Green electricity consumption carbon emission deduction benefit calculation method, system and device and medium

Abstract

The invention is suitable for the technical field of carbon emission deduction calculation, and provides a green electricity consumption carbon emission deduction benefit calculation method, system and device and a medium, and the method comprises the steps: obtaining green electricity consumption data of a target enterprise in a preset time period; determining a time period type based on the load characteristic of the power utilization timestamp of the target enterprise and the output characteristic of the renewable energy source; determining a region type according to the topological structure of the target enterprise power utilization region power grid code and the clean energy consumption capability; according to the time period type and the area type, calling a preset fusion rule base to calculate a dynamic carbon emission factor, and extracting a corresponding power grid reference carbon emission factor from a preset power grid carbon emission factor database; according to the dynamic carbon emission factor, the power grid reference carbon emission factor and the electricity consumption, calculating the carbon emission deduction amount; and outputting a standardized carbon emission reduction voucher containing the space-time identifier and the carbon emission reduction amount. The calculation precision of the carbon emission deduction amount is improved from multiple dimensions, and the problem of lack of space-time dynamics is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission reduction calculation, and in particular to a method, system, device and medium for calculating the benefits of carbon emission reduction from green electricity consumption. Background Art

[0002] With global attention paid to climate change, the role of green electricity consumption in reducing carbon emissions is becoming increasingly prominent. Calculating the carbon emission reduction benefits of green electricity consumption, a clean energy source, is crucial for driving green transformation and achieving carbon reduction targets. Accurately calculating this benefit can provide companies with carbon reduction incentives, promote renewable energy consumption, and facilitate the transition to a low-carbon, cleaner energy structure.

[0003] Existing technologies often use simple static carbon emission factors to calculate the carbon emission reduction benefits of green electricity consumption, failing to fully consider the dynamic characteristics of electricity consumption time and regional power grids. This approach, based on regional carbon emission intensity, ignores changes in the grid's energy structure over time, as well as differences in regional grid topology and clean energy absorption capacity. This static calculation method results in inaccurate carbon emission reduction results, failing to reflect the actual carbon emission reduction benefits of green electricity consumption under different temporal and spatial conditions. This impacts both corporate enthusiasm for green electricity consumption and the effectiveness of carbon emission reduction efforts. Summary of the Invention

[0004] This application provides a method, system, device and medium for calculating the benefits of carbon emission reduction from green electricity consumption, which are used to solve the problem of inaccurate calculation results of carbon emission reduction.

[0005] The first aspect of this application provides a method for calculating the carbon emission reduction benefits of green electricity consumption, including:

[0006] Obtaining green electricity consumption data of the target enterprise within a preset time period, the green electricity consumption data including the electricity consumption timestamp, the power grid code of the electricity consumption area, and the corresponding electricity consumption;

[0007] Determine the time period type based on the load characteristics of the target enterprise's electricity consumption timestamp and the renewable energy output characteristics; determine the region type based on the topological structure of the power grid code and the clean energy absorption capacity of the target enterprise's electricity consumption area;

[0008] According to the time period type and area type, a preset fusion rule library is called to calculate a dynamic carbon emission factor, and at the same time, a corresponding grid benchmark carbon emission factor is extracted from a preset grid carbon emission factor database;

[0009] Calculate the carbon emission deduction amount of the target enterprise's green electricity consumption based on the dynamic carbon emission factor, the grid benchmark carbon emission factor and the target enterprise's electricity consumption;

[0010] The output includes a standardized carbon reduction certificate with a time and space identifier and the amount of carbon emission reduction.

[0011] Furthermore, the determining of the time period type based on the load characteristics and renewable energy output characteristics of the target enterprise's electricity consumption timestamp includes:

[0012] Obtaining the real-time load rate of the power grid in the power consumption area and the ratio of the instantaneous output points of renewable energy sources within a preset time period;

[0013] Dynamically determine the time period type based on preset time period division rules; wherein, when the real-time load rate is higher than a first threshold and the instantaneous output ratio of renewable energy is lower than a second threshold, a peak period is triggered; when the real-time load rate is lower than a third threshold, or the instantaneous output ratio of renewable energy is higher than a fourth threshold, a valley period is triggered; when the real-time load rate and the instantaneous output ratio of renewable energy do not meet the peak period or valley period triggering conditions, it is determined to be a normal period;

[0014] When it is detected that the renewable energy output fluctuation rate exceeds a preset fluctuation threshold, the time period type is dynamically modified; wherein, if the fluctuation rate continues to exceed the threshold for a preset period, the current time period type is switched to a transition state of an adjacent time period type.

[0015] Furthermore, the determining of the regional type according to the topological structure of the power grid coding and the clean energy absorption capacity of the target enterprise's power consumption area includes:

[0016] Analyze the topological data corresponding to the power grid code of the power consumption area to identify the number of renewable energy hub nodes in the power grid and the density of cross-regional transmission channels;

[0017] Obtain real-time clean energy consumption indicators for the region, including curtailment rate, energy storage regulation capacity, and local consumption ratio;

[0018] The regional type is dynamically determined based on the preset regional classification rules. The first type of region is triggered when the number of renewable energy hub nodes exceeds the preset threshold and the power curtailment rate is lower than the dynamic adjustment threshold. The second type of region is triggered when the density of inter-regional transmission channels is greater than the preset density value and the local consumption ratio is lower than the benchmark threshold. When the real-time clean energy consumption index meets some of the conditions of the first and second types of regions at the same time, the dominant type is assigned according to the priority rule.

[0019] If a major change is detected in the topology of the area, the area type is updated.

[0020] Furthermore, the calculation of the dynamic carbon emission factor by calling a preset fusion rule base according to the time period type and the region type includes:

[0021] According to the combination pattern of the time period type and the area type, the corresponding fusion rule is matched from the preset fusion rule library, and the dynamic carbon emission factor is calculated according to the matched fusion rule.

[0022] Furthermore, the calculation of the dynamic carbon emission factor according to the matched fusion rule includes:

[0023] When the combined mode is during peak hours and the target area is the first type of area, the dynamic carbon emission factor is calculated according to the thermal power-dominated dynamic compensation strategy;

[0024] When the combination mode is during the off-peak period and the target area is the second type of area, the dynamic carbon emission factor is calculated according to the cross-regional green electricity priority deduction strategy;

[0025] When the target area in the combination mode is a mixed area and is in a transitional state of time period type switching, the dynamic carbon emission factor is calculated based on the spatiotemporal difference smoothing strategy.

[0026] Furthermore, extracting the corresponding grid benchmark carbon emission factor from a preset grid carbon emission factor database includes:

[0027] Real-time access to unit carbon emission monitoring data from the power dispatching system, renewable energy output forecast data provided by the meteorological department, and a database of historical power grid carbon emission factors;

[0028] Update the benchmark factor based on the real-time load rate and clean energy consumption ratio of the power grid in the power consumption area within a preset time period; if the deviation between the real-time monitoring data and the predicted value exceeds the dynamic adjustment threshold, use the sliding window algorithm to perform weighted correction on the historical data;

[0029] The benchmark factors are divided into regional type and time period type to construct a multidimensional matrix containing region, time and factor type.

[0030] Furthermore, the calculating of the carbon emission deduction amount of the target enterprise's green electricity consumption based on the dynamic carbon emission factor, the grid benchmark carbon emission factor and the target enterprise's electricity consumption includes:

[0031] According to the combination mode of the time period type and the regional type, a corresponding dynamic adjustment coefficient is matched from a preset weight rule library, wherein the dynamic adjustment coefficient includes a peak period coefficient and a regional correction coefficient; wherein the peak period coefficient is dynamically adjusted based on the real-time load rate and the proportion of thermal power output, and the regional correction coefficient is proportionally increased or decreased according to the clean energy absorption capacity of the regional type;

[0032] Calculate the carbon emission reduction deduction amount based on the difference between the dynamic carbon emission factor and the grid benchmark carbon emission factor, the electricity consumption and the dynamic adjustment coefficient;

[0033] If it is detected that the update delay of the dynamic carbon emission factor exceeds a preset threshold, the deduction amount is compensated and corrected based on the volatility of historical data for the same period.

[0034] The second aspect of the present application provides a green electricity consumption carbon emission deduction benefit calculation system, comprising:

[0035] A data acquisition unit, configured to acquire green electricity consumption data of a target enterprise within a preset time period, wherein the green electricity consumption data includes at least a timestamp of electricity consumption, a power grid code of a power consumption area, and corresponding power consumption;

[0036] A time period type and region type determination unit, configured to determine the time period type based on the load characteristics and renewable energy output characteristics of the target enterprise's electricity consumption timestamp; determine the region type based on the topological structure of the power grid code and the clean energy absorption capacity of the target enterprise's electricity consumption area;

[0037] a dynamic carbon emission factor and grid benchmark carbon emission factor determination unit, configured to call a dynamic carbon emission factor model to calculate a dynamic carbon emission factor according to the time period type and the region type, and extract a corresponding grid benchmark carbon emission factor from a preset grid carbon emission factor database;

[0038] a carbon emission deduction calculation unit, configured to calculate the carbon emission deduction of the target enterprise's green electricity consumption based on the dynamic carbon emission factor, the grid benchmark carbon emission factor, and the target enterprise's electricity consumption;

[0039] The standardized carbon emission reduction certificate output unit is used to output the standardized carbon emission reduction certificate containing the time and space identification and the carbon emission deduction amount.

[0040] The third aspect of the present application provides a computer device, including a memory, a transceiver, a processor and a bus system; wherein the memory is used to store programs; the processor is used to execute the programs in the memory, including executing the green electricity consumption carbon emission deduction benefit calculation method as described in any one of the above; the bus system is used to connect the memory and the processor so that the memory and the processor can communicate.

[0041] In a fourth aspect, the present application provides a readable storage medium comprising instructions, which, when executed on a computer, enables the computer to execute the green electricity consumption carbon emission deduction benefit calculation method as described in any one of the above.

[0042] It can be seen from the above technical solutions that this application has the following advantages:

[0043] After obtaining the electricity consumption timestamp, electricity consumption area grid code and corresponding electricity consumption within a preset time period, this application determines the time period type and regional type of the target enterprise's green electricity consumption based on the obtained data; effectively combines the load and energy output characteristics in the time dimension, and the grid topology and absorption capacity in the spatial dimension; calculates the dynamic carbon emission factor based on the said time period type and regional type, and extracts the corresponding grid benchmark carbon emission factor from the preset grid carbon emission factor database. The dynamic carbon emission factor reflects the changes in carbon emission factors in different time periods and regions in real time, and is more in line with the actual energy structure than the static factor; calculates the carbon emission deduction amount of the target enterprise's green electricity consumption based on the dynamic carbon emission factor, the grid benchmark carbon emission factor and the target enterprise's electricity consumption, and improves the calculation accuracy of the deduction amount based on the quantification of the dynamic carbon emission factor and the grid benchmark carbon emission factor; outputs a standardized carbon emission reduction certificate containing time and space identification and carbon emission deduction amount, which is convenient for enterprises to use in scenarios such as carbon trading and carbon tariff negotiations, and improves the versatility and credibility of the certificate. This application improves the calculation accuracy of carbon emission deductions from multiple dimensions, solves the problems of lack of temporal and spatial dynamics and non-standardized certificates, and provides a more scientific and reliable data basis for corporate carbon management and green development. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic flow chart of an embodiment of a method for calculating carbon emission reduction benefits of green electricity consumption in the present invention;

[0045] Figure 2 This is a structural block diagram of an embodiment of a green electricity consumption carbon emission deduction benefit calculation system in the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] The green electricity consumption carbon emission reduction benefit calculation method in this embodiment is used to improve the accuracy of carbon emission reduction calculations and comprehensively reflect the actual carbon emission reduction benefits of green electricity consumption under different temporal and spatial conditions. The implementation method in this embodiment can be implemented in the system, on a server, or on a terminal, without specific limitations.

[0048] Example 1

[0049] See also Figure 1 An embodiment of the carbon emission reduction benefit of green electricity consumption in the present invention includes the following steps:

[0050] S11. Obtain the target enterprise's green electricity consumption data within a preset time period. The green electricity consumption data includes the electricity consumption timestamp, the power grid code of the electricity consumption area, and the corresponding electricity consumption;

[0051] In this embodiment, the preset time period defaults to sliding forward one hour from the current time point and can be configured to 15 minutes, one hour, or a custom duration to accommodate the scheduling needs of different regional power grids. Through the power trading platform API or the smart meter Internet of Things (IoT) system, real-time, minute-accurate timestamps and electricity consumption data for target enterprises are collected. Based on the topological database published by the power grid company, the grid codes for the electricity consumption areas, which comply with the IEEE 1547 standard, are parsed and associated with the grid structure information for the physical node where the target enterprise is located. Timestamps are standardized to eliminate outliers. The grid codes are validated, and reverse mapping from the topological database verifies whether there are any transmission and distribution path conflicts in the area corresponding to the codes. Electricity consumption data is cross-checked with electricity transaction settlement documents, and manual review is triggered if the deviation exceeds ±2%. The cleaned data is stored in a distributed time series database using a dual primary key of "timestamp-grid code," supporting millisecond-level query responses. Electricity consumption data is standardized to MWh, and the grid codes are converted to 16-bit hash values ​​to protect privacy. The transmission link is encrypted using the national SM4 algorithm, and the original data is stored on-chain using blockchain technology to ensure data immutability. Set dynamic access permissions to only authorize the carbon accounting engine to access the data storage layer through the OAuth 2.0 protocol.

[0052] S12. Determine the time period based on the load characteristics of the target enterprise's electricity consumption timestamp and the renewable energy output characteristics; determine the region type based on the topological structure of the target enterprise's electricity consumption area power grid code and the clean energy absorption capacity;

[0053] S121. Determine the time period type based on the load characteristics of the target enterprise's electricity consumption timestamp and the renewable energy output characteristics, including the following steps:

[0054] 1. Obtain the real-time load rate of the power grid in the power consumption area and the instantaneous output point ratio of renewable energy within a preset time period;

[0055] The grid's energy management system (EMS) collects real-time total power load data for the target area. A new energy monitoring platform (such as a wind power / photovoltaic SCADA system) obtains the real-time total output of renewable energy units within the area. The preset time settings are the same as above and are not detailed here.

[0056] 2. Dynamically determine the time period type based on preset time period classification rules. Peak periods are triggered when the real-time load rate is higher than a first threshold and the instantaneous output ratio of renewable energy is lower than a second threshold. Off-peak periods are triggered when the real-time load rate is lower than a third threshold or the instantaneous output ratio of renewable energy is higher than a fourth threshold. Normal periods are determined when the real-time load rate and the instantaneous output ratio of renewable energy do not meet the peak or off-peak period triggering conditions.

[0057] The first threshold is the lower limit of the load factor during peak hours, set at 85% of the regional grid's rated capacity, based on the definition of peak load in the "Guidelines for Power System Security and Stability." For example, if a regional grid has a rated capacity of 10GW and the load factor exceeds 8.5GW, the peak period is triggered. The second threshold is the upper limit of renewable energy output during peak hours, set at 30%, based on historical data analysis. When the renewable energy output ratio falls below 30%, the grid must rely on thermal power for peak load regulation. The third threshold is the upper limit of the load factor during off-peak hours, set at 40%, reflecting the grid's low-load operation. The fourth threshold is the lower limit of renewable energy output during off-peak hours, set at 60%, indicating that renewable energy can meet the majority of electricity demand during these periods.

[0058] Peak period trigger conditions: real-time load rate > first threshold (85%) and renewable energy output ratio <

[0059] The second threshold (30%), for example, a load factor of 88% and a renewable energy share of 25%, is considered peak time. Off-peak time triggering conditions: real-time load factor < the third threshold (40%) or renewable energy output share > the fourth threshold (60%); for example, a load factor of 35% triggers off-peak time, and a renewable energy share of 65% triggers off-peak time. Normal time determination: If neither the peak nor off-peak period conditions are met, the system automatically classifies the period as normal time.

[0060] 3. When it is detected that the renewable energy output fluctuation rate exceeds the preset fluctuation threshold, the time period type is dynamically modified; among them, if the fluctuation rate continues to exceed the threshold for a preset period, the current time period type is switched to a transition state of the adjacent time period type.

[0061] The default fluctuation threshold is set at 20%, adjustable based on regional characteristics. The transition state is triggered when the volatility exceeds the threshold for three consecutive statistical periods (each period is 5 minutes). The correction strategy here involves switching the current period type to a transition state of an adjacent type, such as peak-to-flat or valley-to-flat. During the transition state, the carbon emission factor is calculated using a mixed time-decay weight. This prevents period type jumps caused by sudden output changes and ensures smooth deduction calculations.

[0062] S122. Determine the regional type based on the topological structure of the target enterprise's power grid and its clean energy absorption capacity, including the following steps:

[0063] 1. Analyze the topological data corresponding to the power grid code of the power consumption area to identify the number of renewable energy hub nodes in the power grid and the density of cross-regional transmission channels;

[0064] Using the power grid company's Geographic Information System (GIS) platform, obtain real-time topological data corresponding to the target area's grid code, including the coordinates and connectivity of substations, transmission lines, and renewable energy power station nodes. A renewable energy hub node is a wind power / photovoltaic centralized control station or energy storage station with a capacity of 100 MW or greater, connected to at least two inter-regional transmission lines. Count the number of trunk transmission lines within the target area that connect to other areas.

[0065] 2. Obtain real-time clean energy consumption indicators for the region, including curtailment rate, energy storage regulation capacity, and local consumption ratio;

[0066] The curtailment rate is determined by obtaining data on curtailed wind and solar power from the new energy monitoring platform. The energy storage management system (EMS) provides real-time information on dispatchable storage capacity, including battery SOC. The consumption ratio is determined based on the proportion of local clean energy consumption to total output.

[0067] 3. Dynamically determine the regional type based on preset regional classification rules. The first type of region is triggered when the number of renewable energy hub nodes exceeds a preset threshold and the curtailment rate is lower than the dynamically adjusted threshold. The second type of region is triggered when the density of inter-regional transmission channels exceeds a preset density value and the proportion of local consumption is lower than the benchmark threshold. When the real-time clean energy consumption indicators meet some of the conditions of both the first and second types of regions, the dominant type is assigned according to the priority rule.

[0068] The first type of area here is the number of renewable energy hub nodes ≥ 3; the threshold for dynamic adjustment of power abandonment rate: default ≤ 8%, which can be adjusted according to the season. The second type of area is the density of inter-regional transmission channels ≥ 0.1 / km 2 , the local consumption ratio benchmark threshold is less than 40%. It can be seen that the triggering conditions for the first type of area are that the number of renewable energy hub nodes is greater than the preset threshold (3) and the power curtailment rate is less than the dynamic adjustment threshold (8%); the triggering conditions for the second type of area are that the density of inter-regional transmission channels is greater than the preset density value (0.1 channel / km 2 ) and the local consumption ratio is less than the benchmark threshold (40%). Mixed regional processing: When some conditions are met at the same time (such as the number of hub nodes = 3 and density = 0.11 / km 2 ), the dominant type is assigned according to the following priorities; power curtailment rate priority: if the power curtailment rate is less than the dynamic threshold, it is prioritized as the first category; energy storage capacity suboptimal: if the energy storage capacity is greater than 200MWh, it is prioritized as the first category; default rule: other cases are prioritized as the second category.

[0069] 4. If a major change in the topology of the region is detected, the region type will be updated.

[0070] A major change refers to the addition or removal of a renewable energy hub node with a capacity of 100 MW or more, or a change in the number of inter-regional transmission channels by two or more. Real-time monitoring involves monitoring the CDC logs of the power grid topology database to capture node / line change events. The rules engine then matches pre-defined change conditions. Upon detecting a major change, an event message is generated, and steps 1-3 above are invoked to reparse the topology data, calculate the absorption index, and update the region type in the cache (e.g., Redis) and database. Finally, the change timestamp and the previous and next region types are recorded to support traceability.

[0071] S13. Based on the time period type and regional type, the preset fusion rule library is called to calculate the dynamic carbon emission factor, and the corresponding grid benchmark carbon emission factor is extracted from the preset grid carbon emission factor database;

[0072] S131. Based on the time period type and the region type, the preset fusion rule base is called to calculate the dynamic carbon emission factor, including the following steps:

[0073] According to the combination pattern of time period type and regional type, the corresponding fusion rules are matched from the preset fusion rule library, and the dynamic carbon emission factor is calculated according to the matched fusion rules.

[0074] Specifically, the dynamic carbon emission factors calculated according to the matching fusion rules include the following:

[0075] 1. When the combined mode is during peak hours and the target area is the first category, the dynamic carbon emission factor is calculated according to the thermal power-dominated dynamic compensation strategy;

[0076] The thermal power-dominated dynamic compensation strategy is applicable to peak periods in regions with a high proportion of renewable energy, when the grid relies on thermal power for peak load regulation. This strategy includes: thermal power load compensation: Based on the real-time carbon emission intensity of thermal power units and their output as a proportion of the regional grid's total output, the peak-period thermal power correction factor is used to amplify the carbon emission contribution of thermal power; green power consumption incentives: Based on the equivalent emission reductions of renewable energy units and the effective green power consumption rate, combined with the green power consumption incentive coefficient for Category I regions, thermal power carbon emissions are offset; and dynamic factor generation: A dynamic carbon emission factor is generated by taking the difference between the thermal power compensation value and the green power offset value. During peak periods in renewable energy-rich regions, by compensating for the carbon emissions of thermal power loads and rewarding green power consumption, the balance between peak load regulation pressure and emission reduction benefits is accurately reflected.

[0077] 2. When the combination mode is during the off-peak period and the target area is a Category II area, the dynamic carbon emission factor is calculated based on the cross-regional green electricity priority deduction strategy;

[0078] The inter-regional green electricity priority deduction strategy applies to off-peak hours in regions dominated by traditional energy, when inter-regional green electricity accounts for a high proportion. This strategy includes benchmark factor correction: adjusting the grid's benchmark carbon emission factor based on the grid loss correction factor along the green electricity transmission path; inter-regional green electricity deduction: enhancing the emission reduction benefits of inter-regional green electricity during off-peak hours by using the off-peak inter-regional green electricity deduction factor based on the proportion of inter-regional green electricity power in total output; and dynamic factor generation: generating a dynamic carbon emission factor by deducting the inter-regional green electricity deduction value from the revised benchmark factor. During off-peak hours in regions dominated by traditional energy, inter-regional green electricity is prioritized for deduction, while the impact of transmission losses on the benchmark factor is corrected to strengthen the economic incentives for inter-regional green electricity.

[0079] 3. When the target area in the combination mode is a mixed area and is in a transitional state of time period type switching, the dynamic carbon emission factor is calculated based on the spatiotemporal difference smoothing strategy.

[0080] The spatiotemporal difference smoothing strategy addresses factor jumps during time period or regional type switching. It involves dynamic weight allocation: calculating transition state weights based on the ratio of the remaining time period to the total transition time period; factor fusion calculation: linearly weighting the dynamic carbon emission factors calculated by the first and second modes according to the weights; and dynamic factor generation: generating a smoothly transitioning dynamic carbon emission factor from the weighted fusion value. Dynamic weight allocation ensures a smooth transition of carbon emission factors during grid operation state switching, preventing jumps in accounting results and improving the stability of corporate carbon management.

[0081] S132. Based on the time period type and the region type, the dynamic carbon emission factor model is called to calculate the dynamic carbon emission factor, including the following steps:

[0082] 1. Real-time access to unit carbon emission monitoring data from the power dispatching system, renewable energy output forecast data provided by the meteorological department, and a database of historical power grid carbon emission factors;

[0083] Integrating the power dispatch system, weather forecast data, and historical factor libraries supports dynamic updates of benchmark factors. Real-time carbon emission monitoring data from turbines is obtained through the OPC UA protocol. Access to the Meteorological Bureau API provides 24-hour wind and solar output forecasts. The benchmark factors for the past three years are retrieved from a distributed database.

[0084] 2. Update the benchmark factor based on the real-time load rate and clean energy consumption ratio of the power grid in the power consumption area within a preset time period; if the deviation between the real-time monitoring data and the predicted value exceeds the dynamic adjustment threshold, use the sliding window algorithm to perform weighted correction on the historical data;

[0085] The benchmark factor is updated on a rolling basis based on the real-time load rate and clean energy consumption ratio. The preset time period is one hour, with updates triggered every hour on the hour. If the real-time load rate fluctuates by more than ±10% or the clean energy consumption ratio changes by ±15%, an update is triggered immediately. The sliding window correction window size is the last six hours of data, and the weighting rule is exponential decay weighting.

[0086] 3. Build a multidimensional matrix containing regions, times and factor types based on the benchmark factors according to regional types and time period types.

[0087] Benchmark factors are stored in a three-dimensional structure based on region, time, and factor type. The region dimension includes first-category regions coded as 1 and second-category regions coded as 2. The time dimension includes peak, flat, and valley periods. Factor types include basic factors, dynamic correction factors, and low-carbon threshold factors. Finally, we use the time series database InfluxDB, indexed by the dual primary key of "region-time."

[0088] S14. Calculate the carbon emission deduction amount for the target enterprise’s green electricity consumption based on the dynamic carbon emission factor, the grid benchmark carbon emission factor, and the target enterprise’s electricity consumption;

[0089] 1. Based on the combination of time period type and regional type, the corresponding dynamic adjustment coefficient is matched from the preset weight rule library. The dynamic adjustment coefficient includes a peak period coefficient and a regional correction coefficient. The peak period coefficient is dynamically adjusted based on the real-time load rate and the proportion of thermal power output, and the regional correction coefficient is proportionally increased or decreased according to the clean energy absorption capacity of the regional type.

[0090] 2. Calculate the carbon emission reduction deduction based on the difference between the dynamic carbon emission factor and the grid benchmark carbon emission factor, electricity consumption, and the dynamic adjustment coefficient;

[0091] 3. If it is detected that the update delay of the dynamic carbon emission factor exceeds the preset threshold, the deduction amount will be compensated based on the volatility of historical data for the same period.

[0092] First, based on the combination of time period type (peak / valley) and regional type (Class I / Class II), a dynamic adjustment coefficient is extracted from the rule base and dynamically adjusted based on the real-time load rate and the proportion of thermal power output. For example, for every 5% increase in the load rate, the coefficient is reduced by 0.1. The emission reduction benefits are proportionally amplified or reduced according to the regional clean energy absorption capacity. For example, the coefficient for Class I regions is 1.1, and for Class II regions is 0.9. The difference between the dynamic factor and the baseline factor is multiplied by the electricity consumption and the dynamic adjustment coefficient to calculate the carbon emission deduction amount. If the dynamic factor update delay exceeds the preset threshold (e.g., >30 minutes), the deduction amount is compensated and corrected based on the volatility of historical data for the same period (e.g., the standard deviation of the same period in the past 7 days). For example, if a company uses 100MWh of electricity during off-peak hours, has a dynamic factor of 0.3tCO2 / MWh, a base factor of 0.5, and a regional factor of 1.1, the deduction amount will be (0.5-0.3) × 100 × 1.1 = 22tCO2. If the data is delayed by 45 minutes and the historical volatility is 10%, the corrected deduction amount will be 22 × 1.1 = 24.2tCO2. By adjusting the coefficient in real time based on the load factor and the proportion of thermal power, the grid's operating status can be accurately reflected.

[0093] S15. Output standardized carbon emission reduction certificates containing time and space identifiers and carbon emission deduction amounts.

[0094] The system binds the electricity consumption timestamp, regional grid code, and carbon emission deduction amount, and adds a digital signature from the grid dispatcher. A JSON-formatted certificate is generated based on the international renewable energy certificate data structure, standardizing the format. The certificate is uploaded to the consortium blockchain, generating an unalterable hash value and timestamp authentication, supporting the traceability verification required by the EU Carbon Border Adjustment Mechanism (CBAM). For example, if a manufacturing company consumes 100MWh of northwest wind power at noon, the system automatically generates a certificate and pushes it to the company's carbon account, which can be directly used to declare carbon tariffs for export products or trade in the carbon market.

[0095] Example 2

[0096] See also Figure 2 An embodiment of a green electricity consumption carbon emission deduction benefit calculation system of the present invention includes the following steps:

[0097] A data acquisition unit is used to acquire the green electricity consumption data of the target enterprise within a preset time period, where the green electricity consumption data includes at least the electricity consumption timestamp, the power grid code of the electricity consumption area, and the corresponding electricity consumption;

[0098] The time period type and region type determination unit is used to determine the time period type based on the load characteristics and renewable energy output characteristics of the target enterprise's electricity consumption timestamp; determine the region type based on the topological structure of the power grid code and the clean energy absorption capacity of the target enterprise's electricity consumption area;

[0099] A dynamic carbon emission factor and grid benchmark carbon emission factor determination unit is used to call a dynamic carbon emission factor model to calculate a dynamic carbon emission factor according to a time period type and a regional type, and to extract a corresponding grid benchmark carbon emission factor from a preset grid carbon emission factor database;

[0100] A carbon emission deduction calculation unit, configured to calculate the carbon emission deduction for the target enterprise's green electricity consumption based on the dynamic carbon emission factor, the grid benchmark carbon emission factor, and the target enterprise's electricity consumption;

[0101] The standardized carbon emission reduction certificate output unit is used to output the standardized carbon emission reduction certificate containing the time and space identification and the carbon emission deduction amount.

[0102] Example 3

[0103] The present invention provides a computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above method are implemented.

[0104] Those skilled in the art will appreciate that the units of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0105] In the embodiments provided herein, it should be understood that the division of units is merely a logical functional division. In actual implementation, other division methods may be employed, such as combining multiple units into one unit, splitting a unit into multiple units, or ignoring certain features. Furthermore, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically as a separate unit, or two or more units may be integrated into a single unit. These integrated units may be implemented in either hardware or software functional units.

[0106] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nly Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program code.

[0107] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A method for calculating the carbon emission reduction benefits of green electricity consumption, characterized in that: include: Obtaining green electricity consumption data of the target enterprise within a preset time period, the green electricity consumption data including the electricity consumption timestamp, the power grid code of the electricity consumption area, and the corresponding electricity consumption; Determining a time period based on the load characteristics of the target enterprise's electricity consumption timestamp and renewable energy output characteristics; Determine the regional type based on the topological structure of the power grid code and the clean energy absorption capacity of the target enterprise's power consumption area; According to the time period type and area type, a preset fusion rule library is called to calculate a dynamic carbon emission factor, and at the same time, a corresponding grid benchmark carbon emission factor is extracted from a preset grid carbon emission factor database; Calculate the carbon emission deduction amount of the target enterprise's green electricity consumption based on the dynamic carbon emission factor, the grid benchmark carbon emission factor and the target enterprise's electricity consumption; The output includes a standardized carbon reduction certificate with a time and space identifier and the amount of carbon emission reduction.

2. The green electricity consumption carbon emission deduction benefit calculation method according to claim 1 is characterized in that: The determining of the time period type based on the load characteristics and renewable energy output characteristics of the target enterprise's electricity consumption timestamp includes: Obtaining the real-time load rate of the power grid in the power consumption area and the ratio of the instantaneous output points of renewable energy sources within a preset time period; Dynamically determine the time period type based on preset time period division rules; wherein, when the real-time load rate is higher than a first threshold and the instantaneous output ratio of renewable energy is lower than a second threshold, a peak period is triggered; when the real-time load rate is lower than a third threshold, or the instantaneous output ratio of renewable energy is higher than a fourth threshold, a valley period is triggered; when the real-time load rate and the instantaneous output ratio of renewable energy do not meet the peak period or valley period triggering conditions, it is determined to be a normal period; When it is detected that the renewable energy output fluctuation rate exceeds a preset fluctuation threshold, the time period type is dynamically modified; wherein, if the fluctuation rate continues to exceed the threshold for a preset period, the current time period type is switched to a transition state of an adjacent time period type.

3. The green electricity consumption carbon emission deduction benefit calculation method according to claim 1 is characterized in that: The determining of the regional type according to the topological structure of the power grid coding of the target enterprise's power consumption area and the clean energy absorption capacity includes: Analyze the topological data corresponding to the power grid code of the power consumption area to identify the number of renewable energy hub nodes in the power grid and the density of cross-regional transmission channels; Obtain real-time clean energy consumption indicators for the region, including curtailment rate, energy storage regulation capacity, and local consumption ratio; The regional type is dynamically determined based on the preset regional classification rules. The first type of region is triggered when the number of renewable energy hub nodes exceeds the preset threshold and the power curtailment rate is lower than the dynamic adjustment threshold. The second type of region is triggered when the density of inter-regional transmission channels is greater than the preset density value and the local consumption ratio is lower than the benchmark threshold. When the real-time clean energy consumption index meets some of the conditions of the first and second types of regions at the same time, the dominant type is assigned according to the priority rule. If a major change is detected in the topology of the area, the area type is updated.

4. The green electricity consumption carbon emission deduction benefit calculation method according to claim 1 is characterized in that: The step of calculating the dynamic carbon emission factor by calling a preset fusion rule base according to the time period type and the region type includes: According to the combination pattern of the time period type and the area type, the corresponding fusion rule is matched from the preset fusion rule library, and the dynamic carbon emission factor is calculated according to the matched fusion rule.

5. The green electricity consumption carbon emission deduction benefit calculation method according to claim 1 is characterized in that: The calculation of the dynamic carbon emission factor according to the matching fusion rules includes: When the combined mode is during peak hours and the target area is the first type of area, the dynamic carbon emission factor is calculated according to the thermal power-dominated dynamic compensation strategy; When the combination mode is during the off-peak period and the target area is the second type of area, the dynamic carbon emission factor is calculated according to the cross-regional green electricity priority deduction strategy; When the target area in the combination mode is a mixed area and is in a transitional state of time period type switching, the dynamic carbon emission factor is calculated based on the spatiotemporal difference smoothing strategy.

6. The green electricity consumption carbon emission deduction benefit calculation method according to claim 1 is characterized in that: The extracting of the corresponding grid benchmark carbon emission factor from a preset grid carbon emission factor database includes: Real-time access to unit carbon emission monitoring data from the power dispatching system, renewable energy output forecast data provided by the meteorological department, and a database of historical power grid carbon emission factors; Update the benchmark factor based on the real-time load rate and clean energy consumption ratio of the power grid in the power consumption area within a preset time period; if the deviation between the real-time monitoring data and the predicted value exceeds the dynamic adjustment threshold, use the sliding window algorithm to perform weighted correction on the historical data; The benchmark factors are divided into regional type and time period type to construct a multidimensional matrix containing region, time and factor type.

7. The green electricity consumption carbon emission deduction benefit calculation method according to claim 1 is characterized in that: The calculating of the carbon emission deduction amount of the target enterprise's green electricity consumption based on the dynamic carbon emission factor, the grid benchmark carbon emission factor and the target enterprise's electricity consumption includes: According to the combination mode of the time period type and the regional type, a corresponding dynamic adjustment coefficient is matched from a preset weight rule library, wherein the dynamic adjustment coefficient includes a peak period coefficient and a regional correction coefficient; wherein the peak period coefficient is dynamically adjusted based on the real-time load rate and the proportion of thermal power output, and the regional correction coefficient is proportionally increased or decreased according to the clean energy absorption capacity of the regional type; Calculate the carbon emission reduction deduction amount based on the difference between the dynamic carbon emission factor and the grid benchmark carbon emission factor, the electricity consumption and the dynamic adjustment coefficient; If it is detected that the update delay of the dynamic carbon emission factor exceeds a preset threshold, the deduction amount is compensated and corrected based on the volatility of historical data for the same period.

8. A green electricity consumption carbon emission deduction benefit calculation system, characterized in that: The method for calculating the carbon emission reduction benefits of green electricity consumption according to any one of claims 1 to 7 comprises: A data acquisition unit, configured to acquire green electricity consumption data of a target enterprise within a preset time period, wherein the green electricity consumption data includes at least a timestamp of electricity consumption, a power grid code of a power consumption area, and corresponding power consumption; A time period type and region type determination unit, configured to determine the time period type based on the load characteristics and renewable energy output characteristics of the target enterprise's electricity consumption timestamp; determine the region type based on the topological structure of the power grid code and the clean energy absorption capacity of the target enterprise's electricity consumption area; a dynamic carbon emission factor and grid benchmark carbon emission factor determination unit, configured to call a dynamic carbon emission factor model to calculate a dynamic carbon emission factor according to the time period type and the region type, and extract a corresponding grid benchmark carbon emission factor from a preset grid carbon emission factor database; a carbon emission deduction calculation unit, configured to calculate the carbon emission deduction of the target enterprise's green electricity consumption based on the dynamic carbon emission factor, the grid benchmark carbon emission factor, and the target enterprise's electricity consumption; The standardized carbon emission reduction certificate output unit is used to output the standardized carbon emission reduction certificate containing the time and space identification and the carbon emission deduction amount.

9. A computer device, characterized in that: include: Memory, transceiver, processor and bus system; wherein the memory is used to store programs; The processor is used to execute the program in the memory, including executing the green electricity consumption carbon emission deduction benefit calculation method as described in any one of claims 1 to 7; the bus system is used to connect the memory and the processor so that the memory and the processor can communicate.

10. A readable storage medium, characterized in that: The method comprises instructions which, when executed on a computer, enable the computer to execute the method for calculating the carbon emission reduction benefit of green electricity consumption according to any one of claims 1 to 7.

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