prerequisite for ensuring the safe and stable operation of the grid. If the voltage, frequency, and other parameters of green electricity are unstable, it may cause grid load fluctuations after connection, or even lead to large-scale power outages. Furthermore, some green electricity may be mixed with non-renewable energy sources (such as coal mixed with biomass power generation). If it directly enters the trading market, it will violate the environmental attributes of green electricity and mislead consumer spending. Moreover, traditional grid connection verification models have significant drawbacks: first, they rely on manual review, which is extremely inefficient; second, review standards vary across different regions; and third, the review process is opaque. In this step, blockchain verification nodes periodically scan the dataset of inactive digital green certificates and generate verification instructions containing grid connection standard thresholds for each green certificate. The smart contract automatically executes the instructions, comparing the green electricity data with the thresholds. After successful verification, the green certificate is marked as "tradable," and verification information is added to generate a dataset of tradable digital green certificates. The processes of "generating grid connection verification instructions" and "updating to a tradable state" are completed automatically through blockchain smart contracts without human intervention, reducing the verification cycle to a few hours and significantly improving transaction efficiency. This not only ensures the safety of grid operation but also standardizes the green electricity market access mechanism. It is an essential step for green electricity to enter the legal trading process and is of key significance for maintaining market order and protecting user rights.

[0039] S203. Obtain the power grid dynamic parameter set, and based on the power grid dynamic parameter set and the tradable digital green certificate dataset, generate a green certificate real-time pricing dataset through a spatiotemporal equity dynamic pricing strategy.

[0040] The power grid dynamic parameter set can be a collection of parameters reflecting the real-time operating status of the power grid, including the real-time total output of the regional power grid, the real-time grid congestion level, and the real-time grid load status. The data comes from the power dispatching system. The spatiotemporal rights dynamic pricing strategy can be a strategy that combines time (peak / off-peak periods), space (supply-demand tension / abundance areas), and green electricity environmental rights (emission reductions) to formulate real-time prices. The core principle is "high demand, high price; abundant supply, low price; high environmental value, premium." The green certificate real-time pricing dataset can be a set of tradable digital green certificate real-time prices generated based on the spatiotemporal rights dynamic pricing strategy.

[0041] Specifically, the value of green electricity exhibits significant temporal and spatial variability and environmental externalities. Traditional fixed pricing models fail to reflect its true value, becoming a major obstacle to the absorption of green electricity. From a temporal perspective, the supply guarantee value of green electricity during peak electricity consumption periods (such as summer evenings) is far higher than during off-peak periods, but fixed prices fail to incentivize power generation companies to increase output during peak periods, leading to a supply-demand mismatch. From a spatial perspective, green electricity in power-scarce areas has lower transportation costs and higher practical value, but fixed prices fail to reflect regional differences, resulting in resource waste. From an environmental perspective, the emission reduction effects of different types of green electricity vary significantly (e.g., photovoltaic emission reduction is higher than wind power), but fixed prices fail to reflect their environmental value, weakening the motivation to produce high-purity green electricity. This rigid pricing mechanism directly leads to: insufficient green electricity supply during peak periods, forcing the grid to rely on thermal power to supplement it, violating the "carbon reduction" target; and an oversupply of green electricity during off-peak periods, resulting in a large amount of renewable energy being abandoned due to its inability to be absorbed. This step utilizes dynamic parameters from the power grid monitoring system, combined with information such as the purity and emission reduction of tradable digital green certificates, to automatically calculate green certificate prices (increasing during peak hours and decreasing during off-peak hours, adjusting based on regional supply and demand and environmental value) through a spatiotemporal dynamic pricing strategy, generating a real-time pricing dataset for green certificates. By "acquiring the power grid dynamic parameter set," real-time changes in power grid supply and demand are captured, providing a data foundation for pricing. The "spatiotemporal dynamic pricing strategy" overcomes the limitations of traditional pricing by incorporating time, space, and environmental factors into the model, enabling green certificate prices to flexibly respond to market changes. The combination of these two approaches is a core means to achieve optimal allocation of green electricity resources and improve the renewable energy absorption rate.

[0042] S204. Based on the real-time pricing dataset for green certificates, verify the digital green certificates during the user's electricity consumption process and output a green electricity consumption traceability report.

[0043] A green electricity consumption traceability report can be a report that records the entire process of green electricity from generation and trading to consumption, including the real-time equity price of the green certificate NFT code, the corresponding cumulative value of the power generation, the consumption time point, and the geographical location information.

[0044] Specifically, the closed-loop management of green electricity trading relies on a strict rights redemption mechanism and full-process traceability capabilities. Traditional models have significant shortcomings in these two aspects: Firstly, if digital green certificates are not verified, fraudulent activities such as "selling one certificate multiple times" and "reusing" may occur. This means some companies may sell the rights corresponding to the same green electricity multiple times, resulting in users paying fees but not receiving actual green electricity, severely damaging market trust. Secondly, green electricity traceability relies on manual recording or centralized archiving methods, resulting in fragmented and easily tampered data. Users cannot confirm the true source and environmental attributes of the green electricity they purchase; for example, they cannot verify whether the electricity corresponding to a certain green certificate truly comes from a photovoltaic power station or is thermal power sold under the guise of green electricity. In this step, when a user consumes electricity, they send a confirmation command to the blockchain. The smart contract automatically verifies the corresponding green certificate and marks its status. After verification, the blockchain node integrates the full-process data to generate a green electricity consumption traceability report, which is then sent to the user. By verifying digital green certificates and leveraging the immutability of blockchain, each green certificate can only be used once, technically preventing "one certificate sold multiple times" and ensuring the uniqueness and fairness of transactions. The output of green electricity consumption traceability reports integrates data from the entire process of power generation, verification, transaction, and verification, allowing users to clearly view the source, flow, and environmental contribution of green electricity through the reports.

[0045] This solution first acquires encrypted real-time green electricity data from the intelligent monitoring equipment of distributed green power plants, uploads it to the blockchain node, and generates inactive digital green certificates containing basic green electricity information but without trading eligibility. Next, the power grid company's verification node generates grid connection instructions according to standards. After verification by the smart contract, the inactive green certificates are updated to tradable green certificates with compliance identifiers. Then, real-time grid parameters are acquired and combined with the tradable green certificates to generate a real-time pricing dataset through a spatiotemporal equity strategy. Finally, after a user purchases electricity, the smart contract verifies the green certificates, and the blockchain node integrates the entire process data to output a consumption traceability report, which is then sent to the user. Real-time encryption and blockchain storage ensure the authenticity and security of green electricity data, solving the problems of data fraud and privacy leaks in traditional transactions; automated grid connection verification improves transaction efficiency and compliance, unifies standards and shortens cycles, and ensures grid security; dynamic pricing of green electricity resources through time and space rights optimizes the allocation of green electricity resources, flexibly responding to market supply and demand to improve the renewable energy consumption rate; the green certificate verification mechanism and consumption traceability report enhance market trust and participation, eliminate fraud, and clearly present the entire green electricity process; ultimately, it helps the low-carbon transformation of the energy structure and promotes the fair and orderly development of the green electricity trading market.

[0046] In some embodiments, the real-time green electricity encrypted dataset includes a power generation data stream, millisecond-precision timestamp data, and geographic coordinate data of green electricity generator sets. Based on the real-time green electricity encrypted dataset, a preset green certificate generation verification logic is triggered: based on the millisecond-precision timestamp data, the fluctuation characteristics of the power generation data stream within the corresponding time interval are analyzed to see if they conform to a preset green electricity output mode; based on the geographic coordinate data, it is verified whether the green electricity generator sets are located within a preset list of trusted green electricity regions; when the fluctuation characteristics of the power generation data stream conform to the green electricity output mode and the green electricity generator sets are located within the list of trusted green electricity regions, the blockchain node determines that the verification is successful; after the blockchain node verifies the verification, the real-time green electricity encrypted dataset is encapsulated into a real-time green electricity encrypted data unit with a unique identifier, and the real-time green electricity encrypted data unit is recorded in the corresponding blockchain node on the blockchain to generate an inactive digital green certificate dataset.

[0047] The power generation data stream can be a sequence of electrical energy data generated by green power generators within a unit of time, and is the core data reflecting the actual output of green electricity. Millisecond-precision timestamp data can be time-stamped data with millisecond-level precision at the moment the power generation data stream is collected, used to accurately record the time point of green electricity generation. The geographical coordinate data of green power generators can be the latitude and longitude information of the location of green power generators (such as photovoltaic power plants and wind farms), and is key data for identifying the location of green electricity production. The green electricity output pattern can be the typical characteristics and patterns of power generation changes over time for different types of green power generators (such as photovoltaic and wind power) during normal operation. The trusted green electricity area list can be a pre-defined, certified set of legal green electricity production areas, used to verify whether green power generators are located within compliant production areas. The real-time green electricity encrypted data unit can be an independent data unit formed by encapsulating a single set of data (containing power generation, timestamp, and geographical coordinates at a certain moment) from the real-time green electricity encrypted dataset; it is the basic unit of blockchain storage.

[0048] Specifically, the core premise of green electricity trading is the authenticity of the "green electricity" identity, that is, the electricity traded must indeed come from renewable energy sources (such as wind power and solar power), rather than non-clean energy sources such as thermal power. If there is a lack of strict verification and storage mechanisms for green electricity data, "fake green electricity" may be mixed into the trading, undermining the fairness of the green electricity market. The inactive digital green certificate dataset is a necessary transitional form for digital green certificates from "data" to "tradable assets". Only after being verified by blockchain nodes and generating inactive digital green certificates can they be converted into a tradable state through subsequent grid connection verification instructions. If this step is skipped and unverified green electricity data is used directly for trading, a large amount of invalid or false data may enter the market, increasing trading risks and the probability of disputes. To address the above issues, this step involves the generator set sensors first collecting real-time power generation data, equipment geographic coordinates, and millisecond-level timestamp data. An encrypted data stream is then generated via a hardware encryption module. This data is transmitted to an edge computing node via a 5G private network, where a digital signature is added to form a real-time encrypted green electricity dataset. Next, this dataset is uploaded to a blockchain node, automatically triggering a pre-defined green certificate generation and verification logic. The timestamp analysis module extracts the power generation data stream and analyzes its fluctuation characteristics (such as power change rate and intermittent periods), comparing it with a pre-defined green electricity output mode library and outputting a similarity score (e.g., wind power similarity > 90%). Simultaneously, the geographic coordinate verification module... The blockchain resolves the coordinates of the generating units and performs geofencing matching with the list of trusted green electricity areas stored on the blockchain. The blockchain node determines that the verification is successful only if the timestamp verification score reaches or exceeds a set threshold (e.g., similarity > 90%) and the coordinates are within the trusted list. The node then generates a unique hash identifier (e.g., 0x3a7b...c21d) for the dataset and packages the hash value, original encrypted data, timestamp, and coordinates into a real-time encrypted green electricity data unit. This unit is written into a new blockchain block and marked as "inactive." Finally, the blockchain network broadcasts this new block, and each node synchronously stores the data unit, generating a globally searchable but non-tradable inactive digital green certificate dataset.

[0049] This solution employs dual verification of power generation data flow fluctuations and geographic coordinates to filter out fraudulent green electricity data at the source. This ensures that the generated inactive digital green certificates correspond to the actual green electricity production process, providing a reliable credential basis for subsequent green electricity transactions and reducing transaction disputes caused by fraudulent data. Utilizing the distributed storage and immutability of blockchain nodes, verified green electricity data is permanently recorded in the ledger. Combined with millisecond-level timestamps and geographic coordinates, this achieves full traceability of green electricity from production to green certificate generation, meeting the regulatory authorities' verification requirements for green electricity compliance and providing users with transparent information on the source of green electricity.

[0050] In some embodiments, each green certificate in the inactive digital green certificate dataset is NFT-encoded to generate a grid connection verification instruction containing the green certificate NFT code, the corresponding cumulative power generation value, geographical location information, and the planned grid connection time. Based on the grid connection verification instruction, the feasibility of physical transmission of the cumulative power generation value at the planned grid connection time is analyzed. When the cumulative power generation value is transmittable, a verification pass signal is returned to the blockchain node. In response to the verification pass signal, the blockchain node updates the state attribute of the corresponding green certificate NFT code from inactive to tradable. Based on all green certificate NFT codes updated to tradable, a tradable digital green certificate dataset is generated.

[0051] The cumulative power generation value refers to the total amount of green electricity generated by the green power generating units corresponding to inactive digital green certificates within a specific time period, serving as the core quantitative basis for the value of green certificates. Geographical location information refers to the actual geographical coordinates of the green power generating units, used to determine the production location of green electricity and subsequently analyze its physical transmission path to the grid connection node. The planned grid connection time point refers to the specific time when green electricity is planned to connect to the grid, preset by the green electricity producer based on the power generation plan and grid dispatch requirements, used to match the grid's transmission capacity at that time. Physical transmission feasibility refers to the actual possibility of green electricity being transmitted from the generator unit's location to the grid connection node at the planned grid connection time point, requiring a comprehensive judgment considering factors such as grid topology, transmission line capacity, and grid load rate at that time. The verification pass signal is a confirmation signal sent by the grid dispatch system or a trusted third-party verification node to the blockchain node when the analysis confirms that the cumulative power generation value has physical transmission feasibility at the planned grid connection time point, triggering a green certificate status update.

[0052] Specifically, the core of green electricity trading is the simultaneous delivery of the environmental value of green electricity and the physical power. If only production-side verification is completed (generating inactive green certificates) without confirming whether the green electricity can actually be connected to the grid, there is a risk that the green certificates may be traded but the corresponding green electricity cannot be delivered. For example, although the green certificates generated by a wind power project have passed production verification, the green electricity cannot actually be connected to the grid because the transmission lines are at full load at the planned grid connection time. In this case, the traded green certificates are essentially "virtual certificates," which violates the physical attributes of green electricity trading. Moreover, as a value carrier, digital green certificates must have unique identification and anti-tampering characteristics. If NFT encoding is not performed, the same green electricity may be repeatedly generated into multiple green certificates, or the green certificate information may be tampered with (such as falsely reporting power generation), which undermines market fairness. To address the above issues, this step first iterates through the inactive digital green certificate dataset, performing NFT encoding on each certificate: extracting its power generation data stream (e.g., 10MW of power generation data from a wind farm at 15:30:00:00), geographical location information (e.g., 39.90°N, 116.41°E), and planned grid connection time (e.g., 14:00:00 on 2025-06-01), calling the smart contract encoding engine to generate a unique NFT code (e.g., "0x8a5d...c3f2") and binding the data, encapsulating it into a grid connection verification instruction set; subsequently, the power grid dispatch system receives the instructions and performs physical transmission feasibility verification: first, based on the geographical location, the power grid access node (e.g., node A in the East China power grid) is located, and its real-time transmission capacity (e.g., 500MW) is retrieved; second, the planned grid connection time of the green certificate is compared with the power grid load forecast curve (e.g., verifying whether it falls within the evening peak congestion period of 18:00-20:00). Finally, the node's absorption capacity is calculated by combining the cumulative power generation value (e.g., 50MWh) and the line loss model (e.g., ±3% loss rate). If all three checks pass (e.g., transmission margin > 60MWh, non-congested period, acceptable loss), a digital signature verification pass signal is generated and returned to the blockchain node. Ultimately, the blockchain node listens to this signal, calls the updateStatus() function of the smart contract, updates the matching NFT encoded status attribute from "inactive" to "tradable", records the change timestamp (e.g., 2025-05-20 09:15:23) and signal hash value (e.g., "sha256:7d4a...b9e1") in the distributed ledger, and aggregates all updated status green certificates to generate a tradable digital green certificate dataset.

[0053] This scheme verifies the feasibility of physical transmission, enabling tradable green certificates to have actual grid connection capabilities, avoiding "virtual green certificate" transactions, ensuring that the environmental value of green electricity obtained by buyers is supported by real power delivery, and enhancing the credibility of green electricity transactions. The combination of green certificate NFT encoding and blockchain technology fundamentally eliminates problems such as green certificate forgery and duplicate transactions, providing transparent and traceable digital credentials for the green electricity market and maintaining fair market competition. Based on the feasibility analysis of planned grid connection timelines and grid load, the scheme matches green electricity grid connection plans with grid dispatching needs, reducing power curtailment, increasing green electricity absorption rates, and promoting the efficient utilization of new energy sources.

[0054] In some embodiments, the base layer value is determined by multiplying the cumulative power generation value by a preset unit fixed emission reduction, and a base layer value is assigned to each green certificate NFT code in the tradable digital green certificate dataset; the dynamic layer value adjustment factor is obtained by analyzing the power grid dynamic parameter set, the dynamic layer value is determined, and a dynamic layer value is assigned to each green certificate NFT code in the tradable digital green certificate dataset; the base layer value of each green certificate NFT code is multiplied by the corresponding dynamic layer value adjustment factor to generate the real-time equity price of the green certificate NFT code; the real-time equity prices of all green certificate NFT codes are aggregated to generate a green certificate real-time pricing dataset.

[0055] Fixed emission reduction per unit refers to a fixed reference value for the carbon emissions reduced by green electricity per unit of power generation compared to traditional fossil fuel power generation (such as thermal power). It is a fundamental quantitative indicator for measuring the environmental value of green electricity. The basic layer value can be the inherent environmental value of green certificates, which does not fluctuate with real-time market changes, reflecting the fundamental contribution of green electricity to reducing carbon emissions and promoting low-carbon transformation. The dynamic layer value adjustment factor can be a coefficient used to quantify the impact of grid dynamic parameters on the value of green certificates, reflecting the market adaptability value of green certificates under different environmental rights (such as the ratio of green to thermal power output), spatial (such as regions with tight / sparse power supply and demand), and temporal (such as peak / valley power periods). The dynamic layer value can refer to the value generated by green certificates due to the real-time operating status of the grid, reflecting the actual contribution of green certificates to grid stability and power supply and demand balance under specific spatiotemporal conditions. The real-time equity price can be the actual trading price of the green certificate NFT code at a certain moment, comprehensively reflecting the basic environmental value and real-time market adaptability value of green certificates.

[0056] Specifically, the core of green electricity trading is to unify the environmental and market value of green electricity. The pricing mechanism is the key to connecting supply and demand and ensuring fair and efficient trading. Traditional green electricity pricing methods have significant limitations: on the one hand, prices are determined solely based on power generation or fixed subsidies, ignoring the core environmental value of green electricity (such as emission reduction contributions), leading to an underestimation of its environmental value; on the other hand, the spatiotemporal dynamic characteristics of grid operation (such as peak-valley differences and regional supply-demand imbalances) are not considered, resulting in prices failing to reflect the actual utility of green electricity in different scenarios, thus leading to problems such as low green electricity consumption efficiency and insufficient market activity. To address the above issues, this step first extracts the cumulative power generation value (e.g., 50 MWh) bound to the NFT code of the tradable digital green certificate dataset. It then calls a preset fixed emission reduction parameter (e.g., 0.8 tons CO2 / MWh) and multiplies the two to generate the base layer value (e.g., 50 × 0.8 = 40 tons CO2 emission reduction equivalent). Subsequently, it acquires a real-time set of dynamic grid parameters, including regional net load volatility (e.g., current load / predicted load = 1.15), key section congestion coefficient (e.g., actual power flow / safety limit = 0.95), and renewable energy consumption priority indicators (e.g., green electricity accounting for 30% in dispatch instructions). These parameters are then analyzed using a rule engine. Parameter analysis: If the regional net load volatility is greater than the threshold (e.g., >1.1) and the blockage coefficient is close to the critical value (e.g., >0.9), an upward floating factor (e.g., 1.25) is generated. If the absorption priority is high and the load volatility is mild (e.g., volatility <0.9), a downward floating factor (e.g., 0.8) is generated. Then, the base layer value is multiplied by the dynamic layer value adjustment factor (e.g., 40 × 1.25 = 50 environmental equity units), and converted to fiat currency pricing through an on-chain exchange rate contract (e.g., 1 unit = 10 yuan). Finally, all green certificate NFT codes are traversed to generate a real-time equity price mapping relationship, which is encapsulated into a key-value pair data structure to form a real-time pricing dataset for green certificates.

[0057] This scheme transforms the environmental emission reduction contribution of green electricity into a tradable value indicator through the basic layer value, while the dynamic layer value adjustment factor reflects the actual utility of green electricity under different temporal and spatial conditions. The combination of the two allows the price of green certificates to reflect both environmental attributes and adapt to market dynamics, achieving a unity of "green value" and "market value." The temporal and spatial equity dynamic pricing strategy guides green electricity to be prioritized for consumption in key scenarios such as peak power periods and load centers through price signals, reducing the phenomenon of green electricity curtailment. At the same time, it lowers prices during off-peak power periods or in areas with ample supply and demand, encouraging users to consume more green electricity, balancing the grid load, and improving the stability of grid operation.

[0058] In some embodiments, the power grid dynamic parameter set includes the real-time total output value of the regional power grid, the real-time power grid congestion level, and the real-time power grid load status; the real-time total output value of the regional power grid is analyzed to determine the real-time environmental gain / loss coefficient of the regional power grid cleanliness of the corresponding green certificate NFT code on the base layer value; the real-time power grid congestion level is analyzed to determine the spatial compensation coefficient required for the equivalent transmission loss from the geographical location of the corresponding green certificate NFT code to the target load center; the real-time power grid load status is analyzed to determine the time gain / loss coefficient required when the current time is during the peak or valley period of the power grid; the real-time environmental gain / loss coefficient, the spatial compensation coefficient, and the time gain / loss coefficient are multiplied to generate the dynamic layer value adjustment factor for each green certificate NFT code.

[0059] The real-time total output of a regional power grid can be the total power generation of the power grid within a specific region at a certain moment, that is, the sum of the electrical energy output by all power generation equipment (including thermal power, wind power, photovoltaic, etc.) in that region in real time. Real-time grid congestion level can be a quantitative indicator characterizing the degree of power transmission obstruction caused by excessive power or equipment capacity limitations in transmission lines or key nodes within the power grid. Real-time grid load status can be the total and distributed electricity demand carried by the power grid at a certain moment, mainly used to distinguish whether the current moment is during peak hours (high electricity demand) or off-peak hours (low electricity demand). Regional power grid cleanliness can be an indicator used to measure the proportion of clean energy (such as wind power, photovoltaic, hydropower, etc.) output in the total output of a specific regional power grid, reflecting the low-carbon and environmentally friendly nature of the power generation structure of the regional power grid. The real-time environmental gain / loss coefficient can be a coefficient used to correct the environmental value of the green certificate base layer based on the proportion of clean energy in the real-time total output of the regional power grid. When the proportion of clean energy is high, the coefficient is greater than 1 (gain); when the proportion of clean energy is low, the coefficient is less than 1 (loss). Equivalent transmission loss can be calculated as the equivalent proportion or quantified value of energy loss during transmission, taking into account factors such as the transmission distance between the green energy generator's geographical location and the target load center, and the grid congestion level. Spatial compensation coefficient is a coefficient that adjusts the value of the green certificate's base layer based on spatial costs, taking into account the transmission loss from the green energy generator's geographical location to the target load center; the greater the transmission distance and the greater the loss, the smaller the coefficient. Time gain / loss coefficient is a coefficient that adjusts the value of the green certificate's base layer based on real-time grid load conditions (peak / off-peak periods), adjusting for time value; during peak periods when electricity demand is high, the coefficient is greater than 1 (gain); during off-peak periods when electricity demand is high, the coefficient is less than 1 (loss).

[0060] Specifically, in the electricity market, the grid status (such as the proportion of clean energy, congestion, and load levels) changes in real time, and the value of green certificates should also be dynamically adjusted accordingly. Using a single dimension for adjustment will lead to system imbalance: relying solely on environmental coefficients will ignore the physical constraints of the grid, and using only spatial compensation will result in value distortion across time periods. Therefore, it is necessary to achieve this through a three-factor product calculation. If there is a lack of comprehensive dynamic layer value adjustment factors, green certificate pricing will lag behind market changes, which may lead to unfair phenomena such as high-value green electricity being traded at low prices or low-value green electricity being traded at high prices, thus dampening the enthusiasm of green electricity producers and consumers. However, by comprehensively calculating the real-time environmental gain / loss coefficient, spatial compensation coefficient, and time gain / loss coefficient, it can be ensured that the green certificate price reflects market dynamics in real time and protects the fair rights and interests of both parties in the transaction. To address the above issues, this step first separates fossil fuel and clean energy output data based on the real-time total output value of the regional power grid in the power grid dynamic parameter set, calculates the proportion of clean energy output, and generates a real-time environmental gain / loss coefficient accordingly. For example, when the proportion of clean energy is higher than the regional benchmark, the coefficient increases by a certain value (e.g., 0.1) for every certain percentage (e.g., 10%) exceeding the benchmark, and decreases by a certain value (e.g., 0.15) for every lower proportion. Then, using the generator geographical coordinates in the green certificate NFT code, the power grid digital twin system is called to determine the electrical distance from the generator to the target load center. Combined with the real-time power grid congestion level (e.g., each increase in congestion level corresponds to a 3% increase in loss rate), the approved loss rate is calculated, and a spatial compensation coefficient is generated, with a value of 1 plus the approved loss rate. Next, the real-time power grid load status is analyzed. When the load rate reaches or exceeds a certain high threshold (e.g., 85%), it is determined to be a peak period. The time gain coefficient is 1 plus (real-time load rate minus the high threshold) multiplied by a coefficient (e.g., 0.2). When the load rate is below a certain low threshold (e.g., 40%), it is determined to be a low period. The time loss coefficient is 1 minus (the low threshold minus the real-time load rate) multiplied by a coefficient (e.g., 0.1). During flat periods, the coefficient remains at 1.0. Finally, for each green certificate NFT encoding in the tradable digital green certificate dataset, the calculated real-time environmental gain / loss coefficient, spatial compensation coefficient, and time gain / loss coefficient are multiplied. That is, the dynamic layer value adjustment factor = real-time environmental gain / loss coefficient × spatial compensation coefficient × time gain / loss coefficient, thereby batch outputting the dynamic layer value adjustment factor of all green certificates.

[0061] This scheme comprehensively considers the real-time environment of the power grid, spatial transmission costs, and temporal load conditions, enabling green certificate prices to move beyond static environmental values ​​and reflect real-time market dynamics. This allows for a more accurate match between the actual value of green electricity and the actual value of green energy, avoiding resource misallocation caused by pricing discrepancies. The environmental coefficient dynamically responds to changes in the power grid's carbon intensity, increasing the premium rate of green certificates during high-pollution periods and truly reflecting the marginal benefits of emission reduction. The introduction of a spatial compensation coefficient balances the transmission costs of long-distance green electricity, enhances the market competitiveness of clean energy in remote areas, promotes the efficient flow of green electricity from the production end to load centers, and optimizes the allocation range of clean energy. The time coefficient makes green certificates more valuable during peak hours and more price-competitive during off-peak hours, incentivizing users to consume more green electricity during off-peak hours and rationally control electricity consumption during peak hours, thereby alleviating the peak-valley load difference in the power grid and improving the stability of power grid operation.

[0062] In some embodiments, the real-time green power output of the green power generating units in the current time period is determined based on the real-time total power output of the regional power grid and the power generation data stream; the real-time cleanliness percentage of the regional power grid is determined based on the ratio of the real-time green power output to the real-time total power output of the regional power grid; the real-time cleanliness percentage of the regional power grid is compared with a preset cleanliness benchmark threshold to determine the direction of deviation of the real-time cleanliness percentage of the regional power grid relative to the preset cleanliness benchmark threshold, and the deviation magnitude is determined; when the real-time cleanliness percentage of the regional power grid is higher than the preset cleanliness benchmark threshold, a real-time environmental gain coefficient with a value greater than 1 is generated, and the real-time environmental gain coefficient is dynamically increased according to the deviation magnitude; when the real-time cleanliness percentage of the regional power grid is lower than the preset cleanliness benchmark threshold, a real-time environmental loss coefficient with a value less than 1 is generated, and the real-time environmental loss coefficient is dynamically decreased according to the deviation magnitude.

[0063] Real-time green electricity output value refers to the actual power generation of green electricity generators during the current time period, and is a key indicator for measuring the contribution of green electricity to the regional power grid. The real-time cleanliness percentage of the regional power grid refers to the proportion of green electricity generation to total power generation in the regional power grid during the current time period, used to quantify the cleanliness of the regional power grid. The preset cleanliness benchmark threshold is a benchmark value set based on regional energy policies, environmental goals, and historical power grid cleanliness data, serving as a reference standard for judging whether the cleanliness of the regional power grid meets the standards, and can be dynamically adjusted according to the actual situation in the region. The real-time environmental gain coefficient is an adjustment coefficient generated when the real-time cleanliness percentage of the regional power grid is higher than the preset cleanliness benchmark threshold; its value is greater than 1, used to amplify the basic layer value of green certificates, reflecting the additional environmental contribution of green electricity in a high-cleanliness power grid. The real-time environmental depletion coefficient is an adjustment coefficient generated when the real-time cleanliness percentage of the regional power grid is lower than the preset cleanliness benchmark threshold; its value is less than 1, used to reduce the basic layer value of green certificates, reflecting the weakening of the environmental contribution of green electricity in a low-cleanliness power grid.

[0064] Specifically, the base value of green certificates is determined by the product of cumulative power generation and a preset fixed emission reduction per unit. This only reflects the inherent environmental value of green electricity but fails to reflect the impact of the real-time operation of the power grid on the value of green certificates. For example, the environmental contribution of green certificates of the same amount is relatively more significant in a power grid with a high proportion of clean energy; however, in a power grid with a high proportion of thermal power, its environmental value will be partially offset. Pricing based solely on the base value would lead to a disconnect between the value of green certificates and actual environmental benefits. Therefore, dynamic correction using real-time environmental gain / loss coefficients is necessary. To address these issues, this step first obtains encrypted power generation data streams from blockchain nodes, decrypts them, and extracts the real-time green power output value of the target area within the current time period (e.g., 150MW). Simultaneously, it pulls the total power output value of the regional power grid in real-time from the power grid dispatch center API (e.g., 500MW). After verification, this data is input into the smart contract. The smart contract then performs a cleanliness percentage calculation: real-time green power output value ÷ total regional power grid output value × 100% (e.g., 150 / 500 × 100% = 30%). Next, it calls the preset cleanliness benchmark threshold (…). Deviation analysis is performed on the result of the deviation (e.g., 30%). If the result is higher than the threshold (e.g., 35%), it is marked as a positive deviation and the deviation magnitude is calculated (e.g., 5%). A real-time environmental gain coefficient greater than 1 is generated (e.g., 1 + (5 / 30) × 0.5 ≈ 1.08). If the result is lower than the threshold (e.g., 25%), it is marked as a negative deviation and the magnitude is calculated (e.g., 5%). A real-time environmental loss coefficient less than 1 is generated (e.g., 1 - (5 / 30) × 0.5 ≈ 0.92). Finally, the generated coefficient is written to the corresponding green certificate NFT metadata field for method calls.

[0065] This scheme uses real-time environmental gain / loss coefficients to dynamically link the value of green certificates with the cleanliness of the regional power grid, accurately reflecting the actual environmental contribution of green electricity in different power grid environments and avoiding value distortion. It guides green electricity generating units to be tilted towards areas with low cleanliness, maximizing the emission reduction benefits of green electricity, while avoiding excessive investment in green electricity in areas with high cleanliness, thereby improving the overall efficiency of energy resource utilization.

[0066] In some embodiments, the geographical location information of the target load center is extracted according to the grid connection verification command; based on the real-time grid congestion level, the tolerance threshold of the current grid congestion state for power transmission distance is analyzed, and the maximum effective transmission radius is dynamically generated; based on the geographical coordinate data of the green power generator and the geographical location information of the target load center, the actual spatial distance from the green power generator to the target load center is determined; the actual spatial distance is dynamically compared with the maximum effective transmission radius: when the actual spatial distance is less than or equal to the maximum effective transmission radius, a spatial compensation coefficient with a base value of 1 is generated; when the actual spatial distance is greater than the maximum effective transmission radius, the spatial compensation coefficient is dynamically adjusted down according to the ratio of the excess distance to the maximum effective transmission radius, based on a preset nonlinear attenuation rule and with a value of 1 as the base.

[0067] The target load center can be the core geographical location of a concentrated power consumption area (such as an industrial park or urban business district). The maximum effective transmission radius can be the maximum distance threshold under the current grid congestion state, where the transmission loss is within an acceptable range when power is transmitted from the generator to the load center. Its value changes dynamically with the real-time grid congestion level.

[0068] Specifically, the geographical locations of green power generating units vary significantly: some units may be close to load centers (such as photovoltaic power stations around cities), with short transmission distances and low losses; others may be located in remote areas (such as desert wind power bases), with long transmission distances and high losses. If these geographical differences are not considered and green certificates are priced using a uniform standard, it will lead to an unfair situation where the value of green certificates for units near load centers is overestimated and the value of green certificates for units far from load centers is underestimated, thus discouraging the enthusiasm for green power development in remote areas. At the same time, grid congestion is a common problem in power transmission: when a grid line is overloaded, the power transmission capacity will be limited. In this case, even if the generating unit is close to the load center, the actual transmission loss may increase significantly due to congestion. Conversely, if the grid congestion level is low and the transmission channel is unobstructed, even if the distance is slightly longer, the loss may be within an acceptable range. To address the above issues, this step first extracts the geographical location information (such as latitude and longitude coordinates) of the target load center from the grid connection verification command, and obtains the geographical coordinate data of the generator set bound to the green certificate NFT code from the blockchain node. Next, based on the real-time grid congestion level (e.g., congestion level 1 indicates unobstructed flow, congestion level 5 indicates severe congestion), the maximum effective transmission radius is dynamically generated (e.g., congestion level 1 corresponds to 500km, congestion level 3 corresponds to 300km). Then, the geographic information system is called to calculate the actual spatial distance from the generator set to the target load center, and the actual distance is dynamically compared with the current maximum effective transmission radius: if the actual distance is less than or equal to the maximum radius (e.g., the actual distance is 280km while the maximum radius is 300km), a spatial compensation coefficient of 1.0 is directly assigned; if the actual distance is greater than the maximum radius (e.g., the actual distance is 360km while the maximum radius is 300km, the excess ratio is 20%), a preset nonlinear attenuation rule is applied to adjust the coefficient according to the excess ratio (e.g., the coefficient is reduced to 0.9 when the excess ratio does not exceed 0.3, and the coefficient is reduced to 0.5 when the excess ratio exceeds 0.3).

[0069] This scheme utilizes the dynamic adjustment of the spatial compensation coefficient to guide green electricity trading towards lower losses and higher efficiency: for green electricity located close to load centers and less affected by grid congestion, the spatial compensation coefficient is better, and the green certificate price is more competitive, thus being prioritized for consumption. For green electricity located far away but with reduced grid congestion, the compensation coefficient is increased, and reasonable pricing can also be obtained. This efficiency-first, dynamic balance mechanism can promote the precise matching of green electricity resources and load demand, reduce ineffective transmission losses, improve the overall green electricity consumption efficiency, and help the energy structure transformation.

[0070] In some embodiments, based on millisecond-level timestamp data, the precise millisecond-level time point of the corresponding green electricity generation is extracted; based on the real-time grid load status and the precise millisecond-level time point, the real-time load value corresponding to the precise millisecond-level time point is determined; the changing trend of the real-time load value within a preset time window is dynamically evaluated to determine the load change direction: when the load change direction indicates that the load is continuously rising, a time gain coefficient with a value greater than 1 is generated, and the time gain coefficient is dynamically increased according to the deviation of the number of continuously rising real-time load values ​​within the preset time window from the preset rise intensity threshold; when the load change direction indicates that the load is continuously falling, a time loss coefficient with a value less than 1 is generated, and the time loss coefficient is dynamically decreased according to the deviation of the number of continuously falling real-time load values ​​within the preset time window from the preset fall intensity threshold.

[0071] Precise millisecond-level time points can be the specific millisecond-level moments of actual green electricity generation extracted from millisecond-precision timestamp data, serving as a time benchmark linking green electricity generation behavior with grid load status. Real-time load values ​​can be the specific numerical values ​​corresponding to the real-time grid load status at precise millisecond-level time points, quantifying the size of the grid load. Load change direction can refer to the overall trend of real-time load value changes within a preset time window, including both "continuous increase" and "continuous decrease" scenarios. Preset increase intensity thresholds can be pre-set benchmark values ​​used to measure the intensity of load increase, derived from historical load increase data, reflecting the average intensity of load increase under normal grid conditions. Time gain coefficients can be coefficients greater than 1 generated when the load change direction is continuously increasing, used to reflect the higher value of green electricity during peak hours in green electricity pricing, and are a component of the dynamic layer value adjustment factor. Preset decrease intensity thresholds can be pre-set benchmark values ​​used to measure the intensity of load decrease, derived from historical load decrease data, reflecting the average intensity of load decrease under normal grid conditions. The time loss factor can be a coefficient with a value less than 1 generated when the load change direction is continuously decreasing. It is used to reflect the relatively low value of green electricity during off-peak hours in green electricity pricing and is also a component of the dynamic layer value adjustment factor.

[0072] Specifically, the generation of green electricity (such as wind power and photovoltaic power) fluctuates due to natural conditions, while the load demand of the power grid also changes regularly over time (such as peak industrial electricity consumption during the day and off-peak residential electricity consumption at night). During peak load periods, the power grid has an urgent need for electricity, and green electricity connected at this time can effectively alleviate the power grid supply pressure and reduce the peak-shaving demand of traditional thermal power, with higher environmental and practical value. However, during off-peak periods, the power grid supply exceeds demand, and the connection of green electricity may face difficulties in absorption, resulting in a relatively lower value. If the impact of time factors on the value of green electricity is ignored and a fixed pricing method is adopted, the value of green electricity during peak periods will be underestimated and the value during off-peak periods will be overestimated. This will not only fail to reflect the actual contribution of green electricity but also reduce the incentive for power generators to increase the supply of green electricity during peak periods, which is not conducive to the efficient absorption of green electricity. To address the above issues, this step first obtains the precise millisecond-level time point of the green certificate from the blockchain node (e.g., 2023-10-05T14:30:25.456Z), and simultaneously pulls the real-time power grid load status data stream through the power grid dispatch center API; then, using the current time point as a reference, it slides forward a preset time window (e.g., 5 minutes), extracts all millisecond-level load values ​​within the window, calculates the load difference between adjacent time points, and counts the number of consecutive positive differences (increasing) or negative differences (decreasing) to determine the direction of load change; if the load continues to increase, it counts the number of consecutively increasing real-time load values ​​(e.g., 15 times). Compare it with a preset rise intensity threshold (e.g., 10 times) and calculate the deviation (e.g., (15-10) / 10=0.5), generating a time gain coefficient (e.g., 1+0.1×0.5=1.05); if the load continues to decrease, count the number of consecutive decreases (e.g., 12 times) and compare it with a preset fall intensity threshold (e.g., 8 times) to calculate the deviation (e.g., (12-8) / 8=0.5), generating a time loss coefficient (e.g., 1-0.05×0.5=0.975); finally, set coefficient boundary constraints (e.g., upper limit of gain coefficient 2.0, lower limit of loss coefficient 0.5) to prevent pricing distortion in extreme scenarios.

[0073] This solution, based on millisecond-level timestamps and real-time load status analysis, enables the time coefficient to match the instantaneous changes in grid load in real time, avoiding pricing deviations caused by coarse time granularity and ensuring that the value of green electricity is truly reflected at different points in time. By distinguishing the direction of load changes and combining the deviation magnitude adjustment coefficient, it ensures that the revenue of the power generator matches the actual contribution of green electricity to the grid, while also allowing the power consumer to obtain a reasonable green electricity price based on the actual load status, thus balancing the interests of both parties in the transaction.

[0074] In some embodiments, the system obtains redemption requests from target users for specific green certificate NFT codes in the real-time pricing dataset for green certificates, analyzes the target redemption electricity value specified in the redemption request, verifies whether the current state of the specific green certificate NFT code is tradable, and when the verification passes, marks the cumulative power generation value corresponding to the specific green certificate NFT code as redeemed on the blockchain node based on the target redemption electricity value. The system also aggregates the real-time equity price, corresponding cumulative power generation value, consumption time point, and geographical location information of all redeemed green certificate NFT codes to generate a green electricity consumption traceability report.

[0075] The target amount of green electricity to be reimbursed can be the amount of green electricity that the user wants to consume through this reimbursement, as specified in the reimbursement request. It serves as the basis for determining whether the cumulative amount of electricity generated corresponding to the green certificate meets the user's needs.

[0076] Specifically, from the perspective of the completeness of the transaction loop, the entire process of green electricity trading includes green certificate generation, status activation, and pricing. Verification is the crucial step in realizing the transfer of green electricity rights from market circulation to actual user use. Without the verification process, green certificates will remain in a "tradable" state, unable to complete the transformation from "goods" to "services," leading to a break in the transaction process. Users will not be able to actually enjoy green electricity rights, and the power generation of green electricity generators will not accurately match user needs, ultimately affecting the normal operation of the green electricity market. From the perspective of the clarity of rights ownership, digital green certificates achieve uniqueness through NFT encoding. However, before verification, the corresponding cumulative power generation value remains in a "tradable" state, potentially facing the risk of duplicate trading or misappropriation. The verification process, by marking the cumulative power generation value of a specific green certificate NFT encoding as "verified" on the blockchain node, clearly identifies that this portion of the rights belongs to the user who initiated the verification request, avoiding disputes caused by ambiguous rights ownership. To address the above issues, in this step, the user selects the target green certificate NFT code through the front end, inputs the target redeemed electricity value (e.g., 150.5 kWh), and submits it with a signature. The blockchain node receives the request and parses the green certificate NFT code and the target redeemed electricity value. Next, the node queries the current status attribute field of the green certificate NFT code. If the status is not "tradable" (e.g., redeemed), the request is rejected and an error code (e.g., STATUS_INVALID) is returned. If the verification passes, the smart contract executes the following operations: reads the current cumulative power generation value, compares the target redeemed electricity value with the remaining electricity (if the limit is exceeded, the redemption fails), and further... The new cumulative power generation is the original cumulative value minus the target write-off power value (e.g., from 1000kWh to 849.5kWh), and a "write-off mark" is written to the blockchain to record the write-off volume and remaining power. Then, the node automatically captures the real-time equity price of the write-off (e.g., 0.12 yuan / kWh), the target write-off power value, the consumption time point (the blockchain timestamp when the request is completed, e.g., 2025-03-15T14:23:05.876Z), and the geographical location information (e.g., latitude and longitude [38.9072, -77.0369]), generates a structured report according to a preset template (e.g., JSON format containing NFT ID, consumed power, unit price, time, and location), and stores the report on the blockchain after hashing, and returns a verifiable link to the user.

[0077] This solution ensures the integrity of the closed-loop green electricity trading system, creating a closed loop from green certificate generation and pricing to final user consumption. This guarantees that green electricity rights can be effectively transformed into users' actual electricity needs, promoting a virtuous cycle in the green electricity market. It also enhances the clarity and security of rights ownership. Through the state marking and immutability of the blockchain, it avoids the risks of duplicate trading and misappropriation of green electricity rights, clarifies the ownership of rights for each write-off, and reduces transaction disputes. Furthermore, it strengthens regulatory and compliance support. The green electricity consumption traceability report provides regulatory authorities with traceable and transparent green electricity consumption data, meeting the regulatory requirements of environmental policies and energy regulations for green electricity trading, and contributing to the achievement of clean energy development goals.

[0078] In some embodiments, based on the green electricity consumption traceability report, real-time exchange rate data of the carbon market is associated when the transaction settlement is completed; according to the real-time exchange rate data of the carbon market, the number of carbon allowance tons that can be exchanged for the tradable digital green certificate represented by the unit green certificate NFT code is dynamically calculated; on the blockchain node, according to the preset exchange ratio, the green certificate rights corresponding to the green certificate NFT code of the completed transaction are transferred to the corresponding number of carbon allowance tons, and recorded in the transaction party's account in the carbon market.

[0079] Real-time exchange rate data in the carbon market refers to the real-time exchange rate between carbon allowances and currencies, reflecting the current market value of carbon allowances. This data typically originates from the real-time data interface of carbon trading methods (such as official data channels of national or regional carbon trading markets). Carbon allowance tonnage refers to the number of tons of greenhouse gases (measured in carbon dioxide equivalent) permitted for emission in the carbon market. It is the core trading instrument in the carbon market, used to quantify the carbon emission rights of enterprises or institutions.

[0080] Specifically, from the perspective of quantifying environmental value, the core value of green electricity lies in the carbon emissions reduction it provides by replacing fossil fuel power generation. Carbon quotas are currently the globally recognized standard tool for quantifying carbon emission rights. However, in green electricity trading, it is difficult to directly reflect its emission reduction benefits and its connection to the carbon market solely through "green certificate pricing." For example, 1 megawatt-hour of green electricity can reduce approximately 0.6 tons of carbon dioxide emissions (this varies slightly depending on the region's energy structure). But if this emission reduction cannot be converted into tradable carbon quotas in the carbon market, its environmental value will remain "implicit." From the perspective of market synergy, there is currently a certain degree of separation between the green electricity market and the carbon market: green electricity trading mainly focuses on the energy value of electricity itself, while the carbon market focuses on the trading of carbon emission rights. The value chains of the two are not fully integrated. This separation limits the economic attractiveness of green electricity, meaning that the benefits for companies purchasing green electricity only come from electricity cost savings or brand image enhancement, and they cannot directly obtain benefits from the carbon market. To address the above issues, in this step, upon completion of the green certificate transaction settlement, the blockchain node obtains real-time exchange rate data (e.g., carbon allowance unit price of 100 yuan / ton) from an authoritative carbon exchange via an API interface, verifies the signature, and writes it into the smart contract. Subsequently, it parses the green electricity consumption traceability report, extracts the consumption electricity (e.g., 1000kWh) and real-time equity price (e.g., 200 yuan) corresponding to the target green certificate NFT code, and first converts the consumption electricity into basic carbon according to a preset exchange ratio (e.g., 1MWh green electricity = 0.5 tons of carbon allowance). The quota (e.g., 1000kWh × 0.5 tons / MWh = 0.5 tons) is then converted into additional carbon allowances based on the real-time carbon price (e.g., 100 yuan / ton) (e.g., 200 yuan ÷ 100 yuan / ton = 2 tons), ultimately yielding the total carbon allowance tonnage (e.g., 0.5 tons + 2 tons = 2.5 tons). The smart contract automatically destroys the green certificate NFT code and triggers on-chain transfer, recording the calculated result (e.g., 2.5 tons) in the counterparty's blockchain account in the carbon market, completing the lossless conversion of environmental rights.

[0081] This plan converts green certificate rights into carbon allowances, directly reflecting the environmental value of green electricity into economic value. Enterprises that purchase green electricity can not only obtain electrical energy but also gain additional revenue through carbon allowance trading, thereby incentivizing more enterprises to participate in green electricity trading and expanding the scale of green electricity consumption. It breaks down the separation between the two markets, forming a complete value chain of "green electricity production - green certificate trading - carbon allowance conversion", promoting the cross-market flow of environmental rights and achieving a synergistic effect of "1+1>2".

Claims

1. A power trading method based on blockchain consensus, characterized in that, include: Obtain the real-time encrypted green electricity dataset, upload the real-time encrypted green electricity dataset to the blockchain node, and generate an inactive digital green certificate dataset; Based on the inactive digital green certificate dataset, a grid connection verification instruction is generated to update the inactive digital green certificate dataset to a tradable state, generating a tradable digital green certificate dataset. This includes: NFT encoding each green certificate in the inactive digital green certificate dataset to generate a grid connection verification instruction containing the green certificate NFT encoding, the corresponding cumulative power generation value, geographical location information, and the planned grid connection time; analyzing, based on the grid connection verification instruction, whether the cumulative power generation value has physical transmission feasibility at the planned grid connection time; when the cumulative power generation value is transmittable, returning a verification pass signal to the blockchain node; the blockchain node responds to the verification pass signal by updating the state attribute of the corresponding green certificate NFT encoding from inactive to tradable; generating the tradable digital green certificate dataset based on all the green certificate NFT encodings whose states are updated to tradable; obtaining a set of grid dynamic parameters, and based on the grid dynamic parameter set and the tradable... The digital green certificate dataset generates a real-time pricing dataset for green certificates through a spatiotemporal dynamic pricing strategy. This includes: determining a base layer value based on the product of the cumulative power generation and a preset fixed emission reduction per unit, and assigning this base layer value to each green certificate NFT code in the tradable digital green certificate dataset; analyzing the power grid dynamic parameter set to obtain a dynamic layer value adjustment factor, determining the dynamic layer value, and assigning this dynamic layer value to each green certificate NFT code in the tradable digital green certificate dataset; multiplying the base layer value of each green certificate NFT code with the corresponding dynamic layer value adjustment factor to generate a real-time equity price for the green certificate NFT code; aggregating the real-time equity prices of all green certificate NFT codes to generate the green certificate real-time pricing dataset; and, based on the green certificate real-time pricing dataset, verifying digital green certificates during user electricity consumption and outputting a green electricity consumption traceability report.

2. The power trading method based on blockchain consensus according to claim 1, characterized in that, The step of uploading the real-time green electricity encrypted dataset to the blockchain node to generate an inactive digital green certificate dataset includes: the real-time green electricity encrypted dataset includes a power generation data stream, millisecond-level precision timestamp data, and geographic coordinate data of the green electricity generator set; based on the real-time green electricity encrypted dataset, a preset green certificate generation verification logic is triggered: based on the millisecond-level precision timestamp data, the fluctuation characteristics of the power generation data stream within the corresponding time interval are analyzed to see if they conform to a preset green electricity output mode; based on the geographic coordinate data, the green electricity generator set is verified to be located within a preset trusted green electricity area list; when the fluctuation characteristics of the power generation data stream conform to the green electricity output mode and the green electricity generator set is located within the trusted green electricity area list, the blockchain node determines that the verification is successful; after the blockchain node verifies the verification, the real-time green electricity encrypted dataset is encapsulated into a real-time green electricity encrypted data unit with a unique identifier, and the real-time green electricity encrypted data unit is recorded in the corresponding blockchain node on the blockchain to generate the inactive digital green certificate dataset.

3. The power trading method based on blockchain consensus according to claim 2, characterized in that, The analysis of the power grid dynamic parameter set to obtain the dynamic layer value adjustment factor includes: the power grid dynamic parameter set including the real-time total output value of the regional power grid, the real-time power grid congestion level, and the real-time power grid load status; analyzing the real-time total output value of the regional power grid to determine the real-time environmental gain / loss coefficient of the regional power grid cleanliness corresponding to the green certificate NFT code on the base layer value; analyzing the real-time power grid congestion level to determine the spatial compensation coefficient required for the equivalent transmission loss from the geographical location of the corresponding green certificate NFT code to the target load center; analyzing the real-time power grid load status to determine the time gain / loss coefficient required when the current time is during the power grid peak or valley period; and multiplying the real-time environmental gain / loss coefficient, the spatial compensation coefficient, and the time gain / loss coefficient to generate the dynamic layer value adjustment factor for each green certificate NFT code.

4. The power trading method based on blockchain consensus according to claim 3, characterized in that, The analysis of the real-time total output value of the regional power grid and the determination of the real-time environmental gain / loss coefficient of the regional power grid cleanliness corresponding to the green certificate NFT code to the base layer value include: determining the real-time green power output value of the green power generating unit in the current time period based on the real-time total output value of the regional power grid and the power generation data stream; determining the real-time cleanliness percentage of the regional power grid based on the ratio of the real-time green power output value to the real-time total output value of the regional power grid; comparing the real-time cleanliness percentage of the regional power grid with the preset cleanliness benchmark threshold based on the preset cleanliness benchmark threshold, determining the deviation direction of the real-time cleanliness percentage of the regional power grid relative to the preset cleanliness benchmark threshold, and determining the deviation magnitude value; when the real-time cleanliness percentage of the regional power grid is higher than the preset cleanliness benchmark threshold, generating a real-time environmental gain coefficient with a value greater than 1, and dynamically increasing the real-time environmental gain coefficient according to the deviation magnitude value; when the real-time cleanliness percentage of the regional power grid is lower than the preset cleanliness benchmark threshold, generating a real-time environmental loss coefficient with a value less than 1, and dynamically decreasing the real-time environmental loss coefficient according to the deviation magnitude value.

5. The power trading method based on blockchain consensus according to claim 4, characterized in that, The analysis of the real-time grid congestion level to determine the spatial compensation coefficient required for the equivalent transmission loss from the geographical location corresponding to the green certificate NFT code to the target load center includes: extracting the geographical location information of the target load center according to the grid connection verification command; analyzing the tolerance threshold of the current grid congestion state for power transmission distance based on the real-time grid congestion level, and dynamically generating the maximum effective transmission radius; determining the actual spatial distance from the green power generator to the target load center based on the geographical coordinate data of the green power generator and the geographical location information of the target load center; dynamically comparing the actual spatial distance with the maximum effective transmission radius: when the actual spatial distance is less than or equal to the maximum effective transmission radius, generating the spatial compensation coefficient with a base value of 1; when the actual spatial distance is greater than the maximum effective transmission radius, dynamically adjusting the spatial compensation coefficient based on the ratio of the excess distance to the maximum effective transmission radius, according to a preset nonlinear attenuation rule, with a base value of 1.

6. The power trading method based on blockchain consensus according to claim 4, characterized in that, The analysis of the real-time grid load status to determine the time gain / loss coefficient required for the current time to be in a peak or off-peak period includes: extracting the precise millisecond-level time point of the corresponding green electricity generation based on the millisecond-level timestamp data; determining the real-time load value corresponding to the precise millisecond-level time point based on the real-time grid load status and the precise millisecond-level time point; dynamically evaluating the changing trend of the real-time load value within a preset time window to determine the load change direction: when the load change direction indicates that the load is continuously rising, a time gain coefficient with a value greater than 1 is generated, and the time gain coefficient is dynamically increased according to the deviation of the number of continuously rising real-time load values ​​within the preset time window from a preset rise intensity threshold; when the load change direction indicates that the load is continuously falling, a time loss coefficient with a value less than 1 is generated, and the time loss coefficient is dynamically decreased according to the deviation of the number of continuously falling real-time load values ​​within the preset time window from a preset fall intensity threshold.

7. The power trading method based on blockchain consensus according to claim 4, characterized in that, The step of redeeming digital green certificates during user electricity consumption based on the real-time pricing dataset of green certificates and outputting a green electricity consumption traceability report includes: obtaining redemption requests from target users for specific green certificate NFT codes in the real-time pricing dataset of green certificates; analyzing the target redemption electricity value specified in the redemption request; verifying whether the current state of the specific green certificate NFT code is tradable; when the verification passes, marking the cumulative power generation value corresponding to the specific green certificate NFT code as redeemed on the blockchain node based on the target redemption electricity value; and collecting the real-time equity price, the corresponding cumulative power generation value, the consumption time point, and the geographical location information of all redeemed green certificate NFT codes to generate a green electricity consumption traceability report.

8. The power trading method based on blockchain consensus according to claim 7, characterized in that, The method further includes: based on the green electricity consumption traceability report, linking real-time exchange rate data of the carbon market when completing transaction settlement; dynamically calculating the carbon allowance tons that can be redeemed by the tradable digital green certificate represented by the green certificate NFT code based on the real-time exchange rate data of the carbon market; on the blockchain node, according to the preset exchange ratio, transferring the green certificate rights corresponding to the green certificate NFT code after the transaction is completed to the corresponding carbon allowance tons in an equivalent value, and recording it in the transaction party's account in the carbon market.

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