A power grid planning method and terminal based on provincial power grid marginal emission factor

Abstract

The application discloses a power grid planning method and a terminal based on a provincial power grid marginal emission factor, calculates the power consumption of the provincial power grid in each month, calculates the carbon emission of the provincial power grid in each month according to the power consumption of the provincial power grid in each month, calculates the difference between the carbon emission of the provincial power grid in each month and the carbon emission of the next month and the difference between the power consumption of the provincial power grid in each month and the power consumption of the next month, takes the ratio of the carbon emission difference and the power consumption difference as the marginal emission factor of the provincial power grid in each month, and provides low-carbon planning for power consumption enterprises according to the marginal emission factor of the provincial power grid. The marginal carbon emission factor can reflect the ability of the spatial and temporal difference of the power grid carbon emission, depict the influence of the above factors on the power grid carbon emission, and meet the requirements of carbon emission evaluation and analysis of the new power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid planning, in particular to a power grid planning method based on a provincial power grid marginal emission factor and a terminal. BACKGROUND

[0002] Climate change is one of the biggest challenges facing the world in the 21st century. The power industry plays a crucial role in total carbon emissions, and the power industry has played a key role in achieving the carbon peak on schedule.

[0003] In the prior art, the most commonly used method for calculating carbon emissions in the power industry is the emission factor estimation method, that is, the carbon emissions can be obtained by multiplying the power generation of the power system by the emission factor. Therefore, the accurate evaluation of the emission factor of the power grid plays a decisive role in the quality of carbon emission accounting. At present, the average emission factor is generally used to evaluate and analyze the carbon emissions of the power grid. However, the average emission factor is calculated in units of years, which ignores factors such as time, climate, policy, and generator set proportion changes, making it difficult to represent the time-varying nature of power grid carbon emissions. Under the rapid construction of the new power system and the promotion of the "double carbon" target, using the average emission factor to analyze and quantify the carbon emissions of the power grid has gradually shown its disadvantages. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a power grid planning method based on a provincial power grid marginal emission factor and a terminal, which can reflect the ability to reflect the spatial and temporal differences of power grid carbon emissions.

[0005] To solve the above technical problems, the technical scheme adopted by the present application is:

[0006] A power grid planning method based on a provincial power grid marginal emission factor, comprising the steps of:

[0007] S1, calculating the power consumption of the provincial power grid each month;

[0008] S2, calculating the carbon emissions of the provincial power grid each month according to the power consumption of the provincial power grid each month;

[0009] S3, calculating the difference between the carbon emissions of the provincial power grid each month and the next month, and the difference between the power consumption of the provincial power grid each month and the next month, and taking the ratio of the carbon emission difference and the power consumption difference as the marginal emission factor of the provincial power grid each month;

[0010] S4, providing low-carbon planning for power users according to the marginal emission factor of the provincial power grid.

[0011] To solve the above technical problems, another technical scheme adopted by the present application is:

[0012] A power grid planning terminal based on a provincial power grid marginal emission factor, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0013] S1, calculating the power consumption of the provincial power grid in each month;

[0014] S2, calculating the carbon emission of the provincial power grid in each month according to the power consumption of the provincial power grid in each month;

[0015] S3, calculating the difference between the carbon emission of the provincial power grid in each month and the carbon emission of the next month, and the difference between the power consumption of the provincial power grid in each month and the power consumption of the next month, and taking the ratio of the difference in carbon emission and the difference in power consumption as the marginal emission factor of the provincial power grid in each month;

[0016] S4, providing low-carbon planning for power-using enterprises according to the marginal emission factor of the provincial power grid.

[0017] The present application has the beneficial effect that a power grid planning method and terminal based on a provincial power grid marginal emission factor, the marginal carbon emission factor can reflect the ability of the spatial and temporal difference of the power grid carbon emission, and depict the influence of the spatial and temporal difference on the power grid carbon emission, and according to the marginal emission factor of the provincial power grid, low-carbon planning is provided for power-using enterprises, which can meet the requirements of carbon emission evaluation and analysis of the new power system. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 It is a flowchart of a power grid planning method based on a provincial power grid marginal emission factor of the embodiment of the present application;

[0019] Figure 2 It is a schematic diagram of the power generation characteristics of Fujian Province related to the embodiment of the present application;

[0020] Figure 3 It is a schematic diagram of the power grid MEF of Fujian Province in each month in 2011 related to the embodiment of the present application;

[0021] Figure 4 It is a schematic diagram of the power grid MEF of Fujian Province in each month from 12 to 20 related to the embodiment of the present application;

[0022] Figure 5 It is a schematic diagram of the carbon emission before and after optimization related to the embodiment of the present application;

[0023] Figure 6 It is a schematic diagram of the power grid load before and after optimization related to the embodiment of the present application;

[0024] Figure 7 It is a structural schematic diagram of a power grid planning terminal based on a provincial power grid marginal emission factor of the embodiment of the present application.

[0025] Label description:

[0026] 1. A power grid planning terminal based on a provincial power grid marginal emission factor; 2. a processor; 3. a memory. DETAILED DESCRIPTION

[0027] To illustrate the technical content of the present application, the purposes and effects achieved are described in detail below in conjunction with the embodiments and the accompanying drawings.

[0028] Please refer to Figures 1 to 6 A power grid planning method based on a provincial power grid marginal emission factor, comprising the steps of:

[0029] S1, calculating the power consumption of the provincial power grid each month;

[0030] S2, calculating the carbon emissions of the provincial power grid each month according to the power consumption of the provincial power grid each month;

[0031] S3, calculating the difference between the current month's carbon emissions and the next month's carbon emissions of the provincial power grid each month, and the difference between the current month's power consumption and the next month's power consumption of the provincial power grid each month, and taking the ratio of the carbon emission difference and the power consumption difference as the marginal emission factor of the provincial power grid each month;

[0032] S4, providing low-carbon planning for power-using enterprises according to the marginal emission factor of the provincial power grid.

[0033] From the above description, the beneficial effects of the present application are that a power grid planning method and terminal based on a provincial power grid marginal emission factor, the marginal carbon emission factor can reflect the ability of spatial and temporal difference of power grid carbon emissions, and depict the influence of the above factors on power grid carbon emissions, which can meet the requirements of new power system carbon emission evaluation and analysis.

[0034] Further, the step S4 is specifically:

[0035] Solving the objective function:

[0036]

[0037] In the formula, △C y represents the annual emission reduction of the enterprise, T represents the number of days per month, P t - and P t + represent the daily load reduction and increase of the enterprise under low-carbon corresponding conditions, MEF P,m is the marginal emission factor of the provincial power grid in the mth month.

[0038] From the above description, the target is to maximize the emission reduction in the target year.

[0039] Further, the objective function has constraints:

[0040]

[0041] P t +P t + ≤P t dL ;

[0042] P t -P t - ≥0;

[0043]

[0044]

[0045]

[0046] In the formula, ΔP t u represents the upper limit of the load that can be adjusted per day; and is a (0, 1) variable; P i dL is the upper limit of the load per day; P mL is the upper limit of the load per month.

[0047] From the above description, the constraints indicate that after the enterprise optimizes the electricity consumption behavior, the single-day load cannot exceed the upper limit, nor can it be lower than the lower limit, which indicates that after the enterprise optimizes the electricity consumption behavior, the monthly load cannot exceed the upper limit; ensures that the enterprise cannot be in the state of increasing load and decreasing load at the same time; indicates that after the enterprise optimizes the electricity consumption behavior, the total load of the year remains unchanged.

[0048] Further, the power consumption specifically includes power consumption of thermal power generation, hydroelectric power generation, nuclear power generation, wind power generation, and solar power generation;

[0049] In the step S1, the power consumption G of the provincial power grid each month is calculated according to the following formula P,m :

[0050] G P,m = G Grid,m +∑ j G P,m,j +∑ k G C,m,k ;

[0051] In the formula, m = 1, 2, … 12 represents the month; G Grid,m represents all power supply in the power grid operating area in the mth month, in units of ten thousand kilowatt-hours; G P,m,j and GC,m,k The net power consumption from the jth province or kth country in the mth month, in units of 104kWh;

[0052] The total power supply G in the mth month in the power grid operating area Grid,m is expressed as:

[0053] G Grid,m = G T,m + G N,m + G H,m + G W,m + G S,m ;

[0054] In the formula, G T,m , G N,m , G H,m , G W,m , and G S,m are the power generation amounts of the jth province in the mth month by thermal power, nuclear power, hydroelectric power, wind power, and solar power, respectively, in units of 104kWh.

[0055] As described above, the formula for calculating the power consumption is given, and the calculation of the power consumption is achieved.

[0056] Further, in the step S2, the carbon emissions W P,m of the provincial power grid in each month are calculated according to the following formula:

[0057]

[0058] In the formula, W Grid,m is the CO2 emissions of the provincial power grid in the mth month, in units of tons; EF P,j and EF C,k are the power grid carbon emission factors of the jth province or kth country in the mth month.

[0059] W Grid,m The carbon emissions generated by clean energy power generation are considered and are specifically expressed as:

[0060] W Grid,m = W T,m + W N,m + W H,m + W W,m + W S,m ;

[0061] In the formula, W T,m , W N,m , W H,m , W W,m , and W S,m are the carbon emissions generated by thermal power generation, nuclear power generation, hydroelectric power generation, wind power generation, and solar power generation of the provincial power grid in the mth month, respectively, W N,m , W H,m , WW,m W S,m is calculated according to the following formula:

[0062]

[0063] EF N , EF H , EF W , and EF S are carbon emission factors of nuclear power, hydroelectric power, wind power, and solar power, respectively, and W T,m is calculated according to the following formula:

[0064]

[0065] In the formula, i is a mineral fuel type consumed by the power grid in the mth month; FC i,m is the consumption of the mineral fuel i in the mth year, in units of ten thousand tons or ten thousand cubic meters; QDW i is the average low heat value of the mineral fuel i, in units of megajoules per ton or megajoules per ten thousand cubic meters; and EF i is the emission factor of the mineral fuel i.

[0066] The calculation formula of EF i is as follows:

[0067]

[0068] In the formula, F i represents the carbon emission coefficient of the fuel, i.e., the average carbon content of the fuel, in units of tons per terajoule; O i represents the carbon oxidation rate of the i-th energy source, in units of %; and 44 / 12 represents the molecular weight ratio of carbon dioxide to carbon.

[0069] As can be seen from the above description, the calculation formula of the carbon emission is given, and the calculation of the carbon emission is realized.

[0070] A power grid planning terminal based on a provincial power grid marginal emission factor, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0071] S1, calculating the power consumption of the provincial power grid each month;

[0072] S2, calculating the carbon emission of the provincial power grid each month according to the power consumption of the provincial power grid each month;

[0073] S3, calculating the difference between the carbon emission of the current month and the carbon emission of the next month, and the difference between the power consumption of the current month and the power consumption of the next month of the provincial power grid each month, and taking the ratio of the carbon emission difference and the power consumption difference as the marginal emission factor of the provincial power grid each month.

[0074] S4, providing low-carbon planning for the power consumption enterprise according to the marginal emission factor of the provincial power grid.

[0075] From the above description, the beneficial effects of the present application are that a power grid planning method and terminal based on a marginal emission factor of a provincial power grid, the marginal carbon emission factor can reflect the ability of spatial and temporal differences of power grid carbon emission, and depict the influence of the above factors on power grid carbon emission, which can meet the requirements of carbon emission evaluation and analysis of new power system.

[0076] Further, the step S4 is specifically:

[0077] Solving the objective function:

[0078]

[0079] In the formula, △C y represents the annual emission reduction of the enterprise, T represents the number of days per month, P t - and P t + represent the daily load reduction and increase of the enterprise under the corresponding low-carbon conditions, MEF P,m is the marginal emission factor of the provincial power grid in the mth month.

[0080] From the above description, the target is to maximize the emission reduction in the target year.

[0081] Further, the objective function has constraints:

[0082]

[0083] P t +P t + ≤P t dL ;

[0084] P t -P t - ≥0;

[0085]

[0086]

[0087]

[0088] In the formula, ΔP t u represents the upper limit of the load that can be adjusted per day; and are (0, 1) variables; Pt dL is the upper limit of daily load; P mL is the upper limit of monthly load.

[0089] From the above description, the constraint indicates that the single-day load of the enterprise after optimizing the power consumption behavior cannot exceed the upper limit, and cannot be lower than the lower limit, which indicates that the monthly load of the enterprise after optimizing the power consumption behavior cannot exceed the upper limit; ensures that the enterprise cannot be in the state of increasing load and reducing load at the same time; and indicates that the total load amount of the enterprise in the year remains unchanged after optimizing the power consumption behavior.

[0090] Further, the power consumption specifically includes power consumption of thermal power generation, hydroelectric power generation, nuclear power generation, wind power generation, and solar power generation.

[0091] In the step S1, the power consumption G of the provincial power grid in each month is calculated according to the following formula P,m :

[0092] G P,m = G Grid,m +∑ j G P,m,j +∑ k G C,m,k ;

[0093] In the formula, m = 1, 2, … 12 represents the month; G Grid,m represents all power supply in the power grid operating area in the mth month, in units of ten thousand kilowatt hours; G P,m,j and G C,m,k are the net power transferred from the jth province or the kth country in the mth month, in units of ten thousand kilowatt hours.

[0094] All power supply G Grid,m in the power grid operating area in the mth month is represented as:

[0095] G Grid,m = G T,m + G N,m + G H,m + G W,m + G S,m ;

[0096] In the formula, G T,m , G N,m , G H,m , G W,m , and G S,m are the power generation amounts of the province in the mth month of thermal power, nuclear power, hydroelectric power, wind power, and solar power, respectively, in units of ten thousand kilowatt hours.

[0097] From the above description, the calculation formula of the power consumption is given, and the calculation of the power consumption is realized.

[0098] Further, in the step S2, the carbon emission W of the provincial power grid in each month is calculated according to the following formula P,m :

[0099] W P,m = W Grid,m +∑ j (G P,m,j ×EF P,j )+∑ k (G C,m,k ×EF C,k );

[0100] In the formula, W Grid,m is the CO2 emission of the mth month provincial power grid, in tons; EF P,j and EF C,k are the power grid carbon emission factors of the jth province or the kth country in the mth month, respectively.

[0101] W Grid,m The carbon emission generated by clean energy power generation is considered, and is specifically expressed as:

[0102] W Grid,m = W T,m +W N,m +W H,m +W W,m +W S,m ;

[0103] In the formula, W T,m , W N,m , W H,m , W W,m and W S,m are the carbon emissions generated by the thermal power generation, nuclear power generation, hydroelectric power generation, wind power generation and solar power generation of the provincial power grid in the mth month, respectively, and W N,m , W H,m , W W,m and W S,m are calculated according to the following formula:

[0104]

[0105] In the formula, EF N , EF H , EF W and EF S are the carbon emission factors of nuclear power generation, hydroelectric power generation, wind power generation and solar power generation, respectively, and W T,m is calculated according to the following formula:

[0106]

[0107] In the formula, i is the mineral fuel type consumed by the thermal power of the power grid in the mth month; FC i,mis the consumption of fossil fuel i in the mth year, in units of ten thousand tons or ten thousand cubic meters; QDW i is the average low calorific value of fossil fuel i, in units of megajoules per ton or megajoules per ten thousand cubic meters; EF i is the emission factor of fossil fuel i;

[0108] EF i The calculation formula is:

[0109]

[0110] In the formula, F i represents the carbon emission coefficient of the fuel, i.e., the average carbon content of the fuel, in units of tons per terajoule; O i represents the carbon oxidation rate of the i-th energy source, in units of %; 44 / 12 represents the molecular weight ratio of carbon dioxide to carbon.

[0111] As can be seen from the above description, the calculation formula of carbon emissions is given, and the calculation of carbon emissions is realized.

[0112] The present application is used in the planning of provincial power grids, and provides a basis for energy-saving and emission-reducing planning of provincial power grids.

[0113] Please refer to Figures 1 to 6 , the first embodiment of the present application is:

[0114] A power grid planning method based on marginal emission factors of provincial power grids, comprising the following steps:

[0115] S1, calculate the monthly power consumption of the provincial power grid.

[0116] It includes the power consumption of thermal power generation, hydropower generation, nuclear power generation, wind power generation and solar power generation.

[0117] Specifically, it is assumed that the power consumption G P,m of a certain provincial power grid in a certain month is:

[0118] G P,m =G Grid,m +∑ j G P,m,j +∑ k G C,m,k ;

[0119] In the formula, m=1,2,…12 represents the month; G Grid,m represents all the power supply in the mth month in the power grid operating area, in units of ten thousand kilowatt-hours; G P,m,j and G C,m,k are the net power imported from the jth province or the kth country in the mth month, in units of ten thousand kilowatt-hours.

[0120] Among them, G Grid,m can be represented as:

[0121] G Grid,m =G T,m +G N,m +G H,m +G W,m +G S,m ;

[0122] In the formula, G T,m G N,m G H,m G W,m G S,m These figures represent the province's thermal, nuclear, hydropower, wind, and solar power generation in month m, expressed in ten thousand kilowatt-hours.

[0123] S2. Calculate the monthly carbon emissions of the provincial power grid based on the monthly electricity consumption of the provincial power grid.

[0124] It takes into account factors such as power generation from provincial power grids, clean energy power generation, and inter-provincial power exchange.

[0125] Specifically, suppose that the carbon emissions of a province's power grid in a certain month are W P,m for:

[0126] W P,m =W Grid,m +∑ j (G P,m,j ×EF P,j )+∑ k (G C,m,k ×EF C,k );

[0127] In the formula, W Grid,m EF represents the CO2 emissions from the power grid of a certain province in month m, expressed in tons. P,j EF C,k These are the power grid carbon emission factors for province j or country k in month m, respectively.

[0128] W Grid,m Carbon emissions from clean energy power generation were taken into account, specifically as follows:

[0129] W Grid,m =W T,m +W N,m +W H,m +W W,m +W S,m ;

[0130] In the formula, W T,m W N,m W H,m W W,m With W S,mThe carbon emissions of the province's thermal power generation, nuclear power generation, hydroelectric power generation, wind power generation and solar power generation in a certain month, respectively. N,m , W H,m , W W,m and W S,m are calculated according to the following formula:

[0131]

[0132] In the formula, EF N , EF H , EF W , and EF S are the carbon emission factors of nuclear power generation, hydroelectric power generation, wind power generation and solar power generation, respectively. T,m are calculated according to the following formula:

[0133]

[0134] In the formula, i is the type of mineral fuel consumed by the power grid in the mth month; FC i,m is the consumption of mineral fuel i in the mth year, in units of ten thousand tons or ten thousand cubic meters; QDW i is the average low heat value of mineral fuel i, in units of megajoules per ton or megajoules per ten thousand cubic meters; and EF i is the emission factor of mineral fuel i.

[0135] The calculation formula of EF i is:

[0136]

[0137] In the formula, F i represents the carbon emission coefficient of the fuel, i.e., the average carbon content of the fuel, in units of tons per terajoule; O i represents the carbon oxidation rate of the i-th energy source, in units of %; and 44 / 12 represents the molecular weight ratio of carbon dioxide to carbon.

[0138] S3, calculate the difference between the carbon emissions of the provincial power grid in the current month and the next month, and the difference between the power consumption in the current month and the next month, and take the ratio of the difference in carbon emissions and the difference in power consumption as the marginal emission factor of the provincial power grid.

[0139] Specifically, the difference between the carbon emissions of the provincial power grid in the current month and the next month, ΔW P,m , is calculated as follows:

[0140] ΔW P,m = W P,m - W P,m+1 ;

[0141] The difference between the power consumption in the current month and the next month, ΔWG,m :

[0142] ΔG P,m = G P,m -G P,m+1 ;

[0143] The marginal emission factor of the mth month of the provincial power grid is calculated as MEFm= ∑Gm / ∑Pm. P,m :

[0144]

[0145] S4, providing low-carbon planning for power-using enterprises based on the marginal emission factor of the provincial power grid.

[0146] Specifically, a low-carbon planning model for enterprises can be established based on the marginal emission factor of the provincial power grid, and the objective is to maximize the emission reduction in the target year, and the objective function is:

[0147]

[0148] In the formula, △C y represents the annual emission reduction of the enterprise; T represents the number of days in each month; P t - and P t + represent the daily load reduction and increase of the enterprise under low-carbon conditions, which need to meet the following constraints:

[0149]

[0150] It indicates that after optimizing the power consumption behavior, the daily load of the enterprise cannot exceed the upper limit or be lower than the lower limit:

[0151] P t +P t + ≤P t dL ;

[0152] P t -P t - ≥0;

[0153] It indicates that after optimizing the power consumption behavior, the monthly load of the enterprise cannot exceed the upper limit:

[0154]

[0155] It ensures that the enterprise cannot be in the state of load increase and load reduction at the same time on a certain day:

[0156]

[0157] It shows that the total load remains unchanged after the optimization of the power consumption behavior of the enterprise:

[0158]

[0159] where ΔP t u represents the upper limit of the daily adjustable load; and is a (0, 1) variable; P t dL is the upper limit of the daily load; P mL is the upper limit of the monthly load.

[0160] The above method is further illustrated in combination with specific implementation cases as follows:

[0161] Taking the power generation data of Fujian Province in 2011 as an example, the total power generation is 1565.7 billion kWh, and thermal power generation accounts for a dominant position, accounting for as high as 79.83%; followed by hydropower, accounting for 18.86%; other includes wind power, solar power, accounting for only 1.31%. According to the attached Figure 2 It can be known that the power generation characteristics of Fujian Province every year: summer (June-September) and winter (November-January) are the peak period of electricity. According to the power generation statistics of different types of units, thermal power generation plays a peak shaving function in this period because of its strong adjustability and low priority of power generation. Because of the poor regulation ability of hydropower, it is greatly affected by the change of dry and wet seasons, so when the hydropower is insufficient in the dry period (for example, April), the thermal power generation undertakes the role of peak shaving to fill the supply gap; while in the wet period (for example, May), the thermal power generation is obviously reduced. In addition, the proportion of clean energy in Fujian Province in 2011 is low (1.31%), which has little effect on the remaining two types of power generation units.

[0162] According to the "Compilation of Electric Power Industry Statistics", Fujian Province sent out a total of 63,384,300 kWh of electricity in 2011, all to Zhejiang; Fujian Province received 25,120,000 kWh of electricity from Zhejiang Province. In calculating the carbon emissions of cross-provincial electricity exchange, the regional average carbon emission factor EF is used to reduce the calculation complexity, and the emission factor of East China in 2011 is EF 华东 = 0.7842 tCO2 / MWh.

[0163] According to the relevant data and formula (10), the MEF of the power grid of Fujian Province in each month of 2011 is calculated, and the results are shown in Figure 3 MEF is a time-dependent variable. Since the proportion of thermal power generation is close to 80% in this year, the change trend of MEF is close to that of thermal power generation. It can be found that the change trend of MEF mainly includes three stages:

[0164] (1) Stage one (January-April)

[0165] In January, the power load was high due to the influence of winter cold waves and cold air, leading to an increase in thermal power generation, thus increasing the MEF. In February, the Spring Festival holiday led to a decrease in load demand due to the shutdown of part of the secondary industry (i.e., industry and construction) and the tertiary industry (i.e., service industry and commerce), as well as population flow, and at the same time, thermal power output also decreased, thus reducing the MEF in February. After the holiday, various industries resumed work, and the load rebounded from March to May, but the structure of the generator set output changed: in March and April, Fujian Province experienced a drought period, leading to a decline in hydropower generation, thus significantly increasing thermal power generation, which led to an increase in the proportion of MEF, with MEF P,4 = 1.1371 tCO2 / MWh; while in May, Fujian Province entered the flood season, and the amount of hydropower increased, while the amount of thermal power decreased, with the proportion of hydropower being 30%, thus the MEF reached a low value of 0.5944 tCO2 / MWh in a year. It can be seen that MEF has the ability to reflect the influence of different proportions of generator set output on carbon emissions.

[0166] (2) Stage two (May-September)

[0167] This stage is the rising period of MEF. Due to the gradual rise in temperature in Fujian Province, high temperature led to the start of a large number of cooling equipment in factories, commerce, and residents, thus accelerating the growth of load. In addition, the flood season has passed, and the output of hydropower is stable, while thermal power as a peak shaving unit, the proportion of output increases again, and the proportion of power generation in June-August reaches more than 80%. Therefore, the MEF also shows an upward trend, with MEF P,8 = 1.1495 tCO2 / MWh. It can be seen that MEF can reflect the influence of seasonal changes on the carbon emissions of the power grid.

[0168] (3) Stage three (October-December)

[0169] From October, the temperature in Fujian Province fell, the demand side cooling equipment was turned off, the load decreased, and the thermal power output decreased, thus the MEF was lower than in summer. In December, with the arrival of cold air, the heating load accelerated, the power load climbed again, and the output of hydropower decreased due to the dry season in winter, thus the thermal power generation further increased, leading to an increase in MEF.

[0170] In summary, MEF has the ability to reflect the spatial and temporal differences of carbon emissions of the power grid, and can depict the influence of season, climate, policy, clean energy, etc. on the carbon emissions of the power grid, meeting the requirements of carbon emissions evaluation and analysis of new power systems.

[0171] The MEF of Fujian Province from 2012 to 2020 can be calculated in the same way, and the results are shown in the attached Figure 4The results of the calculation of the emission factors of the power system in Fujian Province in 2011 and 2020 can be found that:

[0172] (1) Since 2015, MEF has been significantly reduced, mainly due to the rapid development of clean energy in Fujian Province. For example, nuclear power in Fujian Province was put into use in 2013, but the stability of power generation was initially insufficient, and the share of power generation was low, with a total power generation of 8305 million kilowatt-hours. However, by 2015, nuclear power has become a stable and reliable power supply method in Fujian Province, with power generation increasing to 287.46 billion kilowatt-hours, and continuously stable output in the following years.

[0173] (2) The MEF of Fujian Province power grid has two low values in the first half of each year, one of which occurs in January or February due to the Spring Festival holiday, and the other low value occurs in April-June, which is the flood season in Fujian Province, and hydropower has squeezed the share of thermal power generation, resulting in a decrease in MEF. It is worth noting that in 2016, Fujian Province experienced climate anomalies, with average annual rainfall reaching a near-decade peak of 2432.6 mm, and water power generation increased by more than 100 billion kilowatt-hours compared with previous years, so the overall power grid emission factor of Fujian Province was lower.

[0174] (3) The MEF of Fujian Province power grid will reach a peak value from July to September, because summer is the peak period of electricity consumption, and power supply is mainly borne by thermal power, so MEF is at a high level.

[0175] By summarizing the change rule of the carbon emission factor of the power grid in Fujian Province from 2011 to 2020, electricity users can benefit from the MEF "peak-valley difference" to develop low-carbon planning and optimize electricity consumption behavior to reduce the carbon emissions of enterprises.

[0176] Taking the load data of a photovoltaic enterprise in Fujian Province in 2018 as an example, based on Figure 5 the emission factor data of the power grid in Fujian Province in 2018, the simulation calculation of emission reduction potential is carried out according to the formula of step S4. The optimization results are shown in the accompanying Figure 5 It can be seen that when the enterprise can perceive the carbon emission difference caused by electricity consumption time, it optimizes its electricity consumption behavior by using the "peak-valley difference" of MEF under the condition of meeting all constraints, that is, it reduces electricity consumption in the period of high emission factor (such as July-September), and compensates for electricity consumption in the period of low emission factor (such as February and June).

[0177] In order to further understand the electricity performance of enterprises before and after low-carbon planning, Figure 6The load of the enterprise in June is shown. Considering that the MEF value of the power grid in this period is the lowest, in order to minimize the carbon emissions of the enterprise in the whole year, the productivity of the enterprise in this month should be improved as much as possible. Compared with the original load, the enterprise maintains a higher power demand in most working days after the low-carbon planning. According to the data in the figure, the monthly power consumption of the enterprise before and after the low-carbon planning is 53.95 MWh and 93.09 MWh respectively.

[0178] Table 1 lists the load of the enterprise before and after the low-carbon planning in 2018 and the corresponding emission reduction potential. Wherein, △L represents the difference between the monthly load before and after the planning. It can be seen that the total load of the enterprise in 2018 does not change, only the power consumption behavior of the enterprise is adjusted according to the MEF of the provincial power grid in each month. After the planning, the emission reduction of the enterprise in this year reaches about 43.75 tons of CO2, while ensuring that no additional power consumption cost is needed. In addition, if the price incentive provided by the national certified voluntary emission reduction project is combined, the enterprise can obtain economic benefits through low-carbon planning, thereby further improving the emission reduction willingness of the enterprise.

[0179] Table 1 Low-carbon planning of the enterprise

[0180]

[0181] In summary, the MEF of the provincial power grid can be used as a guiding signal to develop a “load side” emission reduction mechanism, provide an emission reduction path for power-consuming enterprises, and tap the emission reduction potential of enterprises.

[0182] Please refer to Figure 7 , embodiment two of the present application is:

[0183] A power grid planning terminal 1 based on a marginal emission factor of a provincial power grid, comprising a memory 3, a processor 2 and a computer program stored on the memory 3 and executable on the processor 2, and the processor 2 implements the steps of the above-mentioned embodiment one when executing the computer program.

[0184] In summary, the present application provides a power grid planning method and terminal based on a marginal emission factor of a provincial power grid. The marginal carbon emission factor can reflect the ability of the spatial and temporal difference of the power grid carbon emission, and depict the influence of the above-mentioned factors on the power grid carbon emission, which can meet the requirements of carbon emission evaluation and analysis of the new power system.

[0185] The above-mentioned is only an embodiment of the present application, and does not limit the patent range of the present application, any equivalent transformation made by using the content of the specification and drawings, or direct or indirect application in related technical fields, are also included in the patent protection range of the present application.

Claims

1. A power grid planning method based on the marginal emission factor of a provincial power grid, characterized in that, Including the following steps: S1. Calculate the monthly electricity consumption of the provincial power grid; S2. Calculate the monthly carbon emissions of the provincial power grid based on the monthly electricity consumption of the provincial power grid; S3. Calculate the difference between the carbon emissions of the current month and the carbon emissions of the next month for each month of the provincial power grid, and the difference between the electricity consumption of the current month and the electricity consumption of the next month for each month. Use the ratio of the difference in carbon emissions to the difference in electricity consumption as the marginal emission factor of the provincial power grid for each month. S4. Provide low-carbon planning for electricity-consuming enterprises based on the marginal emission factors of the provincial power grid; Specifically, step S4 is as follows: Solve for the objective function: ； In the formula, △ C y This indicates the company's total emission reductions for the entire year. T This indicates the number of days in each month. and MEF represents the daily load reduction and increase of an enterprise under low-carbon response conditions. P,m This represents the marginal emission factor of the provincial power grid in month m. The objective function has constraints: ； ； ； ； ； ； In the formula, This indicates the daily adjustable load limit; and The variable is (0,1); This is the daily load limit; This is the monthly load limit.

2. The power grid planning method based on the marginal emission factor of a provincial power grid according to claim 1, characterized in that, The electricity consumption specifically includes the electricity consumption of thermal power generation, hydropower generation, nuclear power generation, wind power generation, and solar power generation; In step S1, the monthly electricity consumption of the provincial power grid is calculated according to the following formula. G P,m : ； In the formula, m =1,2,…12 represents the month; G Grid,m Indicates the first m The total electricity supply within the monthly power grid operating area is expressed in ten thousand kilowatt-hours. G P,m,j and G C,m,k For from the first m Moon j Province or k Net electricity imported into the country, in ten thousand kilowatt-hours; The total power supply G within the power grid operating area in month m Grid,m Represented as: ； In the formula, G T,m , G N,m , G H,m , G W,m , G S,m The province was in the m The monthly electricity generation from thermal, nuclear, hydro, wind, and solar power, measured in ten thousand kilowatt-hours.

3. The power grid planning method based on the marginal emission factor of a provincial power grid according to claim 2, characterized in that, In step S2, the monthly carbon emissions of the provincial power grid are calculated according to the following formula. W P,m : ； In the formula, W Grid,m For the first m Monthly CO2 emissions from provincial power grids, in tons; EF P,j , EF C,k The first m moon j Province or k The carbon emission factor of the country's power grid; W Grid,m Carbon emissions from clean energy power generation were taken into account, specifically as follows: ； In the formula, W T,m , W N,m , W H,m , W W,m and W S,m These figures represent the carbon emissions from thermal power generation, nuclear power generation, hydropower generation, wind power generation, and solar power generation generated by the provincial power grid in month m. W N,m , W H,m , W W,m and W S,m The value is calculated according to the following formula: ； In the formula, EF N , EF H , EF W , EF S These are the carbon emission factors for nuclear, hydro, wind, and solar power generation, respectively. W T,m It is calculated using the following formula: ； In the formula, i For the first m Types of fossil fuels consumed by thermal power plants in the monthly power grid; FC i,m For the first m Annual fossil fuels i The consumption amount is expressed in tens of thousands of tons or tens of thousands of cubic meters. QDW i fossil fuels i The average lower heating value, expressed in megajoules per ton or megajoules per 10,000 cubic meters; EF i fossil fuels i Emission factors; EF i The calculation formula is: ； In the formula, F i The carbon emission factor of a fuel is expressed as the average carbon content of the fuel, measured in tons per terajoule (TJ). O i Indicates the first i The carbon oxidation rate of this energy source is expressed as %; 44 / 12 represents the molecular weight ratio of carbon dioxide to carbon.

4. A power grid planning terminal based on the marginal emission factor of a provincial power grid, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it performs the following steps: S1. Calculate the monthly electricity consumption of the provincial power grid; S2. Calculate the monthly carbon emissions of the provincial power grid based on the monthly electricity consumption of the provincial power grid; S3. Calculate the difference between the carbon emissions of the current month and the carbon emissions of the next month for each month of the provincial power grid, and the difference between the electricity consumption of the current month and the electricity consumption of the next month for each month. Use the ratio of the difference in carbon emissions to the difference in electricity consumption as the marginal emission factor of the provincial power grid for each month. S4. Provide low-carbon planning for electricity-consuming enterprises based on the marginal emission factors of the provincial power grid; Specifically, step S4 is as follows: Solve for the objective function: ； In the formula, △ C y This indicates the company's total emission reductions for the entire year. T This indicates the number of days in each month. and MEF represents the daily load reduction and increase of an enterprise under low-carbon response conditions. P,m This represents the marginal emission factor of the provincial power grid in month m. The objective function has constraints: ； ； ； ； ； ； In the formula, This indicates the daily adjustable load limit; and The variable is (0,1); This is the daily load limit; This is the monthly load limit.

5. A power grid planning terminal based on the marginal emission factor of a provincial power grid according to claim 4, characterized in that, The electricity consumption specifically includes the electricity consumption of thermal power generation, hydropower generation, nuclear power generation, wind power generation, and solar power generation; In step S1, the monthly electricity consumption of the provincial power grid is calculated according to the following formula. G P,m : ； In the formula, m =1,2,…12 represents the month; G Grid,m Indicates the first m The total electricity supply within the monthly power grid operating area is expressed in ten thousand kilowatt-hours. G P,m,j and G C,m,k For from the first m Moon j Province or k Net electricity imported into the country, in ten thousand kilowatt-hours; The total power supply G within the power grid operating area in month m Grid,m Represented as: ； In the formula, G T,m , G N,m , G H,m , G W,m , G S,m The province was in the m The monthly electricity generation from thermal, nuclear, hydro, wind, and solar power, measured in ten thousand kilowatt-hours.

6. A power grid planning terminal based on the marginal emission factor of a provincial power grid according to claim 5, characterized in that, In step S2, the monthly carbon emissions of the provincial power grid are calculated according to the following formula. W P,m : ； In the formula, W Grid,m For the first m Monthly CO2 emissions from provincial power grids, in tons; EF P,j , EF C,k The first m moon j Province or k The carbon emission factor of the country's power grid; W Grid,m Carbon emissions from clean energy power generation were taken into account, specifically as follows: ； In the formula, W T,m , W N,m , W H,m , W W,m and W S,m These figures represent the carbon emissions from thermal power generation, nuclear power generation, hydropower generation, wind power generation, and solar power generation generated by the provincial power grid in month m. W N,m , W H,m , W W,m and W S,m The value is calculated according to the following formula: ； In the formula, EF N , EF H , EF W , EF S These are the carbon emission factors for nuclear, hydro, wind, and solar power generation, respectively. W T,m It is calculated using the following formula: ； In the formula, i For the first m Types of fossil fuels consumed by thermal power plants in the monthly power grid; FC i,m For the first m Annual fossil fuels i The consumption amount is expressed in tens of thousands of tons or tens of thousands of cubic meters. QDW i fossil fuels i The average lower heating value, expressed in megajoules per ton or megajoules per 10,000 cubic meters; EF i fossil fuels i Emission factors; EF i The calculation formula is: ； In the formula, F i The carbon emission factor of a fuel is expressed as the average carbon content of the fuel, measured in tons per terajoule (TJ). O i Indicates the first i The carbon oxidation rate of this energy source is expressed as %; 44 / 12 represents the molecular weight ratio of carbon dioxide to carbon.

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