Multi-element electric power scene adaptive low-carbon electricity utilization carbon beneficiation method and system
By adopting a low-carbon electricity consumption carbon benefit method adapted to multiple electricity scenarios, the peak, off-peak, and valley periods and the grid carbon factor are dynamically adjusted. Combined with the differences in the number of users, a data interaction mechanism is constructed, which solves the problems of scenario adaptability, accounting accuracy and rigid incentives in existing carbon benefit methods. This achieves the optimization of new energy consumption and grid load, and improves residents' participation and emission reduction effect.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-17
- Publication Date
- 2026-04-10
AI Technical Summary
Existing carbon inclusion methods suffer from poor adaptability in terms of scenarios, insufficient accuracy in terms of accounting, isolated data, and rigid incentives, resulting in poor effects on renewable energy consumption and grid load shaving and valley filling, as well as low resident participation.
By adapting to diverse power scenarios, the system divides the scenarios for renewable energy consumption in spring and autumn and power supply guarantee in summer and winter, dynamically adjusts peak, flat and valley periods, and constructs a data interaction mechanism by combining dynamic grid carbon factors and differences in the number of users. This achieves a closed loop for emission reduction accounting and incentives, and uses meter account number binding to ensure data uniqueness.
It has improved scenario adaptability, enhanced accounting accuracy and data security, and increased resident participation. It is expected that the renewable energy consumption capacity will increase by 300,000 kilowatts, the peak load of residents will decrease by 10%, the calculation error of emission reduction will be reduced to within 5%, and the resident participation rate will increase to over 60%.
Smart Images
Figure CN121836740A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of low-carbon energy management and carbon credit technology, specifically to a low-carbon electricity consumption carbon credit method and system adapted to multiple electricity scenarios. Background Technology
[0002] Driven by the "dual carbon" goals, carbon emissions from residential consumption account for a significant portion of total emissions. Data from the United Nations Environment Programme shows that approximately two-thirds of global greenhouse gas emissions originate from household consumption, and in China, residential consumption accounts for one-third of the country's total carbon emissions (reaching 3.724 billion tons in 2019), with residential electricity consumption accounting for over 30% of these emissions. To guide residents towards low-carbon electricity use, various regions are exploring carbon credit mechanisms, but existing technologies suffer from the following shortcomings and deficiencies: (1) The scene dimension is too simple and the adaptability is poor. Existing carbon credit methods only divide the time periods into "peak / flat / valley" periods, without dynamically adjusting them based on the characteristics of renewable energy output and seasonal electricity consumption patterns. For example, during the peak solar power generation in spring and autumn, there is no specific "valley incentive period" set up. In summer and winter, when air conditioning load accounts for 50% of the grid's peak load (residential air conditioning load accounts for 60% of the total air conditioning load), the peak time period division does not match the peak electricity consumption under extreme weather conditions (such as the surge in electricity consumption during the high temperature period from 2 PM to 4 PM in summer), resulting in poor renewable energy absorption and peak shaving and valley filling effects.
[0003] (2) The accounting dimensions are too broad and the accuracy is insufficient. The existing method does not take into account the difference in the number of users: it uses "household" as the accounting unit and ignores the difference in the number of people per household (such as the different electricity demand of a 3-person household and a 1-person household), which causes the baseline emission to deviate from the actual value. The "dynamic grid carbon factor" is not coupled: the annual average carbon factor is fixed and is not combined with real-time adjustments to the output of new energy sources (e.g., the carbon factor is very low when photovoltaic output is high at noon, but higher when thermal power dominates during peak hours, and the calculation results are out of sync with the actual emission reduction contribution).
[0004] (3) Data dimensions are isolated and prone to duplicate reporting. The existing system relies solely on the power company as a data source and has failed to establish a data exchange mechanism. On the one hand, it cannot eliminate abnormal electricity consumption data (such as electricity theft or equipment failure), and on the other hand, it lacks unique verification of "one meter per household - emission reduction verification", which poses a risk of duplicate emission reduction declarations for the same electricity consumption behavior.
[0005] (4) Incentive dimensions are rigid and participation is low. The carbon inclusive benchmark adjustment factor is a fixed value and is not dynamically optimized in combination with real-time grid load and the pressure of new energy consumption (such as not further reducing the off-peak factor to encourage electricity consumption when photovoltaic output exceeds expectations), resulting in a mismatch between the incentive and grid demand, and insufficient willingness of residents to participate. Summary of the Invention
[0006] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, one objective of this invention is to propose a low-carbon electricity consumption carbon reduction method and system adapted to multiple power scenarios, applicable to the quantification, verification, and universal incentive of carbon emission reductions for residential electricity consumption within a provincial administrative region, which can achieve synergy between promoting the consumption of new energy sources, peak shaving and valley filling of power grid load, and guiding residents' low-carbon behavior.
[0007] To address the above problems, this invention provides a low-carbon electricity consumption carbon-inclusive method adapted to multiple electricity scenarios, comprising the following steps: S1: Determine the accounting boundary to obtain the accounting boundary range. The accounting boundary includes the geographical boundary, which is limited to the provincial administrative region; the electricity user boundary, which is limited to residential users and excludes public institutions and industrial enterprise dormitories; the property type boundary, which is limited to residential buildings with traceable data; and the greenhouse gas boundary, which is limited to carbon dioxide emissions. S2: Based on the accounting boundary range obtained in S1, identify the emission sources and accounting objects to obtain the emission source and accounting object identifiers. The emission source is the indirect emission of electricity consumption corresponding to residential electricity consumption, and the accounting object is the residential users in the 10kV power supply area. The unique identifier is achieved by binding the meter account number. S3: Based on the accounting object identifier obtained in S2, the baseline scenario and peak-valley period are divided to obtain the scenario and period division results. Among them, the spring and autumn new energy consumption scenario and the summer and winter power supply guarantee scenario are divided based on seasonal demand, and the peak, flat and valley periods are set differently. The spring and autumn scenario is applicable to February-June and October-November, and the summer and winter scenario is applicable to July-September and December-January. S4: Based on the scenario and time period division results obtained in S3, basic data collection and key parameter determination are carried out to obtain basic data and key parameters. The basic data includes the grid carbon emission factor by month and time period, the historical electricity consumption of the distribution area, the actual electricity consumption of users and the number of users. The key parameters include the total number of households in the distribution area and the carbon inclusive benchmark adjustment factor. The carbon inclusive benchmark adjustment factor is dynamically set according to the scenario and time period. S5: Based on the basic data and key parameters obtained in S4, the baseline emissions are calculated to obtain the baseline emissions data. The data is calculated by combining dynamic grid carbon emission factors, historical electricity consumption of the distribution area, and carbon inclusive benchmark adjustment factors with different scenarios and time periods. The per capita daily baseline emissions are calculated by allocating the total number of households in the distribution area, the average number of people per household, and the number of days in the month. S6: Based on the basic data and key parameters obtained in S4, calculate the project scenario emissions to obtain project scenario emissions data. Among them, the actual carbon dioxide emissions of users are calculated by multiplying the dynamic power grid carbon emission factor by the actual electricity consumption of users, taking users and time periods as the dimensions. S7: Based on the baseline emission data obtained in S5 and the project scenario emission data obtained in S6, calculate the emission reduction of residential electricity consumption and obtain the emission reduction result. The emission reduction is calculated by comparing the baseline emission and the project scenario emission, and the emission reduction is calculated separately for peak, flat and valley periods. The total emission reduction is then obtained by summing them up, and only the effective contribution with a total emission reduction ≥ 0 is retained. S8: Based on the emission reduction results obtained in S7, perform data verification and result confirmation to obtain the emission reduction data that has passed the verification. The verification includes uniqueness verification to verify the binding relationship of the electricity meter account number to avoid duplicate reporting, and total amount verification to compare the total emission reduction of the transformer area with the total emission reduction of the user. The result is confirmed to be valid when the deviation is ≤5%. S9: Based on the emission reduction data that has passed verification obtained in S8, output the emission reduction results to obtain the final carbon inclusive incentive basis. The emission reduction results are synchronized to the power low-carbon scenario application component for the distribution of carbon inclusive incentives.
[0008] Preferably, in S3, the peak period for the spring and autumn renewable energy consumption scenario is 20:00-24:00, the valley period includes the midday valley period of 0:00-6:00 and 10:00-15:00, and the normal period is 6:00-10:00 and 15:00-20:00; the peak period for the summer and winter power supply guarantee scenario is 15:00-24:00, the valley period is 0:00-7:00, and the normal period is 7:00-15:00.
[0009] Preferably, the carbon inclusive benchmark adjustment factor in S4 is dynamically set as follows: under the spring and autumn renewable energy consumption scenario, the peak period factor is 1.13, the valley period factor is 0.99, and the normal period factor is 1; under the summer and winter power supply guarantee scenario, the peak period factor is 1.28, the valley period factor is 0.74, and the normal period factor is 1.
[0010] Preferably, the formula for calculating the baseline emissions in S5 includes: For the scenario of new energy consumption in spring and autumn, according to the formula BE 1, p , i = NEFelec × ESt 0, p , i calculate; For power supply security scenarios in summer and winter, according to the formula BE 2, p , i = NEFelec × ESt 0, p , i calculate; in, BE 1, p , i andBE 2, p , i As the baseline emissions, EFelec As a carbon emission factor of the power grid, ESt 0, p , i This refers to the historical electricity consumption of the power station area. N This represents the total number of households in the Taiwan area.
[0011] Preferably, the formula for calculating emissions in the S6 project scenario is as follows: PEp , i = NEFelec × ECn , i ,in PEp , i Emissions from the project site EFelec As a carbon emission factor of the power grid, ECn , i The actual electricity consumption of the user N This represents the total number of households in the Taiwan area.
[0012] Preferably, the emission reduction calculation in S7 includes: Total emission reductions are calculated using the formula ERi = ERp , i + ERg , i + ERf , i calculate; Peak period emission reductions are calculated using the formula. ERp , i =( BEp , i - PEp , i )× ωi calculate; Emission reduction during off-peak hours is calculated using the formula. ERg , i =( BEg , i - PEg , i )× ωi calculate; Normal emission reduction is calculated using the formula ERf , i =( BEf , i - PEf , i )× ωi calculate; in, ERi For total emission reductions, ERp , i , ERg , i ,ERf , i To reduce emissions in different time periods, BEp , i , BEg , i , BEf , i As the baseline emissions, PEp , i , PEg , i , PEf , i Emissions from the project site ωi This is the carbon inclusive benchmark adjustment factor.
[0013] The system used in a low-carbon electricity consumption carbon benefit method adapted to multiple electricity scenarios includes: The data aggregation module is used to integrate registered user information, number of residential electricity users, average number of people per household, and peak, flat, and valley electricity consumption data to build an emission reduction accounting model. The electricity conservation scenario module provides users with an entry point to participate in electricity conservation activities and displays emission reductions and historical data; The backend information management module is used to manage registered user information, electricity-saving behavior information, and rules; The backend model management module embeds the power grid carbon emission factor, carbon emission accounting formula and emission reduction accounting model, and automatically calculates and generates monthly emission reductions based on the input data; The data service module utilizes big data technology to provide data storage, computing, and security. The modules work together through the low-carbon power scenario application components. The data aggregation module provides data input to the back-end model management module, the output of the back-end model management module is used to display the power saving scenario module, the back-end information management module ensures the uniqueness of the data, and the data service module supports the operation of the system.
[0014] Preferably, the system also includes a uniqueness verification unit to verify the binding relationship between the electricity meter account number and the individual carbon account, so as to avoid the same user from repeatedly declaring emission reductions.
[0015] Preferably, the data service module is integrated into the provincial carbon inclusion platform, supporting data interaction with local government departments, power grid companies and residential users, and dynamically updating the power grid carbon emission factor.
[0016] Preferably, after the system outputs the emission reduction results, it automatically triggers the carbon incentive distribution mechanism to achieve a closed-loop incentive of emission reduction, credits, and electricity price discounts.
[0017] The advantages of this invention compared to the prior art are: (1) Stronger scenario adaptability: For the first time, the “extreme weather emergency scenario” is introduced, and the peak and valley periods are dynamically adjusted to deeply couple carbon benefits with new energy consumption (extended midday valley in spring and autumn) and power grid supply guarantee (optimized peak periods in summer and winter). It is expected that the new energy consumption capacity will increase by 300,000 kW / 1% of the electricity load growth in spring and autumn, and the peak load of residents will decrease by 10% in summer and winter (alleviating the power shortage of 2.5 million kW).
[0018] (2) Higher accounting accuracy: The accounting unit is "per person per day", and the number of users and dynamic carbon factors are included to solve the problem of "household per day accounting" being too crude; through dynamic factors, the emission reduction is matched with the actual carbon contribution, and the accounting error is reduced to within 5%.
[0019] (3) Better data security: A two-way verification mechanism for “low-carbon power application components” is constructed. Combined with meter account number binding and abnormal data cleaning, duplicate applications are completely eliminated, and data credibility is greatly improved.
[0020] (4) Higher resident participation: Dynamic adjustment factors link the incentive intensity with grid demand (such as enhanced incentives during off-peak periods when photovoltaic power generation is high), and with the incentive closed loop of "emission reduction - points - electricity discount", the resident participation rate is expected to increase to over 60%. Attached Figure Description
[0021] 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0022] Figure 1 This is a system application architecture diagram of the present invention. Detailed Implementation
[0023] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.
[0024] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.
[0025] The present invention will now be described in further detail with reference to the accompanying drawings.
[0026] The present invention provides a low-carbon electricity consumption carbon-inclusive method and system for adapting to multiple power scenarios, comprising: (1) Accounting Boundary This method calculates the carbon credit emission reductions generated by residential electricity consumption within residential buildings. It establishes the scope, boundaries, calculation methods, and scalable logic by constructing metering data for the 10kV power distribution area of each residential building as a baseline. Piloted in Anhui Province, and referencing actual data from the State Grid Anhui Electric Power Company and other feasible conditions, this method defines the statistical and accounting boundaries of carbon credits at the distribution area level, separately calculating the emissions of residential users and charging stations within each area. The advantage of using the distribution area as the unit is that it allows for convenient downward aggregation of actual electricity usage from individual users, reducing the risk of privacy leaks; it also allows for upward aggregation to the city or even provincial level, effectively avoiding duplicate accounting.
[0027] The greenhouse gas emissions from residential electricity consumption calculated using this method include only carbon dioxide.
[0028] (2) Calculation method for baseline emissions The emission reduction scenarios in this research report are those involving registered users engaging in low-carbon electricity consumption. To avoid data collection delays and ensure fairness in user participation, the data collection period for each month will be from 00:00:00 on the first day of the month to 23:59:59 on the last day. The plan sets two baseline scenarios: spring / autumn renewable energy consumption and summer / winter power supply assurance. Following the "Notice on Further Optimizing Peak-Valley Time-of-Use Electricity Pricing Policies and Other Related Matters" issued by the Anhui Provincial Development and Reform Commission and the Anhui Provincial Energy Bureau, the months are divided into seasons: spring (February-June), summer (July-September), autumn (October-November), and winter (December-January). Furthermore, it distinguishes between peak, flat, and valley periods. If necessary and data conditions permit, the peak, flat, and valley periods can be further broken down to hourly or finer time granularity to flexibly support renewable energy output fluctuations and consumption under different seasons.
[0029] 1) Spring and Autumn New Energy Consumption Scenario 1 During the year from February to June and from September to November, the peak period is from 20:00 to 24:00, and the off-peak period is from 0:00 to 6:00 and from 10:00 to 15:00 (midday valley in spring and autumn). Other periods are flat. The emissions during the peak, valley and flat periods of the baseline spring and autumn new energy consumption scenario 1 are calculated according to formula (1): In the formula: BE 1,p,i Scenario 1: Baseline emissions during peak, valley and normal periods in the month (i month) when the emission reduction scenario occurs for registered users, in kilograms of carbon dioxide (kgCO2). EF elec The carbon emission factor of the power grid in Anhui Province in 2021 is 0.7075 kg CO2 / kWh, with reference to the announcement of the Ministry of Ecology and Environment and the National Bureau of Statistics on the release of the carbon dioxide emission factor of the power grid in 2021. ES t0,p,i :Registered users' emission reduction scenario t0 year (previous year) i month peak valley normal period pilot area residential electricity consumption, unit is kilowatt-hour (kWh); N: Total number of residential electricity users in the transformer area; 2) Peak Dust Season Scenario 2 (Winter and Summer) Throughout the year, from July to September and from December to January, the peak period is from 15:00 to 24:00, the trough period is from 0:00 to 7:00, and the other periods are flat. The emissions during the peak, trough and flat periods of the baseline peak intensity winter and summer scenario 2 are calculated according to formula (2): In the formula: BE 2,p,i Scenario 2: Baseline emissions during peak, valley and normal periods in the month (i month) when the emission reduction scenario occurs for registered users, in kilograms of carbon dioxide (kgCO2). ES t0,p,i :Registered users' emission reduction scenario t0 year (previous year) i month peak valley normal period pilot area residential electricity consumption, unit is kilowatt-hour (kWh); (3) Calculation of emission reduction from residential electricity consumption 1) Emissions calculation method in emission reduction scenarios The emissions of registered users during peak, valley and normal periods are calculated according to formula (5): In the formula: PE p,i Emissions of registered users during different peak, valley and normal periods in the month (i month) of emission reduction scenario, in kilograms of carbon dioxide (kgCO2). EF elecThe carbon emission factor of the power grid in Anhui Province in 2021 is 0.7075 kg CO2 / kWh, with reference to the announcement of the Ministry of Ecology and Environment and the National Bureau of Statistics on the release of the carbon dioxide emission factor of the power grid in 2021. EC p,i Electricity consumption during different peak, valley and normal periods in the month (i month) when emission reduction scenarios occur for registered users, in kilowatt-hours (kWh); 2) Emission Reduction Calculation The emission reduction from low-carbon electricity consumption by residents is calculated using formula (6): In the formula: E Ri : Monthly emission reduction of registered users (i month) in which emission reduction scenarios occur, in kilograms of carbon dioxide (kgCO2). ER p,i : Emission reduction amount during the peak period of the month (i month) when the emission reduction scenario of the registered user occurs, in kilograms of carbon dioxide (kgCO2). ER g,i : Emission reduction amount during the off-peak period of the month (i month) in which the emission reduction scenario of the registered user occurred, in kilograms of carbon dioxide (kgCO2). ER f,i : Emission reduction amount during normal periods in the month (i month) when the emission reduction scenario of the registered user occurs, in kilograms of carbon dioxide (kgCO2). Peak-hour emission reductions are calculated using formula (7): In the formula: BE p,i : Baseline emissions during the peak period of the month (i month) in which the emission reduction scenario occurs for registered users, expressed in kilograms of carbon dioxide (kgCO2). If the peak period applies to Scenario 1, then BE p,i =BE 1,p,i If the peak period applies to scenario 2, then BE p,i =BE 2,p,i . This is a carbon inclusive benchmark adjustment factor used to balance different types of electricity consumption at different times and seasons. The emission reduction during the off-peak period is calculated according to formula (8): In the formula: BE g,i : Baseline emissions during the off-peak period of the month (i month) in which the emission reduction scenario occurs for registered users, expressed in kilograms of carbon dioxide (kgCO2). If the peak period applies to Scenario 1, then BE g,i =BE 1,g,i If the valley period applies to scenario 2, then BEg,i =BE 2,g,i .
[0030] The emission reduction during normal periods is calculated according to formula (9): In the formula: BE f,i : Baseline emissions during the off-peak period of the month (i month) in which the emission reduction scenario occurs for registered users, expressed in kilograms of carbon dioxide (kgCO2). If scenario 1 applies during normal periods, then BE f,i =BE 1,f,i If the normal procedure applies to scenario 2, then BE f,i =BE 2,f,i .
[0031] (4) Adjustment of carbon inclusive benchmark Based on the two baseline scenarios above, the carbon emission reductions under each scenario are calculated using equations (6)-(9). This is a carbon inclusive benchmark adjustment factor used to balance different types of electricity consumption at different times and seasons, in order to achieve peak shaving and valley filling. During peak electricity consumption periods, A value greater than 1 is used to "punish" registered users who consume excessive electricity during peak hours; while during off-peak hours, A value less than 1 is set to encourage registered users to consume electricity, thereby maintaining the security of the power grid and achieving peak shaving and valley filling of the power load.
[0032] Table 1. Time-based / Seasonal Baseline Scenarios
[0033] (3) Construction of low-carbon electricity consumption and carbon credit application scenarios Data aggregation: Integrate data such as registered users, total number of residential electricity users, average number of permanent residents per household, and peak, flat, and valley electricity consumption to build an emission reduction accounting model and achieve data visualization.
[0034] Electricity conservation scenarios: Provides an entry point for registered users to participate in electricity conservation activities, and offers display of emission reductions and historical data viewing.
[0035] Backend information management: functions include managing registered user information, electricity-saving behavior information, and rule management.
[0036] Backend model management: Embeds electricity carbon emission factors and electricity carbon emission accounting calculation formulas, embeds baseline scenario and project scenario carbon emission accounting and emission reduction accounting models, automatically calculates results based on input data, and generates monthly emission reductions.
[0037] Data services: Utilizing big data technology, we provide data storage, computing, and research and development services to improve data processing, verification, and measurement capabilities, and enhance platform and data security.
[0038] To more clearly illustrate the specific embodiments of the present invention, an example is provided below: (I) Implementation Process S1: Determine the accounting boundaries (technical prerequisite) Electricity user boundaries: limited to residential users, excluding public institutions such as schools and social welfare institutions that are subject to residential electricity prices, as well as industrial enterprise dormitories; Property type boundaries: Only commercial housing, affordable housing, collectively built buildings, self-built houses by work units, and urban villages are included in the electricity consumption calculation, excluding special properties whose data are difficult to trace; Greenhouse gas boundary: Only carbon dioxide emissions are counted, excluding other types of greenhouse gases.
[0039] S2: Identify emission sources and accounting objects (core technical positioning) Sources of emissions: “Indirect emissions from electricity consumption” corresponding to daily electricity use in residential life – that is, carbon dioxide generated during the electricity production process (such as the combustion of fossil fuels in thermal power plants), which is not directly emitted by residential users themselves; Accounting objects: residential users in 10kV power supply areas. The "transformer area" is the basic accounting unit. When it comes to the user dimension, it needs to be bound to the "meter account number".
[0040] S3: Delineating the baseline scenario and peak / off-peak / valley periods (technical framework construction) Baseline scenario division: Scenario 1 (Spring and Autumn Renewable Energy Consumption): Applicable periods are February to June and October to November, with the core objective of promoting midday photovoltaic consumption; Scenario 2 (Summer and Winter Power Supply Guarantee): Applicable periods are July-September and December-January, with the core objective of reducing peak air conditioning load; Peak, off-peak, and valley time periods (defined according to different scenarios): Scenario 1 (Spring and Autumn): Peak hours are 20:00-24:00, valley hours are 0:00-6:00 and 10:00-15:00 (PV power output is matched during the midday valley hours), and the rest of the time is flat. Scenario 2 (Summer and Winter): Peak hours are 15:00-24:00 (covering peak air conditioning electricity consumption), valley hours are 0:00-7:00, and the rest of the time is flat.
[0041] S4: Collect basic data and determine key parameters (technical input preparation) Basic data collection: Historical electricity consumption in the transformer substation: Collects the total electricity consumption of residents in the transformer substation during the same month of the previous year, divided into peak and off-peak periods; Actual electricity consumption of users: Collect and calculate the actual electricity consumption data of residential users during the current month and peak and off-peak periods; Dynamic power grid carbon emission factor: Collects power grid carbon dioxide emission factors by month and by peak and off-peak periods; User number data: Collect the number of people in the household as reported by resident users. If no report is submitted, the average number of people per household in the pilot area's statistical yearbook will be used. Key parameters determined: Total number of households in the transformer substation area: This determines the total number of residential users covered by the 10kV transformer substation area within the accounting scope; Carbon Inclusive Benchmark Adjustment Factor: Set according to scenario and time period. The factor is less than 1 during the off-peak period in spring and autumn (encouraging electricity consumption), and the factor is greater than 1 during the peak period in summer and winter (constraining electricity consumption). The calculation logic is the ratio of the carbon factor during peak hours to that during off-peak hours.
[0042] S5: Calculate baseline emissions (Core technology calculation 1) Using different scenarios and time periods as dimensions, the dynamic power grid carbon emission factor is multiplied by the historical electricity consumption of the corresponding time period, then multiplied by the carbon inclusive benchmark adjustment factor, and then divided by the total number of households, the average number of people per household, and the number of days in the month to finally obtain the "average daily baseline emission per capita"—that is, the average carbon emission level of residents under the corresponding scenario and time period when low-carbon electricity use is not implemented (the calculation logic of the two types of scenarios is consistent, and the results are different only due to the difference in the time period factor).
[0043] S6: Calculate project scenario emissions (Core technology calculation 2) By dividing the data into time periods and users, the actual carbon dioxide emissions of a user during a given time period are obtained by multiplying the dynamic grid carbon emission factor by the user's actual electricity consumption during that time period in the current month. This results in the user's actual carbon dioxide emissions during that time period (i.e., the actual emission level after implementing low-carbon electricity use, with the core data coming from the user's actual electricity consumption records).
[0044] S7: Calculate emission reductions from residential electricity consumption (core technical output) Time-based emission reduction: For each peak, flat, and valley period, multiply the per capita daily baseline emission for that period by the actual number of users and the number of days in the month to obtain the user's baseline total emission for that period; then subtract the user's actual emission for that period from the baseline total emission to obtain the emission reduction for that period. Total emission reduction: This sums up the emission reductions of users during peak, flat, and valley periods. If the total emission reduction is greater than or equal to 0, it is considered an effective emission reduction contribution (included in carbon benefit incentives); if the total emission reduction is less than 0, it is considered no emission reduction contribution.
[0045] S8: Data verification and result confirmation (technical compliance assurance) Uniqueness verification: The binding relationship of the "electricity meter account number" is verified by the power low-carbon scenario application component to ensure that the same user does not participate in the accounting repeatedly; Total emission reduction verification: Calculate the total emission reduction of the entire distribution area and compare it with the total emission reduction of all users summarized by the low-carbon power scenario application components; if the deviation is less than or equal to 5%, the calculation result is confirmed to be valid; if the deviation is greater than 5%, check for issues such as duplicate reporting and abnormal power consumption, and re-execute the data collection and emission reduction calculation process.
[0046] S9: Output emission reduction results (technology application implementation) The verified emission reduction results will be synchronized to the power low-carbon scenario application components to realize the application of technological achievements: Record the current emission reductions for residents; Carbon incentives are issued based on emission reductions; The emission reduction filing and public announcement have been completed, meeting the verification requirements of Anhui Province's carbon benefit system.
[0047] (II) Explanation of Attachments Table 2 Data Requirements Table
[0048] The full-process technical accounting S of this invention includes a complete process of "determining accounting boundaries - identifying emission sources and accounting objects - dividing baseline scenarios and time periods - data acquisition and parameter determination - calculating baseline / project scenario emissions - calculating emission reductions - data verification - outputting results", which is described in detail below: a) Defining the Accounting Boundaries: Clearly define the accounting scope for carbon credits related to low-carbon electricity consumption by residents to avoid accounting bias. Four types of boundaries need to be defined: First, geographical boundaries (limited to the target administrative region); second, electricity user boundaries (only including residential users, excluding public institutions such as schools and social welfare institutions, and industrial enterprise dormitories); third, property type boundaries (only covering traceable properties such as commercial housing, affordable housing, collectively built buildings, self-built housing by work units, and urban villages); and fourth, greenhouse gas boundaries (only counting carbon dioxide emissions).
[0049] b) Identification and location of emission sources and accounting objects: The core objects and emission sources for accounting. Among them, emission sources are "indirect emissions from electricity consumption" corresponding to daily residential electricity consumption (i.e., carbon dioxide produced by the combustion of fossil fuels during the electricity production process, which is not directly emitted by residents); accounting objects are residential users covered by the 10kV power supply area, which need to be uniquely identified by binding "electricity meter account number".
[0050] c) Baseline Scenario and Time Period Division: Establish a seasonal and demand-based accounting time framework. Baseline scenarios are divided into two demand categories: "new energy consumption" and "power supply guarantee". Spring and autumn (February-June, October-November) are new energy consumption scenarios, while summer and winter (July-September, December-January) are power supply guarantee scenarios. At the same time, peak, flat, and valley periods are set differently (such as peak period 20:00-24:00 in spring and autumn, valley period 0:00-6:00 and 10:00-15:00) to match the output characteristics of new energy and the grid load pattern.
[0051] d) Data Collection and Parameter Determination: Collect the basic data required for accounting and define key parameters. The basic data to be collected includes: monthly and time-period grid carbon emission factors, historical electricity consumption of the same month in the previous year (peak and valley periods) in the distribution area, actual electricity consumption of residential users in the current month (peak and valley periods), and the number of residential users who have reported. The key parameters to be determined include: the total number of households in the distribution area and the carbon inclusion benchmark adjustment factor (set according to the scenario period to balance the differences in electricity consumption in different periods).
[0052] e) Baseline Emission Calculation: Calculate the average carbon emission level of residents when low-carbon electricity use is not implemented. Using "per capita daily" as the accounting dimension, combined with dynamic grid carbon emission factors for different scenarios and time periods, historical electricity consumption of the distribution area, carbon inclusive benchmark adjustment factors, and then allocating the emissions to the per capita daily baseline emission level by the total number of households in the distribution area, the average number of people per household, and the number of days in the month.
[0053] f) Calculate the actual carbon emission level of residents after they adopt low-carbon electricity consumption. Based on the principle of "by user and by time period", the actual carbon dioxide emission of the user during the corresponding time period of the month is obtained by multiplying the dynamic grid carbon emission factor by the actual electricity consumption of the residential user during that time period.
[0054] g) Quantify the actual emission reduction contribution of residential low-carbon electricity use. Calculate for three periods: peak, flat, and valley. Multiply the per capita daily baseline emission for each period by the actual number of users and the number of days in the month to obtain the user's baseline total emission for that period. Subtract the user's actual emission for that period to obtain the emission reduction for that period. After summing the emission reductions for the three periods, only the effective contribution of a total emission reduction ≥ 0 is retained (a total emission reduction < 0 is considered as no emission reduction contribution).
[0055] h) Data verification ensures the uniqueness and accuracy of the calculation results. It includes two layers of verification: first, uniqueness verification (verifying the binding relationship between "meter account number and personal carbon account" to avoid duplicate calculation for the same user); second, total amount verification (comparing the total emission reduction of the distribution area with the total emission reduction of the user. If the deviation is ≤5%, the result is valid. If the deviation is >5%, abnormal data is investigated and recalculated).
[0056] i) Output results to realize the practical application of accounting results. The verified emission reduction results will be synchronized to the power low-carbon scenario application component for the purpose of issuing carbon incentives (such as electricity tariff discounts) based on emission reductions.
[0057] The target audience of this invention includes: local government departments, local State Grid companies, and the general public (residents).
[0058] Application services include three main modules: electricity conservation scenarios, backend information management, and backend model management. It supports users to participate in electricity conservation scenarios through registration, associates and aggregates users' electricity data (such as electricity consumption data), calculates carbon emissions for baseline scenarios and project scenarios, and finally calculates monthly emission reductions. It also supports integration with local carbon credit platforms, model invocation, and data services, as well as the retrieval, viewing, and export of behavioral information and data.
[0059] Finally, any aspects not fully described in this invention utilize existing mature products and technologies.
[0060] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present invention, such designs should fall within the protection scope of the present invention.
Claims
1. A low-carbon electricity consumption carbon-inclusive method adapted to multiple electricity scenarios, characterized in that, Includes the following steps: S1: Determine the accounting boundary to obtain the accounting boundary range; S2: Based on the accounting boundary range obtained in S1, identify the emission sources and accounting objects to obtain the emission source and accounting object identifiers. The emission source is the indirect emission of electricity consumption corresponding to residential electricity consumption, and the accounting object is the residential users in the 10kV power supply area. The unique identifier is achieved by binding the meter account number. S3: Based on the accounting object identifier obtained in S2, perform baseline scenario and peak-valley time period division to obtain scenario and time period division results; S4: Based on the scenario and time period division results obtained in S3, basic data collection and key parameter determination are carried out to obtain basic data and key parameters; S5: Based on the basic data and key parameters obtained in S4, calculate the baseline emissions to obtain baseline emissions data; S6: Based on the basic data and key parameters obtained in S4, calculate the project scenario emissions to obtain the project scenario emissions data; S7: Based on the baseline emission data obtained in S5 and the project scenario emission data obtained in S6, calculate the emission reduction of residential electricity consumption and obtain the emission reduction result; S8: Based on the emission reduction results obtained in S7, perform data verification and result confirmation to obtain the emission reduction data that has passed verification; S9: Based on the emission reduction data that has passed verification obtained in S8, output the emission reduction results to obtain the final carbon inclusive incentive basis. The emission reduction results are synchronized to the power low-carbon scenario application component for the distribution of carbon inclusive incentives.
2. The low-carbon electricity consumption carbon-inclusive method for adapting to multiple electricity scenarios according to claim 1, characterized in that: The peak period for the spring and autumn renewable energy consumption scenario described in S3 is 20:00-24:00, the valley period includes the midday valley period of 0:00-6:00 and 10:00-15:00, and the normal period is 6:00-10:00 and 15:00-20:00; the peak period for the summer and winter power supply guarantee scenario is 15:00-24:00, the valley period is 0:00-7:00, and the normal period is 7:00-15:
00.
3. The low-carbon electricity consumption carbon-inclusive method for adapting to multiple electricity scenarios according to claim 1, characterized in that: The carbon inclusive benchmark adjustment factor described in S4 is dynamically set as follows: under the spring and autumn renewable energy consumption scenario, the peak period factor is 1.13, the valley period factor is 0.99, and the normal period factor is 1; under the summer and winter power supply guarantee scenario, the peak period factor is 1.28, the valley period factor is 0.74, and the normal period factor is 1.
4. The low-carbon electricity consumption carbon-inclusive method for adapting to multiple electricity scenarios according to claim 1, characterized in that: The baseline emission calculation formula mentioned in S5 includes: For the scenario of new energy consumption in spring and autumn, according to the formula BE 1, p , i = NEFelec × ESt 0, p , i calculate; For power supply security scenarios in summer and winter, according to the formula BE 2, p , i = NEFelec × ESt 0, p , i calculate; in, BE 1, p , i and BE 2, p , i As the baseline emissions, EFelec As a carbon emission factor of the power grid, ESt 0, p , i This refers to the historical electricity consumption of the power station area. N This represents the total number of households in the Taiwan area.
5. The low-carbon electricity consumption carbon-inclusive method for adapting to multiple electricity scenarios according to claim 1, characterized in that: The formula for calculating emissions for the project scenario described in S6 is as follows: PEp , i = NEFelec × ECn , i ,in PEp , i Emissions from the project site EFelec As a carbon emission factor of the power grid, ECn , i The actual electricity consumption of the user N This represents the total number of households in the Taiwan area.
6. The low-carbon electricity consumption carbon-inclusive method for adapting to multiple electricity scenarios according to claim 1, characterized in that: The emission reduction calculation described in S7 includes: Total emission reductions are calculated using the formula ERi = ERp , i + ERg , i + ERf , i calculate; Peak period emission reductions are calculated using the formula. ERp , i =( BEp , i - PEp , i )× ωi calculate; Emission reduction during off-peak hours is calculated using the formula. ERg , i =( BEg , i - PEg , i )× ωi calculate; Normal emission reduction is calculated using the formula ERf , i =( BEf , i - PEf , i )× ωi calculate; in, ERi For total emission reductions, ERp , i , ERg , i , ERf , i To reduce emissions in different time periods, BEp , i , BEg , i , BEf , i As the baseline emissions, PEp , i , PEg , i , PEf , i Emissions from the project site ωi This is the carbon inclusive benchmark adjustment factor.
7. A system used in the low-carbon electricity consumption carbon inclusiveness method for adapting to multiple power scenarios as described in claim 1, characterized in that: include: The data aggregation module is used to integrate registered user information, number of residential electricity users, average number of people per household, and peak, flat, and valley electricity consumption data to build an emission reduction accounting model. The electricity conservation scenario module provides users with an entry point to participate in electricity conservation activities and displays emission reductions and historical data; The backend information management module is used to manage registered user information, electricity-saving behavior information, and rules; The backend model management module embeds the power grid carbon emission factor, carbon emission accounting formula and emission reduction accounting model, and automatically calculates and generates monthly emission reductions based on the input data; The data service module utilizes big data technology to provide data storage, computing, and security. The modules work together through the low-carbon power scenario application components. The data aggregation module provides data input to the back-end model management module, the output of the back-end model management module is used to display the power saving scenario module, the back-end information management module ensures the uniqueness of the data, and the data service module supports the operation of the system.
8. The system used in the low-carbon electricity consumption carbon reduction method for adapting to multiple power scenarios according to claim 7, characterized in that: The system also includes a uniqueness verification unit to verify the binding relationship between the electricity meter account number and the individual carbon account, so as to prevent the same user from repeatedly declaring emission reductions.
9. The system used in the low-carbon electricity consumption carbon reduction method for adapting to multiple power scenarios as described in claim 7, characterized in that: The data service module is integrated into the provincial carbon benefit platform, supporting data interaction with local government departments, power grid companies and residential users, and dynamically updating the power grid carbon emission factor.
10. The system used in the low-carbon electricity consumption carbon-inclusive method for adapting to multiple power scenarios according to claim 7, characterized in that: After the system outputs the emission reduction results, it automatically triggers the carbon incentive distribution mechanism to achieve a closed-loop incentive of emission reduction, credits, and electricity price discounts.