A method for calculating dynamic carbon emission factors in provincial regions based on time periods

Abstract

This invention provides a method for calculating dynamic carbon emission factors for provincial regions based on time-segmentation. This method is based on the regional operation characteristics of the 220kV power grid, sets power exchange constraints between the 500kV main grid and the 220kV sub-regional grid, and calculates the active power consumed by various types in the 220kV region of the province at each time period. Using the 220kV power supply area as the unit spatial scale and Δt as the segment interval, the carbon emission factor of regional electricity consumption in a single time period is calculated. Regional electricity consumption curves are classified, and time periods are divided according to the temporal characteristics of regional loads. The average carbon emission factor for each region belonging to the same time period category is calculated. Based on the average carbon emission factor calculation results, a user carbon reduction model based on dynamic carbon emission factors is established. This invention can effectively quantify the carbon emissions of user-side electricity consumption behavior in different regions at different time periods, and further promote low-carbon interaction between source and load from a carbon perspective based on the dynamic carbon emission factor calculation results.

Description

Technical Field

[0001] This invention relates to the field of carbon emission factor calculation, specifically a method for calculating dynamic carbon emission factors for different regions within a province based on time period division. Background Technology

[0002] Power generation and the proportion of power generated by different generating units are direct factors determining carbon emissions. With the accelerated construction of new power systems primarily based on new energy sources, the large-scale integration of centralized and distributed new energy power generation has promoted the decarbonization and diversification of the source side. However, user-side electricity consumption behavior, as an indirect factor influencing carbon emissions, makes the load side the primary responsible party for carbon emissions. Traditional low-carbon planning and operation studies on the generation side cannot fully mobilize the low-carbon demand response capabilities of the user side, nor can they fully mobilize the user-side carbon reduction capabilities from a "carbon perspective." The emission factor method provided by the IPCC is the most widely used carbon emission measurement method, where carbon emissions are determined by consumption and the emission factor. The electricity consumption carbon emission factor is a key parameter for achieving user-side carbon emission measurement, reflecting the impact of differentiated user electricity consumption behavior on source-side carbon emissions. Traditional fixed average electricity consumption carbon emission factors cannot effectively reflect the differences in carbon emissions generated on the generation side by user electricity consumption behavior in different regions. At the same time, while large-scale integration of new energy power generation reduces carbon emissions, its output curve is affected by natural factors and exhibits intermittent and fluctuating characteristics, resulting in significant differences in the power generation ratio of different units at different times on the source side.

[0003] Therefore, it is urgent to consider the spatiotemporal differences in user-side load curves, conduct dynamic carbon emission factor calculations for the power grid, effectively quantify the carbon emissions of user-side electricity consumption behavior in different regions and at different times, further fully explore the carbon emission reduction capabilities of various provincial regions, and promote low-carbon interaction between source and load from a carbon perspective. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method for calculating the dynamic carbon emission factor of provincial regions based on time period division. This method is based on the regional operation characteristics of 220kV power grid, considers the power exchange constraints between the 500kV main grid and the 220kV sub-regional power grid, calculates the active power absorbed by each 220kV region, further divides the time period according to the temporal characteristics of regional load, and obtains the dynamic carbon emission factor calculation result with appropriate time granularity based on the calculation result of regional electricity consumption carbon emission factor in a single time period. It can be applied to calculate the dynamic carbon emission factor of 220kV regional power grids in provincial power grids.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0006] A method for calculating dynamic carbon emission factors for different regions within a province based on time periods includes the following steps:

[0007] (1) Based on the regional operation characteristics of the 220kV power grid, power exchange constraints between the 500kV main grid and the 220kV regional power grid are set, and the active power of each type consumed in the 220kV region of the whole province in each time period is calculated.

[0008] (2) Based on the active power consumed by the 220kV area of ​​the whole province in each time period calculated in step (1), the carbon emission factor of electricity consumption in the area in a single time period is calculated with the 220kV power supply area as the unit spatial scale and Δt as the segment interval.

[0009] (3) Classify the regional electricity consumption curves and divide the time periods according to the regional load time sequence characteristics using the fuzzy C-means clustering method. Based on the single-time-period regional electricity consumption carbon emission factor calculated in step (2), calculate the average carbon emission factor of each region belonging to the same time period.

[0010] Furthermore, it also includes:

[0011] (4) Based on the average carbon emission factor calculated in step (3), establish a user carbon emission reduction model based on dynamic carbon emission factor.

[0012] Furthermore, the power exchange constraints between the 500kV main grid and the 220kV sub-regional grid set in step (1) are specifically as follows:

[0013]

[0014]

[0015]

[0016]

[0017]

[0018]

[0019] In the formula: P represents the active power output of the k-th type of power generation in region n during time period t; n,t,k P n,t These represent the k-th type of active power and the total active power absorbed, respectively. These represent the k-th type of active power transmitted from the 500kV main grid to region n and the k-th type of active power transmitted from region n to the 500kV main grid, respectively. This represents the net active power output of type k in the 500kV main grid, which is equal to the active power output of type k generation connected to the 500kV main grid. Adding all sub-regions and out-of-province active power input to the main grid, and subtracting the k-th type of active power output to out-of-province; the total net active power output Pt M* similar; This indicates the active power transmitted and received across provinces by the 500kV main grid.

[0020] Furthermore, in step (1), the calculation of the various types of active power consumed in the 220kV area of ​​the entire province at each time period is as follows:

[0021] The active power of type k consumed in the 220kV area of ​​the entire province during time period t is as follows:

[0022]

[0023] In the formula: These represent the total active power transmitted from the 500kV main grid to region n and the total active power transmitted from region n to the 500kV main grid, respectively.

[0024] Furthermore, in step (2), when calculating the carbon emission factor of electricity consumption in a single time period, the 220kV area is used as the unit spatial scale for calculating the dynamic carbon emission factor. It is assumed that the unit comprehensive carbon dioxide emission of different generating units of the same type in each sub-region is the same. The carbon emission factor CEF for time period t is obtained by using Δt as the segment interval. n,t as follows:

[0025]

[0026] in CE n,t This represents the total carbon dioxide emissions of region n during time period t. This represents the total carbon dioxide emissions per unit of electricity generated in the k-th type of power generation.

[0027] Furthermore, step (3) includes the following steps:

[0028] 1) Divide the electricity consumption curves of regional users into two categories: active power curves for thermal power and active power curves for wind power, photovoltaic power, and hydropower.

[0029] 2) Considering that user electricity consumption behavior is a key factor in source-side carbon emissions, time periods are divided according to the two types of electricity load curves for each time period of the day in the region, and a uniform value is taken for the carbon emission factor of electricity consumption in the same time period.

[0030] By P n,t,k Forming a user load time series dataset L n =[L n,1 ,L n,2 ,...,L n,T ] T Sample L n,t L represents the two types of electricity consumption in region n during time period t.n,t =[L n,t，1 ,L n,t，2 ] T :

[0031] L n,t，1 =P n,t,k ,k=H

[0032]

[0033] In the formula: L n,t，1 P represents the active power of thermal power absorbed by region n during time period t; n,t,k The k-th type of active power absorbed; H represents thermal power; GT represents all power generation types, including thermal power, hydropower, wind power, and photovoltaic power; L n,t,2 This represents the active power of hydropower, wind power, and photovoltaic power consumed in region n during time period t;

[0034] L is clustered using the fuzzy C-means clustering method. n Fuzzy classification into class C, corresponding to class center V n =[V n,1 V n,2 ,...,V n,C ] T Establish a Euclidean distance d between the sample and the class center. n,c,t and fuzzy membership matrix U n The clustering programming model is as follows:

[0035]

[0036]

[0037] In the formula: u n,c,t L represents the membership degree of the t-th sample in region n to the c-th class; n,t V represents the two types of electrical power consumption in region n during time period t; n,c This represents the c-th class center of region n.

[0038] Constructing the Lagrangian function yields the optimal U. n and class center set V n as follows:

[0039]

[0040]

[0041] In the formula: b is the weighting index; m is the class identifier;

[0042] 3) Based on the above time period division method and the single-time-period regional electricity consumption carbon emission factor calculated in the aforementioned steps, the average carbon emission factor for each region belonging to the same time period category is calculated:

[0043]

[0044] In the formula: T c This represents the total number of time periods belonging to category c; CEF n,c is the average carbon emission factor for region n within the time period of category c.

[0045] Furthermore, the user carbon reduction model based on dynamic carbon emission factors in step (4) is as follows:

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052] In the formula: P n,l,t This represents the active power load of user l in region n during time period t; The variable is 0-1, representing the positive or negative response state of active power load of user l in region n under low-carbon demand response. T represents the corresponding positive and negative response active power. c This represents the total number of time periods belonging to category c; CEF n,t Represents the carbon emission factor of region n during time period t; This represents the upper limit of the total electricity consumption regulation amount for the entire time period; CE n,l This represents the carbon emission reduction of user l in region n during the total optimization period T under low-carbon demand response;

[0053] Based on the dynamic carbon emission factor throughout the day, users can reduce their electricity demand during periods of high carbon emission factor and increase their electricity demand during periods of low carbon emission factor, thereby gaining carbon emission reduction benefits by adjusting their own electricity consumption behavior.

[0054] The beneficial effects of this invention are as follows: This invention explores a method for calculating dynamic carbon emission factors in provincial-level regions. Using a 220kV power supply area as the unit spatial scale, and considering the temporal characteristics of user power consumption, time periods are divided to determine the unit time scale of the dynamic carbon emission factor. The average carbon emission factor for each region belonging to the same time period is calculated, and further used to achieve electricity consumption behavior decisions that maximize user carbon emission reduction. The proposed method for calculating dynamic carbon emission factors in various regions of the provincial power grid takes into account both the temporal and spatial characteristics of the carbon emission factor. Compared with the traditional single average carbon emission factor, it can fully characterize the differences in carbon emissions caused by differentiated user electricity consumption behaviors, which is conducive to achieving low-carbon interaction between the user side and the source side, and provides a feasible approach for further reducing carbon emissions. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating a method for calculating dynamic carbon emission factors for provincial regions based on time period division, according to an embodiment of the present invention.

[0056] Figure 2 This is a graph showing the active power output curves of thermal power, hydropower, wind power, and photovoltaic power generation in various regions of this invention.

[0057] Figure 3 This is a graph showing the active power exchange between the 220kV region and the 500kV main grid in this invention.

[0058] Figure 4 This presents the power absorption results for various types of power in the 220kV region according to the present invention. Detailed Implementation

[0059] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0060] like Figure 1 As shown, this invention provides a method for calculating dynamic carbon emission factors for provincial regions based on time-segmentation. Based on the zonal operation characteristics of the 220kV power grid, power exchange constraints are set between the 500kV main grid and the 220kV sub-regional power grid. The method calculates the active power consumed by various types in the 220kV region of the entire province during each time period. Using the 220kV power supply area as the unit spatial scale and Δt as the segment interval, the carbon emission factor of regional electricity consumption in a single time period is calculated. Based on the different types of power generation consumption in each region during a single time period, the regional electricity consumption curves are classified, and time periods are divided according to the temporal characteristics of the regional load. The average carbon emission factor for each region belonging to the same time period category is calculated. Based on the average carbon emission factor calculation results, a user carbon reduction model based on dynamic carbon emission factors is established. The effectiveness of the proposed method for calculating dynamic carbon emission factors for provincial regions based on time-segmentation is verified using a simulation system.

[0061] The power exchange constraints between the 500kV main grid and the 220kV sub-regional grid are specifically as follows:

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] In the formula: P represents the active power output of the k-th type of power generation in region n during time period t; n,t,k P n,t These represent the k-th type of active power and the total active power absorbed, respectively. These represent the k-th type of active power transmitted from the 500kV main grid to region n and the k-th type of active power transmitted from region n to the 500kV main grid, respectively. This represents the net active power output of type k in the 500kV main grid, which is equal to the active power output of type k generation connected to the 500kV main grid. Adding all sub-regions and out-of-province active power input to the main grid, and subtracting the k-th type of active power output to out-of-province; the total net active power output P t M* similar; This indicates the active power transmitted and received across provinces by the 500kV main grid.

[0069] The calculation of the various types of active power consumed by the 220kV area of ​​the entire province at different time periods is as follows:

[0070] The active power of type k consumed in the 220kV area of ​​the entire province during time period t is as follows:

[0071]

[0072] In the formula: These represent the total active power transmitted from the 500kV main grid to region n and the total active power transmitted from region n to the 500kV main grid, respectively.

[0073] When calculating the carbon emission factor of electricity consumption in a single time period, the 220kV area is used as the unit spatial scale for calculating the dynamic carbon emission factor. It is assumed that the unit comprehensive carbon dioxide emission of different generating units of the same type in each sub-region is the same. The carbon emission factor CEF for time period t is obtained with Δt as the segment interval. n,t as follows:

[0074]

[0075] in CE n,t This represents the total carbon dioxide emissions of region n during time period t. This represents the total carbon dioxide emissions per unit of electricity generated in the k-th type of power generation.

[0076] The calculation of the average carbon emission factor for each region belonging to the same time period includes the following steps:

[0077] 1) Divide the electricity consumption curves of regional users into two categories: active power curves for thermal power and active power curves for wind power, photovoltaic power, and hydropower.

[0078] 2) Considering that user electricity consumption behavior is a key factor in source-side carbon emissions, time periods are divided according to the two types of electricity load curves for each time period of the day in the region, and a uniform value is taken for the carbon emission factor of electricity consumption in the same time period.

[0079] By P n,t,k Forming a user load time series dataset L n =[L n,1 ,L n,2 ,...,L n,T ] T Sample L n,t L represents the two types of electricity consumption in region n during time period t. n,t =[L n,t，1 ,L n,t，2 ] T :

[0080] L n,t，1 =P n,t,k ,k=H

[0081]

[0082] In the formula: L n,t，1 P represents the active power of thermal power absorbed by region n during time period t; n,t,k The k-th type of active power absorbed; H represents thermal power; GT represents all power generation types, including thermal power, hydropower, wind power, and photovoltaic power; L n,t,2 This represents the active power of hydropower, wind power, and photovoltaic power consumed in region n during time period t;

[0083] L is clustered using the fuzzy C-means clustering method. n Fuzzy classification into class C, corresponding to class center V n =[V n,1 V n,2 ,...,V n,C ] TEstablish a Euclidean distance d between the sample and the class center. n,c,t and fuzzy membership matrix U n The clustering programming model is as follows:

[0084]

[0085]

[0086] In the formula: u n,c,t L represents the membership degree of the t-th sample in region n to the c-th class; n,t V represents the two types of electrical power consumption in region n during time period t; n,c This represents the c-th class center of region n.

[0087] Constructing the Lagrangian function yields the optimal U. n and class center set V n as follows:

[0088]

[0089]

[0090] In the formula: b is the weighted index; m is the class identifier.

[0091] 3) Based on the above time period division method and the single-time-period regional electricity consumption carbon emission factor calculated in the aforementioned steps, the average carbon emission factor for each region belonging to the same time period category is calculated:

[0092]

[0093] In the formula: T c This represents the total number of time periods belonging to category c; CEF n,c is the average carbon emission factor for region n within the time period of category c.

[0094] The user carbon reduction model based on dynamic carbon emission factors is as follows:

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101] In the formula: P n,l,tThis represents the active power load of user l in region n during time period t; The variable is 0-1, representing the positive or negative response state of active power load of user l in region n under low-carbon demand response. T represents the corresponding positive and negative response active power. c This represents the total number of time periods belonging to category c; CEF n,t Represents the carbon emission factor of region n during time period t; This represents the upper limit of the total electricity consumption regulation amount for the entire time period; CE n,l This represents the carbon emission reduction of user l in region n during the total optimization period T under low-carbon demand response.

[0102] Based on the dynamic carbon emission factor throughout the day, users can reduce their electricity demand during periods of high carbon emission factor and increase their electricity demand during periods of low carbon emission factor, thereby gaining carbon emission reduction benefits by adjusting their own electricity consumption behavior.

[0103] Numerical example verification and analysis:

[0104] Province A was selected as the case study system, and a verification analysis of the dynamic carbon emission factor calculation method was conducted with a 60-minute optimization time interval. The 220kV power grid of Province A can be divided into six decoupled regions. The active power output of thermal power, hydropower, wind power, and photovoltaic power generation in each region during the total calculation period is as follows: Figure 2 As shown. The active power exchange between the 220kV area and the 500kV main grid is as follows. Figure 3 As shown.

[0105] Results of power absorption for various types in the 220kV region:

[0106] Based on the power generation data of the main grid and various regions, as well as the power exchange data between the main grid and various regions, the actual active power of thermal power, hydropower, wind power, and photovoltaic power absorbed by each 220kV region is calculated, such as... Figure 4 As shown, the load demand in each 220kV area is mainly provided by thermal power. Compared with areas 1 and 6, photovoltaic power generation in areas 2, 3, 4, and 5 significantly reduces the active power consumption of thermal power during the midday peak period.

[0107] Results of dynamic carbon emission factor time period division:

[0108] Based on the two types of electricity load curves for each time period of the day in the region, the time periods were divided, and the results of the time period division for calculating the dynamic carbon emission factor are shown in Table 1. Taking region 4 as an example, the fuzzy C-means clustering method was used to divide the entire time period into 5 categories, and the division results are: time periods 1-8, 9-10, 11-13, 14-17, and 28-24.

[0109] Table 1 Time Period Division Results

[0110]

[0111] Calculation results of dynamic carbon emission factors:

[0112] Carbon emission factors are the same for time periods belonging to the same category. The calculation results of dynamic carbon emission factors for each region are shown in the table below:

[0113] Table 2 Calculation results of dynamic carbon emission factors

[0114]

[0115] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for calculating dynamic carbon emission factors of each region at the provincial level based on time period division, characterized in that, The method comprises the following steps: (1) Based on the operation characteristics of 220 kV power grid partition, set the power exchange constraint of 500 kV main grid and 220 kV regional power grid, and calculate the active power of each type consumed by the 220 kV regional power grid in each period; (2) Based on the calculation of step (1), the 220kV regional consumption of various types of active power in each period is calculated, with 220kV power supply area as the unit spatial scale, and as the interval, the single-period regional carbon emission factor is calculated; (3) Classify the regional power consumption curve, and divide the time period according to the fuzzy C-means clustering method based on the time sequence characteristics of regional load, and calculate the average carbon emission factor of each region belonging to the same time period based on the single-period regional power consumption carbon emission factor calculated in step (2); The power exchange constraint of 500 kV main grid and 220 kV sub-regional power grid set in step (1) is specifically: ； ； ； ； ； ； In the formula: Pn, k (t) represents the active power of the kth type of power generation output of region n in the t time period; , Pn, k (t) and Pn (t) respectively represent the kth type of active power absorbed and the total active power absorbed; , Pn, k (t) and Pn, k (t) respectively represent the kth type of active power transmitted from the 500 kV main grid to region n and the kth type of active power transmitted from region n to the 500 kV main grid; Pn, k (t) represents the kth type of net active power of the 500 kV main grid, which is equal to the kth type of active power of the power generation connected to the 500 kV main grid Pn, k (t) and Pn, k (t) respectively represent the kth type of active power transmitted from the 500 kV main grid to region n and the kth type of active power transmitted from region n to the 500 kV main grid; Pn, k (t) represents the total net active power; , Pn, k (t) represents the active power transmitted across the province by the 500 kV main grid; The step (3) comprises the following steps: 1) The power consumption curve of the regional user is divided into two categories: thermal power active power curve and wind power, photovoltaic and hydropower active power curve; 2) Considering that the user's power consumption behavior is a key factor of source-side carbon emission, the time period is divided according to the two types of power consumption load curve of the region in each period of the day, and the power consumption carbon emission factor of the same time period is taken as a unified value; By forming a user load time series dataset , samples representing two classes of electricity consumption for a time period zone n, ： ； ； In the formula: represents the thermal power type active power consumed in the t period region n; represents the kth type active power consumed; H represents thermal power; GT represents all power generation types, including thermal power, hydropower, wind power, and photovoltaic power; represents the hydropower, wind power, and photovoltaic power type active power consumed in the t period region n; The fuzzy C-means clustering method is used to classify the samples into C classes The fuzzy partition is C classes, and the class centers are The clustering programming model based on the Euclidean distance between the sample and the class center and the fuzzy membership matrix is as follows: and the fuzzy membership matrix ​ ； ； In the formula: represents the membership degree of the tth sample of region n corresponding to the cth class; represents the two-class power of region n at t period; represents the cth class center of region n; The optimal solution can be obtained by constructing a Lagrange function and a set of centroids V n As follows: ； ； In the formula: b is the weighted index; m is the class identifier; 3) Based on the above time period division method and the single-period regional power consumption carbon emission factor calculated in the foregoing step, the average carbon emission factor of each region belonging to the same time period is calculated: ； where: T c represents the total number of periods belonging to the cth class; is the average carbon emission factor for period zone n to which the cth class belongs.

2. The method for calculating dynamic carbon emission factors of each province-level region based on time period division according to claim 1, characterized in that, Further comprising: (4) Based on the average carbon emission factor calculated in step (3), a user carbon emission reduction model based on dynamic carbon emission factor is established. 3.The method of claim 1, wherein, The step (1) of calculating the active power of each type consumed by the 220 kV regional power grid in each period is specifically: The kth type of active power consumed by the 220 kV regional power grid in the t period is as follows: ； In the formula: , Pn500in and Pn500out are the total active power delivered from the 500 kV main grid to the area n and the total active power delivered from the area n to the 500 kV main grid, respectively. 4.The method of claim 1, wherein, In the step (2) of calculating the single-period regional electricity carbon emission factor, 220kV area is taken as the unit space scale of calculating dynamic carbon emission factor, and it is assumed that the unit comprehensive power generation carbon dioxide emission of different units of the same type power generation in each sub-region is the same, and the carbon emission factor of t period is obtained by taking as the interval, as follows: ​ ； wherein ; ； represents the total amount of carbon dioxide emissions in the region n in the time period t; represents the unit integrated power generation carbon dioxide emissions of the kth type of power generation.

5. The method for calculating dynamic carbon emission factors of each province-level region based on time period division according to claim 2, characterized in that, The user carbon emission reduction model based on dynamic carbon emission factor in step (4) is as follows: ； s.t. ; ； ； ； In the formula: represents the active load of user l in region n at time period t; , is a 0-1 variable, representing the positive and negative response state of the active load of user l in region n under low-carbon demand response, , is the corresponding positive and negative response active power; represents the total number of time periods belonging to the cth type; represents the carbon emission factor of region n at time period t; is the upper limit value of the total time period electricity adjustment amount; represents the carbon emission reduction amount of user l in region n under low-carbon demand response within the total optimization time period T; Based on the dynamic carbon emission factor throughout the day, the user can reduce the power consumption demand in the high carbon emission factor period and increase the power consumption demand in the low carbon emission factor period, so as to obtain carbon emission reduction benefit by adjusting the power consumption behavior.

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