CN115392528A - Carbon emission fine metering method for electricity utilization side based on carbon emission flow theory - Google Patents
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Abstract
The invention discloses a carbon emission fine metering method of a power utilization side based on a carbon emission flow theory, and relates to the technical field of calculation of carbon emission flow of a power distribution network, wherein the method comprises the following steps: solving the discharge carbon potential of the energy storage element based on the real-time power value carbon flow rate in the charging process of the energy storage element, wherein the discharge carbon potential is the node carbon potential of the energy storage element at the point discharge moment; obtaining a node carbon potential represented by daily mileage based on the carbon potential of the energy storage element at the discharging moment and the electric quantity probability model of the energy storage element at the initial charging moment; and predicting the node carbon potential distribution based on the node carbon potential represented by the daily mileage. The invention can realize the fine measurement of the carbon emission of the distribution network side, so as to analyze the high-carbon element of the user side, facilitate the monitoring of the carbon emission condition of the user side by related departments and formulate the carbon reduction policy of electric energy consumers.
Description
Technical Field
The invention relates to the technical field of carbon emission flow calculation of a power distribution network, in particular to a carbon emission fine metering method on a power utilization side based on a carbon emission flow theory.
Background
The 'carbon reduction' of the power system needs 'source-network-load' full-chain cooperative coordination, and in order to develop the carbon emission reduction potential of power carbon, power users need to be guided to interactively reduce carbon, the healthy development of the carbon market is supported, and the low-carbon transformation of power economy is promoted. The real-time, accurate and comprehensive statistical accounting of the carbon emission is one of important bases for mastering the current situation and the trend of the carbon emission in the power industry, and provides corresponding policy support for carbon emission reduction.
In the power field, the utility-side carbon emission factor is an important bridge between the end power usage and the carbon emission of the system. Most of the existing computing systems perform accounting according to a power generation side, and basically, the carbon emission is not statistically accounted from a distribution network side, so that the carbon emission statistical accounting needs to be performed on a user side urgently to promote the realization of a low-carbon target.
Disclosure of Invention
The invention aims to solve the technical problem of how to realize the fine calculation of the carbon emission on the side of the distribution network.
The invention solves the technical problems through the following technical means:
the invention provides a carbon emission fine metering method on the electricity utilization side based on a carbon emission flow theory, which comprises the following steps:
solving the discharge carbon potential of the energy storage element based on the real-time power value carbon flow rate in the charging process of the energy storage element, wherein the discharge carbon potential is the node carbon potential at the point discharge moment of the energy storage element;
obtaining a node carbon potential represented by daily mileage based on the carbon potential of the energy storage element at the discharging moment and the electric quantity probability model of the energy storage element at the initial charging moment;
and predicting the node carbon potential distribution based on the node carbon potential represented by the daily mileage.
Further, the solving of the discharge carbon potential of the energy storage element based on the real-time power value carbon flow rate in the charging process of the energy storage element includes:
let the charging time of the energy storage element be t 0 T, the element is in a discharge state after time t, and the discharge carbon potential at time t is:
in the formula: e.g. of the type B (t) a power supply carbon potential at which the energy storage element is switched from a charged state to a discharged state at time t; f 0 And E 0 The residual carbon flow and the electric quantity of the energy storage element are respectively converted from the last discharge state to the charge state; r is b (t) and P b (t) respectively providing the carbon flow rate and the charging power during the charging of the energy storage element; η represents the charge-discharge efficiency of stored energy.
Further, the calculation process of the electric quantity probability model of the energy storage element at the initial charging moment comprises the following steps:
calculating an electric quantity probability model of the energy storage element at the initial charging moment according to charging influence factors of the energy storage element, wherein the charging influence factors comprise charging duration, charging power and battery capacity, and the electric quantity probability model is as follows:
in the formula: s is the daily mileage; w is a group of 100 Electric energy consumed by a hundred kilometers of automobile; eta is charging efficiency; p is c Is the charging power; and E is the electric quantity of the automobile battery.
Further, the obtaining a node carbon potential represented by a daily mileage based on the carbon potential at the discharging time of the energy storage element and the electric quantity probability model at the initial charging time of the energy storage element includes:
setting the linear relation between the carbon flow and the electric quantity at the starting moment of the energy storage element as F 0 ＝kE 0 ，E 0 To initiate the charge moment, F 0 The carbon flow at the initial charging time is k is a constant;
based on the linear relation and based on the carbon potential of the energy storage element at the discharging moment and the electric quantity probability model of the energy storage element at the initial charging moment, the carbon potential of the node represented by the daily mileage is obtained as follows:
in the formula: e.g. of the type B Is the node carbon potential expressed in terms of daily mileage, R b Is the carbon flow rate.
Further, the predicting a node carbon potential distribution based on the node carbon potential represented by the daily mileage includes:
carrying out positive distribution on the basis of the node carbon potential represented by the daily mileage to obtain a carbon potential probability model of the energy storage element;
and multiplying the carbon potential probability model by the node carbon potential vector to predict the node carbon potential distribution.
Further, the calculation of the node carbon potential vector comprises:
solving the active power flow of the power distribution network by adopting a forward-backward substitution method based on branch data and node loads of the power distribution network;
knowing the carbon potential and active output of a main network input point and each distributed generator set, equating the main network access point as a generator set model for calculation, wherein the carbon potential of a node i is the carbon flow density of all active power flows flowing out of the node i, and recording the carbon potential of the node i as the ratio of the carbon flow rate of the node i to the active power:
in the formula: e.g. of the type i Is the carbon potential of node i; i is + A branch set of active power flow flowing into the node i; p is a radical of Br Is the active power flow of the branch r; ρ is a unit of a gradient r The carbon flow density for branch r; p is a radical of Gi Active power flow injected for the generator i; eG i Is the carbon potential of generator i;
expanding the carbon potential of the node i to a power network to obtain a branch tide distribution matrix, a node active flux matrix, a unit injection distribution matrix and a unit carbon emission intensity vector;
based on the branch flow distribution matrix, the node active flux matrix, the unit injection distribution matrix and the unit carbon emission intensity vector, calculating a node carbon potential vector as follows:
in the formula: e G Is the carbon emission intensity vector, P, of the generator set N As a node active flux matrix, P B Is a branch flow distribution matrix, P G And injecting a distribution matrix for the unit.
Further, the method further comprises:
calculating node output distribution factor, namely node active output distribution factor H from node i to node j ij Comprises the following steps:
in the formula: w is a group of ij Is a network flow flowing from node i to adjacent node j via branch ij; w sigma i Is the sum of the network flows flowing into node i; h if there is no branch connection between two nodes or no forward network flow flows in on adjacent branches ij =0, when i = j, H ij ＝1。
Further, the method further comprises:
calculating path output distribution factor, if there are multiple communication paths from node i to node j, the path from node i to node jRadial output distribution factor D ij Comprises the following steps:
in the formula: h st The distribution factor is output for the node, and Λ is the set of paths.
Further, the method further comprises:
determining the contribution condition of the ith unit to all node carbon flows in the power network based on the ith row element of a unit-node incidence matrix, wherein the unit-node incidence matrix is R U-N ：
Further, the method further comprises:
the carbon flow contribution condition of a single machine set to all branches in the network can be calculated based on a machine set-branch incidence matrix, and the machine set-branch incidence matrix
Comprises the following steps:
the contribution condition of all the units in the network to the load carbon flow rate can be obtained based on a unit-load incidence matrix R U-L Comprises the following steps:
the invention has the advantages that: the invention considers the carbon potential time-varying characteristic of the energy storage power supply, provides the moment carbon potential of the energy storage element of the electric automobile, constructs a calculation model of the carbon emission flow at the distribution network side, can obtain the carbon emission condition at the distribution network side, realizes the fine measurement of the carbon emission at the distribution network side, analyzes the high carbon element at the user side according to the carbon emission condition, is convenient for relevant departments to monitor the carbon emission condition at the user side, and makes a carbon reduction policy of an electric energy consumer.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a schematic flow chart of a method for fine metering of carbon emissions on the electricity side based on carbon emission flow theory, as set forth in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a topology of an IEEE33 node distribution network system in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a system of nodes in a region 61 according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a carbon potential at a 100h node 61 in a regional distribution network system according to an embodiment of the present invention;
fig. 5 is a schematic diagram of the carbon potential variation of nodes in an embodiment of the present invention, in which (a) is a carbon potential variation diagram of nodes 10 and 29 in fig. 4, and (b) is a carbon potential variation diagram of node 61 in the distribution network system in fig. 4;
fig. 6 is a schematic diagram of the electric quantity, the carbon flow and the carbon potential of the energy storage battery in a certain area distribution network system in an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present invention and should not be construed as limiting the present invention.
The method for finely metering the carbon emission on the electricity utilization side based on the carbon emission flow theory, which is provided by the embodiment of the invention, is described by referring to the attached figure 1, and comprises the following steps:
s1, solving a discharging carbon potential of an energy storage element based on a real-time power value carbon flow rate in a charging process of the energy storage element, wherein the discharging carbon potential is a node carbon potential at a discharging moment of the energy storage element;
s2, obtaining a node carbon potential represented by daily driving mileage based on the carbon potential of the energy storage element at the discharging moment and the electric quantity probability model of the energy storage element at the initial charging moment;
and S3, predicting the distribution of the node carbon potential based on the node carbon potential represented by the daily mileage.
It should be noted that, in the embodiment, the time-varying characteristic of the carbon potential of the energy storage power supply is taken into consideration, the time-varying carbon potential of the energy storage element of the electric vehicle is provided, the time-varying carbon potential is the node carbon potential represented by the daily mileage, the distribution of the node carbon potential is predicted based on the node carbon potential represented by the daily mileage, the carbon emission situation on the distribution network side can be obtained, the fine measurement of the carbon emission on the distribution network side is realized, the high-carbon element on the user side is analyzed accordingly, the monitoring of the carbon emission situation on the user side by relevant departments is facilitated, and the carbon reduction policy of an electric energy consumer is formulated.
Furthermore, the generated energy of the traditional generator set and the new energy generator set at different time periods can be reasonably distributed according to the calculated carbon potential at the moment, so that the generated energy of the traditional generator set is reduced, and the aim of reducing the emission of carbon dioxide is fulfilled.
In an embodiment, the step S1 specifically includes the following steps:
let the charging time of the energy storage element be t 0 T, after time t the element is in a discharge state, the discharge carbon potential at time t is:
in the formula: e.g. of the type B (t) a power supply carbon potential at which the energy storage element is switched from a charged state to a discharged state at time t; f 0 And E 0 The residual carbon flow and the electric quantity of the energy storage element are respectively converted from the last discharge state to the charge state; r is b (t) and P b (t) carbon flow rate and charging power during charging of the energy storage element, respectively; eta represents the charge-discharge efficiency of stored energy.
The energy storage power supply capacity constraint conditions are as follows:
E min ≤E s (t)≤E max
in the formula: e s (t) represents the electrical quantity of the energy storage element at time t; ε represents the self-discharge efficiency of the cell; p B (t) represents the charging and discharging power of the battery at time t, when P B (t) ≥ 0, charging the battery, when P B When (t) is less than or equal to 0, discharging the battery; beta is a beta dis And beta ch The charge and discharge efficiency of the energy storage element is represented; e min And E max Respectively the lower limit and the upper limit of the residual capacity of the energy storage device.
The energy storage power supply power constraint conditions are as follows:
P Bmin ≤P B (t)≤P Bmax
in the formula: p Bmin And P Bmax The lower limit and the upper limit of the charging and discharging power of the energy storage power supply. The charging power of the energy storage power source is considered as a positive load power, whereas the discharging power is a negative load power.
It should be noted that, according to the power distribution network load flow calculation, the target parameter of the carbon flow calculation at the power distribution network side is the node carbon potential, and the node carbon potential is a dependent variable, and the circulation path of carbon emission generated in the system by the unit active power load flow is calculated. For the load on the electricity side, the composition of the carbon emission needs to be analyzed; for a power generating unit, it is necessary to know the ultimate goal of its carbon flow. In the embodiment, considering an energy storage power supply represented by energy storage, a user-side carbon flow operation model containing the energy storage power supply is constructed. The carbon potential of the energy storage power supply represented by the stored energy changes along with the charge and discharge state of the energy storage power supply, so the carbon potential of the energy storage power supply needs to be calculated and modeled again. Because the energy storage power supply is connected to the power distribution system through the node, the carbon potential of the power supply at the discharging moment can be obtained by solving the real-time power value and the carbon flow rate of the energy storage power supply through the node in the charging process.
Further, the method classifies Electric Vehicles (Electric Vehicles) according to different applications and different driving characteristics, and provides a carbon emission measurement model of the EV under the charging probability at the moment by using charging probabilities and charging load influence factors of different types of charging modes of the EV.
(a) Charging mode
Document "electric vehicle conduction charging system part 1, published in 2015 in china: general requirements the charging modes of EVs are divided into slow charging, regular charging and fast charging. Description of various types of charging modes as shown in table 1, the charging suppliers are different in different regions, and thus, various types of power, current, phase voltage, and carbon flow rate are different in the actual operation process. At present, the carbon flow rates of different charging piles are not measured specifically, so that the carbon flow rates are assumed correspondingly.
TABLE 1 charging mode comparison
At present, the electricity supply of China still takes coal electricity as the leading factor, and new energy sources such as photovoltaic energy, nuclear energy and the like are adopted in part of regions for supplying electric energy. The charge carbon flow rate values in the above table are high, and only the fast charge and the conventional charge have a decrease in value.
(b) EV charging mode analysis
EVs can be classified into 6 types of buses, taxis, private cars, business cars, network appointments and sanitation cars according to the using ways of the EVs, and charging time periods and charging probabilities of different charging time periods of the EVs are determined according to the using conditions of different types of vehicles. Charging time periods of different types of EVs in the table 2 are obtained according to travel data of the HE-FEI market EVs, corresponding charging modes are obtained by utilizing areas where the different charging time periods EVs are located, and charging probabilities corresponding to the different time periods are obtained according to probability statistics.
TABLE 2 comparison of charging patterns for different vehicle types
(c) Probability model of electric quantity and carbon potential at initial charging moment
(1) Calculating an electric quantity probability model of the energy storage element at the initial charging moment:
the charging duration, charging power and battery capacity together determine the battery charge at the initial charging moment. The charging duration can be calculated according to the daily mileage, the probability distribution is obtained by utilizing the driving characteristics of the automobile, the charging duration is more scientific, and the charging duration is as follows:
in the formula: t is a unit of c Is the charging time length in unit h; s is the daily mileage in km; w 100 Electric energy consumed by a hundred kilometers of automobile, unit (kW.h)/hundred kilometers; eta is charging efficiency; p is c For charging power, in kW.
The electric quantity of the battery at the initial charging moment can be obtained by utilizing a calculation formula of the charging time, wherein the formula is as follows:
in the formula: s is the daily mileage; w is a group of 100 Electric energy consumed for hundreds of kilometers of automobiles; eta is charging efficiency; p c Is the charging power; and E is the electric quantity of the automobile battery.
According to the formula of electric quantity, the electric quantity E at the moment of initial charging 0 Linearly related to the daily driving range s, so E 0 Also fit into a lognormal distribution, i.e.:
in the formula:
is lnE 0 A mathematical expectation of (d);
is ln E 0 Standard deviation of (a) D Is the standard deviation of ln s.
(2) And obtaining a node carbon potential represented by daily driving mileage based on the carbon potential of the energy storage element at the discharging moment and the electric quantity probability model of the energy storage element at the initial charging moment:
setting the linear relation between the carbon flow and the electric quantity at the starting moment of the energy storage element as F 0 ＝kE 0 ，E 0 To start the charge moment, F 0 The carbon flow at the initial charging time is k is a constant;
based on the linear relation and based on the carbon potential of the energy storage element at the discharging moment and the electric quantity probability model of the energy storage element at the initial charging moment, the carbon potential of the node represented by the daily mileage is obtained as follows:
in the formula: e.g. of the type B Is the node carbon potential expressed in terms of daily mileage, R b Is the carbon flow rate.
Further, node carbon potential e B The node carbon potential e is linearly related to the daily driving mileage s and can be known according to the property of normal distribution B Fit to a normal distribution, i.e.:
in the formula:
for the mathematical expectation of the carbon potential at the node,
is the standard deviation of the node carbon potential.
In an embodiment, the step S3 includes the following steps:
s31, carrying out positive distribution on the basis of the node carbon potential represented by the daily mileage to obtain a carbon potential probability model of the energy storage element;
and S32, multiplying the carbon potential probability model by the node carbon potential vector to predict the node carbon potential distribution.
In one embodiment, in step S32: the calculation of the node carbon potential vector comprises the following steps:
(1) And solving the active power flow of the power distribution network by adopting a forward-backward substitution method based on branch data and node loads of the power distribution network.
It should be noted that the forward-backward generation method includes, but is not limited to, an implicit zbus gaussian method, an improved newton method, an improved fast decoupling method, and the like.
(2) Given the carbon potential and active output of the main network input point and each distributed generator set, the main network access point is equivalent to a generator set model calculation, and assuming that the carbon potential of the ith node is ei (i =1,2, \8230;, n), the node carbon potential vector (nodal carbon potential vector) is defined as: e n ＝[e 1 ,e 2 ,…,e n ] T The carbon potential of the node i is the carbon flow density of all active power flows flowing out of the node i, and the ratio of the carbon flow rate and the active power of the node i is recorded as the carbon potential of the node i:
in the formula: e.g. of the type i Is the carbon potential of node i; i is + A branch set which is the active power flow flowing into the node i; p is a radical of formula Br Is the active power flow of branch r; ρ is a unit of a gradient r The carbon flow density for branch r; p is a radical of formula Gi Active power flow injected for the generator i; eG i Is the carbon potential of generator i;
expanding the carbon potential of the node i to a power network to obtain a branch flow distribution matrix, a node active flux matrix, a unit injection distribution matrix and a unit carbon emission intensity vector;
based on the branch flow distribution matrix, the node active flux matrix, the unit injection distribution matrix and the unit carbon emission intensity vector, calculating a node carbon potential vector as follows:
in the formula: e G Is the carbon emission intensity vector, P, of the generator set N As a node active flux matrix, P B Is a branch flow distribution matrix, P G And injecting a distribution matrix for the unit.
It should be noted that, the carbon emission of the power system is evaluated based on the power flow tracking, and the following assumptions are made: firstly, according to a proportion sharing principle, power flow is distributed to each branch at a node; secondly, the distributed power source-containing node firstly supplies power to the node, and the surplus power is input into a power grid. Given that a certain power network N includes b branches and N nodes, where there are unit injection in k nodes and m nodes are load nodes.
Whether an energy storage element of the electric automobile is in a working state depends on the magnitude of the discharge carbon potential of the power supply and the magnitude of the node carbon potential, and when the power supply carbon potential is smaller than the node carbon potential, the power supply is in a discharge state; otherwise, the system is in a charging state or an off-grid state.
In this embodiment, the calculation of the node carbon potential is determined by the carbon emission flow of the generator set and the carbon emission flows of other nodes, and a node carbon potential vector can be obtained when the carbon potentials of all nodes in the system are taken into consideration. The node carbon potential vector can reflect the carbon potential of all nodes in the system, and further can analyze high carbon elements so as to judge the access condition of the new energy source unit, and meanwhile, the node carbon potential vector is used for calculating the carbon potential of all nodes in the system.
In one embodiment, the carbon flow factor contemplated by the present embodiment includes: a) a carbon emission factor, b) a node output profile factor, c) a path output profile factor, wherein:
a) Carbon emission factor
Average Carbon Emission Factors (CEF) are coefficients of carbon dioxide emission in certain energy-consuming processes, and are defined as follows:
in the formula: c ef Is the carbon emission factor, F is the carbon emission, and W is the electrical quantity.
The carbon emission factors on the electricity utilization side are mainly divided into two categories, wherein the first category is used for calculating the carbon emission generated when unit electric energy is consumed; and the second type is used for calculating the carbon emission which is correspondingly reduced by the unit electric energy generated by the new energy power equipment.
b) Node output distribution factor
The node output distribution factor is used to represent the ratio of network flow (active power flow or carbon emission flow) flowing from an originating node to an adjacent destination node at a certain instant to the total amount of network flow flowing into this originating node. Node active output distribution factor H from node i to node j ij Comprises the following steps:
in the formula: w ij Is a network flow flowing from node i to adjacent node j via branch ij; w sigma i Is the sum of the network flows into node i. H if there is no branch connection between two nodes or no forward network flow flows in on adjacent branches ij =0, in particular H when i = j ij ＝1。
When the starting node and the end node are fixed, the values of the carbon emission flow and the power flow output distribution factor are equal. The two distribution factors are collectively called node output distribution factor in the power network analysis, and the symbol H is used ij And (4) showing.
c) Path output distribution factor
The path output distribution factor is used to represent the contribution rate of the network flow (active power flow or carbon emission flow) flowing out from a starting node to the total amount of the network flow flowing into the target node under a certain path.
Suppose that a communication path L exists between a grid node i and a node j, and the branch set of the path is L, H st The distribution factor is output for the node, thus the output distribution factor of the path l
Comprises the following steps:
if a plurality of communication paths exist between the node i and the node j, assuming that the paths are integrated into lambda, the path from the node i to the node j outputs the distribution factor D ij Comprises the following steps:
for a given start and end node communication path, its carbon emission flow path output distribution factor
And active power flow path distribution factor
Equal, these two distribution factors are collectively referred to as the path output distribution factor, denoted by the symbol D ij And (4) showing.
In this embodiment, the carbon emission factor may be calculated on the power generation side or the power consumption side, and the node output and path output distribution factors are the carbon flow contribution rates of the node nodes and paths in the system (including the power consumption side and the power generation side). The carbon emission factor can obtain the average carbon emission of a certain node, and the average carbon emission is used as a reference condition and a boundary condition of the carbon dioxide emission; the node output distribution factor is used for representing the distribution relation between the power flow and the carbon emission flow when the system is in steady-state operation; the path output distribution factor is path information representing a flow of power and carbon emissions injected into the system by the generator set flowing from the generator set to each of the target nodes.
In an embodiment, the method further comprises:
1) Determining the contribution condition of the ith unit to the carbon flow of all nodes in the power network based on the ith row element of the unit-node incidence matrix:
taking a certain node i in the power grid as an example, the carbon emission flow flowing into the node at each moment is all provided by the generator sets in the power grid, and the specific positions of the generator sets in the system and the carbon flow injected by the generator sets determine the carbon flow contribution rate of each generator set to the node.
The carbon current density of all the power flows from the nodes in the power network is equal to the carbon potential of the nodes. Thus it is first
Carbon flow contribution rate of station generator set to node i
Can be expressed as:
in the formula:
is as follows
Active power output of the platform generator set;
is as follows
Carbon emission intensity of the platform generator set;
is as follows
And outputting a distribution factor by a path between the station generator set and the ith node.
The above formula is expanded into a matrix form, and all the generator sets in the power grid can be obtainedDistribution information of node carbon flow contribution rate, and unit-node carbon flow incidence matrix
And (4) showing.
Binding of E G 、P N 、P B 、P G The formula is simplified, and can be obtained by arranging:
2) And calculating the carbon flow contribution condition of a single unit to all branches in the network based on the unit-branch incidence matrix:
for a branch (i, j) within the grid, the carbon emission flows provided by different generator sets to the branch are different, and the carbon emission flow provided by each generator set to the branch is related to the load flow injection of the set and the position in the grid, and can be represented by a node output distribution factor of the carbon flow.
According to the definition of node output distribution factor, there is the first branch (i, j)
The carbon flow provided by the station-generator set may be expressed as:
the above formula is expanded into a matrix form, the contribution condition of a certain power generation unit to the carbon flow rates of all branches in the power grid can be obtained, and a unit-branch carbon flow correlation matrix is used
And (4) showing. Depending on the nature of the node and path output distribution factors, the formula can be formulated as:
in the formula:
there are k elements of the row vector (only the kth element is 1, the remaining elements are all 0).
Under the condition of obtaining the tidal current in the system, the related tidal current matrix can be used for further simplifying the unit-branch incidence matrix to obtain:
3) The contribution condition of all units in the network to the load carbon flow rate can be obtained on the basis of the unit-load incidence matrix:
according to the proportion sharing principle, a node load exists in the system, and the proportion of the contribution of all the generator sets in the network to the carbon flow rate of the load is equal to the proportion of the contribution of the carbon flow rate of the node to the load. Suppose that the ith node has a load P Li The carbon flow rate corresponding to this load is R Li Is then made of
The carbon flow provided by the platform generator set is as follows:
by expanding the above formula into a matrix form, the distribution of the carbon flow rates of all the generator sets and the rest of the loads in the network can be obtained. Using unit-load carbon flow correlation matrix
And (4) showing. Active flux matrix P of combined node n Definition and properties, and relevant formula adjustment is carried out, so that:
definition P Ln Setting the load of the ith node as P for the node load vector Li Then the node load vector can be expressed as:
P LN ＝[P L1 ,P L2 ,…P LN ] T 。
in this embodiment, the unit node carbon flow incidence matrix is used to calculate a carbon flow rate of carbon injected by all the generator units in the system to a carbon flow rate flowing into a certain node, that is, a contribution condition of all the generator units to the carbon flow distribution of the nodes in the system; the unit-branch incidence matrix is the contribution condition of the kth power generation unit to the system branch. (ii) a The unit-load incidence matrix is the contribution condition of carbon flow rate of carbon emission flow injected by all the generator units to a certain node load.
Simulation analysis was performed as follows:
an IEEE33 node system and a distributed photoelectric 10kV power distribution network in a certain region of Anhui province are selected to verify the feasibility and accuracy of a carbon flow calculation model, and a result obtained by the calculation model is used for analyzing high-carbon users on the power distribution network side.
A) Distribution network system of IEEE33 nodes
The topology of the distribution network system of IEEE33 standard nodes is shown in fig. 2, and the line parameters and load data of the system are shown in appendix a. The node 20 is connected to an energy storage battery, the maximum charge and discharge power of the energy storage battery is 60kW, the charge and discharge efficiency of the energy storage battery is 95%, the self-discharge efficiency is 1%, and the maximum storable electric quantity is 1400kWh. At the initial time, the battery had 686kWh of electricity and 380kg of carbon flow.
The generator in the distribution network system is regarded as a constant power model, and the data of the model is shown in table 1. The node voltage and the branch current of the power distribution network are obtained by solving through a forward-backward substitution algorithm according to the data in the appendix A, and therefore a branch load flow distribution matrix P can be obtained through correlation calculation B 。
TABLE 3 Generator parameters
And (3) the root node at the connection position of the main network and the distribution network is equivalent to a generator set, and the output power of the generator set is recorded as the active power of the branch 1-2 circulation. The generator set G1 at the root node is equivalent to a coal-fired unit, and G2, G3 and G4 are all gas units and are respectively connected to nodes 7, 24 and 29. The carbon emission intensity of the generator set in the known distribution network system and the carbon emission vector E of the generator set G As follows:
E G ＝[0.85 0.55 0.60 0.65] T
the active flux matrix P obtained by using the data N After checking that the matrix is reversible, the carbon potentials of all the nodes can be obtained according to the carbon potential vectors of the nodes, and the result is shown in table 4.
TABLE 4 comparison of node carbon potentials
According to the existing conclusion, when the distribution network system does not contain a distributed power supply and a ring network, the carbon potential of all nodes in the system is equal to that of the main network access point. Table 4 the second column of data is 0.85, which verifies that the carbon potential of all nodes in the system is consistent with the main network connection when no distributed unit exists. The nodes 7, 24, 29 are all connected to the gas turbine units, so that the electric energy originally provided by the main network is converted into the energy supplied by each gas turbine unit, and the carbon potential of each access point and the nodes supplied with the electric energy by the gas turbine units thereafter is obviously reduced.
Taking the system operating for one hour as an example, the power source in the discharging state can be obtained according to the initial state of the energy storage battery, and the relevant values are shown in table 5.
Table 5 energy storage cells discharged for one hour
The contribution of the electricity side units to the node and load carbon flows is shown in tables 6 and 7.
TABLE 6 node carbon flow supply for generator set
TABLE 7 Generator set for load carbon flow supply
Tables 6 and 7 above fully illustrate that the carbon flow at each node and load is provided by different gensets in the system, with different locations of the gensets determining the differences in the composition of the carbon flow at different nodes and loads.
TABLE 8 supply of branch carbon flow to a generator set
The data in table 8 show the supply conditions of all branch carbon streams to each generator set in the distribution network, and the branch without the power supply of the distributed generator set does not have corresponding carbon stream supply.
According to the calculation model, the carbon flow distribution mechanism of the power utilization side can be specifically analyzed, and a new idea is provided for future enterprise carbon flow analysis and government carbon emission policy establishment. The method is based on the calculation of the distribution network system carbon flow distribution situation of the time section, if similar analysis is carried out in continuous time, the change situation of the node carbon potential in the system and the distribution mechanism of branch and load carbon flows can be calculated by using the method, and a reliable calculation model is provided for the power supply of the distributed units according to the time period in the future.
B) 61-node 10kV system for certain region of Anhui province
In order to verify the feasibility and the accuracy of the carbon emission flow model, a 10kV feeder line in operation in a certain region of Anhui province is selected as a research object. The system comprises 61 nodes, wherein the nodes 15, 39 and 57 are connected to a gas turbine unit, the node 61 is connected to a hydroelectric generating unit, and the rest are photovoltaic power generation, and specific data of the photovoltaic power generation are shown in appendix B1. The topology diagram and branch parameters of the feeder system are shown in fig. 3, where node 36 has access to the energy storage element.
Because a large number of new energy generator sets are connected into the system, the carbon potential of each node is obviously reduced. If a certain radiation branch only has a distributed power supply at the root node of the branch, the carbon potential of all nodes of the branch is equal to that of the node; on the contrary, if other distributed power sources exist in the branch, the carbon potential of the node of the branch changes. Fig. 4 is a carbon potential at 100h of 61 st node in the regional distribution network system, and the graph further verifies the carbon potential change condition of each node in the system.
Fig. 5- (a) is a graph of the change in carbon potential at nodes 10 and 29, with run time 567h and load power change over time. No new energy source unit exists at the connection of the node 10 and the main network, so the carbon potential is maintained at 0.85kg 2 kWh. Two photovoltaic units are arranged on a branch of the node 29 connected with the main network and are respectively positioned at the node 18 and the node 19. This causes the carbon potential at node 29 to decrease and vary with power at 0.4 kg 2 Fluctuated around/kWh. Fig. 5- (b) is a carbon potential variation diagram of 61 nodes in the distribution network system. Except that no new energy source unit exists on a branch between the connection of the main network and the corresponding node, the carbon potential of all the nodes with the new energy source unit and the subsequent power supply carbon potential are obviously reduced and fluctuate along with the change of load power.
The node 36 is connected to the energy storage battery, and the charging and discharging states of the energy storage battery can be determined according to the electric quantity of the battery and the carbon potential of the node, and the specific state change of the energy storage battery can be observed from fig. 6. The charge and discharge state of the energy storage battery can be obtained according to the electric quantity and the carbon potential, and the change trends of the energy storage battery and the carbon potential are in one-to-one correspondence.
According to the relative node carbon potential lower than the main network connection in fig. 5 and the discharge state of the energy storage battery in fig. 6, the new energy source unit and the energy storage battery provide low-carbon electric energy into the distribution network. Therefore, the carbon emission condition of each part in the system can be obviously reduced by connecting the distributed renewable energy power generator set and the energy storage element into the distribution network system.
It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description of the specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A method for finely metering carbon emission on a power utilization side based on carbon emission flow theory is characterized by comprising the following steps:
solving the discharge carbon potential of the energy storage element based on the real-time power value carbon flow rate in the charging process of the energy storage element, wherein the discharge carbon potential is the node carbon potential at the point discharge moment of the energy storage element;
obtaining a node carbon potential represented by daily mileage based on the carbon potential of the energy storage element at the discharging moment and the electric quantity probability model of the energy storage element at the initial charging moment;
and predicting the node carbon potential distribution based on the node carbon potential represented by the daily mileage.
2. The method for finely metering carbon emission on the electricity utilization side based on the carbon emission flow theory according to claim 1, wherein the step of solving the discharge carbon potential of the energy storage element based on the real-time power value carbon flow rate in the charging process of the energy storage element comprises the following steps:
let the charging time of the energy storage element be t 0 T, after time t the element is in a discharge state, the discharge carbon potential at time t is:
in the formula: e.g. of the type B (t) a power supply carbon potential at which the energy storage element is switched from a charged state to a discharged state at time t; f 0 And E 0 The residual carbon flow and the electric quantity of the energy storage element are respectively converted from the last discharge state to the charge state; r b (t) and P b (t) respectively providing the carbon flow rate and the charging power during the charging of the energy storage element; η represents the charge-discharge efficiency of stored energy.
3. The method for finely metering carbon emission on the electricity utilization side based on the carbon emission flow theory according to claim 2, wherein the calculation process of the probability model of the electric quantity at the moment of starting charging of the energy storage element comprises the following steps:
calculating an electric quantity probability model of the energy storage element at the initial charging moment according to charging influence factors of the energy storage element, wherein the charging influence factors comprise charging duration, charging power and battery capacity, and the electric quantity probability model is as follows:
in the formula: s is the daily mileage; w 100 Electric energy consumed by a hundred kilometers of automobile; eta is charging efficiency; p c Is the charging power; and E is the electric quantity of the automobile battery.
4. The method for finely measuring carbon emission on the electricity utilization side based on the carbon emission flow theory as claimed in claim 3, wherein the obtaining of the node carbon potential represented by the daily mileage based on the carbon potential at the discharge time of the energy storage element and the power probability model at the initial charge time of the energy storage element comprises:
setting the linear relation between the carbon flow and the electric quantity at the starting moment of the energy storage element as F 0 ＝kE 0 ，E 0 To start the charge moment, F 0 K is a constant for the carbon flow at the initial charging time;
based on the linear relation and based on the carbon potential of the energy storage element at the discharging moment and the electric quantity probability model of the energy storage element at the initial charging moment, the carbon potential of the node represented by the daily mileage is obtained as follows:
in the formula: e.g. of the type B Is the node carbon potential, R, expressed in daily mileage b Is the carbon flow rate.
5. The carbon emission flow theory-based power utilization side carbon emission fine metering method according to claim 1, wherein the predicting a node carbon potential distribution based on the node carbon potential represented by daily mileage comprises:
performing normal distribution based on the node carbon potential represented by the daily mileage to obtain a carbon potential probability model of the energy storage element;
and multiplying the carbon potential probability model by the node carbon potential vector to predict the node carbon potential distribution.
6. The carbon emission flow theory-based power side carbon emission fine metering method according to claim 5, wherein the calculation of the node carbon potential vector comprises:
solving the active power flow of the power distribution network by adopting a forward-backward substitution method based on branch data and node loads of the power distribution network;
knowing the carbon potential and active output of the main network input point and each distributed generator set, equivalently calculating the main network access point as a generator set model, wherein the carbon potential of a node i is the carbon flow density of all active power flows flowing out of the node i, and the carbon potential of the node i is recorded as the ratio of the carbon flow rate of the node i to the active power:
in the formula: e.g. of the type i Is the carbon potential of node i; I.C. A + A branch set of active power flow flowing into the node i; p is a radical of Br Is the active power flow of the branch r; rho r The carbon flow density of branch r; p is a radical of Gi Active power flow injected for the generator i; e.g. of the type Gi Is the carbon potential of generator i;
expanding the carbon potential of the node i to a power network to obtain a branch tide distribution matrix, a node active flux matrix, a unit injection distribution matrix and a unit carbon emission intensity vector;
based on the branch flow distribution matrix, the node active flux matrix, the unit injection distribution matrix and the generator unit carbon emission intensity vector, calculating a node carbon potential vector as follows:
in the formula: e G Is the carbon emission intensity vector, P, of the generator set N As a node active flux matrix, P B Is a branch flow distribution matrix, P G And injecting a distribution matrix for the unit.
7. The carbon emission flow theory-based power side carbon emission fine metering method of claim 1, further comprising:
calculating node output distribution factor, namely node active output distribution factor H from node i to node j ij Comprises the following steps:
in the formula: w ij Is a network flow flowing from node i to adjacent node j via branch ij; w ∑i Is the sum of the network flows flowing into node i; h if there is no branch connection between two nodes or no forward network flow flows in on adjacent branches ij =0, when i = j, H ij ＝1。
8. The carbon emission flow theory-based power side carbon emission fine metering method of claim 1, further comprising:
calculating path output distribution factor, if there are multiple communication paths from node i to node j, the path output distribution factor D from node i to node j ij Comprises the following steps:
in the formula: h st The distribution factor is output for the node, and Λ is the set of paths.
9. The carbon emission flow theory-based power side carbon emission fine metering method of claim 6, further comprising:
determining the contribution condition of the ith unit to all node carbon flows in the power network based on the ith row element of a unit-node incidence matrix, wherein the unit-node incidence matrix is R U-N ：
10. The carbon emission flow theory-based power side carbon emission fine metering method of claim 6, further comprising:
the carbon flow contribution condition of a single machine set to all branches in the network can be calculated based on a machine set-branch incidence matrix, and the machine set-branch incidence matrix
Comprises the following steps:
the contribution condition of all the units in the network to the load carbon flow rate can be obtained based on a unit-load incidence matrix R U-L Comprises the following steps:
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| CN117764798A (en) * | 2024-02-22 | 2024-03-26 | 福建省计量科学研究院（福建省眼镜质量检验站） | Method and system for checking carbon meter measurement data of user |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117371650A (en) * | 2023-10-09 | 2024-01-09 | 国网江苏省电力有限公司连云港供电分公司 | Accurate carbon metering method and system for power distribution network considering load side electric energy substitution |
| CN117371650B (en) * | 2023-10-09 | 2024-06-07 | 国网江苏省电力有限公司连云港供电分公司 | Accurate carbon metering method and system for power distribution network considering load side electric energy substitution |
| CN117764798A (en) * | 2024-02-22 | 2024-03-26 | 福建省计量科学研究院（福建省眼镜质量检验站） | Method and system for checking carbon meter measurement data of user |
| CN117764798B (en) * | 2024-02-22 | 2024-05-24 | 福建省计量科学研究院（福建省眼镜质量检验站） | Method and system for checking carbon meter measurement data of user |
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