Carbon emission tracking method, device and equipment for medium and low voltage power grid, and medium

Abstract

The invention relates to the field of electric power and carbon emission, and discloses a carbon emission tracking method, device, equipment and medium for a medium and low voltage power grid, and the method comprises the steps: selecting a power grid structure model in which wind speed data and illumination intensity data are imported as a target model, and selecting a branch between a node i and a node j in the target model as a target branch; generating an intra-day deviation carbon emission flow of the load of the node i according to the intra-day active power deviation of the load of the node i and the intra-day active power deviation of the node i, and generating an intra-day deviation carbon emission flow of the target branch according to the intra-day active power deviation of the target branch and the intra-day active power deviation of the node i, and based on the intraday deviation carbon emission flow of the load of the node i and the intraday deviation carbon emission flow of the target branch, determining the intraday actual carbon emission flow of the load of the node i and the intraday actual carbon emission flow of the target branch. According to the invention, the problem of how to track the actual carbon emission flow of the medium-low voltage power grid is solved.

Description

Technical Field

[0001] The present invention relates to the fields of electricity and carbon emissions, and in particular to a method, device, equipment and medium for tracking carbon emissions in a medium- and low-voltage power grid. Background Art

[0002] With the rapid growth of my country's economy and the continuous improvement of people's living standards, the demand and scale of electricity in rural areas are continuing to expand. Various household appliances are gradually popularizing in rural households, making rural households increasingly dependent on electric energy. Medium and low voltage distribution networks are important infrastructures that are indispensable for rural development.

[0003] Medium and low voltage power grid refers to the power network with voltage levels between 1kV and 35kV. It is at the end of the power system, directly connected to the users, and is responsible for distributing electricity to various users. The access of distributed power sources will change the power flow distribution and voltage quality of medium and low voltage power grids. By understanding the actual carbon emission flow of medium and low voltage power grids, targeted emission reduction strategies can be formulated. However, the existing technology relies on manual acquisition, which is time-consuming and labor-intensive. Therefore, how to track the actual carbon emission flow of medium and low voltage power grids has become an urgent problem to be solved. Summary of the invention

[0004] The present invention provides a method, device, computer equipment and storage medium for tracking carbon emissions of a medium and low voltage power grid, so as to solve the technical problem of how to track the actual carbon emission flow of the medium and low voltage power grid.

[0005] In a first aspect, a method for tracking carbon emissions of a medium and low voltage power grid is provided, comprising:

[0006] Acquire target data of a medium and low voltage power grid containing distributed power sources, and construct a power grid structure model according to the topological structure, network parameters and power load data in the target data;

[0007] Acquiring wind speed data and light intensity data, and importing the wind speed data and the light intensity data into the power grid structure model;

[0008] Selecting the power grid structure model into which the wind speed data and the light intensity data are imported as a target model, and selecting a branch connecting node i and node j in the target model as a target branch;

[0009] Performing power flow simulation on the target model to obtain simulation data, in which the day-ahead active power of node i, the day-ahead active power of the load of node i, and the day-ahead active power of the target branch are obtained;

[0010] Subtract the day-ahead active power of node i from the day-ahead active power of node i to obtain the day-ahead active power deviation of node i; subtract the day-ahead active power of the load of node i from the day-ahead active power of the load of node i to obtain the day-ahead active power deviation of the load of node i; subtract the day-ahead active power of the target branch from the day-ahead active power of the target branch to obtain the day-ahead active power deviation of the target branch;

[0011] According to the intraday active power deviation of the load at node i and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the load at node i is generated; according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the target branch is generated; based on the intraday deviation carbon emission flow of the load at node i, the intraday deviation carbon emission flow of the target branch and the preset actual carbon emission flow generation model, the intraday actual carbon emission flow of the load at node i and the intraday actual carbon emission flow of the target branch are determined.

[0012] Further, the acquiring of wind speed data and light intensity data, and importing the wind speed data and the light intensity data into the power grid structure model, includes:

[0013] Obtain wind speed data and light intensity data in a day-ahead wind and solar power output scenario, where the day-ahead wind and solar power output scenario describes the output of wind power generation and photovoltaic power generation before a target date;

[0014] The wind speed data and the light intensity data are imported into the power grid structure model.

[0015] Further, the selecting the grid structure model into which the wind speed data and the light intensity data are imported as the target model, and selecting the branch connecting the node i and the node j in the target model as the target branch, comprises:

[0016] Obtaining an import instruction, executing the import instruction, and selecting the power grid structure model that imports the wind speed data and the light intensity data as a target model;

[0017] In the target model, a branch connecting node i and node j in the target model is selected as a target branch.

[0018] Further, the target model is subjected to power flow simulation to obtain simulation data, in which the day-ahead active power of node i, the day-ahead active power of the load of node i, and the day-ahead active power of the target branch are obtained, including:

[0019] Acquire a simulation instruction, execute the simulation instruction, perform power flow simulation on the target model, and obtain simulation data;

[0020] In the simulation data, the simulated value of the active power of node i before the target day is selected as the day-ahead active power of node i, the simulated value of the active power of the load of node i before the target day is selected as the day-ahead active power of the load of node i, and the simulated value of the active power of the target branch before the target day is selected as the day-ahead active power of the target branch.

[0021] Further, the intra-day active power of node i is subtracted from the day-ahead active power of node i to obtain the intra-day active power deviation of node i, the intra-day active power of the load of node i is subtracted from the day-ahead active power of the load of node i to obtain the intra-day active power deviation of the load of node i, and the day-ahead active power of the target branch is subtracted from the day-ahead active power of the target branch to obtain the intra-day active power deviation of the target branch, including:

[0022] Acquire actual data. From the actual data, select the actual value of the active power of node i within the target day as the day-ahead active power of node i, select the actual value of the active power of the load of node i within the target day as the day-ahead active power of the load of node i, and select the actual value of the active power of the target branch within the target day as the day-ahead active power of the target branch;

[0023] Subtract the day-ago active power of node i from the day-ago active power of node i to obtain the day-ago active power deviation of node i. Subtract the day-ago active power of node i from the day-ago active power of node i’s load from the day-ago active power of node i’s load to obtain the day-ago active power deviation of node i’s load. Subtract the day-ago active power of the target branch from the day-ago active power of the target branch to obtain the day-ago active power deviation of the target branch.

[0024] Further, the intraday deviation carbon emission flow of the load of node i is generated according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the target branch is generated according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, and the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch are determined based on the intraday deviation carbon emission flow of the load of node i, the intraday deviation carbon emission flow of the target branch and the preset actual carbon emission flow generation model, including:

[0025] Using a preset deviation carbon emission flow generation model, according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the load of node i is generated; according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the target branch is generated;

[0026] Obtain the day-ahead carbon emission flow of the load of node i, obtain the day-ahead carbon emission flow of the target branch, use the preset actual carbon emission flow generation model, add the day-ahead carbon emission flow of the load of node i and the intraday deviation carbon emission flow of the load of node i, and obtain the intraday actual carbon emission flow of the load of node i; add the day-ahead carbon emission flow of the target branch and the intraday deviation carbon emission flow of the target branch, and obtain the intraday actual carbon emission flow of the target branch.

[0027] Further, after generating the intraday deviation carbon emission flow of the load of node i according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, generating the intraday deviation carbon emission flow of the target branch according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, and determining the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch based on the intraday deviation carbon emission flow of the load of node i, the intraday deviation carbon emission flow of the target branch and the preset actual carbon emission flow generation model, the carbon emission tracking method includes:

[0028] The actual daily carbon emission flow of the load of node i and the actual daily carbon emission flow of the target branch are combined into the carbon emission results of the medium and low voltage power grid, and the carbon emission results are displayed.

[0029] In a second aspect, a carbon emission tracking device for a medium and low voltage power grid is provided, comprising:

[0030] A first acquisition module is used to acquire target data of a medium and low voltage power grid containing distributed power sources, and to construct a power grid structure model according to the topological structure, network parameters and power load data in the target data;

[0031] A second acquisition module is used to acquire wind speed data and light intensity data, and import the wind speed data and the light intensity data into the power grid structure model;

[0032] A third acquisition module is used to select the power grid structure model into which the wind speed data and the light intensity data are imported as a target model, and select a branch connecting node i and node j in the target model as a target branch;

[0033] A simulation module, used to perform power flow simulation on the target model to obtain simulation data, in which the day-ahead active power of node i, the day-ahead active power of the load of node i, and the day-ahead active power of the target branch are obtained;

[0034] The fourth acquisition module is used to subtract the day-ahead active power of node i from the day-ahead active power of node i to obtain the day-ahead active power deviation of node i, subtract the day-ahead active power of the load of node i from the day-ahead active power of the load of node i to obtain the day-ahead active power deviation of the load of node i, and subtract the day-ahead active power of the target branch from the day-ahead active power of the target branch to obtain the day-ahead active power deviation of the target branch;

[0035] A determination module is used to generate an intraday deviation carbon emission flow of the load of node i according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, generate an intraday deviation carbon emission flow of the target branch according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, and determine the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch based on the intraday deviation carbon emission flow of the load of node i, the intraday deviation carbon emission flow of the target branch and a preset actual carbon emission flow generation model.

[0036] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned carbon emission tracking method when executing the computer program.

[0037] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned carbon emission tracking method are implemented.

[0038] The present application provides a method, device, computer equipment and storage medium for tracking carbon emissions of a medium- and low-voltage power grid, which obtains target data of a medium- and low-voltage power grid containing distributed power sources, and constructs a power grid structure model according to the topological structure, network parameters and power load data in the target data; obtains wind speed data and light intensity data, and imports the wind speed data and the light intensity data into the power grid structure model; selects the power grid structure model imported with the wind speed data and the light intensity data as the target model, and selects the branch connecting node i and node j in the target model as the target branch; performs power flow simulation on the target model to obtain simulation data, and in the simulation data In , the day-ahead active power of node i, the day-ahead active power of the load of node i, and the day-ahead active power of the target branch are obtained; the day-ahead active power of node i is subtracted from the day-ahead active power of node i to obtain the day-ahead active power deviation of node i, the day-ahead active power of the load of node i is subtracted from the day-ahead active power of the load of node i to obtain the day-ahead active power deviation of the load of node i, and the day-ahead active power of the target branch is subtracted from the day-ahead active power of the target branch to obtain the day-ahead active power deviation of the target branch; according to the day-ahead active power deviation of the load of node i and the day-ahead active power deviation of node i, the day-ahead deviation carbon emission flow of the load of node i is generated, and according to the day-ahead active power deviation of the target branch The active power deviation and the intraday active power deviation of node i are used to generate the intraday deviation carbon emission flow of the target branch. Based on the intraday deviation carbon emission flow of the load of node i, the intraday deviation carbon emission flow of the target branch and the preset actual carbon emission flow generation model, the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch are determined. The beneficial effects are in two aspects. On the one hand, according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the load of node i is generated. According to the intraday active power deviation of the target branch and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the target branch is generated. Based on node i The intraday deviation carbon emission flow of the load, the intraday deviation carbon emission flow of the target branch and the preset actual carbon emission flow generation model are used to determine the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch. Since the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch are determined, the problem of how to track the actual carbon emission flow of the medium and low voltage power grid is solved. On the other hand, since no manual acquisition is required, the acquisition time of the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch is reduced, which is conducive to improving the acquisition efficiency of the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0040] Figure 1 is a schematic diagram of an application environment of a carbon emission tracking method in one embodiment of the present invention;

[0041] Figure 2 A schematic flow chart of a carbon emission tracking method provided by an embodiment of the present invention;

[0042] Figure 3 yes Figure 2 A schematic flow chart of a specific implementation of step S23;

[0043] Figure 4 yes Figure 2 A schematic flow chart of a specific implementation of step S25;

[0044] Figure 5 yes Figure 2 A schematic flow chart of a specific implementation of step S26;

[0045] Figure 6 is a schematic diagram of a structure of a carbon emission tracking device in one embodiment of the present invention;

[0046] Figure 7 It is a structural diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] See also Figure 1 , Figure 1 is a schematic diagram of an application environment of a carbon emission tracking method according to an embodiment of the present invention. The carbon emission tracking method provided by the embodiment of the present invention can be applied in the following situations: Figure 1 In an application environment, a client communicates with a server through a network.

[0049] The server obtains target data of a medium and low voltage power grid containing distributed power sources through the client, and constructs a power grid structure model according to the topological structure, network parameters and power load data in the target data;

[0050] Acquiring wind speed data and light intensity data, and importing the wind speed data and the light intensity data into the power grid structure model;

[0051] Selecting the power grid structure model into which the wind speed data and the light intensity data are imported as a target model, and selecting a branch connecting node i and node j in the target model as a target branch;

[0052] Performing power flow simulation on the target model to obtain simulation data, in which the day-ahead active power of node i, the day-ahead active power of the load of node i, and the day-ahead active power of the target branch are obtained;

[0053] Subtract the day-ahead active power of node i from the day-ahead active power of node i to obtain the day-ahead active power deviation of node i; subtract the day-ahead active power of the load of node i from the day-ahead active power of the load of node i to obtain the day-ahead active power deviation of the load of node i; subtract the day-ahead active power of the target branch from the day-ahead active power of the target branch to obtain the day-ahead active power deviation of the target branch;

[0054] According to the intraday active power deviation of the load at node i and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the load at node i is generated; according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the target branch is generated; based on the intraday deviation carbon emission flow of the load at node i, the intraday deviation carbon emission flow of the target branch and the preset actual carbon emission flow generation model, the intraday actual carbon emission flow of the load at node i and the intraday actual carbon emission flow of the target branch are determined.

[0055] In the scheme implemented by the above-mentioned carbon emission tracking method, device, equipment and medium, the beneficial effects are in two aspects. On the one hand, according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the load of node i is generated, and according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the target branch is generated. Based on the intraday deviation carbon emission flow of the load of node i, the intraday deviation carbon emission flow of the target branch and the preset actual carbon emission flow generation model, the intraday deviation carbon emission flow of the target branch is determined. The actual daily carbon emission flow of the load of node i and the actual daily carbon emission flow of the target branch are determined, thus solving the problem of how to track the actual carbon emission flow of medium and low voltage power grids; on the other hand, since no manual acquisition is required, the acquisition time of the actual daily carbon emission flow of the load of node i and the actual daily carbon emission flow of the target branch is reduced, which is conducive to improving the acquisition efficiency of the actual daily carbon emission flow of the load of node i and the actual daily carbon emission flow of the target branch.

[0056] The device running the client is referred to as a client device.

[0057] The device running the server is referred to as the server device.

[0058] Among them, client devices include but are not limited to smartphones, personal computers, Internet of Vehicles terminals, tablets and portable wearable devices.

[0059] The server device may be implemented by an independent server or a server cluster composed of multiple servers. The present invention is described in detail below through specific embodiments. Figure 2 , Figure 2 A flow chart of a carbon emission tracking method provided in an embodiment of the present invention includes the following steps:

[0060] S21, obtaining target data of a medium and low voltage power grid containing distributed power sources, and constructing a power grid structure model according to the topological structure, network parameters and power load data in the target data;

[0061] Among them, distributed power generation is a decentralized, modular, small-scale power supply system located at the user end or close to the power load. It is based on renewable energy such as solar energy and wind energy, and provides electricity to users or local power grids through power electronic devices or direct access to the power system.

[0062] The target data of the medium and low voltage power grid containing the distributed power source is obtained, and the power grid structure model is constructed according to the topological structure, network parameters and power load data in the target data, including:

[0063] Obtain target data of distributed power sources connected to 10 kV, 380 V and 220 V power grids, and build a power grid structure model based on the topological structure, network parameters and power load data in the target data.

[0064] The grid structure model is a model used to describe the layout of the components of the grid and their interconnection. This grid structure model can clearly reflect the hierarchical structure of the 10 kV grid, 380 V grid and 220 V grid, the direct and indirect relationships between the elements, and their relative positions.

[0065] For illustration, an example is given below:

[0066] The power grid structure model adopts a double-layer grid structure, the first layer of the grid structure is a 10 kV grid structure, and the second layer of the grid structure is a 380 / 220 V grid structure.

[0067] S22, acquiring wind speed data and light intensity data, and importing the wind speed data and the light intensity data into the power grid structure model;

[0068] The step of acquiring wind speed data and light intensity data and importing the wind speed data and the light intensity data into the power grid structure model includes:

[0069] Obtain wind speed data and light intensity data in a day-ahead wind and solar power output scenario, where the day-ahead wind and solar power output scenario describes the output of wind power generation and photovoltaic power generation before a target date;

[0070] The wind speed data and the light intensity data are imported into the power grid structure model.

[0071] Among them, wind speed data refers to the speed of air flow at a specific location and time period, usually measured in meters per second (m / s).

[0072] The light intensity data reflects the energy density of solar radiation on the surface, usually expressed in watts per square meter (W / m 2 ) or lux.

[0073] S23, selecting the grid structure model into which the wind speed data and the light intensity data are imported as a target model, and selecting a branch connecting node i and node j in the target model as a target branch;

[0074] Wherein, node i represents the i-th grid node.

[0075] Wherein, node j represents the jth grid node.

[0076] Among them, node i and node j are different power grid nodes.

[0077] Grid nodes refer to the key points in the power network that are used to connect, distribute and control electric energy. Grid nodes include substations, power plants, distribution stations, user terminals or other power facilities.

[0078] S24, performing power flow simulation on the target model to obtain simulation data, in which the day-ahead active power of node i, the day-ahead active power of the load of node i, and the day-ahead active power of the target branch are obtained;

[0079] The load of node i refers to the power demand or power consumption of node i in the power grid, usually measured in kilowatts (kW) or megawatts (MW). This load reflects the power usage at different locations in the power grid and is an important parameter for grid dispatching and operation management.

[0080] The target model is subjected to power flow simulation to obtain simulation data, in which the day-ahead active power of node i, the day-ahead active power of the load of node i, and the day-ahead active power of the target branch are obtained, including:

[0081] Acquire a simulation instruction, execute the simulation instruction, perform power flow simulation on the target model, and obtain simulation data;

[0082] In the simulation data, the simulated value of the active power of node i before the target day is selected as the day-ahead active power of node i, the simulated value of the active power of the load of node i before the target day is selected as the day-ahead active power of the load of node i, and the simulated value of the active power of the target branch before the target day is selected as the day-ahead active power of the target branch.

[0083] S25, subtract the day-ahead active power of node i from the day-ahead active power of node i to obtain the day-ahead active power deviation of node i, subtract the day-ahead active power of the load of node i from the day-ahead active power of the load of node i to obtain the day-ahead active power deviation of the load of node i, and subtract the day-ahead active power of the target branch from the day-ahead active power of the target branch to obtain the day-ahead active power deviation of the target branch;

[0084] The intraday active power deviation of the load at node i refers to the difference between the intraday active power of the load at node i and the expected day-ahead active power on a specific natural day. This difference may be caused by a variety of factors, including fluctuations in electricity demand caused by weather changes, changes in residents' living habits, etc.

[0085] The target branch’s intraday active power deviation refers to the difference between the target branch’s intraday active power and the expected day-ahead active power on a specific natural day. This difference may be caused by a variety of factors, including but not limited to changes in load demand, power outages caused by grid failures or maintenance, and instability of the power generation equipment itself.

[0086] S26, based on the intraday active power deviation of the load at node i and the intraday active power deviation of node i, generate the intraday deviation carbon emission flow of the load at node i, based on the intraday active power deviation of the target branch and the intraday active power deviation of node i, generate the intraday deviation carbon emission flow of the target branch, based on the intraday deviation carbon emission flow of the load at node i, the intraday deviation carbon emission flow of the target branch and the preset actual carbon emission flow generation model, determine the intraday actual carbon emission flow of the load at node i and the intraday actual carbon emission flow of the target branch.

[0087] Wherein, the actual carbon emission flow generation model is:

[0088]

[0089] is the actual carbon emission flow of the load at node i during the day;

[0090] R Li is the day-ahead carbon emission flow of the load at node i;

[0091] ΔR Li is the intraday deviation carbon emission flow of the load of node i;

[0092] is the actual carbon emission flow of the target branch in a day;

[0093] R ij is the day-ahead carbon emission flow of the target branch;

[0094] ΔR ij is the intraday deviation carbon emission flow of the target branch.

[0095] Among them, the actual carbon emission flow within the day is the actual carbon emission flow within the day.

[0096] Among them, the day-ahead carbon emission flow is the day-ahead carbon emission flow, that is, the expected carbon emission flow.

[0097] Among them, the intraday deviation carbon emission flow is the deviation value of the carbon emission flow within the day.

[0098] Among them, by monitoring and analyzing the deviation values ​​of the daily carbon emission flow, data support can be provided for further carbon emission management.

[0099] Among them, by determining the actual daily carbon emission flow of the load of node i and the actual daily carbon emission flow of the target branch, the abnormal situation of carbon emissions can be discovered in time and the effect of emission reduction measures can be evaluated.

[0100] Wherein, after generating the intraday deviation carbon emission flow of the load of node i according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, generating the intraday deviation carbon emission flow of the target branch according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, and determining the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch based on the intraday deviation carbon emission flow of the load of node i, the intraday deviation carbon emission flow of the target branch and the preset actual carbon emission flow generation model, the carbon emission tracking method includes:

[0101] The actual daily carbon emission flow of the load of node i and the actual daily carbon emission flow of the target branch are combined into the carbon emission results of the medium and low voltage power grid, and the carbon emission results are displayed.

[0102] In the embodiment of the present invention, the beneficial effects lie in two aspects. On the one hand, according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the load of node i is generated. According to the intraday active power deviation of the target branch and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the target branch is generated. Based on the intraday deviation carbon emission flow of the load of node i, the intraday deviation carbon emission flow of the target branch and the preset actual carbon emission flow generation model, the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch are determined. Since the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch are determined, the problem of how to track the actual carbon emission flow of the medium and low voltage power grids is solved. On the other hand, since no manual acquisition is required, the acquisition time of the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch is reduced, which is conducive to improving the acquisition efficiency of the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch.

[0103] See also Figure 3 , Figure 3 yes Figure 2 A specific implementation flow diagram of step S23 is described in detail as follows:

[0104] S31, obtaining an import instruction, executing the import instruction, and selecting the power grid structure model that imports the wind speed data and the light intensity data as a target model;

[0105] Among them, the power grid structure model that imports the wind speed data and the light intensity data is selected as the target model. Since the target model imports the wind speed data and the light intensity data, the target model can simulate the operation of the wind power generation system and the photovoltaic power generation system.

[0106] S32, in the target model, selecting a branch connecting node i and node j in the target model as a target branch.

[0107] In the embodiment of the present invention, the branch connecting the node i and the node j in the target model is selected as the target branch, so that the target branch can be determined.

[0108] See also Figure 4 , Figure 4 yes Figure 2 A specific implementation flow diagram of step S25 is described in detail as follows:

[0109] S41, obtaining actual data, selecting the actual value of the active power of node i within the target day as the day-ahead active power of node i, selecting the actual value of the active power of the load of node i within the target day as the day-ahead active power of the load of node i, and selecting the actual value of the active power of the target branch within the target day as the day-ahead active power of the target branch;

[0110] S42, subtract the day-ago active power of node i from the day-ago active power of node i to obtain the day-ago active power deviation of node i, subtract the day-ago active power of node i from the day-ago active power of the load of node i to obtain the day-ago active power deviation of the load of node i, subtract the day-ago active power of the target branch from the day-ago active power of the target branch to obtain the day-ago active power deviation of the target branch.

[0111] In an embodiment of the present invention, determining the intra-day active power deviation of the load at node i and the intra-day active power deviation of the target branch can serve as a direct basis for power system dispatching decisions. Power system dispatchers can quickly take adjustment measures, such as adjusting generator output, optimizing power grid currents, etc., to avoid the risk of power supply interruption caused by power grid overload or underload, thereby improving energy utilization efficiency and ensuring the safety, reliability and economy of power supply.

[0112] See also Figure 5 , Figure 5 yes Figure 2 A specific implementation flow diagram of step S26 is described in detail as follows:

[0113] S51, using a preset deviation carbon emission flow generation model, generating an intraday deviation carbon emission flow of the load of node i according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, and generating an intraday deviation carbon emission flow of the target branch according to the intraday active power deviation of the target branch and the intraday active power deviation of node i;

[0114] Wherein, the deviation carbon emission flow generation model is:

[0115]

[0116] Among them, the first node is node i, and the second node is node j;

[0117] Among them, ΔR Li is the intraday deviation carbon emission flow of the load of node i;

[0118] Among them, ΔP Li is the intra-day active power deviation of the load at node i;

[0119] Among them, ΔP i is the intra-day active power deviation of node i;

[0120] Among them, e i is an n-dimensional column vector whose i-th component is 1 and the other components are 0. The symbol T represents the transpose operation;

[0121] Among them, ΔA u is the intraday deviation power flow distribution matrix;

[0122] Where ΔP G is an n-dimensional column vector consisting of the daily output deviation of the Gth generator, and diag represents an operation or function that creates a diagonal matrix from a column vector;

[0123] Among them, EG is the n-dimensional column vector composed of the carbon emission intensity of the unit;

[0124] Among them, ΔR ij is the deviation carbon emission flow of the target branch, and the target branch refers to the branch connecting node i and node j;

[0125] Among them, ΔP ij is the daily active power deviation of the target branch.

[0126] Among them, the intraday deviation power flow distribution matrix ΔA u , expressed as:

[0127]

[0128] ΔP ij is the intra-day active power deviation of the target branch;

[0129] ΔP j is the intra-day active power deviation of node j;

[0130] U j is the set of nodes where the active power deviation directly flows into node j.

[0131] Among them, the intraday deviation power flow distribution matrix is ​​the deviation value of the power flow distribution matrix within the day.

[0132] Among them, the power flow distribution matrix is ​​an important tool in power system power flow analysis, which is used to describe and represent the power flow relationship between node i and the target branch and the power flow relationship between node j and the target branch in the power system.

[0133] Among them, the actual carbon emission flow within the day is the actual carbon emission flow within the day.

[0134] Among them, the day-ahead carbon emission flow is the day-ahead carbon emission flow, that is, the expected carbon emission flow.

[0135] Among them, the intraday deviation carbon emission flow is the deviation value of the carbon emission flow within the day.

[0136] The intraday active power deviation is the deviation value of the intraday active power.

[0137] Among them, the daily output deviation is the deviation between the actual output and the expected output within a day. The output of the generator is affected by many factors, such as weather conditions, equipment status, fuel supply, etc. The dynamic changes of these factors within a day will cause the deviation between the actual output and the expected output.

[0138] Among them, the carbon emission intensity of the unit refers to the carbon dioxide emissions generated per unit of power generation, which is an important indicator to measure the environmental performance of the generator set.

[0139] S52, obtain the day-ahead carbon emission flow of the load of node i, obtain the day-ahead carbon emission flow of the target branch, use the preset actual carbon emission flow generation model, add the day-ahead carbon emission flow of the load of node i and the intraday deviation carbon emission flow of the load of node i, and obtain the intraday actual carbon emission flow of the load of node i, add the day-ahead carbon emission flow of the target branch and the intraday deviation carbon emission flow of the target branch, and obtain the intraday actual carbon emission flow of the target branch.

[0140] Among them, by recording the actual daily carbon emission flow of the load of node i and the actual daily carbon emission flow of the target branch, inefficient links and high emission sources in energy use can be identified, and optimization measures can be taken, such as adjusting the energy structure, improving equipment efficiency or introducing clean energy technology, to directly reduce carbon emission intensity, reduce unnecessary waste, and thus improve energy utilization efficiency.

[0141] In an embodiment of the present invention, the day-ahead carbon emission flow of the load of node i and the intraday deviation carbon emission flow of the load of node i are added to obtain the actual intraday carbon emission flow of the load of node i, and the day-ahead carbon emission flow of the target branch and the intraday deviation carbon emission flow of the target branch are added to obtain the actual intraday carbon emission flow of the target branch. Since the actual intraday carbon emission flow of the load of node i and the actual intraday carbon emission flow of the target branch are determined, the problem of how to track the actual carbon emission flow of medium and low voltage power grids is solved.

[0142] See also Figure 6 , Figure 6 is a schematic diagram of a carbon emission tracking device in one embodiment of the present invention. Figure 6 As shown, the carbon emission tracking device includes a first acquisition module 101, a second acquisition module 102, a third acquisition module 103, a simulation module 104, a fourth acquisition module 105, and a determination module 106. The functional modules are described in detail as follows:

[0143] The first acquisition module 101 is used to acquire target data of a medium and low voltage power grid containing distributed power sources, and to construct a power grid structure model according to the topological structure, network parameters and power load data in the target data;

[0144] A second acquisition module 102 is used to acquire wind speed data and light intensity data, and import the wind speed data and the light intensity data into the power grid structure model;

[0145] A third acquisition module 103 is used to select the power grid structure model into which the wind speed data and the light intensity data are imported as a target model, and select a branch connecting node i and node j in the target model as a target branch;

[0146] The simulation module 104 is used to perform power flow simulation on the target model to obtain simulation data, in which the day-ahead active power of the node i, the day-ahead active power of the load of the node i, and the day-ahead active power of the target branch are obtained;

[0147] The fourth acquisition module 105 is used to subtract the day-ahead active power of node i from the day-ahead active power of node i to obtain the day-ahead active power deviation of node i, subtract the day-ahead active power of the load of node i from the day-ahead active power of the load of node i to obtain the day-ahead active power deviation of the load of node i, and subtract the day-ahead active power of the target branch from the day-ahead active power of the target branch to obtain the day-ahead active power deviation of the target branch;

[0148] Determination module 106 is used to generate the intraday deviation carbon emission flow of the load of node i according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, generate the intraday deviation carbon emission flow of the target branch according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, and determine the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch based on the intraday deviation carbon emission flow of the load of node i, the intraday deviation carbon emission flow of the target branch and a preset actual carbon emission flow generation model.

[0149] In the embodiment of the present invention, the beneficial effects lie in two aspects. On the one hand, according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the load of node i is generated. According to the intraday active power deviation of the target branch and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the target branch is generated. Based on the intraday deviation carbon emission flow of the load of node i, the intraday deviation carbon emission flow of the target branch and the preset actual carbon emission flow generation model, the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch are determined. Since the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch are determined, the problem of how to track the actual carbon emission flow of the medium and low voltage power grids is solved. On the other hand, since no manual acquisition is required, the acquisition time of the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch is reduced, which is conducive to improving the acquisition efficiency of the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch.

[0150] For the specific limitations of the carbon emission tracking device, please refer to the limitations of the carbon emission tracking method above, which will not be repeated here.

[0151] Each module in the above carbon emission tracking device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0152] See also Figure 7 , Figure 7 is another structural diagram of a computer device in one embodiment of the present invention. In one embodiment, a computer device is provided. The computer device is a server device or a client device. Its internal structure diagram can be as shown in FIG. Figure 7As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external database. When the computer program is executed by the processor, the functions or steps of a carbon emission tracking method for a medium and low voltage power grid can be implemented.

[0153] In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor.

[0154] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant description of the aforementioned method embodiment. In order to avoid repetition, they will not be described one by one here.

[0155] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0156] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. Technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. Technicians can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

Claims

1. A method for tracking carbon emissions in a medium and low voltage power grid, characterized in that: include: Acquire target data of a medium and low voltage power grid containing distributed power sources, and construct a power grid structure model according to the topological structure, network parameters and power load data in the target data; Acquiring wind speed data and light intensity data, and importing the wind speed data and the light intensity data into the power grid structure model; Selecting the grid structure model imported with the wind speed data and the light intensity data as the target model, and selecting the branch connecting the node i and the node j in the target model as the target branch; Performing power flow simulation on the target model to obtain simulation data, in which the day-ahead active power of node i, the day-ahead active power of the load of node i, and the day-ahead active power of the target branch are obtained; Subtract the day-ahead active power of node i from the day-ahead active power of node i to obtain the day-ahead active power deviation of node i; subtract the day-ahead active power of the load of node i from the day-ahead active power of the load of node i to obtain the day-ahead active power deviation of the load of node i; subtract the day-ahead active power of the target branch from the day-ahead active power of the target branch to obtain the day-ahead active power deviation of the target branch; According to the intraday active power deviation of the load at node i and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the load at node i is generated; according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the target branch is generated; based on the intraday deviation carbon emission flow of the load at node i, the intraday deviation carbon emission flow of the target branch and the preset actual carbon emission flow generation model, the intraday actual carbon emission flow of the load at node i and the intraday actual carbon emission flow of the target branch are determined.

2. The carbon emission tracking method according to claim 1, characterized in that: The obtaining of wind speed data and light intensity data, and importing the wind speed data and the light intensity data into the power grid structure model, comprises: Obtain wind speed data and light intensity data in a day-ahead wind and solar power output scenario, where the day-ahead wind and solar power output scenario describes the output of wind power generation and photovoltaic power generation before a target date; The wind speed data and the light intensity data are imported into the power grid structure model.

3. The carbon emission tracking method according to claim 1, characterized in that: The step of selecting the grid structure model into which the wind speed data and the light intensity data are imported as a target model, and selecting a branch connecting a node i and a node j in the target model as a target branch, comprises: Obtaining an import instruction, executing the import instruction, and selecting the power grid structure model that imports the wind speed data and the light intensity data as a target model; In the target model, a branch connecting node i and node j in the target model is selected as a target branch.

4. The carbon emission tracking method according to claim 1, characterized in that: The power flow simulation is performed on the target model to obtain simulation data, in which the day-ahead active power of node i, the day-ahead active power of the load of node i, and the day-ahead active power of the target branch are obtained, including: Acquire a simulation instruction, execute the simulation instruction, perform power flow simulation on the target model, and obtain simulation data; In the simulation data, the simulated value of the active power of node i before the target day is selected as the day-ahead active power of node i, the simulated value of the active power of the load of node i before the target day is selected as the day-ahead active power of the load of node i, and the simulated value of the active power of the target branch before the target day is selected as the day-ahead active power of the target branch.

5. The carbon emission tracking method according to claim 1, characterized in that: The method of subtracting the day-ahead active power of node i from the day-ahead active power of node i to obtain the day-ahead active power deviation of node i, subtracting the day-ahead active power of the load of node i from the day-ahead active power of the load of node i to obtain the day-ahead active power deviation of the load of node i, and subtracting the day-ahead active power of the target branch from the day-ahead active power of the target branch to obtain the day-ahead active power deviation of the target branch includes: Acquire actual data. From the actual data, select the actual value of the active power of node i within the target day as the day-ahead active power of node i, select the actual value of the active power of the load of node i within the target day as the day-ahead active power of the load of node i, and select the actual value of the active power of the target branch within the target day as the day-ahead active power of the target branch; Subtract the day-ago active power of node i from the day-ago active power of node i to obtain the day-ago active power deviation of node i. Subtract the day-ago active power of node i from the day-ago active power of node i’s load from the day-ago active power of node i’s load to obtain the day-ago active power deviation of node i’s load. Subtract the day-ago active power of the target branch from the day-ago active power of the target branch to obtain the day-ago active power deviation of the target branch.

6. The carbon emission tracking method according to claim 1, characterized in that: The method of generating the intraday deviation carbon emission flow of the load of node i according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, generating the intraday deviation carbon emission flow of the target branch according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, and determining the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch based on the intraday deviation carbon emission flow of the load of node i, the intraday deviation carbon emission flow of the target branch and a preset actual carbon emission flow generation model, includes: Using a preset deviation carbon emission flow generation model, according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the load of node i is generated; according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, the intraday deviation carbon emission flow of the target branch is generated; Obtain the day-ahead carbon emission flow of the load of node i, obtain the day-ahead carbon emission flow of the target branch, use the preset actual carbon emission flow generation model, add the day-ahead carbon emission flow of the load of node i and the intraday deviation carbon emission flow of the load of node i, and obtain the intraday actual carbon emission flow of the load of node i; add the day-ahead carbon emission flow of the target branch and the intraday deviation carbon emission flow of the target branch, and obtain the intraday actual carbon emission flow of the target branch.

7. The carbon emission tracking method according to claim 1, characterized in that: After generating the intraday deviation carbon emission flow of the load of node i according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, generating the intraday deviation carbon emission flow of the target branch according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, and determining the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch based on the intraday deviation carbon emission flow of the load of node i, the intraday deviation carbon emission flow of the target branch and a preset actual carbon emission flow generation model, the carbon emission tracking method includes: The actual daily carbon emission flow of the load of node i and the actual daily carbon emission flow of the target branch are combined into the carbon emission results of the medium and low voltage power grid, and the carbon emission results are displayed.

8. A carbon emission tracking device for a medium and low voltage power grid, characterized in that: include: A first acquisition module is used to acquire target data of a medium and low voltage power grid containing distributed power sources, and to construct a power grid structure model according to the topological structure, network parameters and power load data in the target data; A second acquisition module is used to acquire wind speed data and light intensity data, and import the wind speed data and the light intensity data into the power grid structure model; A third acquisition module is used to select the power grid structure model into which the wind speed data and the light intensity data are imported as a target model, and select a branch connecting node i and node j in the target model as a target branch; A simulation module, used to perform power flow simulation on the target model to obtain simulation data, in which the day-ahead active power of node i, the day-ahead active power of the load of node i, and the day-ahead active power of the target branch are obtained; The fourth acquisition module is used to subtract the day-ahead active power of node i from the day-ahead active power of node i to obtain the day-ahead active power deviation of node i, subtract the day-ahead active power of the load of node i from the day-ahead active power of the load of node i to obtain the day-ahead active power deviation of the load of node i, and subtract the day-ahead active power of the target branch from the day-ahead active power of the target branch to obtain the day-ahead active power deviation of the target branch; A determination module is used to generate an intraday deviation carbon emission flow of the load of node i according to the intraday active power deviation of the load of node i and the intraday active power deviation of node i, generate an intraday deviation carbon emission flow of the target branch according to the intraday active power deviation of the target branch and the intraday active power deviation of node i, and determine the intraday actual carbon emission flow of the load of node i and the intraday actual carbon emission flow of the target branch based on the intraday deviation carbon emission flow of the load of node i, the intraday deviation carbon emission flow of the target branch and a preset actual carbon emission flow generation model.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the carbon emission tracking method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the carbon emission tracking method according to any one of claims 1 to 7 are implemented.

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