Power grid dynamic carbon emission factor calculation method and system considering bidirectional traceability correction

Abstract

The invention discloses a power grid dynamic carbon emission factor calculation method and system considering bidirectional traceability correction, and the method comprises the steps: carrying out the carbon flow calculation based on a power grid power flow result, tracing a carbon emission source from a network layer to nodes from top to bottom, and obtaining a carbon emission factor; then, performing countercurrent tracing from the park to the node from bottom to top to obtain a carbon emission reduction intensity factor; and finally, correcting a carbon emission factor based on the carbon emission reduction intensity factor to obtain a dynamic carbon emission factor. The dynamic carbon emission factors obtained by the method can better adapt to changes of regional multi-main-body carbon emission and carbon emission reduction behaviors through top-down and bottom-up bidirectional traceability, and dynamic updating of the carbon emission factors is realized.

Description

Technical Field

[0001] The present invention belongs to the field of electric power systems, and in particular relates to a method and system for calculating a dynamic carbon emission factor of a power grid taking into account bidirectional traceability correction. Background Art

[0002] As the global climate change problem becomes increasingly serious, the effective management and accurate accounting of carbon emissions have become the focus of attention of governments and the power industry. In the power system, traditional carbon emission factors are usually extrapolated from the generator node or substation bus node to the user node. Although this method can reflect the carbon emissions of the entire power grid, it often cannot accurately capture the impact of the user-side emission reduction interaction on the distribution of carbon emissions in the power grid. This makes it difficult for the existing power grid carbon emission model to support the user-side interactive regulation mechanism aimed at reducing carbon emissions.

[0003] In a regional multi-agent environment, users' electricity consumption and trading behaviors are highly coupled with the physical processes of power distribution and carbon flow distribution. The carbon emissions and emission reduction behaviors of each user are interrelated, and these behaviors have a significant impact on the carbon emission distribution of the power grid. However, current calculation methods fail to effectively consider these dynamic interactions, resulting in possible inaccuracies when formulating emission reduction policies or optimizing power dispatch.

[0004] Therefore, in order to more accurately evaluate the comprehensive impact of carbon emissions and emission reduction behaviors of multiple entities in the region on the carbon emission distribution of the power grid, it is urgent to propose a method and system for calculating the dynamic carbon emission factor of the power grid that considers two-way traceability correction. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for calculating the dynamic carbon emission factor of a power grid taking into account two-way traceability correction. The node carbon emission factor can better adapt to the changes in the carbon emissions and carbon reduction behaviors of multiple subjects in the region through top-down and bottom-up two-way traceability, and realize the dynamic update of the carbon emission factor; the storage method based on blockchain technology performs multi-temporal and spatial storage of the dynamic carbon emission factor to ensure that the dynamic carbon emission factor data cannot be tampered with and the emission process can be traced.

[0006] In order to achieve the above object, the solution of the present invention is:

[0007] A method for calculating the dynamic carbon emission factor of a power grid considering bidirectional source tracing correction includes:

[0008] Carbon flow calculation is performed based on the power grid flow results, and the carbon emission sources are traced from the network layer to the node from top to bottom to obtain the carbon emission factor;

[0009] Tracing the source from the park to the node from bottom to top, we can get the carbon reduction intensity factor;

[0010] The carbon emission factor is corrected based on the carbon emission reduction intensity factor to obtain a dynamic carbon emission factor.

[0011] Among them, the carbon flow calculation is carried out based on the power grid flow results, and the carbon emission sources are traced from the network layer to the node from top to bottom to obtain the carbon emission factors, including:

[0012] Obtain real-time data of power grid parameters;

[0013] Perform AC power flow calculation to obtain power flow and network loss of each branch of the power grid;

[0014] According to the calculation results of AC power flow, the generated power is decomposed into the power drawn from the generator by each node load, the power drawn from the generator by each branch, and the network loss power borne by the generator;

[0015] Based on the above power generation decomposition results, the power generation carbon emission is decomposed into the total carbon flow rate borne by each node load, each branch and network loss respectively;

[0016] The carbon emission factor of each node is calculated based on the total inflow carbon flow rate and total outflow power of the branch where each node is located.

[0017] Among them, according to the calculation results of AC power flow, the power generation is decomposed into the power drawn from the generator by each node load, the power drawn from the generator by each branch, and the network loss power borne by the generator, including:

[0018] Construct downstream power flow distribution matrix T u :

[0019] T u P=P G

[0020]

[0021] Where P G is the active output matrix of each node generator, P = [P1 P2 ... P n ] T is the outflow power matrix of each node, T u,ki is the matrix T u The element in row k and column i; P ki is the power of branch ki; U k is the upstream node set of node k, that is, the node set directly connected to node k and with active power flowing to k; P i is the active power flowing through node i, n is the number of nodes in the power system;

[0022] The power calculation method of the node load drawn from the generator is:

[0023] The power component P drawn by the node k load from the node i generator Lk,Gi It is expressed as:

[0024]

[0025] Where P Lk is the load of node k, P Gi is the generator output of node i, P k is the power flowing through node k;

[0026] The power drawn from the generator by the branch is calculated as follows:

[0027] The power P of the generator connected to node i to branch kj kj Contribution share P kj,Gi for:

[0028]

[0029] The calculation method of the grid loss power borne by the generator is as follows:

[0030] Similarly, the network loss of the generator connected to node i on branch kj is Contribution share It is expressed as:

[0031]

[0032] Among them, based on the above-mentioned power generation decomposition results, the power generation carbon emissions are decomposed into the total carbon flow rate borne by each node load, each branch and network loss, including:

[0033] The load of node k bears the carbon flow rate component C of the unit connected to node i Lk,Gi for:

[0034]

[0035] In the formula, e Gi is the carbon emission intensity of the generator set connected to node i;

[0036]

[0037] In the formula, C Lk is the total carbon flow rate of node k;

[0038] Branch kj bears the carbon flow rate component C of the generator set connected to node i kj,Gi for:

[0039]

[0040] In the formula, C kj is the total carbon flow rate of branch kj;

[0041] The network loss of branch kj is the carbon flow rate component of the generator set connected to node i for:

[0042]

[0043] In the formula, is the total carbon flow rate of branch kj network loss.

[0044] Among them, the calculation of the node carbon emission factor includes, for node j, its carbon emission factor is expressed as:

[0045]

[0046] In the formula, E Nj is the carbon emission factor of node j; C kj is the total carbon flow rate of branch kj; is the total carbon flow rate of the branch kj network loss; U j is the upstream node set of node j; P j is the active power flowing through node j.

[0047] Among them, the carbon emission reduction intensity factor is obtained by tracing back from the park to the node from bottom to top, including:

[0048] The power changes of low-carbon behaviors of multiple users in the park are integrated to obtain the integrated power of low-carbon behaviors;

[0049] Equivalently converting the fused combined power of the low-carbon behavior into the virtual generator injection power;

[0050] Construct a countercurrent power flow distribution matrix to decompose the equivalent generator injection power into contributions to each node load, each branch, and network loss;

[0051] Based on the above injection power decomposition results, the equivalent generator injected carbon emission is decomposed into the total carbon flow rate borne by each node load, each branch and network loss respectively;

[0052] The carbon emission reduction intensity factor of each node is calculated based on the total inflow carbon flow rate and total outflow power of the branch where each node is located.

[0053] Among them, the park's multi-subject users include residential users, small and medium-sized industrial and commercial users, and distributed clean energy;

[0054] The combined expression of low-carbon behavior power changes of multiple users at the park level is:

[0055]

[0056] Where P sum(t) is the fusion power of low carbon behavior, ΔP r,i (t) is the reduction in electricity consumption of residential users, ΔP b,i is the reduction in electricity consumption by small and medium-sized industrial and commercial users, ΔP d,i (t) is the change in power generation of distributed clean energy; N1 is the number of residential users, N2 is the number of small and medium-sized industrial and commercial users, and N3 is the number of distributed clean energy;

[0057] From the park to the node, we can trace the source from the bottom up, and integrate the low-carbon behaviors of multiple users in the park into a combined power equivalent to the power injected by the virtual generator:

[0058] P F (t)=-P sum (t)

[0059] Where P F (t) is the injected power of the equivalent generator;

[0060] Decompose the equivalent generator injected power:

[0061] B d P=P F

[0062]

[0063] In the formula, B d is the countercurrent flow distribution matrix, B d,ki is the matrix B d The element in the kth row and ith column, P = [P1 P2 ...P n ] T is the outflow power matrix of each node; P F is the injection power matrix of the equivalent generator at each node, P Fi is the equivalent generator power connected to node i; P Lk,Fi is the contribution of the equivalent generator power connected to node i to the load of node k, P kj,Fi is the contribution of the equivalent generator power connected to node i to the power of branch kj, is the contribution of the equivalent generator power of access node i to the network loss of branch kj;

[0064] Inject the equivalent generator into the carbon exhaust to decompose:

[0065]

[0066] In the formula, C Lk,Fi is the contribution of the equivalent generator carbon flow rate connected to node i to the load of node k, e Fi is the carbon emission factor of node i, C Lk is the total carbon flow rate of node k load;

[0067]

[0068] In the formula, C kj,Fi is the contribution of the equivalent generator carbon flow rate of node i to branch kj, C kj is the total carbon flow rate of branch kj;

[0069]

[0070] In the formula, is the contribution of the equivalent generator carbon flow rate of node i to the network loss of branch kj, is the total carbon flow rate of the branch kj network loss;

[0071] The carbon reduction intensity factor is calculated according to the following formula:

[0072]

[0073] Where ΔE Nj is the carbon emission reduction intensity factor of node j.

[0074] The carbon emission factor is corrected based on the carbon emission reduction intensity factor to obtain a dynamic carbon emission factor, including:

[0075] The carbon emission factor is corrected according to the following formula:

[0076] E' Nj =E Nj +ΔE Nj

[0077] Where, E' Nj is the dynamic carbon emission factor; E Nj is the carbon emission factor of node j, ΔE Nj is the carbon emission reduction intensity factor of node j.

[0078] The method also includes a blockchain-based evidence storage method to achieve multi-temporal and spatial evidence storage of dynamic carbon emission factors, including:

[0079] Step a: Determine the type and architecture of the blockchain platform and select a blockchain platform that meets the requirements; design smart contracts for data storage, including defining storage structure, access rights, and data verification rules;

[0080] Step b, determine the source of data related to network carbon emission intensity and carbon emission reduction, including user entities, electricity consumption, electricity savings, and electricity consumption types; store the collected data on the blockchain and update relevant data records in real time;

[0081] Step c: deploy the designed smart contract to the selected blockchain platform and ensure the correct execution of the contract;

[0082] Step d: When a specific event occurs, the smart contract generates a corresponding evidence record; the evidence data includes the relevant timestamp, dynamic carbon emission factor, and emission reduction amount;

[0083] Step e: Design a user interface and data visualization tool for reviewing and analyzing the stored data.

[0084] A power grid dynamic carbon emission factor calculation system considering bidirectional source tracing correction includes:

[0085] The carbon emission factor calculation module is configured to calculate the carbon flow based on the power grid flow results, trace the carbon emission sources from the network layer to the node from top to bottom, and obtain the carbon emission factor;

[0086] The carbon reduction intensity factor calculation module is configured to trace the source from the park to the node from bottom to top to obtain the carbon reduction intensity factor; and,

[0087] The correction module is configured to correct the carbon emission factor based on the carbon emission reduction intensity factor to obtain a dynamic carbon emission factor.

[0088] After adopting the above scheme, the beneficial effect of the present invention is that, compared with the prior art, the method and system for calculating the dynamic carbon emission factor of the power grid considering two-way traceability correction in the present invention takes into account the impact of the emission reduction interactive behavior of park users on the carbon emission distribution of the power grid. The node carbon emission factor is traced bidirectionally from top to bottom and from bottom to top, which can better adapt to the changes in carbon emissions and carbon emission reduction behaviors of multiple subjects in the region and realize the dynamic update of the carbon emission factor; the evidence storage method based on blockchain technology realizes multi-temporal and spatial evidence storage of dynamic carbon emission factors, ensuring that the dynamic carbon emission factor data cannot be tampered with and the emission process can be traced. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 It is a schematic diagram of the process of the present invention;

[0090] Figure 2 It is the topological structure diagram of IEEE33-node power grid system;

[0091] Figure 3 is the node load distribution diagram;

[0092] Figure 4 This is a schematic diagram of top-down carbon emission tracing and carbon emission factor calculation results;

[0093] Figure 5 Schematic diagram of bottom-up carbon emission reduction tracing and carbon emission factor calculation results. DETAILED DESCRIPTION

[0094] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in the present invention are only embodiments of a part of the present invention, not all embodiments. Based on the spirit of the present invention, all other embodiments not recorded in the present invention obtained by ordinary technicians in this field according to the embodiments recorded in the present invention without making creative work should belong to the protection scope of the present invention.

[0095] Figure 1 The first aspect of the present invention relates to a method for calculating a dynamic carbon emission factor of a power grid considering bidirectional source tracing correction, and the method comprises steps (1), (2) and (3).

[0096] Step (1) calculates the carbon flow based on the power grid flow results, traces the carbon emission sources from the network layer to the node from top to bottom, and obtains the node carbon emission factor.

[0097] Furthermore, step (1) specifically includes:

[0098] (11) Carbon flow calculation is performed based on the power grid flow results, and the carbon emission sources are traced from the network layer to the node from top to bottom to obtain the node carbon emission factors, including:

[0099] Obtain real-time data, such as node load, generator output and network topology parameters;

[0100] AC power flow calculation to obtain the power flow of each branch and network loss;

[0101] Decomposing the generated power, allocating the generated power to each branch, each node load and network loss; wherein, according to the calculation results of the AC power flow, constructing a downstream power flow distribution matrix, according to which the generated power is decomposed into the power drawn from the generator by each node load, the power drawn from the generator by each branch and the network loss power borne by the generator;

[0102] Power generation carbon emission allocation: Based on the above power generation power decomposition results, power generation carbon emission is allocated to each branch, each node load and network loss;

[0103] Calculate the node carbon emission factor and calculate the node carbon intensity.

[0104] (12) The power decomposition of the generated power includes the power drawn from the generator by the load, the power drawn from the generator by the branch, and the network loss power borne by the generator. Taking a power system with n nodes as an example, a downstream power flow distribution moment is defined to reflect the relationship between the generator power and the power flowing through the node:

[0105] Tu P=P G

[0106]

[0107] Where P G is the active output matrix of each node generator, P=[P1 P2 ... P n ] T is the outflow power matrix of each node, T u is the downstream power flow distribution matrix, T u,ki is the matrix T u The element in row k and column i in P ki is the power of branch ki; U k is the upstream node set of node k, that is, the node set directly connected to node k and with active power flowing to k; P i is the active power flowing through node i.

[0108] The power drawn by the load from the generator is calculated as follows:

[0109] The power component P drawn by the node k load from the generator connected to node i Lk,Gi It is expressed as:

[0110]

[0111] Where P Lk is the load of node k, P Gi is the generator output of node i, P k is the power flowing through node k.

[0112] The power drawn from the generator by the branch is calculated as follows:

[0113] The power P of the generator connected to node i to branch kj kj Contribution share P kj,Gi for:

[0114]

[0115] The calculation method of the grid loss power borne by the generator is as follows:

[0116] Similarly, the network loss of the generator connected to node i on branch kj is Contribution share It can be expressed as:

[0117]

[0118] (13) The carbon emission allocation for power generation includes allocating the carbon emission for power generation to each node load, each branch power and network loss. Based on the aforementioned power generation allocation results, the power allocation results borne by each branch, each node load and each generator are multiplied by the carbon emission intensity of their corresponding generator sets, thereby obtaining the carbon emission allocation results for power generation of each branch, each node load and each generator.

[0119] The expression for allocating power generation carbon emissions to each node load is:

[0120]

[0121] In the formula, C Lk,Gi is the carbon flow rate component of the unit connected to node i that node k load needs to bear, e Gi is the carbon emission intensity of the generator set connected to node i.

[0122]

[0123] In the formula, C Lk is the total carbon flow rate of node k, and n is the number of nodes in the power system.

[0124] The expression for allocating the carbon emissions from power generation to the power of each branch is:

[0125]

[0126] In the formula, C kj,Gi Branch kj needs to bear the carbon flow rate component of the generator set connected to node i.

[0127]

[0128] In the formula, C kj is the total carbon flow rate of branch kj.

[0129] Among them, the expression for allocating carbon emissions from power generation to grid losses is:

[0130] The carbon flow rate corresponding to the active power loss of branch kj is:

[0131]

[0132] In the formula, is the carbon flow rate component of the generator set connected to node i that needs to bear the network loss of branch kj.

[0133]

[0134] In the formula, is the total carbon flow rate of branch kj network loss.

[0135] (14) The calculation method of the node carbon emission factor is:

[0136] The node carbon emission factor is the ratio of the sum of the branch carbon flow rates flowing into the node to the total outflow power of the node, which describes the equivalent carbon dioxide released by the node per unit of electricity used and attributed to the power generation side, and the unit is tCO2 / (MW·h). For node j, its node carbon emission factor can be expressed as:

[0137]

[0138] In the formula, E Nj is the carbon emission factor of node j, U j is the set of upstream nodes of node j.

[0139] Step (2), considering the impact of the interactive emission reduction behavior of park users on the carbon emission distribution of the power grid, tracing the source from the park to the node from bottom to top, correcting the node carbon emission factor, and realizing the dynamic update of the carbon emission factor;

[0140] Furthermore, step (2) specifically includes:

[0141] (21) Considering the impact of the interactive emission reduction behavior of park users on the carbon emission distribution of the power grid, the carbon emission factors of the nodes are corrected by tracing the source from the park to the node from bottom to top, including:

[0142] Integrate and aggregate the power changes of low-carbon behaviors of multiple users in the park;

[0143] Trace the source from the park to the node from bottom to top and calculate the dynamic carbon emission factor.

[0144] (22) The multi-subject users in the park mainly include residential users, small and medium-sized industrial and commercial users, and distributed clean energy. The emission reduction interactive behaviors of park users mainly include demand response, electricity conservation, and green electricity trading.

[0145] The combined expression of low-carbon behavior power changes of multiple users at the park level is:

[0146]

[0147] Where P sum (t) is the fusion power of low carbon behavior, ΔP r,i (t) is the reduction in electricity consumption of residential users, ΔP b,i is the reduction in electricity consumption by small and medium-sized industrial and commercial users, ΔP d,i (t) is the change in power generation of distributed clean energy; N1 is the number of residential users, N2 is the number of small and medium-sized industrial and commercial users, and N3 is the number of distributed clean energy.

[0148] (23) From the park to the node, we can trace the source from the bottom up and integrate the low-carbon behaviors of multiple users in the park into a combined power equivalent to the power injected by the virtual generator.

[0149] P F (t)=-P sum (t)

[0150] Where P F (t) is the injected power of the equivalent generator.

[0151] (24) The equivalent generator injection power is apportioned in proportion:

[0152] B d P=P F

[0153]

[0154] In the formula, B d is the countercurrent flow distribution matrix, B d,ki is the matrix B d The element in the kth row and ith column, P = [P1 P2 ...P n ] T is the outflow power matrix of each node, P F is the injection power matrix of the equivalent generator at each node, P Fi is the equivalent generator power connected to node i, P Lk,Fi is the contribution of the equivalent generator power connected to node i to the load of node k, P kj,Fi is the contribution of the equivalent generator power connected to node i to the power of branch kj, is the contribution of the equivalent generator power connected to node i to the network loss of branch kj.

[0155] (25) The equivalent generator injected carbon emissions are apportioned in proportion:

[0156]

[0157]

[0158] In the formula, C Lk,Fi is the contribution of the equivalent generator carbon flow rate connected to node i to the load of node k, e Fi is the carbon emission factor of node i calculated from top-down downstream tracing, C Lk is the total carbon flow rate of node k load.

[0159]

[0160] In the formula, C kj,Fiis the contribution of the equivalent generator carbon flow rate of node i to branch kj, C kj is the total carbon flow rate of branch kj.

[0161]

[0162] In the formula, is the contribution of the equivalent generator carbon flow rate of node i to the network loss of branch kj, is the total carbon flow rate of branch kj network loss.

[0163] (26) Similar to the top-down hierarchical tracing method, a method for calculating the carbon emission reduction intensity factor that takes into account the low-carbon interactive behavior of park users can be obtained to achieve the update and correction of the dynamic carbon emission factor.

[0164]

[0165] E' Nj =E Nj +ΔE Nj

[0166] In the formula, ΔE Nj is the carbon reduction intensity factor of node j, E' Nj is the carbon emission factor of node j after correction.

[0167] Step (3), based on the blockchain technology, a storage method is used to realize multi-temporal and spatial storage of dynamic carbon emission factors, ensuring that the dynamic carbon emission factor data cannot be tampered with and the emission process can be traced.

[0168] Furthermore, step (3) specifically includes:

[0169] (31) Blockchain technology is used to store and trace electronic data information such as carbon emission intensity factors and timestamps at multiple time sections. The authenticity, integrity and validity of electronic data are guaranteed by combining abstract algorithms, digital signatures, digital certificates and other technical means. The use of blockchain technology makes data tamper-proof, and the data or information stored in it forms a linear, time-ordered "event chain" with the characteristics of being unforgeable, leaving traces throughout the process, traceable, open and transparent, and collectively maintained.

[0170]

[0171] (32) The multi-temporal and spatial evidence storage method for dynamic carbon emission factors specifically includes the following steps:

[0172] 1) Design the blockchain evidence storage system architecture

[0173] a) Determine the type and architecture of the blockchain platform and select a suitable blockchain platform.

[0174] b) Design smart contracts for data storage, including defining storage structure, access rights, and data verification rules.

[0175] 2) Data collection and storage

[0176] a) Determine the sources of data related to network carbon emission intensity and carbon emission reduction, including user entities, electricity consumption, electricity savings, electricity consumption types, etc.

[0177] b) Design a safe and reliable data collection system to ensure the authenticity and credibility of data sources.

[0178] c) Store the collected data on the blockchain and update relevant data records in real time.

[0179] 3) Smart contract operation and data verification

[0180] a) Deploy the designed smart contract to the selected blockchain platform and ensure the correct execution of the contract.

[0181] b) Implement relevant data verification logic in smart contracts, including rule checking and confirmation of carbon emission intensity and carbon emission reduction data.

[0182] 4) Generate evidence records

[0183] a) When a specific event occurs (such as the launch of a new carbon reduction project, the achievement of a carbon reduction target, etc.), the smart contract will generate a corresponding evidence record.

[0184] b) The stored data will contain relevant timestamps, dynamic carbon emission factors, emission reductions, etc. to ensure the data is tamper-proof and complete.

[0185] 5) Data visualization and traceability

[0186] a) Design user interfaces and data visualization tools to enable stakeholders to easily access and analyze deposited data.

[0187] b) Support the traceability and flow analysis of carbon emission intensity and carbon emission reduction data in order to fully understand the source and change process of relevant data.

[0188] The second aspect of the present invention relates to a system for calculating a dynamic carbon emission factor of a power grid taking into account bidirectional traceability correction, the system comprising a calculation module and a proof storage module; wherein:

[0189] The calculation module is used for top-down and bottom-up tracing, taking into account the interactive emission reduction behaviors of park users, and updating the dynamic carbon emission factors; specifically, it includes:

[0190] The carbon emission factor calculation module is configured to calculate the carbon flow based on the power grid flow results, trace the carbon emission sources from the network layer to the node from top to bottom, and obtain the carbon emission factor;

[0191] The carbon reduction intensity factor calculation module is configured to trace the source from the park to the node from bottom to top to obtain the carbon reduction intensity factor; and,

[0192] A correction module is configured to correct the carbon emission factor based on the carbon emission reduction intensity factor to obtain a dynamic carbon emission factor;

[0193] The evidence storage module is used to perform multi-temporal and spatial evidence storage and traceability of the dynamic carbon emission factors obtained by the calculation module, so as to ensure that the carbon emission intensity data cannot be tampered with and the emission process can be traced.

[0194] The system provided by the embodiment of the present invention can be implemented in software and / or hardware, and the system can be configured in a terminal device and can be used to execute the method provided by the embodiment of the present invention, and has the corresponding functions and beneficial effects of the execution method.

[0195] It is worth noting that in the above-mentioned determination system embodiment, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented only by hardware, as long as the function or effect can be achieved.

[0196] The present invention is verified by simulation as follows.

[0197] This article takes the IEEE33 node distribution network system as an example. The topology diagram is as follows Figure 2 Assume that nodes 1, 21, and 30 are the upper power sources, respectively. The carbon emission intensity at different nodes is different. The carbon emission intensity factors of the upper power sources are set to 0.5703tCO2 / MWh, 0.525tCO2 / MWh, and 0.875tCO2 / MWh, respectively.

[0198] 33 node load distribution diagram as shown Figure 3As shown, each node has different electricity consumption and carbon emission behaviors at different times. It can be found that the load demand of nodes 24 and 25 is higher, and the load demand of node 11 is lower.

[0199] The network topology is traced from top to bottom and the carbon emission factor is calculated. The results are as follows: Figure 4 As shown. The figure shows the branch flow, branch carbon flow density distribution and node carbon emission factor calculation results caused by user carbon emission behavior. The different environmental properties of multiple types of upper power sources will lead to differences in the carbon emission factors of nodes. From the simulation results, it can be seen that the carbon emission factors of nodes closer to high carbon emission power sources are higher, while the carbon emission factors of nodes closer to low carbon emission power sources are lower. Among the three upper power sources set, the carbon emission factor of node 30 is the highest, and the carbon emission factor of node 21 is the lowest. The node carbon emission factor is related to the carbon emission intensity of the injected power. Near node 30, the carbon emission factors of the nodes are mostly 0.875. For nodes 2-7, 18-20, 23-24, 26-28, and 31-33, the flow flowing into these nodes only comes from a certain upper power source or a node adjacent to it. Therefore, the node carbon emission factors of these nodes should be consistent with the carbon emission intensity of the nodes or upper power sources that provide active flow upstream.

[0200] Similarly, taking the IEEE33-node distribution network system as an example, considering the carbon emission reduction behavior of users, a bottom-up carbon emission traceability is performed. Assuming that users at nodes 8 and 24 have emission reduction behaviors, reducing electricity consumption by 0.1MWh and 0.2MWh respectively, their electricity reduction can be equivalent to the injected power of the virtual generator and its injected carbon emission intensity can be set to a negative value. According to the calculation results of the node carbon emission intensity in the top-down hierarchical traceability method, the initial carbon emission intensity factors of nodes 8 and 24 are set to 0.5506tCO2 / MWh and 0.5703tCO2 / MWh.

[0201] The calculation results of bottom-up carbon reduction traceability and carbon reduction intensity factor are as follows: Figure 5 As shown. The figure shows the branch flow changes caused by the user's carbon emission reduction behavior, the branch carbon flow density distribution and the calculation results of the node carbon emission reduction intensity factor. When a negative carbon emission intensity is injected into the power grid, each node has a corresponding carbon emission reduction intensity factor. The carbon emission reduction intensity factor of the node is determined by the injected carbon emission factor of the emission reduction node. It can be found that the node carbon emission reduction intensity factor injected by the No. 24 virtual generator is -0.0119, and the node carbon emission reduction intensity factor injected by the No. 8 virtual generator is -0.0058. The injected power of individual nodes is jointly provided by nodes 8 and 24, and the node carbon emission reduction intensity factor is a linear combination of the two.

[0202] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for calculating the dynamic carbon emission factor of a power grid considering bidirectional traceability correction, characterized in that: include, Carbon flow calculation is performed based on the power grid flow results, and the carbon emission sources are traced from the network layer to the node from top to bottom to obtain the carbon emission factor; Tracing the source from the park to the node from bottom to top, we can get the carbon reduction intensity factor; The carbon emission factor is corrected based on the carbon emission reduction intensity factor to obtain a dynamic carbon emission factor.

2. The method according to claim 1, characterized in that: Carbon flow calculation is performed based on the power grid flow results, and the carbon emission sources are traced from the network layer to the node from top to bottom to obtain the carbon emission factors, including: Obtain real-time data of power grid parameters; Perform AC power flow calculation to obtain power flow and network loss of each branch of the power grid; According to the calculation results of AC power flow, the generated power is decomposed into the power drawn from the generator by each node load, the power drawn from the generator by each branch, and the network loss power borne by the generator; Based on the above power generation decomposition results, the power generation carbon emissions are decomposed into the total carbon flow rate borne by each node load, each branch and network loss respectively; The carbon emission factor of each node is calculated based on the total inflow carbon flow rate and total outflow power of the branch where each node is located.

3. The method according to claim 2, characterized in that: According to the calculation results of AC power flow, the power generation is decomposed into the power drawn from the generator by each node load, the power drawn from the generator by each branch, and the network loss power borne by the generator, including: Construct downstream power flow distribution matrix T u : T u P=P G Where P G is the active output matrix of each node generator, P = [P1 P2 ... P n ] T is the outflow power matrix of each node, T u,ki is the matrix T u The element in row k and column i; P ki is the power of branch ki; U k is the upstream node set of node k, that is, the node set directly connected to node k and with active power flowing to k; P i is the active power flowing through node i, n is the number of nodes in the power system; The power calculation method of the node load drawn from the generator is: The power component P drawn by the node k load from the node i generator Lk,Gi It is expressed as: Where P Lk is the load of node k, P Gi is the generator output of node i, P k is the power flowing through node k; The power drawn from the generator by the branch is calculated as follows: The power P of the generator connected to node i to branch kj kj Contribution share P kj,Gi for: The calculation method of the grid loss power borne by the generator is as follows: Similarly, the network loss of the generator connected to node i on branch kj is Contribution share It is expressed as:

4. The method according to claim 3, characterized in that: Based on the above power generation decomposition results, the power generation carbon emission is decomposed into the total carbon flow rate borne by each node load, each branch and network loss, including: The load of node k bears the carbon flow rate component C of the unit connected to node i Lk,Gi for: In the formula, e Gi is the carbon emission intensity of the generator set connected to node i; In the formula, C Lk is the total carbon flow rate of node k; Branch kj bears the carbon flow rate component C of the generator set connected to node i kj,Gi for: In the formula, C kj is the total carbon flow rate of branch kj; The network loss of branch kj is the carbon flow rate component of the generator set connected to node i for: In the formula, is the total carbon flow rate of branch kj network loss.

5. The method according to claim 1, characterized in that: Calculate the node carbon emission factor, including, for node j, its carbon emission factor is expressed as: In the formula, E Nj is the carbon emission factor of node j; C kj is the total carbon flow rate of branch kj; is the total carbon flow rate of the branch kj network loss; U j is the upstream node set of node j; P j is the active power flowing through node j.

6. The method according to claim 1, characterized in that: Tracing back from the park to the node from bottom to top, we can get the carbon reduction intensity factor, including: The power changes of low-carbon behaviors of multiple users in the park are integrated to obtain the integrated power of low-carbon behaviors; Equivalently converting the fused combined power of the low-carbon behavior into the virtual generator injection power; Construct a countercurrent power flow distribution matrix to decompose the equivalent generator injection power into contributions to each node load, each branch, and network loss; Based on the above injection power decomposition results, the equivalent generator injected carbon emission is decomposed into the total carbon flow rate borne by each node load, each branch and network loss respectively; The carbon emission reduction intensity factor of each node is calculated based on the total inflow carbon flow rate and total outflow power of the branch where each node is located.

7. The method according to claim 6, characterized in that: The park's multi-subject users include residential users, small and medium-sized industrial and commercial users, and distributed clean energy; The combined expression of low-carbon behavior power changes of multiple users at the park level is: Where P sum (t) is the fusion power of low carbon behavior, ΔP r,i (t) is the reduction in electricity consumption of residential users, ΔP b,i is the reduction in electricity consumption by small and medium-sized industrial and commercial users, ΔP d,i (t) is the change in power generation of distributed clean energy; N1 is the number of residential users, N2 is the number of small and medium-sized industrial and commercial users, and N3 is the number of distributed clean energy; From the park to the node, we can trace the source from the bottom up, and integrate the low-carbon behaviors of multiple users in the park into a combined power equivalent to the power injected by the virtual generator: P F (t)=-P sum (t) Where P F (t) is the injected power of the equivalent generator; Decompose the equivalent generator injected power: B d P=P F In the formula, B d is the countercurrent flow distribution matrix, B d,ki is the matrix B d The element in the kth row and ith column, P = [P1 P2 ... P n ] T is the outflow power matrix of each node; P F is the injection power matrix of the equivalent generator at each node, P Fi is the equivalent generator power connected to node i; P Lk,Fi is the contribution of the equivalent generator power connected to node i to the load of node k, P kj,Fi is the contribution of the equivalent generator power connected to node i to the power of branch kj, is the contribution of the equivalent generator power of access node i to the network loss of branch kj; Inject the equivalent generator into the carbon exhaust to decompose: In the formula, C Lk,Fi is the contribution of the equivalent generator carbon flow rate connected to node i to the load of node k, e Fi is the carbon emission factor of node i, C Lk is the total carbon flow rate of node k load; In the formula, C kj,Fi is the contribution of the equivalent generator carbon flow rate of node i to branch kj, C kj is the total carbon flow rate of branch kj; In the formula, is the contribution of the equivalent generator carbon flow rate of node i to the network loss of branch kj, is the total carbon flow rate of the branch kj network loss; The carbon reduction intensity factor is calculated according to the following formula: In the formula, ΔE Nj is the carbon emission reduction intensity factor of node j.

8. The method according to claim 1, characterized in that: The carbon emission factor is corrected based on the carbon emission reduction intensity factor to obtain a dynamic carbon emission factor, including: The carbon emission factor is corrected according to the following formula: E' Nj =E Nj +ΔE Nj Where, E' Nj is the dynamic carbon emission factor; E Nj is the carbon emission factor of node j, ΔE Nj is the carbon emission reduction intensity factor of node j.

9. The method according to claim 1, characterized in that: It also includes a blockchain-based evidence storage method to achieve multi-temporal and spatial evidence storage of dynamic carbon emission factors, including: Step a: Determine the type and architecture of the blockchain platform and select a blockchain platform that meets the requirements; Design smart contracts for data storage, including defining storage structure, access rights, and data verification rules; Step b, determining the source of data related to network carbon emission intensity and carbon emission reduction, including user entities, electricity consumption, electricity savings, and electricity consumption types; Store the collected data on the blockchain and update relevant data records in real time; Step c: deploy the designed smart contract to the selected blockchain platform and ensure the correct execution of the contract; Step d: When a specific event occurs, the smart contract generates a corresponding evidence record; the evidence data includes the relevant timestamp, dynamic carbon emission factor, and emission reduction amount; Step e: Design a user interface and data visualization tool for reviewing and analyzing the stored data.

10. A power grid dynamic carbon emission factor calculation system considering bidirectional traceability correction, characterized by: include, The carbon emission factor calculation module is configured to calculate the carbon flow based on the power grid flow results, trace the carbon emission sources from the network layer to the node from top to bottom, and obtain the carbon emission factor; The carbon reduction intensity factor calculation module is configured to trace the source from the park to the node from bottom to top to obtain the carbon reduction intensity factor; as well as, The correction module is configured to correct the carbon emission factor based on the carbon emission reduction intensity factor to obtain a dynamic carbon emission factor.

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