An algorithm for carbon footprint for a typical day

Abstract

This invention discloses a carbon footprint algorithm for typical days, comprising the following steps: S1. Selecting typical days from the year; S2. Setting the corresponding electricity source proportion and calling various emission factors; S3. Dividing the study area and obtaining the regional proportion; S4. Adjusting the electricity consumption of each region according to the load coefficient of different time periods; S5. Calculating the electricity consumption of each region in each time period and summing them to obtain the total electricity consumption; S6. Weighting each emission factor to obtain the comprehensive emission factor; S7. Multiplying the total electricity consumption by the comprehensive emission factor to obtain the total carbon emissions of the typical day, and the contribution of various power sources can be broken down; S8. Outputting the visualization results. This invention, by selecting representative typical days and combining regional and time-of-use calculations, achieves an accurate reflection of carbon emission characteristics under both normal and special scenarios, and can be used to identify high-carbon links, formulate emission reduction strategies, evaluate the effectiveness of measures, and support climate change research.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission accounting and energy system analysis technology, and in particular relates to a carbon footprint algorithm for a typical day. Background Technology

[0002] Carbon footprint (CF) is an indicator that measures the total amount of greenhouse gas emissions directly or indirectly generated by a specific entity over a certain period of time, usually expressed as carbon dioxide equivalent. By quantifying emission sources, emission reduction potential can be identified and a basis for low-carbon decision-making can be provided. Traditional carbon emission statistics methods mostly rely on annual or quarterly total data, which, while reflecting macro trends, are difficult to describe the short-term fluctuations in emissions under different scenarios.

[0003] To address this deficiency, the "typical day" calculation method has gained increasing attention. This method captures the differences in energy consumption between different time periods by selecting representative time slices such as the average temperature baseline day, extreme high-temperature days, holidays, and morning / evening rush hours. For example, the carbon footprint of an extreme high-temperature day may be about 30% higher than that of the baseline day, and the carbon footprint of transportation activities during holidays may also increase by about 15%. By constructing a typical day carbon emission structure model, it is possible to more clearly reveal "when, where, and why" emissions peak, thus providing a scientific basis for developing targeted intervention measures.

[0004] Taking Shanghai as an example, as a megacity, its energy demand is easily affected by climate change and social rhythms. Analysis of the carbon footprint on typical days can reveal the vulnerability of the energy system under extreme weather conditions. For instance, under extreme heat conditions, if electricity sources remain primarily coal-fired, both the carbon footprint and power supply pressure will increase simultaneously, exposing the contradiction between a high-carbon energy structure and climate risks. Furthermore, analysis of the carbon footprint proportion of electricity sources on typical days (such as the increased proportion of thermal power due to insufficient renewable energy output on days with extreme heat) can provide a basis for energy storage system deployment or demand-side response strategies, thereby reducing carbon emissions while ensuring power supply security.

[0005] Existing carbon footprint calculation methods are mainly based on annual or quarterly total accounting, which has the following limitations: annual or quarterly carbon footprint results can only reflect the overall trend and cannot capture emission characteristics under special circumstances such as extreme high temperatures, holidays, and morning and evening rush hours, nor can they support refined analysis of the operational status of cities or systems; due to the long statistical period, existing results cannot reflect the impact of short-term events such as adjustments in residents' behavior, industrial staggered production, abnormal climate, and temporary traffic changes on carbon emissions in a timely manner, which is not conducive to short-term management and rapid decision-making; the update frequency of annual or quarterly accounting is low, and the results are often lagging behind the actual situation, which cannot provide timely reference for responding to sudden extreme weather, rapidly changing electricity load, or traffic tides.

[0006] In summary, traditional carbon footprint accounting methods are insufficient to meet the needs of refined urban management, extreme weather response, and short-cycle low-carbon regulation. It is necessary to develop carbon footprint calculation methods that can reflect short-term fluctuation characteristics and have scenario recognition capabilities. Summary of the Invention

[0007] This invention provides a carbon footprint algorithm for a typical day to solve existing technical problems.

[0008] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0009] A carbon footprint algorithm for a typical day includes the above steps:

[0010] S1. Select a typical day from the year and obtain the basic information corresponding to the typical day:

[0011] S2. Based on the season of a typical day, set the corresponding electricity source ratio and call up each emission factor;

[0012] S3. Divide the study area, and correct the proportion of the regular area based on the characteristics of typical days to obtain the area proportion;

[0013] S4. Divide a typical 24-hour day into multiple time periods and adjust the electricity consumption of each area according to the load factor of different time periods;

[0014] S5. Calculate the electricity consumption of each region in each time period, and sum them up to obtain the total electricity consumption;

[0015] S6. Based on the power source structure, the emission factors are weighted to obtain the comprehensive emission factor;

[0016] S7. Multiply the total electricity consumption by the comprehensive emission factor to obtain the total carbon emissions for a typical day, and the contribution of various power sources can be broken down.

[0017] S8. Output visualization results and a chart of carbon emissions for a typical day.

[0018] As a further improvement to the above technical solution:

[0019] In S1, the typical days include typhoon extreme weather days, extreme high temperature days, May Day holiday days, and winter average days.

[0020] In S2, the power sources include thermal power, hydropower, and new energy sources, and the source emission factors include the thermal power emission factor, hydropower emission factor, and new energy source emission factor corresponding to the season.

[0021] In S3, the defined study area includes the central urban area, industrial area, residential area, transportation hub, commercial area, and drainage facilities.

[0022] In S4, the time period includes peak hours, normal hours, and off-peak hours.

[0023] In S4, the peak period is 10 hours with a load factor K of 1.2, the normal period is 7 hours with a load factor K of 1.0, and the off-peak period is 7 hours with a load factor K of 0.6.

[0024] In S5, the electricity consumption of each region in each time period is calculated. The electricity consumption calculation formula is: E i,j =P×T×R i ×K j Where i represents the region, j represents the time period, and E i,j Let P be the electricity consumption in region i and time period j, and T be the total load on a typical day. j Let R be the duration of the j-th time period. i K represents the corrected proportion of the i-th region. j Let j be the load factor for time period j. The total electricity consumption is obtained by summing the values ​​for all regions and all time periods: Among them, E 总 Let n be the total electricity consumption, n be the number of regions, m be the number of time periods, and E be the total electricity consumption. i,j Let represent the electricity consumption in the i-th region during the j-th time period.

[0025] In S6, the weighted calculation formula is: EF 综合 =S 火电 ×EF 火电 +S 水电 ×EF 水电 +S 新能源 ×EF 新能源

[0026] , where EF 综合 S is the comprehensive emission factor after weighting by power source. 火电 For the proportion of thermal power in the power supply, EF 火电 S is the emission factor for thermal power plants. 水电 For the proportion of hydropower in the power supply, EF 水电 S is a hydroelectric discharge factor. 新能源 For the proportion of power generation from new energy sources, EF 新能源 It is a new energy emission factor.

[0027] In S7, the formula for calculating the total carbon emissions is: After simplification, we get: C 总 =E 总 ×EF 综合 , where C 总 E represents total carbon emissions. 总 S represents the total electricity consumption. k For the k-th type of power supply, EFk Let be the emission factor for the k-th type of power source, where k represents any of the three types: thermal power, hydropower, and new energy sources.

[0028] In S8, the carbon emission chart includes a line graph of carbon emissions by time period and a data table of carbon emissions by time period.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0030] This algorithm selects typical days that represent normal life and production conditions, avoiding interference from holidays and extreme weather events on carbon emission analysis, thus making the calculation results more universal and stable. Simultaneously, by partitioning and characterizing the typical daily energy consumption structure of different organizations such as enterprises, schools, and hospitals, it can accurately identify high-carbon links, providing a basis for formulating emission reduction strategies. Furthermore, the algorithm can reflect the characteristics of carbon emission and load changes under holidays and extreme weather conditions, offering a new analytical perspective for energy conservation and emission reduction. It can also be used to predict future carbon emission trends and evaluate the effectiveness of emission reduction measures, providing quantifiable and verifiable empirical data for climate change research. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a line graph showing carbon emissions by region and time period on typhoon days.

[0033] Figure 2 This is a line graph showing carbon emissions by region and time period during the May Day holiday. Detailed Implementation

[0034] To facilitate understanding of the present invention, the present invention will be described more fully and in detail below with reference to the accompanying drawings and preferred embodiments, but the scope of protection of the present invention is not limited to the following specific embodiments.

[0035] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by those skilled in the art. The technical terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the scope of the invention.

[0036] Unless otherwise specified, all raw materials, reagents, instruments and equipment used in this invention can be purchased from the market or prepared by existing methods.

[0037] Example: Taking Shanghai in 2024 as an example, four representative typical days were selected from the whole year for calculation, namely: (1) Typhoon extreme weather day: September 16 (autumn); (2) Extreme high temperature day: August 11 (summer); (3) May Day holiday (spring); (4) Winter average day: January 20 (winter). The selection of typical days is based on meteorological characteristics, urban operation characteristics and energy load characteristics.

[0038] For example, August 11th was the peak load day in summer, with the load reaching 40.55 million kilowatts at 11:23 AM, setting a historical record. The core meteorological characteristics of that day are shown in Table 1. The multi-year average temperature of January 20th is consistent with the overall winter average level in Shanghai (3–9℃), making it suitable as a winter average day. The temperature characteristics of that day are shown in Table 2.

[0039] Table 1. Core Meteorological Characteristics on August 11, 2024:

[0040]

[0041] Table 2. Core Meteorological Characteristics on January 20, 2024:

[0042] Temperature index Daily average Shanghai winter average Representativeness analysis Average maximum temperature 7℃ 8-12℃ Located in the lower-middle range of winter temperatures, it meets the characteristics of midwinter. Average minimum temperature 1℃ 0-4℃ It is close to the average minimum value in winter, reflecting the cold characteristics of winter. Diurnal temperature range Approximately 6℃ 6-8℃ This is consistent with the average daily temperature range in Shanghai during winter, and conforms to the climate characteristics.

[0043] A carbon footprint algorithm for a typical day includes the above steps:

[0044] First, basic information is acquired for each typical day, including the average temperature, weather conditions, holiday attributes, and load characteristics, for subsequent regional adjustments and time-of-use electricity calculations. Second, the power source structure of Shanghai in 2024 (as shown in Table 3) is seasonally adjusted according to the season of the typical day. Power sources are divided into three categories: thermal power, hydropower, and new energy. Adjustments are made according to their proportion in the power grid in different seasons, which serves as the basis for subsequent weighted emission factors.

[0045] Table 3. Shanghai's Electricity Source Structure in 2024:

[0046]

[0047] Electricity usage percentages in Shanghai are calculated by dividing the city into four zones: central urban area, industrial area, residential area, and transportation hub. The base percentages under typical scenarios are: central urban area 25%, industrial area 35%, residential area 30%, and transportation hub 10%. For different typical days, the regional percentages are adjusted based on their characteristics, for example:

[0048] —During typhoon extreme weather days, electricity consumption in industrial areas, transportation hubs, and central urban areas decreases, while drainage facilities increase significantly due to the heavy rainfall load;

[0049] —During days of extreme heat, electricity consumption increases in residential and commercial areas;

[0050] —Electricity consumption decreased in industrial areas during the May Day holiday, while it increased in commercial areas and transportation hubs;

[0051] —The average daily temperature in winter remains within the normal range.

[0052] The revised proportions are directly used in subsequent calculations. A typical 24-hour day is divided into peak hours, normal hours, and off-peak hours. Peak hours are 8:00–15:00 and 18:00–21:00; normal hours are 6:00–8:00, 15:00–18:00, and 21:00–23:00; and off-peak hours are 23:00–6:00 the next day. Each of these three time periods corresponds to a different load factor K. j This is used to reflect the differences in electricity demand on a typical day. The following examples illustrate this: a day of extreme typhoon weather and a day of extreme high temperatures.

[0053] Table 4. Letters used in the formulas and their definitions:

[0054]

[0055] The relevant data sources in Table 4 are shown in Table 5.

[0056] Table 5. Data Source Table

[0057]

[0058] Based on the above regional proportions and time-of-use design, the regional time-of-use electricity consumption formula is used:

[0059] E i,j =P×T×R i ×K,

[0060] Calculate the electricity consumption of region i during time period j, where P is the typical daily total load, Tj is the length of the time period, Ri is the regional correction percentage, and Kj is the time period load factor.

[0061] Summing over all regions and all time periods yields the typical daily total electricity consumption:

[0062]

[0063] Where n is the number of regions and m is the number of time periods;

[0064] After obtaining the total electricity consumption, the proportion of thermal power, hydropower, and new energy power (S) is adjusted according to the seasonally adjusted proportions. k and its emission factor EF k The weighted formula is used:

[0065] EF综合 =S 火电 ×EF 火电 +S 水电 ×EF 水电 +S 新能源 ×EF 新能源 ,

[0066] The comprehensive emission factor EF after weighting by power source was obtained. 综合 .

[0067] Then use the total carbon footprint formula:

[0068]

[0069] After simplification, we get: C 总 =E 总 ×EF 综合 ,

[0070] The total carbon footprint of a typical day is obtained, where C 总 The total carbon emissions can be further broken down to obtain the contribution ratio of each power source type to carbon emissions.

[0071] Taking typhoon extreme weather days and extreme high-temperature days as examples, the regional proportions are adjusted according to their actual characteristics. Then, the time-of-use electricity consumption, total electricity consumption, and corresponding comprehensive carbon emission factors for each region are calculated using the formulas described above, thus obtaining the total carbon footprint for that typical day. The calculation formulas and results for typhoon extreme weather days are shown in Table 6, the data on carbon emissions by region and time period on typhoon days are shown in Table 7, and the line graphs of carbon emissions by region and time period on typhoon days are shown in... Figure 1 As shown.

[0072] Table 6. Calculation Formulas and Results for Typhoon Extreme Weather Days:

[0073]

[0074]

[0075] Table 7. Data on carbon emissions by region and time of day during typhoons:

[0076]

[0077] The same process was used for the May Day holiday and the average daily carbon emissions in winter, adjusting for regional proportions based on holiday characteristics and seasonal temperature characteristics, and then calculating the corresponding carbon footprints. The calculation formula and results for the May Day holiday are shown in Table 8, the data on carbon emissions by region and time period during the May Day holiday are shown in Table 9, and the line graphs of carbon emissions by region and time period during the May Day holiday are shown below. Figure 2 As shown.

[0078] Table 8. Calculation Formulas and Results for Typhoon Extreme Weather Days:

[0079]

[0080] Table 9. Data on carbon emissions by region and time period during the May Day holiday:

[0081]

[0082] By following the steps above, a carbon footprint calculation model for four typical days is established. This model can be used to assess the differences in carbon emissions under different seasons, weather conditions, and urban operating scenarios, providing a typical day basis for the annual carbon footprint estimation of the city.

Claims

1. A carbon footprint algorithm for a typical day, characterized in that, Including the above steps: S1. Select a typical day from the year and obtain the basic information corresponding to the typical day: S2. Based on the season of a typical day, set the corresponding electricity source ratio and call up each emission factor; S3. Divide the study area, and correct the proportion of the regular area based on the characteristics of typical days to obtain the area proportion; S4. Divide a typical 24-hour day into multiple time periods and adjust the electricity consumption of each area according to the load factor of different time periods; S5. Calculate the electricity consumption of each region in each time period, and sum them up to obtain the total electricity consumption; S6. Based on the power source structure, the emission factors are weighted to obtain the comprehensive emission factor; S7. Multiply the total electricity consumption by the comprehensive emission factor to obtain the total carbon emissions for a typical day, and the contribution of various power sources can be broken down. S8. Output visualization results and a chart of carbon emissions for a typical day.

2. The carbon footprint algorithm for a typical day according to claim 1, characterized in that, In S1, the typical days include typhoon extreme weather days, extreme high temperature days, May Day holiday days, and winter average days.

3. The carbon footprint algorithm for a typical day according to claim 2, characterized in that, In S2, the power sources include thermal power, hydropower, and new energy sources, and the source emission factors include the thermal power emission factor, hydropower emission factor, and new energy source emission factor corresponding to the season.

4. The carbon footprint algorithm for a typical day according to claim 3, characterized in that, In S3, the defined study area includes the central urban area, industrial area, residential area, transportation hub, commercial area, and drainage facilities.

5. The carbon footprint algorithm for a typical day according to claim 4, characterized in that, In S4, the time period includes peak hours, normal hours, and off-peak hours.

6. The carbon footprint algorithm for a typical day according to claim 5, characterized in that, In S4, the peak period is 10 hours with a load factor K of 1.2, the normal period is 7 hours with a load factor K of 1.0, and the off-peak period is 7 hours with a load factor K of 0.

6.

7. The carbon footprint algorithm for a typical day according to claim 6, characterized in that, In S5, the total load, regional proportion, and time period coefficient are used to calculate the electricity consumption of each region in each time period. The electricity consumption calculation formula is: E i,j =P×T×R i ×K j Where i represents the region, j represents the time period, and E i,j Let P be the electricity consumption in region i and time period j, and T be the total load on a typical day. j Let R be the duration of the j-th time period. i K represents the corrected proportion of the i-th region. j Let j be the load factor for time period j. The total electricity consumption is obtained by summing the values ​​for all regions and all time periods: Among them, E 总 Let n be the total electricity consumption, n be the number of regions, m be the number of time periods, and E be the total electricity consumption. i,j Let represent the electricity consumption in the i-th region during the j-th time period.

8. The carbon footprint algorithm for a typical day according to claim 7, characterized in that, In S6, the weighted calculation formula is: EF 综合 =S 火电 ×EF 火电 +S 水电 ×EF 水电 +S 新能源 ×EF 新能源 , where EF 综合 S is the comprehensive emission factor after weighting by power source. 火电 For the proportion of thermal power in the power supply, EF 火电 S is the emission factor for thermal power plants. 水电 For the proportion of hydropower in the power supply, EF 水电 S is a hydroelectric discharge factor. 新能源 For the proportion of power generation from new energy sources, EF 新能源 It is a new energy emission factor.

9. The carbon footprint algorithm for a typical day according to claim 8, characterized in that, In S7, the formula for calculating the total carbon emissions is: After simplification, we get: C 总 =E 总 ×EF 综合， Among them, C 总 E represents total carbon emissions. 总 S represents the total electricity consumption. k For the k-th type of power supply, EF k Let be the emission factor for the k-th type of power source, where k represents any of the three types: thermal power, hydropower, and new energy sources.

10. The carbon footprint algorithm for a typical day according to claim 9, characterized in that, In S8, the carbon emission chart includes a line graph of carbon emissions by time period and a data table of carbon emissions by time period.

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