CN116227822A - Power grid planning method and terminal based on provincial power grid marginal emission factors - Google Patents

Power grid planning method and terminal based on provincial power grid marginal emission factors Download PDF

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CN116227822A
CN116227822A CN202211643168.5A CN202211643168A CN116227822A CN 116227822 A CN116227822 A CN 116227822A CN 202211643168 A CN202211643168 A CN 202211643168A CN 116227822 A CN116227822 A CN 116227822A
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陈思敏
陈津莼
杜翼
项康利
蔡期塬
李源非
林晓凡
陈晗
陈晚晴
郑楠
李益楠
陈柯任
施鹏佳
林昶咏
林晗星
陈文欣
陈劲宇
陈紫晗
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State Grid Fujian Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Fujian Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Fujian Electric Power Co Ltd
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Abstract

The invention discloses a power grid planning method and a terminal based on a marginal emission factor of a provincial power grid, and the power consumption of the provincial power grid in each month is calculated; calculating the carbon emission of each month of the provincial power grid according to the electric quantity consumption of each month of the provincial power grid; calculating the difference between the current month carbon emission and the next month carbon emission of the provincial power grid and the difference between the current month electricity consumption and the next month electricity consumption of each month, and taking the ratio of the difference between the carbon emission and the difference between the electricity consumption as the marginal emission factor of the provincial power grid of each month; and providing low-carbon planning for power enterprises according to the marginal emission factors of the provincial power grid. The marginal carbon emission factor can reflect the capability of space-time variability of the carbon emission of the power grid, and can characterize the influence of the factors on the carbon emission of the power grid, so that the requirements of carbon emission evaluation and analysis of a novel power system can be met.

Description

Power grid planning method and terminal based on provincial power grid marginal emission factors
Technical Field
The invention relates to the technical field of power grid planning, in particular to a power grid planning method and a terminal based on provincial power grid marginal emission factors.
Background
Climate change problems are one of the biggest challenges facing the 21 st century worldwide. The power industry plays a critical role in the total carbon emission, and on this factor, the power industry plays a critical role in achieving the carbon peak as expected.
In the prior art, the most commonly used carbon emission calculation method in the electric power industry is an emission factor estimation method, namely, the generated energy of an electric power system is multiplied by an emission factor to obtain the carbon emission. Thus, an accurate assessment of the grid emission factor plays a decisive role in the quality of the carbon emission accounting. Currently, the prior art generally adopts average emission factors to evaluate and analyze the carbon emission of the power grid. However, the average emission factor is calculated in units of years, and the factors such as time, climate, policy and generator set duty ratio change are ignored, so that the time-varying property of the carbon emission of the power grid is hard to characterize. Under the promotion of the rapid construction of a novel power system and the 'double carbon' target, the analysis and quantification of the carbon emission of a power grid by utilizing the average emission factor have the progressive disadvantages.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the utility model provides a power grid planning method and terminal based on provincial power grid marginal emission factors, which can reflect the capability of space-time variability of power grid carbon emission.
In order to solve the technical problems, the invention adopts the following technical scheme:
a power grid planning method based on provincial power grid marginal emission factors comprises the following steps:
s1, calculating the electricity consumption of the provincial power grid in each month;
s2, calculating the carbon emission of each month of the provincial power grid according to the electric quantity consumption of each month of the provincial power grid;
s3, calculating the difference between the current month carbon emission and the next month carbon emission of the provincial power grid and the difference between the current month electric quantity consumption and the next month electric quantity consumption of each month, and taking the ratio of the difference between the carbon emission and the difference between the electric quantity consumption as the marginal emission factor of the provincial power grid of each month;
and S4, providing low-carbon planning for power enterprises according to the marginal emission factors of the provincial power grid.
In order to solve the technical problems, the invention adopts another technical scheme that:
a power grid planning terminal based on provincial power grid marginal emission factors, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
s1, calculating the electricity consumption of the provincial power grid in each month;
s2, calculating the carbon emission of each month of the provincial power grid according to the electric quantity consumption of each month of the provincial power grid;
s3, calculating the difference between the current month carbon emission and the next month carbon emission of the provincial power grid and the difference between the current month electric quantity consumption and the next month electric quantity consumption of each month, and taking the ratio of the difference between the carbon emission and the difference between the electric quantity consumption as the marginal emission factor of the provincial power grid of each month;
and S4, providing low-carbon planning for power enterprises according to the marginal emission factors of the provincial power grid.
The invention has the beneficial effects that: a power grid planning method and a terminal based on a provincial power grid marginal emission factor can reflect the capability of space-time variability of power grid carbon emission, and describe the influence of space-time variability on power grid carbon emission, and provide low-carbon planning for power enterprises according to the provincial power grid marginal emission factor, so that the requirements of carbon emission assessment and analysis of a novel power system can be met.
Drawings
FIG. 1 is a schematic flow chart of a power grid planning method based on a provincial power grid marginal emission factor according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a Fujian province power generation feature according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a grid MEF of 2011, fowler-packard province for each month according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a power grid MEF of 12-20 years fowler-packard province for each month according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of carbon emissions before and after optimization in accordance with an embodiment of the present invention;
FIG. 6 is a schematic diagram of grid loads before and after optimization according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a power grid planning terminal based on a provincial power grid marginal emission factor according to an embodiment of the present invention.
Description of the reference numerals:
1. a power grid planning terminal based on provincial power grid marginal emission factors; 2. a processor; 3. a memory.
Detailed Description
In order to describe the technical contents, the achieved objects and effects of the present invention in detail, the following description will be made with reference to the embodiments in conjunction with the accompanying drawings.
Referring to fig. 1 to 6, a power grid planning method based on provincial power grid marginal emission factors includes the steps of:
s1, calculating the electricity consumption of the provincial power grid in each month;
s2, calculating the carbon emission of each month of the provincial power grid according to the electric quantity consumption of each month of the provincial power grid;
s3, calculating the difference between the current month carbon emission and the next month carbon emission of the provincial power grid and the difference between the current month electric quantity consumption and the next month electric quantity consumption of each month, and taking the ratio of the difference between the carbon emission and the difference between the electric quantity consumption as the marginal emission factor of the provincial power grid of each month;
and S4, providing low-carbon planning for power enterprises according to the marginal emission factors of the provincial power grid.
From the above description, the beneficial effects of the invention are as follows: a power grid planning method and a terminal based on provincial power grid marginal emission factors can reflect the capability of space-time variability of power grid carbon emission, and describe the influence of the factors on the power grid carbon emission, so that the requirements of carbon emission assessment and analysis of a novel power system can be met.
Further, the step S4 specifically includes:
solving an objective function:
Figure BDA0004008580290000031
in the formula DeltaC y Represents annual capacity reduction of enterprises, T represents days per month and P t - And P t + Represents the daily load reduction and increase of enterprises under the corresponding low-carbon conditions, MEF P,m And (5) saving a marginal emission factor for the level grid for the mth month.
From the above description, it is the goal to maximize the volume reduction of the target year.
Further, the objective function has a constraint:
Figure BDA0004008580290000032
P t +P t + ≤P t dL ；
P t -P t - ≥0；
Figure BDA0004008580290000041
Figure BDA0004008580290000042
Figure BDA0004008580290000043
wherein DeltaP t u Represents an upper daily adjustable load limit;
Figure BDA0004008580290000045
and->
Figure BDA0004008580290000046
Is a (0, 1) variable; p (P) i dL Is the daily upper load limit; p (P) mL Is the upper monthly load limit.
From the above description, the constraint indicates that the daily load of the enterprise does not exceed the upper limit after the power consumption behavior is optimized, and the daily load of the enterprise does not exceed the lower limit after the power consumption behavior is optimized; ensuring that the enterprises cannot be in the load increasing and load reducing states at the same time in a certain day; indicating that the total load amount of the enterprise in the current year remains unchanged after optimizing the electricity consumption behavior.
Further, the electric power consumption specifically includes electric power consumption of thermal power generation, hydroelectric power generation, nuclear power generation, wind power generation and solar power generation;
in the step S1, the electricity consumption G of each month of the provincial power grid is calculated according to the following formula P,m ：
G P,m ＝G Grid,m +∑ j G P,m,j +∑ k G C,m,k ；
Wherein m=1, 2, … represents month; g Grid,m Representing all power supply quantities in a menstrual region of an mth month power grid, wherein the unit is ten thousand watt hours; g P,m,j And G C,m,k The unit is kilowatt-hour for the net electric quantity transferred from month m and from country j or country k;
all power supply G in m-th month power grid menstrual area Grid,m Expressed as:
G Grid,m ＝G T,m +G N,m +G H,m +G W,m +G S,m ；
wherein G is T,m 、G N,m 、G H,m 、G W,m 、G S,m The unit is ten thousand watt hours of the generated energy of the fire power, nuclear energy, hydraulic power, wind power and solar energy saved in the m month.
From the above description, a calculation formula of the electricity consumption is given, and the electricity consumption is calculated.
Further, in the step S2, the carbon emission W of the provincial power grid for each month is calculated according to the following formula P,m ：
Figure BDA0004008580290000051
In which W is Grid,m CO for a month-saving grid 2 Discharge in tons; EF (electric F) P,j 、EF C,k And the carbon emission factors of the power grid in the j province of the mth month or the k country respectively.
W Grid,m Considering carbon emission generated by clean energy power generation, the method is specifically expressed as:
W Grid,m ＝W T,m +W N,m +W H,m +W W,m +W S,m ；
in which W is T,m 、W N,m 、W H,m 、W W,m And W is equal to S,m Respectively generating carbon emission by thermal power generation, nuclear power generation, hydroelectric power generation, wind power generation and solar power generation of the m th month of the provincial power grid, W N,m 、W H,m 、W W,m And W is equal to S,m Is calculated according to the following formula:
Figure BDA0004008580290000052
in the formula, EF N 、EF H 、EF W 、EF S Carbon emission factors of nuclear power, hydraulic power, wind power and solar power generation respectively, W T,m Calculated from the following formula:
Figure BDA0004008580290000053
wherein i is the type of fossil fuel consumed by thermal power of an m-th month power grid; FC (fiber channel) i,m The consumption of fossil fuel i in the m year is ten thousand tons or ten thousand cubic meters; QDW i The average low-grade heating value of the fossil fuel i is expressed as megajoules/ton or megajoules/ten thousand cubic meters; EF (electric F) i An emission factor for fossil fuel i;
EF i the calculation formula of (2) is as follows:
Figure BDA0004008580290000054
wherein F is i Represents the carbon emission coefficient of the fuel, i.e. the average carbon content of the fuel, in tons/too-coke; o (O) i Represents the carbon oxidation rate of the ith energy source in units of; 44/12 represents the molecular weight ratio of carbon dioxide to carbon.
From the above description, a calculation formula of the carbon emission amount is given, and calculation of the carbon emission amount is realized.
A power grid planning terminal based on provincial power grid marginal emission factors, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
s1, calculating the electricity consumption of the provincial power grid in each month;
s2, calculating the carbon emission of each month of the provincial power grid according to the electric quantity consumption of each month of the provincial power grid;
s3, calculating the difference between the current month carbon emission and the next month carbon emission of the provincial power grid and the difference between the current month electric quantity consumption and the next month electric quantity consumption of each month, and taking the ratio of the difference between the carbon emission and the difference between the electric quantity consumption as the marginal emission factor of the provincial power grid of each month;
and S4, providing low-carbon planning for power enterprises according to the marginal emission factors of the provincial power grid.
From the above description, the beneficial effects of the invention are as follows: a power grid planning method and a terminal based on provincial power grid marginal emission factors can reflect the capability of space-time variability of power grid carbon emission, and describe the influence of the factors on the power grid carbon emission, so that the requirements of carbon emission assessment and analysis of a novel power system can be met.
Further, the step S4 specifically includes:
solving an objective function:
Figure BDA0004008580290000061
in the formula DeltaC y Represents annual capacity reduction of enterprises, T represents days per month and P t - And P t + Represents the daily load reduction and increase of enterprises under the corresponding low-carbon conditions, MEF P,m And (5) saving a marginal emission factor for the level grid for the mth month.
From the above description, it is the goal to maximize the volume reduction of the target year.
Further, the objective function has a constraint:
Figure BDA0004008580290000062
P t +P t + ≤P t dL ；
P t -P t - ≥0；
Figure BDA0004008580290000063
Figure BDA0004008580290000071
Figure BDA0004008580290000072
wherein DeltaP t u Represents an upper daily adjustable load limit;
Figure BDA0004008580290000074
and->
Figure BDA0004008580290000075
Is a (0, 1) variable; p (P) t dL Is the daily upper load limit; p (P) mL Is the upper monthly load limit.
From the above description, the constraint indicates that the daily load of the enterprise does not exceed the upper limit after the power consumption behavior is optimized, and the daily load of the enterprise does not exceed the lower limit after the power consumption behavior is optimized; ensuring that the enterprises cannot be in the load increasing and load reducing states at the same time in a certain day; indicating that the total load amount of the enterprise in the current year remains unchanged after optimizing the electricity consumption behavior.
Further, the electric power consumption specifically includes electric power consumption of thermal power generation, hydroelectric power generation, nuclear power generation, wind power generation and solar power generation;
in the step S1, the electricity consumption G of each month of the provincial power grid is calculated according to the following formula P,m ：
G P,m ＝G Grid,m +∑ j G P,m,j +∑ k G C,m,k ；
Wherein m=1, 2, … represents month; g Grid,m Representing all power supply quantities in a menstrual region of an mth month power grid, wherein the unit is ten thousand watt hours; g P,m,j And G C,m,k The unit is kilowatt-hour for the net electric quantity transferred from month m and from country j or country k;
all power supply G in m-th month power grid menstrual area Grid,m Expressed as:
G Grid,m ＝G T,m +G N,m +G H,m +G W,m +G S,m ；
wherein G is T,m 、G N,m 、G H,m 、G W,m 、G S,m The unit is ten thousand watt hours of the generated energy of the fire power, nuclear energy, hydraulic power, wind power and solar energy saved in the m month.
From the above description, a calculation formula of the electricity consumption is given, and the electricity consumption is calculated.
Further, in the step S2, the carbon emission W of the provincial power grid for each month is calculated according to the following formula P,m ：
W P,m ＝W Grid,m +∑ j (G P,m,j ×EF P,j )+∑ k (G C,m,k ×EF C,k )；
In the method, in the process of the invention,W Grid,m CO for a month-saving grid 2 Discharge in tons; EF (electric F) P,j 、EF C,k And the carbon emission factors of the power grid in the j province of the mth month or the k country respectively.
W Grid,m Considering carbon emission generated by clean energy power generation, the method is specifically expressed as:
W Grid,m ＝W T,m +W N,m +W H,m +W W,m +W S,m ；
in which W is T,m 、W N,m 、W H,m 、W W,m And W is equal to S,m Respectively generating carbon emission by thermal power generation, nuclear power generation, hydroelectric power generation, wind power generation and solar power generation of the m th month of the provincial power grid, W N,m 、W H,m 、W W,m And W is equal to S,m Is calculated according to the following formula:
Figure BDA0004008580290000081
in the formula, EF N 、EF H 、EF W 、EF S Carbon emission factors of nuclear power, hydraulic power, wind power and solar power generation respectively, W T,m Calculated from the following formula:
Figure BDA0004008580290000082
wherein i is the type of fossil fuel consumed by thermal power of an m-th month power grid; FC (fiber channel) i,m The consumption of fossil fuel i in the m year is ten thousand tons or ten thousand cubic meters; QDW i The average low-grade heating value of the fossil fuel i is expressed as megajoules/ton or megajoules/ten thousand cubic meters; EF (electric F) i An emission factor for fossil fuel i;
EF i the calculation formula of (2) is as follows:
Figure BDA0004008580290000083
wherein F is i Represents the carbon emission coefficient of the fuel, i.e. the average carbon content of the fuel, in tons/too-coke; o (O) i Represents the carbon oxidation rate of the ith energy source in units of; 44/12 represents the molecular weight ratio of carbon dioxide to carbon.
From the above description, a calculation formula of the carbon emission amount is given, and calculation of the carbon emission amount is realized.
The method is used for planning the provincial power grid, and provides a basis for energy conservation and emission reduction planning of the provincial power grid.
Referring to fig. 1 to 6, a first embodiment of the present invention is as follows:
a power grid planning method based on provincial power grid marginal emission factors comprises the following steps:
s1, calculating the electricity consumption of the provincial power grid in each month.
The method comprises thermal power generation, hydroelectric power generation, nuclear power generation, wind power generation and solar power generation.
Specifically, assume that the power G consumed by a power grid in a certain province is a month P,m The method comprises the following steps:
G P,m ＝G Grid,m +∑ j G P,m,j +∑ k G C,m,k ；
wherein m=1, 2, … represents month; g Grid,m Representing all power supply quantities in a menstrual region of an mth month power grid, wherein the unit is ten thousand watt hours; g P,m,j And G C,m,k The unit is kilowatt-hours for the net transfer of electricity from month m from province j or country k.
Wherein G is Grid,m Can be expressed as:
G Grid,m ＝G T,m +G N,m +G H,m +G W,m +G S,m ；
wherein G is T,m 、G N,m 、G H,m 、G W,m 、G S,m The unit is ten thousand watt hours of the generated energy of the fire power, nuclear energy, hydraulic power, wind power and solar energy saved in the m month.
And S2, calculating the carbon emission of each month of the provincial power grid according to the electric quantity consumption of each month of the provincial power grid.
The power generation of the provincial power grid, the clean energy power generation, the cross-power-saving exchange and other factors are considered.
Specifically, assume that a certain power grid carbon emission W is given in a certain month P,m The method comprises the following steps:
W P,m ＝W Grid,m +∑ j (G P,m,j ×EF P,j )+∑ k (G C,m,k ×EF C,k )；
in which W is Grid,m CO for the power grid of the mth month of a certain province 2 Discharge in tons; EF (electric F) P,j 、EF C,k And the carbon emission factors of the power grid in the j province of the mth month or the k country respectively.
W Grid,m Considering carbon emission generated by clean energy power generation, the method is specifically expressed as:
W Grid,m ＝W T,m +W N,m +W H,m +W W,m +W S,m ；
in which W is T,m 、W N,m 、W H,m 、W W,m And W is equal to S,m The carbon emissions generated by thermal power generation, nuclear power generation, hydroelectric power generation, wind power generation and solar power generation in the province are respectively. W (W) N,m 、W H,m 、W W,m And W is equal to S,m Reference is made to the following formula:
Figure BDA0004008580290000091
in the formula, EF N 、EF H 、EF W 、EF S And the carbon emission factors are nuclear energy, hydraulic power, wind power and solar power generation respectively. W (W) T,m Calculated from the following formula:
Figure BDA0004008580290000101
wherein i is the type of fossil fuel consumed by thermal power of an m-th month power grid; FC (fiber channel) i,m The consumption of fossil fuel i in the m year is ten thousand tons or ten thousand cubic meters; QDW i Is the average of fossil fuels iThe low-position heating value is expressed as megajoules/ton or megajoules/ten thousand cubic meters; EF (electric F) i Is the emission factor of fossil fuel i.
EF i The calculation formula of (2) is as follows:
Figure BDA0004008580290000102
wherein F is i Represents the carbon emission coefficient of the fuel, i.e. the average carbon content of the fuel, in tons/too-coke; o (O) i Represents the carbon oxidation rate of the ith energy source in units of; 44/12 represents the molecular weight ratio of carbon dioxide to carbon.
S3, calculating the difference between the current month carbon emission and the next month carbon emission of the provincial power grid and the difference between the current month electricity consumption and the next month electricity consumption, and taking the ratio of the difference between the carbon emission and the electricity consumption as the marginal emission factor of the provincial power grid.
Specifically, the difference DeltaW between the current month carbon emission and the next month carbon emission of the provincial power grid is calculated P,m ：
ΔW P,m ＝W P,m -W P,m+1 ；
Calculating the difference DeltaW between the current month power consumption and the next month power consumption G,m ：
ΔG P,m ＝G P,m -G P,m+1 ；
Calculating the marginal emission factor of the m month-saving level power grid as MEF P,m ：
Figure BDA0004008580290000103
And S4, providing low-carbon planning for power enterprises according to the marginal emission factors of the provincial power grid.
Specifically, an enterprise low-carbon planning model can be established based on the provincial power grid marginal emission factor, the objective is to maximize the emission reduction of the target year, and then the objective function is as follows:
Figure BDA0004008580290000104
in the formula DeltaC y Representing annual emission reduction of enterprises; t represents the number of days per month; p (P) t - And P t + Representing the daily load decrease and increase of enterprises under low carbon corresponding conditions, they need to meet the following constraints:
Figure BDA0004008580290000111
after optimizing the electricity consumption behavior, the daily load of the enterprise is not required to exceed the upper limit and is not required to be lower than the lower limit:
P t +P t + ≤P t dL ；
P t -P t - ≥0；
the method shows that the month load cannot exceed the upper limit after the enterprise optimizes the electricity consumption behavior:
Figure BDA0004008580290000112
ensuring that the enterprises cannot be in the load increasing and load reducing states at the same time in a certain day:
Figure BDA0004008580290000113
the total load amount of the enterprise in the current year is kept unchanged after the power consumption behavior is optimized:
Figure BDA0004008580290000114
wherein DeltaP t u Represents an upper daily adjustable load limit;
Figure BDA0004008580290000115
and->
Figure BDA0004008580290000116
Is a (0, 1) variable; p (P) t dL Is the daily upper load limit; p (P) mL Is the upper monthly load limit.
The above method is further described in connection with the specific embodiments below:
taking the electricity generation data of 2011 of Fujian province as an example, wherein the total electricity generation amount is 1565.7 hundred million kilowatt-hours, and the thermal power generation takes the dominant place and accounts for up to 79.83%; secondly, hydroelectric generation is carried out, and the ratio is 18.86%; the other components comprise wind power and solar power generation, and the ratio is only 1.31 percent. From fig. 2, the annual power generation characteristics of the fowler province can be seen: the summer (6 months-9 months) and the winter (11 months-1 month) are the power peak periods. According to the statistics of the generated energy of different types of units, the thermal power generation has a peak regulation function in the period, and the thermal power generation has strong adjustability and low power generation priority. Because the hydroelectric power generation regulation capability is poor and the influence of the variation of the full-grown season is great, when the hydroelectric power generation is insufficient in the drought period (for example, 4 months), the thermal power generation takes on the role of peak regulation so as to fill the supply gap; while in the period of high water (for example, 5 months) the water power output is increased, the thermal power generation is obviously reduced. In addition, the clean energy proportion of Fujian province in 2011 is low (1.31%), and the influence on the other two generator sets is small.
According to the electric power industry statistics data assembly, 633843 kilowatts of electricity is sent to Zhejiang in 2011 of Fujian province; the Fujian province receives 2512 kilowatts of electricity from the Zhejiang province. In calculating the carbon emission calculation of the cross-power-saving amount exchange, the area average carbon emission factor EF is adopted to reduce the calculation complexity, wherein the emission factor of the 2011 east China area is EF East China ＝0.7842tCO 2 /MWh。
According to the related data and formula (10), the power grid MEF of each month of Fujian province in 2011 is calculated, and the result is shown in fig. 3. MEF is a time dependent variable. The thermal power generation ratio in the year is close to 80%, so the MEF change trend is close to that of thermal power output. The trend of MEF changes can be found to be mainly divided into three phases:
(1) Stage one (1 month-4 months)
The power generation amount of the MEF is increased due to the fact that the power generation amount is increased as the power load is higher due to the fact that the MEF is influenced by winter chill and cold air in 1 month. The holiday of spring festival is 2 months, and part of the second industry (i.e. industry and architecture, etc.) and the third industry (i.e. service industry and business) are shut down, and the population flow causes the load demand to decrease, and the thermal power output also decreases accordingly, so the MEF of 2 months decreases. After the holiday is finished, the load of each industry is rebounded for 3-5 months, but the output structure of the generator set is changed: in the drought period of 3 months and 4 months Fujian province, the hydroelectric power generation falls to the valley, so that the thermal power generation is obviously increased, the MEF is obviously increased due to the increase of the duty ratio, and the MEF P,4 ＝1.1371tCO 2 MWh; the water power generation capacity is improved, the water power generation capacity is reduced, and the water power ratio is 30% in the 5-month Fujian province entering the flood season, so that the MEF also reaches the low valley value of one year, namely 0.5944tCO 2 /MWh. It can be seen that MEFs have the ability to reflect the effects of different genset output duty cycles on carbon emissions.
(2) Stage two (5 month-9 month)
This stage is the rising period of MEF, and since the air temperature of foodborne province rises gradually, the high temperature causes a large number of refrigeration equipment such as factories, businesses, residents and the like to start up, so the load increases rapidly. In addition, after the flood season, the hydroelectric power is stably output, the thermal power is used as a peak shaver unit, the power output ratio rises again, and the power generation ratio in 6-8 months is more than 80%. Thus, MEF is also in an upward trend, wherein MEF P,8 ＝1.1495tCO 2 /MWh. It can be seen that MEFs can reflect the effects of seasonal variations on grid carbon emissions.
(3) Stage three (10 month-12 month)
The temperature returns from Fujian province after 10 months, the refrigerating equipment at the demand side is closed, the load is reduced, and the thermal power output is reduced, so that the MEF is lower than that in summer. The heating load is released in an accelerating way and the electricity load rises again after the cold air comes in 12 months, and the water power output is reduced due to the dead water period in winter, so that the MEF is increased again due to the further rising of the thermal power generation quantity.
In conclusion, the MEF has the capability of reflecting the space-time variability of the carbon emission of the power grid, can characterize the influence of seasons, climates, policies, clean energy sources and the like on the carbon emission of the power grid, and meets the requirements of carbon emission evaluation and analysis of a novel power system.
Similarly, MEFs in 2012-2020 of Fujian province can be calculated, and the result is shown in fig. 4. Combining the calculation results of the emission factors of the power system of the Fujian province in 2011 and 2020, it can be found that:
(1) Since 2015, MEF has been significantly reduced, mainly because clean energy has been strongly developed in the foodborne province. If the Fujian province nuclear power is put into use in 2013, the stability of power generation is insufficient at first, the power generation share is low, and the annual total power generation amount is 83.05 hundred million kilowatt-hours. However, by 2015, nuclear power has evolved into a stable and reliable power supply for foodservice, with power generation increasing to 287.46 hundred million kilowatt-hours, and sustained stable output for the next few years.
(2) The last half year of the Fujian province power grid MEF comes from two low valleys, one of which occurs in 1 month or 2 months, due to the spring festival holidays; the other valley occurs in 4 months to 6 months, and the analysis shows that the period is the fowls flood-saving period, and the hydropower generation amount share is occupied by hydropower, so that MEF is reduced. Notably, the Fujian province of 2016 years suffers from abnormal climate, the precipitation is more in four seasons of the year, the average annual precipitation amount is the last ten years peak, the water power generation capacity reaches 2432.6 millimeters, and the water power generation capacity is improved by more than 100 hundred million kilowatt-hours compared with the past year, so that the whole power grid emission factor of the Fujian province of the year is lower.
(3) The MEF of the power grid in Fujian province reaches a peak value in 7-9 months, because the power supply is mainly borne by thermal power in the peak period of electricity consumption in summer, and therefore the MEF is in a high position.
By summarizing the change rule of the carbon emission factor of the power grid in 2011-2020 of Fujian province, the power utilization enterprises can be beneficial to making low-carbon plans of MEF peak valley difference and optimizing power utilization behaviors so as to reduce the carbon emission of the enterprises.
Taking load data of a photoelectric enterprise 2018 in Fujian province as an example, based on power grid emission factor data of the photoelectric enterprise 2018 in Fujian province in fig. 5, performing simulation calculation of emission reduction potential according to a formula in step S4. The optimization results are shown in fig. 5. It can be seen that when the enterprise is able to perceive the carbon emission difference due to electricity consumption time, the utilization of the MEF's "peak-to-valley difference" optimizes its own electricity consumption behavior under all constraint conditions, i.e. reduces the electricity consumption during the period of high emission factor (e.g. 7 months to 9 months), and compensates the electricity consumption during the period of low emission factor (e.g. 2 months and 6 months).
To further understand the electricity usage of the enterprise before and after low-carbon planning, fig. 6 shows the loading of the enterprise for 6 months. Considering that the MEF value of the power grid is the lowest in this period, in order to reduce electricity and carbon emission in the whole year to the maximum extent, enterprises should improve the productivity of the month as much as possible. Compared with the original load, the enterprise has higher electricity demand in most working days after low-carbon planning. According to the data in the graph, the month electricity consumption of the enterprise before and after the low-carbon planning is calculated to be 53.95MWh and 93.09MWh respectively.
Table 1 lists the load of 2018 enterprises before and after low-carbon planning and their corresponding emission reduction potential. Wherein Δl represents the month load difference before and after planning. It can be seen that the total load of the enterprise is not changed in 2018, and the power consumption behavior of the enterprise is adjusted only according to the MEF of the power grid of the provincial level of each month. The annual emission reduction of the enterprise after planning reaches about 43.75 tons of CO 2 While ensuring that no additional electricity costs are expended. In addition, if the price incentive effect provided for enterprises by the national evidence voluntary emission reduction project is combined on the basis, the enterprises can obtain economic benefits through low-carbon planning, so that the emission reduction willingness of the enterprises is further improved.
TABLE 1 carbon reduction potential of enterprises under Low carbon planning
Figure BDA0004008580290000141
In summary, the provincial power grid MEF is taken as a guide signal, so that an emission reduction mechanism aiming at a load side can be developed, an emission reduction path is provided for an electricity utilization enterprise, and the emission reduction potential of the enterprise is excavated.
Referring to fig. 7, a second embodiment of the present invention is as follows:
the utility model provides a power grid planning terminal 1 based on provincial power grid marginal emission factor, includes memory 3, processor 2 and stores on memory 3 and can run on processor 2's computer program, and the step of the first embodiment is realized to the processor 2 when executing the computer program.
In summary, the power grid planning method and the terminal based on the provincial power grid marginal emission factor provided by the invention can reflect the capability of the power grid carbon emission space-time variability and characterize the influence of the factors on the power grid carbon emission, and can meet the requirements of the novel power system carbon emission evaluation and analysis.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent changes made by the specification and drawings of the present invention, or direct or indirect application in the relevant art, are included in the scope of the present invention.

Claims (10)

1. The utility model provides a power grid planning method based on provincial power grid marginal emission factor, which is characterized by comprising the following steps:
s1, calculating the electricity consumption of the provincial power grid in each month;
s2, calculating the carbon emission of each month of the provincial power grid according to the electric quantity consumption of each month of the provincial power grid;
s3, calculating the difference between the current month carbon emission and the next month carbon emission of the provincial power grid and the difference between the current month electric quantity consumption and the next month electric quantity consumption of each month, and taking the ratio of the difference between the carbon emission and the difference between the electric quantity consumption as the marginal emission factor of the provincial power grid of each month;
and S4, providing low-carbon planning for power enterprises according to the marginal emission factors of the provincial power grid.
2. The power grid planning method based on the provincial power grid marginal emission factor according to claim 1, wherein the step S4 specifically is:
solving an objective function:
Figure FDA0004008580280000011
in the formula DeltaC y Represents annual capacity reduction of enterprises, T represents days per month and P t - And P t + Represents the daily load reduction and increase of enterprises under the corresponding low-carbon conditions, MEF P,m And (5) saving a marginal emission factor for the level grid for the mth month.
3. A method of grid planning based on provincial grid marginal emission factors as claimed in claim 2, wherein the objective function has the constraint:
Figure FDA0004008580280000012
P t +P t + ≤P t dL ；
P t -P t - ≥0；
Figure FDA0004008580280000013
Figure FDA0004008580280000014
Figure FDA0004008580280000015
wherein DeltaP t u Represents an upper daily adjustable load limit;
Figure FDA0004008580280000016
and->
Figure FDA0004008580280000017
Is a (0, 1) variable; p (P) t dL Is the daily upper load limit; p (P) mL Is the upper monthly load limit.
4. The power grid planning method based on the provincial power grid marginal emission factor according to claim 1, wherein the power consumption specifically comprises power consumption of thermal power generation, hydroelectric power generation, nuclear power generation, wind power generation and solar power generation;
in the step S1, the electricity consumption G of each month of the provincial power grid is calculated according to the following formula P,m ：
G P,m ＝G Grid,m +∑ j G P,m,j +∑ k G C,m,k ；
Wherein m=1, 2, … represents month; g Grid,m Representing all power supply quantities in a menstrual region of an mth month power grid, wherein the unit is ten thousand watt hours; g P,m,j And G C,m,k The unit is kilowatt-hour for the net electric quantity transferred from month m and from country j or country k;
all power supply G in m-th month power grid menstrual area Grid,m Expressed as:
G Grid,m ＝G T,m +G N,m +G H,m +G W,m +G S,m ；
wherein G is T,m 、G N,m 、G H,m 、G W,m 、G S,m The unit is ten thousand watt hours of the generated energy of the fire power, nuclear energy, hydraulic power, wind power and solar energy saved in the m month.
5. The power grid planning method based on the marginal emission factor of the provincial power grid according to claim 4, wherein in the step S2, the monthly carbon emission W of the provincial power grid is calculated according to the following formula P,m ：
W P,m ＝W Grid,m +∑ j (G P,m,j ×EF P,j )+∑ k (G C,m,k ×EF C,k )；
In which W is Grid,m CO for a month-saving grid 2 Discharge in tons; EF (electric F) P,j 、EF C,k The carbon emission factors of the power grid in the month j province or the country k are respectively;
W Grid,m considering carbon emission generated by clean energy power generation, the method is specifically expressed as:
W Grid,m ＝W T,m +W N,m +W H,m +W W,m +W S,m ；
in which W is T,m 、W N,m 、W H,m 、W W,m And W is equal to S,m Respectively generating carbon emission by thermal power generation, nuclear power generation, hydroelectric power generation, wind power generation and solar power generation of the m th month of the provincial power grid, W N,m 、W H,m 、W W,m And W is equal to S,m Is calculated according to the following formula:
Figure FDA0004008580280000031
in the formula, EF N 、EF H 、EF W 、EF S Carbon emission factors of nuclear power, hydraulic power, wind power and solar power generation respectively, W T,m Calculated from the following formula:
Figure FDA0004008580280000032
wherein i is the type of fossil fuel consumed by thermal power of an m-th month power grid; FC (fiber channel) i,m The consumption of fossil fuel i in the m year is ten thousand tons or ten thousand cubic meters; QDW i The average low-grade heating value of the fossil fuel i is expressed as megajoules/ton or megajoules/ten thousand cubic meters; EF (electric F) i An emission factor for fossil fuel i;
EF i the calculation formula of (2) is as follows:
Figure FDA0004008580280000033
wherein F is i Represents the carbon emission coefficient of the fuel, i.e. the average carbon content of the fuel, in tons/too-coke; o (O) i Carbon oxidation representing the ith energy sourceThe rate in units of; 44/12 represents the molecular weight ratio of carbon dioxide to carbon.
6. 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 operable on the processor, wherein the computer program when executed by the processor implements the steps of:
s1, calculating the electricity consumption of the provincial power grid in each month;
s2, calculating the carbon emission of each month of the provincial power grid according to the electric quantity consumption of each month of the provincial power grid;
s3, calculating the difference between the current month carbon emission and the next month carbon emission of the provincial power grid and the difference between the current month electric quantity consumption and the next month electric quantity consumption of each month, and taking the ratio of the difference between the carbon emission and the difference between the electric quantity consumption as the marginal emission factor of the provincial power grid of each month;
and S4, providing low-carbon planning for power enterprises according to the marginal emission factors of the provincial power grid.
7. The grid planning terminal based on the provincial grid marginal emission factor according to claim 6, wherein the step S4 is specifically:
solving an objective function:
Figure FDA0004008580280000041
in the formula DeltaC y Represents annual capacity reduction of enterprises, T represents days per month and P t - And P t + Represents the daily load reduction and increase of enterprises under the corresponding low-carbon conditions, MEF P,m And (5) saving a marginal emission factor for the level grid for the mth month.
8. A provincial grid marginal emission factor-based grid planning terminal as claimed in claim 7, wherein the objective function has the constraint:
Figure FDA0004008580280000042
P t +P t + ≤P t dL ；
P t -P t - ≥0；
Figure FDA0004008580280000043
Figure FDA0004008580280000044
Figure FDA0004008580280000045
wherein DeltaP t u Represents an upper daily adjustable load limit;
Figure FDA0004008580280000046
and->
Figure FDA0004008580280000047
Is a (0, 1) variable; p (P) t dL Is the daily upper load limit; p (P) mL Is the upper monthly load limit.
9. The power grid planning terminal based on a provincial power grid marginal emission factor according to claim 6, wherein the power consumption specifically comprises power consumption of thermal power generation, hydroelectric power generation, nuclear power generation, wind power generation and solar power generation;
in the step S1, the electricity consumption G of each month of the provincial power grid is calculated according to the following formula P,m ：
G P,m ＝G Grid,m +∑ j G P,m,j +∑ k G C,m,k ；
Wherein m=1, 2, … represents month; g Grid,m Representing all power supply quantities in a menstrual region of an mth month power grid, wherein the unit is ten thousand watt hours; g P,m,j And G C,m,k The unit is kilowatt-hour for the net electric quantity transferred from month m and from country j or country k;
all power supply G in m-th month power grid menstrual area Grid,m Expressed as:
G Grid,m ＝G T,m +G N,m +G H,m +G W,m +G S,m ；
wherein G is T,m 、G N,m 、G H,m 、G W,m 、G S,m The unit is ten thousand watt hours of the generated energy of the fire power, nuclear energy, hydraulic power, wind power and solar energy saved in the m month.
10. The grid planning terminal based on the marginal emission factor of the provincial grid according to claim 9, wherein in step S2, the monthly carbon emission W of the provincial grid is calculated according to the following formula P,m ：
W P,m ＝W Grid,m +∑ j (G P,m,j ×EF P,j )+∑ k (G C,m,k ×EF C,k )；
In which W is Grid,m CO for a month-saving grid 2 Discharge in tons; EF (electric F) P,j 、EF C,k The carbon emission factors of the power grid in the month j province or the country k are respectively;
W Grid,m considering carbon emission generated by clean energy power generation, the method is specifically expressed as:
W Grid,m ＝W T,m +W N,m +W H,m +W W,m +W S,m ；
in which W is T,m 、W N,m 、W H,m 、W W,m And W is equal to S,m Respectively generating carbon emission by thermal power generation, nuclear power generation, hydroelectric power generation, wind power generation and solar power generation of the m th month of the provincial power grid, W N,m 、W H,m 、W W,m And W is equal to S,m Is calculated according to the following formula:
Figure FDA0004008580280000051
in the formula, EF N 、EF H 、EF W 、EF S Carbon emission factors of nuclear power, hydraulic power, wind power and solar power generation respectively, W T,m Calculated from the following formula:
Figure FDA0004008580280000052
wherein i is the type of fossil fuel consumed by thermal power of an m-th month power grid; FC (fiber channel) i,m The consumption of fossil fuel i in the m year is ten thousand tons or ten thousand cubic meters; QDW i The average low-grade heating value of the fossil fuel i is expressed as megajoules/ton or megajoules/ten thousand cubic meters; EF (electric F) i An emission factor for fossil fuel i;
EF i the calculation formula of (2) is as follows:
Figure FDA0004008580280000061
wherein F is i Represents the carbon emission coefficient of the fuel, i.e. the average carbon content of the fuel, in tons/too-coke; o (O) i Represents the carbon oxidation rate of the ith energy source in units of; 44/12 represents the molecular weight ratio of carbon dioxide to carbon.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116823295A (en) * 2023-08-31 2023-09-29 国网山东省电力公司营销服务中心(计量中心) Method, system, equipment and medium for measuring carbon emission in steel industry
CN116823295B (en) * 2023-08-31 2024-04-19 国网山东省电力公司营销服务中心(计量中心) Method, system, equipment and medium for measuring carbon emission in steel industry

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