CN114611827A - Community electrical carbon factor calculation and prediction method - Google Patents
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Abstract
The invention discloses a method for calculating and predicting a community electrical carbon factor, which comprises the following steps: determining a path mainly comprising a power supply source of a community; setting a calculation method of the community electric carbon factor according to the main approach of a power supply source; setting constraint conditions according to the actual power balance and the actual power operation range; initializing a threshold value and a weight value; setting a teacher signal; calculating a forward propagating signal; improvement of weight value and error check. The method focuses on different specific power utilization scenes, so that the calculation result of the electrical carbon factor is more suitable for the community, and the calculation accuracy is higher. Meanwhile, the delay of the calculation of the electrical carbon factor is relieved to a certain extent by the provision of the prediction method.
Description
Technical Field
The invention relates to the technical field of electric carbon silver calculation methods, in particular to a community electric carbon factor calculation and prediction method.
Background
The average carbon emission factor of the regional power grid is also called a power grid electricity emission factor or a power consumption emission factor, and the average carbon emission factor represents the carbon dioxide emission generated by unit electricity. The power grid average emission factor is mainly used for calculating carbon emission generated by electricity and is a calculation element for calculating carbon emission of energy main body. Currently, the average carbon emission factor of a regional power grid is divided into six regions of north China, northeast China, east China, northwest China and south China according to regions, and different carbon emission factors are used according to regional conditions. The problems that exist are mainly: firstly, from the view of an accounting subject, the current power grid carbon emission factor is subjected to factor measurement in a macro area covering multiple provinces, the obtained factor result reflects average factor values of multiple regions and multiple industries, and the level of the electric carbon factor of a certain specific area may not be accurately reflected, for example, in a low-carbon industrial park or a residential community, the electric carbon factor may be greatly different from the area average value. In addition, the accounting period and the release time of the electrical carbon factor have certain delay, the total electric quantity and the total carbon emission of the power acquisition link in the area need to be comprehensively counted and then calculated, and the real-time performance needs to be further improved.
At present, the technical scheme of the electric carbon factor calculation method for the microscopic main body is relatively few, and the macroscopic electric carbon factor is difficult to accurately reflect the carbon emission condition of the actual and accurate power acquisition stage of the energy consumption main body or the energy consumption scene. But as an accounting method with wider applicability, the method is suitable for carbon emission calculation of enterprises in various industries and various energy consumption main bodies.
Disclosure of Invention
The invention aims to solve the technical problem of how to provide a method for calculating and predicting a community electrical carbon factor with high calculation precision.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a method for calculating and predicting community electrical carbon factors is characterized by comprising the following steps:
determining a path mainly comprising a power supply source of a community;
setting a calculation method of a community electric carbon factor according to a main path of a power supply source;
setting constraint conditions according to the actual power balance and the actual power operation range;
initializing a threshold and a weight;
setting a teacher signal;
calculating a forward propagating signal;
improving the weight;
and (5) error checking.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in: the method focuses on different specific power utilization scenes, so that the calculation result of the electrical carbon factor is more suitable for the community, and the calculation accuracy is higher. Meanwhile, the delay of the calculation of the electrical carbon factor is relieved to a certain extent by the provision of the prediction method.
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The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow chart of a method according to an embodiment of the invention;
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, an embodiment of the present invention discloses a method for calculating and predicting a community electrical carbon factor, including the following steps:
step 1: the approach mainly comprises determining the power supply source of the community. The power supply source of the set community comprises the following five ways:
source 1: the community adopts a conventional mode of purchasing electric quantity from a power grid, and the electric quantity is Q1Carbon emission of E1；
Source 2: the community purchases the electric quantity of a certain coal-fired power plant through the electric power market, and the electric quantity is directly conveyed to the community for use, wherein the power supply quantity is Q2Carbon emission of E2；
Source 3: the community purchases the electric quantity of a certain gas power plant through the electric power market, and the electric quantity is directly conveyed to the community for use, and the power supply quantity is Q3Carbon emission of E3；
Source 4: implementing energy-saving transformation in communities, carrying out comprehensive photovoltaic construction on the roof, and automatically using the power supply quantity of Q4；
Source 5: the direct power supply quantity of a clean energy power plant is purchased as the external power purchase through a power trading market or a protocol mode, and the power supply quantity is Q5；
Step 2: and setting a calculation method of the community electrical carbon factor. I.e. the ratio of the total carbon emission of the electricity to the total supply according to all sources.
Eall＝E1+E2+E3…………(2)
Qall＝Q1+Q2+Q3+Q4+Q5…………(3)
E1＝Q1×EFElectric power…………(4)
Wherein, EFElectric powerAnnual average power supply emission factor of regional power grid in the unit of tCO2/MWh；QhThe total on-line electric quantity of the coal-fired unit is calculated; qqThe total online electric quantity of the gas turbine set is obtained; FCiThe consumption (mass or volume unit) of unit fuel i; NCViIs the annual average lower calorific value of the fuel i (solid/liquid: GJ/t; gas: GJ/10)4Nm3)；
CO as fuel i2Emission factor (tCO)2/GJ); i is the type of all fossil fuels consumed by the unit;
the annual average low-grade calorific value measuring and calculating mode comprises the following steps:
(1) coal burning: measuring daily average values of purchased enterprises (the measuring method, laboratory and instrument standards conform to GB/T213 related regulations), and calculating according to daily average low-order calorific value weighted average to obtain annual average value, wherein the weight is daily consumption of coal;
(2) fuel oil: measuring each batch of purchased enterprises (the measuring method, laboratory and instrument standards conform to the related provisions of DL/T567.8) or adopting annual average lower heating value in a trade settlement contract with a supplier, and calculating according to weighted average of the annual average lower heating value of each batch of fuel to obtain an annual average value, wherein the weight is the fuel consumption of each batch;
(3) natural gas: the method is characterized in that the method is measured by a purchasing enterprise every month (a measuring method, a laboratory and an instrument standard conform to GB/T11062 relevant regulations) or is provided by a fossil fuel supplier, if a certain month has a plurality of lower heating value data, a weighted average value is taken as monthly lower heating value, the lower heating value of each month is weighted and averaged to obtain a yearly average value, and the weight is the natural gas consumption;
(4) the biomass mixed fuel generating set and the waste incineration generating set refer to the measurement and calculation modes of fire coal, fuel oil and natural gas. And under the condition that the measuring and calculating method cannot be executed, corresponding default values can be selected for accounting.
And 3) setting constraint conditions. The actual power balance and the actual power operation range need to be considered during calculation;
actual power balance:
the actual total power supply on the power generation side must equal the sum of the expected power demand and the actual power loss in the transmission line:
QiP＝Qi+QiL(i＝1，2，3，4，5)…………(7)
in the formula, QiPFor the actual supply of power, Q, to the generating side in a corresponding source modeiFor load demands corresponding to the source mode, QiLCorresponding to the power line loss of the source mode.
(2) Actual power operating range
The actual power operation constraints are:
QiP，min≤QiP≤QiP，max，(i＝1，2，3，4，5)…………(8)
in the formula, QiP，minAnd QiP，maxThe lowest and highest unit capacity limits for the ith source mode, respectively.
Step 4), initializing a threshold and a weight;
and (3) adopting a back propagation artificial neural network (BP artificial neural network) as knowledge acquisition to construct a dynamic prediction algorithm of the community electrical carbon factor. All weights and neuron thresholds are first randomly given an initial value, and the counter is set to a zero state, i.e., t is 0.
Step 5), setting a teacher signal;
given input data xi (i ═ 1, 2, … …, n) and target output data yk(i＝1，2，……，l)。
Step 6), calculating a forward propagation signal;
for a three-layer artificial neural network with an input layer with n nodes, a hidden layer with m nodes and an output layer, the hidden layer nodes of the input/hidden layer are as follows:
the output nodes of the hidden layer/output layer are:
wherein, the first and the second end of the pipe are connected with each other,
is the weight of the input/hidden layer, hidden layer/output layer, wherein the function of the node is
Step 7), improvement of weight;
the principle of BP neural network weight improvement is that an error signal is connected with a path to carry out back propagation from an output layer, so that the weight is improved. Namely:
Wij 1(t+1)＝Wij 1(t)+θ1δpj 1xi…………(12)
Wjk 2(t+1)＝Wjk 2(t)+η2δpk 2zj…………(13)
wherein the content of the first and second substances,
is an error term, η1、η2Is a gain term, xi、zjAre the values of the input layer and the hidden layer. For the output layer node, the calculation result is shown in formula (14):
δpk 2(t+1)＝yk(1-yk)(yk 0-yk)…………(14)
wherein, ykIs to output the tutor signal.
For nodes that are hidden layers, the calculation result is shown in formula (15):
step 8) error checking:
if the artificial neural network meets the requirement of error precision, the condition to be met is shown as a formula (16);
ΔE＜ε，ΔE(t+1)＝E(t+1)-E(t)…………(16)
wherein epsilon is the requirement of error precision, and epsilon is more than or equal to 0 and less than 1.
Or if the training times of the neural network meet the requirement of the cycle times, namely T is less than or equal to T0Wherein, T0The requirement of cycle times is that the training times are set and are a large positive integer;
if the two criterion formulas are met, the artificial spirit completes network training on the network; otherwise, t +1 → t, turning to the step of setting the instructor signal, and continuing to train the BP artificial neural network.
The method focuses on different specific power utilization scenes, so that the calculation result of the electrical carbon factor is more suitable for the community, and the calculation accuracy is higher. Meanwhile, the delay of the calculation of the electrical carbon factor is relieved to a certain extent by the provision of the prediction method.
Claims (9)
1. A method for calculating and predicting community electrical carbon factors is characterized by comprising the following steps:
determining a path mainly comprising a power supply source of a community;
setting a calculation method of the community electric carbon factor according to the main approach of a power supply source;
setting constraint conditions according to the actual power balance and the actual power operation range;
initializing a threshold value and a weight value;
setting a teacher signal;
calculating a forward propagating signal;
improving the weight;
and (5) error checking.
2. The method for calculating and predicting the community electric carbon factor according to claim 1, wherein the community power supply source mainly comprises the following ways:
source 1: the community adopts a conventional mode of purchasing electric quantity from a power grid, and the electric quantity is Q1Carbon emission of E1；
Source 2: the community purchases the electric quantity of a certain coal-fired power plant through the electric power market, and the electric quantity is directly conveyed to the community for use, wherein the power supply quantity is Q2Carbon emission of E2；
Source 3: the community purchases the electric quantity of a certain gas power plant through the electric power market, and the electric quantity is directly conveyed to the community for use, and the power supply quantity is Q3Carbon emission of E3；
Source 4: implementing energy-saving transformation in communities, carrying out comprehensive photovoltaic construction on the roof, and automatically using the power supply quantity of Q4；
Source 5: the direct power supply quantity of a clean energy power plant is purchased as the external power purchase through a power trading market or a protocol mode, and the power supply quantity is Q5。
3. The method for calculating and predicting the community electric carbon factor according to claim 1, wherein the method for calculating the community electric carbon factor comprises the following steps:
according to the ratio of the total carbon emission of the electric power of all the source modes to the total power supply quantity;
Eall＝E1+E2+E3............(2)
Qall＝Q1+Q2+Q3+Q4+Q5............(3)
E1＝Q1×EFelectric power............(4)
Wherein, EFElectric powerAnnual average power supply emission factor of regional power grid in the unit of tCO2/MWh；QhThe total on-line electric quantity of the coal-fired unit is calculated; qqThe total on-line electric quantity of the gas unit is obtained; FCiThe consumption of unit fuel i; NCViThe annual average lower heating value of fuel i;
CO as fuel i2An emission factor; i is the type of all fossil fuels consumed by the unit;
the average annual low calorific value measuring and calculating mode is as follows:
(1) coal burning: measuring daily average values of purchasing enterprises every day, and calculating according to daily average low-order calorific value weighted average to obtain annual average value, wherein the weight is coal-fired daily consumption;
(2) fuel oil: measuring each batch of purchased enterprises or adopting annual average low heating value in a trade settlement contract with a supplier, and calculating according to the weighted average of the annual average low heating value of each batch of fuel to obtain an annual average value, wherein the weight is the fuel consumption of each batch;
(3) natural gas: the method comprises the steps that monthly measurement is carried out by a purchasing enterprise or the method is provided by a fossil fuel supplier, if a month has several pieces of lower heating value data, a weighted average value is taken as monthly lower heating value, the lower heating value of each month is weighted and averaged to obtain a yearly average value, and the weight is the natural gas consumption;
(4) the biomass mixed fuel generating set and the waste incineration generating set refer to the measurement and calculation modes of fire coal, fuel oil and natural gas.
4. The method for calculating and predicting the community electrical carbon factor of claim 1, wherein:
actual power balance:
the actual total power supply on the power generation side must equal the sum of the expected power demand and the actual power loss in the transmission line:
QiP＝Qi+QiL(i＝1，2，3，4，5)............(7)
in the formula, QiPFor the actual supply of power, Q, to the generating side in a corresponding source modeiFor load demands corresponding to the source mode, QiLCorresponding to the power line loss of the source mode.
(2) Actual power operating range
The actual power operation constraints are:
QiP，min≤QiP≤QiP，max，(i＝1，2，3，4，5)............(8)
in the formula, QiP，minAnd QiP，maxThe lowest and highest unit capacity limits for the ith source mode, respectively.
5. The method for calculating and predicting the electrical carbon factor of community as claimed in claim 1, wherein the threshold and weight are initialized as follows:
the method comprises the steps of adopting a back propagation artificial neural network as knowledge acquisition, constructing a dynamic prediction algorithm of the electrical carbon factor of the community, randomly giving all weights and neuron thresholds, assigning initial values, and setting a counter to be in a zero state, namely t is 0.
6. The method for calculating and predicting the electrical carbon factor of community as claimed in claim 1, wherein the instructor's signal is set by the following method:
given input data xi(i 1, 2.... n) and target output data yk(i＝1，2，......，l)。
7. The method for community electrical carbon factor calculation and prediction according to claim 1, wherein the method for calculating the forward propagating signal is as follows:
for a three-layer artificial neural network with an input layer with n nodes, a hidden layer with m nodes and an output layer, the hidden layer nodes of the input/hidden layer are as follows:
the output nodes of the hidden layer/output layer are:
wherein the content of the first and second substances,
is the weight of the input/hidden layer, hidden layer/output layer, wherein the function of the node is
8. The method for calculating and predicting the community electrical carbon factor according to claim 1, wherein the weight value is improved by the following steps:
the principle of BP neural network weight improvement is that an error signal is connected with a path to carry out back propagation from an output layer, so that the weight is improved. Namely:
Wij 1(t+1)＝Wij 1(t)+η1δpj 1xi............(12)
Wjk 2(t+1)＝Wjk 2(t)+η2δpk 2zj............(13)
wherein the content of the first and second substances,
is an error term, η1、η2Is a gain term, xi、zjAre the values of the input layer and the hidden layer. For the output layer node, the calculation result is shown in formula (14):
δpk 2(t+1)＝yk(1-yk)(yk 0-yk)............(14)
wherein, ykIs to output the tutor signal;
for nodes which are hidden layer nodes, the calculation result is shown in formula (15);
9. the method for calculating and predicting community electrical carbon factor of claim 1, wherein the error checking method comprises:
if the artificial neural network meets the requirement of error precision, the condition to be met is shown as a formula (16);
ΔE＜ε，ΔE(t+1)＝E(t+1)-E(t)............(16)
wherein epsilon is the requirement of error precision, and epsilon is more than or equal to 0 and less than 1.
Or if the training times of the neural network meet the requirement of the cycle times, namely T is less than or equal to T0Wherein, T0The requirement of cycle times is that the training times are set and are a large positive integer;
if the two criterion formulas are met, the artificial spirit completes network training on the network; otherwise, t +1 → t, turning to the step of setting the instructor signal, and continuing to train the BP artificial neural network.
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| CN116542427B (en) * | 2023-07-03 | 2023-10-03 | 国网北京市电力公司 | Power grid power supply structure optimization method, system, equipment and medium |
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