Method and system for quantifying power demand side response carbon emission reduction effect

Abstract

The invention relates to a power demand side response carbon emission reduction effect quantification method. The method comprises the following steps: S1, obtaining user side data and power grid side data; s2, local data preprocessing is carried out through an edge computing node, and then the data are uploaded to a cloud database; s3, according to the preprocessed user side data, obtaining an adjustment load capacity; s4, calculating a dynamic carbon emission factor through a real-time power generation curve and a power grid power generation structure in combination with the preprocessed power grid side data, thereby evaluating marginal carbon emission change caused by demand response, dynamically calculating the real-time carbon emission factor, and dynamically updating EF (t) by using power grid power generation structure data; and S5, calculating a baseline and actual carbon emission according to the baseline load model, the adjusted load data and the EF (t), and finally obtaining an emission reduction effect. According to the invention, dynamic marginal carbon emission factor evaluation and high-precision carbon emission reduction quantification are realized.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission reduction, and in particular to a method and system for quantifying the carbon emission reduction effect of the power demand side response. Background Art

[0002] With the global energy transformation and the proposal of carbon neutrality goals, countries are accelerating the optimization of energy structures and the control of carbon emissions. As a key area of ​​carbon emissions, the power industry has an urgent need to reduce emissions. Demand response (DR), as a flexible energy management method, can achieve dynamic adjustments between the supply and demand sides of electricity, improve energy efficiency through peak shaving and valley filling, load shifting, etc., reduce dependence on high-carbon emission power sources, and promote greener and more low-carbon power grid operations. However, due to the complex impact of demand response on the power generation structure and marginal power demand of the power grid, how to quantitatively evaluate the carbon emission reduction effect brought about by demand response has become a difficulty in current research and application.

[0003] Traditional carbon emission calculation methods typically rely on average carbon emission factors, which cannot accurately capture the dynamic impact of demand response on the grid's marginal power sources. Furthermore, the increasing proportion of renewable energy in the power system and the increasing complexity of the grid's generation structure have significantly increased the temporal and spatial variability of carbon emission factors. Therefore, to accurately quantify carbon emission reductions, it is necessary to comprehensively consider the impact of user-side load changes, the grid's generation structure, and real-time marginal carbon emission factors, and dynamically evaluate the emission reduction benefits of demand response. Summary of the Invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a method and system for quantifying the carbon emission reduction effect of the power demand side response, so as to realize dynamic marginal carbon emission factor evaluation and high-precision carbon emission reduction quantification.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for quantifying the carbon emission reduction effect of power demand-side response includes the following steps:

[0007] S1: Acquire user-side data and grid-side data, wherein the user-side data includes a base load curve and an actual load curve after demand response; the grid-side data includes a real-time power generation curve and a carbon emission factor;

[0008] S2: Preprocess local data through edge computing nodes and then upload to the cloud database;

[0009] S3: Obtaining the adjusted load amount based on the pre-processed user-side data;

[0010] S4: Combine the pre-processed grid-side data, calculate the dynamic carbon emission factor through the real-time power generation curve and the grid generation structure, and thus evaluate the marginal carbon emission changes brought about by demand response. Dynamic calculation of the real-time carbon emission factor, using the grid generation structure data, dynamically update EF(t);

[0011] S5: Calculate the baseline and actual carbon emissions based on the baseline load model, the adjusted load data, and EF(t), and finally obtain the emission reduction effect.

[0012] Furthermore, the load adjustment amount is obtained based on the pre-processed user-side data, as follows:

[0013] The linear regression model and time series analysis model ARIMA are used to analyze the user's basic load data to establish a forecast load model during the demand response period and generate the user's baseline load curve L baseline (t);

[0014] Combined with the actual load curve data L after demand response actual (t), evaluate the peak shaving or valley filling load after the user response, and adjust the load to: ΔL(t) = L baseline (t)-L actual (t).

[0015] Furthermore, the grid-side carbon emission factor EF(t) changes dynamically according to the real-time power generation curve and the power generation structure of the grid. The total emission factor calculation formula is:

[0016]

[0017] Among them, W i (t) is the output of the i-th generator set at time t; EF i Unit carbon emission factor of the i-th generator set;

[0018] In the cloud, by calculating the real-time power generation data of the power grid and combining it with the load-side adjustment behavior, the marginal carbon emission factor EF is dynamically evaluated using the power generation priority of the power grid. margin (t), assess the marginal emission changes of high-carbon units reduced during peak curtailment and low-carbon units introduced through valley filling.

[0019] Furthermore, the baseline load model, the adjusted load data, and EF(t) are combined to calculate the baseline and actual carbon emissions, and the emission reduction effect is finally obtained, as follows:

[0020] Calculate the carbon emissions at each moment under the baseline conditions:

[0021] E baseline (t) = L baseline (t)·EF(t);

[0022] Carbon emissions after actual load response:

[0023] E actual (t) = L actual (t)·EF(t);

[0024] Integrate the emissions according to the time interval [t0, t1] to obtain the total carbon emissions under the baseline and response conditions:

[0025]

[0026] The carbon emission reduction benefits are quantified as:

[0027] ΔE=E baseline -E actual ;

[0028] Conduct aggregate analysis of all users’ demand response behaviors and calculate the overall carbon emission reduction benefits of the regional power grid.

[0029] A quantitative system for responding to carbon emission reduction effects on the power demand side includes a data acquisition module, a preprocessing module, a load calculation module, a dynamic update module, and an emission reduction analysis module, specifically as follows:

[0030] The data acquisition module obtains user-side data and grid-side data, and the user-side data includes the basic load curve and the actual load curve after demand response; the grid-side data includes the real-time power generation curve and the carbon emission factor; the preprocessing module performs local data preprocessing through the edge computing node and then uploads it to the cloud database; the load calculation module obtains the adjusted load according to the preprocessed user-side data; the dynamic update module combines the preprocessed grid-side data, calculates the dynamic carbon emission factor through the real-time power generation curve and the grid power generation structure, and thus evaluates the marginal carbon emission changes brought about by demand response. The real-time carbon emission factor is dynamically calculated, and EF(t) is dynamically updated using the grid power generation structure data; the emission reduction analysis module calculates the baseline and actual carbon emissions based on the baseline load model, the adjusted load data, and EF(t), and finally obtains the emission reduction effect.

[0031] Furthermore, preprocessing includes normalization, sliding average, and high-frequency compression, as follows:

[0032] The standardization process standardizes data fields from different sources and converts them into a unified JSON format;

[0033] The sliding average processing is to perform sliding average processing on the user side data and the grid side data to eliminate spikes or abnormal values ​​in the signal;

[0034] The high-frequency compression process compresses high-frequency data into N pieces per minute, and extracts key information using average, maximum, and minimum values.

[0035] Furthermore, the load adjustment amount is obtained based on the pre-processed user-side data, as follows:

[0036] The linear regression model and time series analysis model ARIMA are used to analyze the user's basic load data to establish a forecast load model during the demand response period and generate the user's baseline load curve L baseline (t);

[0037] Combined with the actual load curve data L after demand response actual (t), evaluate the peak shaving or valley filling load after the user response, and adjust the load to: ΔL(t) = L baseline (t)-L actual (t).

[0038] Furthermore, the grid-side carbon emission factor EF(t) changes dynamically according to the real-time power generation curve and the power generation structure of the grid. The total emission factor calculation formula is:

[0039]

[0040] Among them, W i (t) is the output of the i-th generator set at time t; EF i Unit carbon emission factor of the i-th generator set;

[0041] In the cloud, by calculating the real-time power generation data of the power grid and combining it with the load-side adjustment behavior, the marginal carbon emission factor EF is dynamically evaluated using the power generation priority of the power grid. margin (t), assess the marginal emission changes of high-carbon units reduced during peak curtailment and low-carbon units introduced through valley filling.

[0042] Furthermore, the baseline load model, the adjusted load data, and EF(t) are combined to calculate the baseline and actual carbon emissions, and the emission reduction effect is finally obtained, as follows:

[0043] Calculate the carbon emissions at each moment under the baseline conditions:

[0044] E baseline (t) = L baseline (t)·EF(t);

[0045] Carbon emissions after actual load response:

[0046] E actual (t) = L actual (t)·EF(t);

[0047] Integrate the emissions according to the time interval [t0, t1] to obtain the total carbon emissions under the baseline and response conditions:

[0048]

[0049] The carbon emission reduction benefits are quantified as:

[0050] ΔE=E baseline -E actual ;

[0051] Conduct aggregate analysis of all users’ demand response behaviors and calculate the overall carbon emission reduction benefits of the regional power grid.

[0052] The present invention has the following beneficial effects:

[0053] This invention combines real-time user load curves, grid power generation curves and dynamic carbon emission factors to construct a full-process calculation model, performs data preprocessing through edge computing nodes, and dynamically updates the marginal carbon emission factors of generator sets in real time. It also combines baseline load modeling and adjustment models to evaluate the carbon emission reduction effects of demand response under different load scenarios, providing strong support for the green development of smart grids, demand-side management optimization and the construction of carbon market mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0055] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0056] A method for quantifying the carbon emission reduction effect of power demand-side response includes the following steps:

[0057] S1: Acquire user-side data and grid-side data, wherein the user-side data includes a base load curve and an actual load curve after demand response; the grid-side data includes a real-time power generation curve and a carbon emission factor;

[0058] S2: Perform local data preprocessing (denoising, compression) through edge computing nodes (such as Raspberry Pi), and then upload to the cloud database (such as InfluxDB);

[0059] S3: Obtaining the adjusted load amount based on the pre-processed user-side data;

[0060] S4: Combine the pre-processed grid-side data, calculate the dynamic carbon emission factor through the real-time power generation curve and the grid generation structure, and thus evaluate the marginal carbon emission changes brought about by demand response. Dynamic calculation of the real-time carbon emission factor, using the grid generation structure data, dynamically update EF(t);

[0061] S5: Calculate the baseline and actual carbon emissions based on the baseline load model, the adjusted load data, and EF(t), and finally obtain the emission reduction effect.

[0062] Obtain user-side data and grid-side data, as follows:

[0063] Obtain historical electricity usage data from the user's power monitoring equipment (such as smart meters) to determine the user's base load curve (i.e., the user's standard electricity usage curve without demand response). The user's actual electricity usage curve during the demand response period is collected and compared with the base curve to reflect the change in load after the user responds (load reduction or peak-to-valley load shifting).

[0064] Through the energy management system (EMS) of the power dispatch center, real-time power generation data of the power grid is obtained, including unit output (MW), such as the proportion and time series data of coal-fired, natural gas, wind power, photovoltaic power generation, etc.

[0065] Obtain the unit carbon emission factors of each unit type on the grid side, including the emission coefficients of coal-fired units, gas-fired units and other renewable energy sources.

[0066] In this embodiment, the preprocessing includes normalization processing, sliding average processing and high-frequency compression processing, which are specifically as follows:

[0067] The standardization process standardizes data fields from different sources and converts them into a unified JSON format;

[0068] The sliding average processing is to perform sliding average processing on the user side data and the grid side data to eliminate spikes or abnormal values ​​in the signal;

[0069] The high-frequency compression process compresses high-frequency data into N pieces per minute, and extracts key information using average, maximum, and minimum values.

[0070] In this embodiment, the load adjustment amount is obtained based on the pre-processed user-side data, as follows:

[0071] The linear regression model and time series analysis model ARIMA are used to analyze the user's basic load data to establish a forecast load model during the demand response period and generate the user's baseline load curve L baseline (t);

[0072] Combined with the actual load curve data L after demand response actual (t), evaluate the peak shaving or valley filling load after the user response, and adjust the load to: ΔL(t) = L baseline (t)-L actual (t).

[0073] In this embodiment, the grid-side carbon emission factor EF(t) changes dynamically based on the real-time power generation curve and the power generation structure of the grid. The total emission factor calculation formula is:

[0074]

[0075] Among them, W i (t) is the output of the i-th generator set at time t; EF i Unit carbon emission factor of the i-th generator set;

[0076] In the cloud, by calculating the real-time power generation data of the power grid, combining the load-side adjustment behavior, and using the power generation priority of the power grid (such as the marginal cost of power generation), the marginal carbon emission factor EF is dynamically evaluated. margin (t), assess the marginal emission changes of high-carbon units reduced during peak curtailment and low-carbon units introduced through valley filling.

[0077] In this embodiment, the baseline load model, the adjusted load data, and EF(t) are combined to calculate the baseline and actual carbon emissions, and the emission reduction effect is finally obtained as follows:

[0078] Calculate the carbon emissions at each moment under the baseline conditions:

[0079] E baseline (t) = L baseline (t)·EF(t);

[0080] Carbon emissions after actual load response:

[0081] E actual (t) = L actual (t)·EF(t);

[0082] Integrate the emissions according to the time interval [t0, t1] to obtain the total carbon emissions under the baseline and response conditions:

[0083]

[0084] The carbon emission reduction benefits are quantified as:

[0085] ΔE=E baseline -E actual ;

[0086] Conduct aggregate analysis of all users’ demand response behaviors and calculate the overall carbon emission reduction benefits of the regional power grid.

[0087] A quantitative system for responding to carbon emission reduction effects on the power demand side includes a data acquisition module, a preprocessing module, a load calculation module, a dynamic update module, and an emission reduction analysis module, specifically as follows:

[0088] The data acquisition module obtains user-side data and grid-side data, and the user-side data includes the basic load curve and the actual load curve after demand response; the grid-side data includes the real-time power generation curve and the carbon emission factor; the preprocessing module performs local data preprocessing through the edge computing node and then uploads it to the cloud database; the load calculation module obtains the adjusted load according to the preprocessed user-side data; the dynamic update module combines the preprocessed grid-side data, calculates the dynamic carbon emission factor through the real-time power generation curve and the grid power generation structure, and thus evaluates the marginal carbon emission changes brought about by demand response. The real-time carbon emission factor is dynamically calculated, and EF(t) is dynamically updated using the grid power generation structure data; the emission reduction analysis module calculates the baseline and actual carbon emissions according to the baseline load model, the adjusted load data, and EF(t), and finally obtains the emission reduction effect.

[0089] In this embodiment, the preprocessing includes normalization processing, sliding average processing and high-frequency compression processing, which are specifically as follows:

[0090] The standardization process standardizes data fields from different sources and converts them into a unified JSON format;

[0091] The sliding average processing is to perform sliding average processing on the user side data and the grid side data to eliminate spikes or abnormal values ​​in the signal;

[0092] The high-frequency compression process compresses high-frequency data into N pieces per minute, and extracts key information using average, maximum, and minimum values.

[0093] In this embodiment, the load adjustment amount is obtained based on the pre-processed user-side data, as follows:

[0094] The linear regression model and time series analysis model ARIMA are used to analyze the user's basic load data to establish a forecast load model during the demand response period and generate the user's baseline load curve L baseline (t);

[0095] Combined with the actual load curve data L after demand response actual (t), evaluate the peak shaving or valley filling load after the user response, and adjust the load to: ΔL(t) = L baseline (t)-L actual (t).

[0096] In this embodiment, the grid-side carbon emission factor EF(t) changes dynamically based on the real-time power generation curve and the power generation structure of the grid. The total emission factor calculation formula is:

[0097]

[0098] Among them, Wi (t) is the output of the i-th generator set at time t; EF i Unit carbon emission factor of the i-th generator set;

[0099] In the cloud, by calculating the real-time power generation data of the power grid, combining the load-side adjustment behavior, and using the power generation priority of the power grid (such as the marginal cost of power generation), the marginal carbon emission factor EF is dynamically evaluated. margin (t), assess the marginal emission changes of high-carbon units reduced during peak curtailment and low-carbon units introduced through valley filling.

[0100] In this embodiment, the baseline load model, the adjusted load data, and EF(t) are combined to calculate the baseline and actual carbon emissions, and the emission reduction effect is finally obtained as follows:

[0101] Calculate the carbon emissions at each moment under the baseline conditions:

[0102] E baseline (t) = L baseline (t)·EF(t);

[0103] Carbon emissions after actual load response:

[0104] E actual (t) = L actual (t)·EF(t);

[0105] Integrate the emissions according to the time interval [t0, t1] to obtain the total carbon emissions under the baseline and response conditions:

[0106]

[0107] The carbon emission reduction benefits are quantified as:

[0108] ΔE=E baseline -E actual ;

[0109] Conduct aggregate analysis of all users’ demand response behaviors and calculate the overall carbon emission reduction benefits of the regional power grid.

[0110] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0114] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

Claims

1. A method for quantifying the carbon emission reduction effect of power demand side response, characterized in that: The following steps are involved: S1: Acquire user-side data and grid-side data, wherein the user-side data includes a base load curve and an actual load curve after demand response; the grid-side data includes a real-time power generation curve and a carbon emission factor; S2: Preprocess local data through edge computing nodes and then upload to the cloud database; S3: Obtaining the adjusted load amount based on the pre-processed user-side data; S4: Combine the pre-processed grid-side data, calculate the dynamic carbon emission factor through the real-time power generation curve and the grid generation structure, and thus evaluate the marginal carbon emission changes brought about by demand response. Dynamic calculation of the real-time carbon emission factor, using the grid generation structure data, dynamically update EF(t); S5: Calculate the baseline and actual carbon emissions based on the baseline load model, the adjusted load data, and EF(t), and finally obtain the emission reduction effect.

2. A method for quantifying the carbon emission reduction effect of power demand side response according to claim 1, characterized in that: The preprocessing includes standardization, sliding average and high-frequency compression, specifically as follows: the standardization standardizes data fields from different sources and converts them into a unified JSON format; The sliding average processing is to perform sliding average processing on the user side data and the grid side data to eliminate spikes or abnormal values ​​in the signal; The high-frequency compression process compresses high-frequency data into N pieces per minute, and extracts key information using average, maximum, and minimum values.

3. The method for quantifying the carbon emission reduction effect of power demand side response according to claim 1 is characterized in that: The load adjustment is obtained based on the pre-processed user-side data, specifically as follows: The linear regression model and time series analysis model ARIMA are used to analyze the user's basic load data to establish a forecast load model during the demand response period and generate the user's baseline load curve L baseline (t); Combined with the actual load curve data L after demand response actual (t), evaluate the peak shaving or valley filling load after the user response, and adjust the load to: ΔL(t) = L baseline (t)-L actual (t).

4. The method for quantifying the carbon emission reduction effect of power demand side response according to claim 1, characterized in that: The grid-side carbon emission factor EF(t) changes dynamically based on the real-time power generation curve and the power generation structure of the grid. The total emission factor is calculated as follows: Among them, W i (t) is the output of the i-th generator set at time t; EF i Unit carbon emission factor of the i-th generator set; In the cloud, by calculating the real-time power generation data of the power grid and combining it with the load-side adjustment behavior, the marginal carbon emission factor EF is dynamically evaluated using the power generation priority of the power grid. margin (t), assess the marginal emission changes of high-carbon units reduced during peak curtailment and low-carbon units introduced through valley filling.

5. A method for quantifying the carbon emission reduction effect of power demand side response according to claim 4, characterized in that: The baseline load model, the adjusted load data, and EF(t) are combined to calculate the baseline and actual carbon emissions, and the emission reduction effect is finally obtained, as follows: Calculate the carbon emissions at each moment under the baseline conditions: E baseline (t)=L baseline (t)·EF(t); Carbon emissions after actual load response: E actual (t)=L actual (t)·EF(t); Integrate the emissions according to the time interval [t0, t1] to obtain the total carbon emissions under the baseline and response conditions: The carbon emission reduction benefits are quantified as: △E=E baseline -AND actual ; Conduct aggregate analysis of all users’ demand response behaviors and calculate the overall carbon emission reduction benefits of the regional power grid.

6. A quantification system for the carbon emission reduction effect of the power demand side, characterized by: It includes a data acquisition module, a preprocessing module, a load calculation module, a dynamic update module and an emission reduction analysis module, specifically as follows: the data acquisition module acquires user-side data and grid-side data, the user-side data includes a basic load curve and an actual load curve after demand response; the grid-side data includes a real-time power generation curve and a carbon emission factor; The pre-processing module performs local data pre-processing through edge computing nodes and then uploads it to the cloud database; The load calculation module obtains the adjusted load according to the pre-processed user-side data; The dynamic update module combines pre-processed grid-side data with real-time power generation curves and grid power generation structure to calculate dynamic carbon emission factors, thereby evaluating marginal carbon emission changes caused by demand response. The dynamic calculation of real-time carbon emission factors uses grid power generation structure data to dynamically update EF(t); The emission reduction analysis module calculates the baseline and actual carbon emissions based on the baseline load model, the adjusted load data, and EF(t), and ultimately obtains the emission reduction effect.

7. A quantification system for carbon emission reduction effect of power demand side response according to claim 6, characterized in that: The preprocessing includes standardization, sliding average and high-frequency compression, specifically as follows: the standardization standardizes data fields from different sources and converts them into a unified JSON format; The sliding average processing is to perform sliding average processing on the user side data and the grid side data to eliminate spikes or abnormal values ​​in the signal; The high-frequency compression process compresses high-frequency data into N pieces per minute, and extracts key information using average, maximum, and minimum values.

8. The quantification system for carbon emission reduction effect of power demand side response according to claim 6 is characterized in that: The load adjustment is obtained based on the pre-processed user-side data, specifically as follows: The linear regression model and time series analysis model ARIMA are used to analyze the user's basic load data to establish a forecast load model during the demand response period and generate the user's baseline load curve L baseline (t); Combined with the actual load curve data L after demand response actual (t), evaluate the peak shaving or valley filling load after the user response, and adjust the load to: ΔL(t) = L baseline (t)-L actual (t).

9. The quantification system for carbon emission reduction effect of power demand side response according to claim 6 is characterized in that: The grid-side carbon emission factor EF(t) changes dynamically based on the real-time power generation curve and the power generation structure of the grid. The total emission factor is calculated as follows: Among them, W i (t) is the output of the i-th generator set at time t; EF i Unit carbon emission factor of the i-th generator set; In the cloud, by calculating the real-time power generation data of the power grid and combining it with the load-side adjustment behavior, the marginal carbon emission factor EF is dynamically evaluated using the power generation priority of the power grid. margin (t), assess the marginal emission changes of high-carbon units reduced during peak curtailment and low-carbon units introduced through valley filling.

10. The quantification system for carbon emission reduction effect of power demand side response according to claim 6, characterized in that: The baseline load model, the adjusted load data, and EF(t) are combined to calculate the baseline and actual carbon emissions, and the emission reduction effect is finally obtained, as follows: Calculate the carbon emissions at each moment under the baseline conditions: E baseline (t)=L baseline (t)·EF(t); Carbon emissions after actual load response: E actual (t)=L actual (t)·EF(t); Integrate the emissions according to the time interval [t0, t1] to obtain the total carbon emissions under the baseline and response conditions: The carbon emission reduction benefits are quantified as: △E=E baseline -AND actual ; Conduct aggregate analysis of all users’ demand response behaviors and calculate the overall carbon emission reduction benefits of the regional power grid.

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