A method, device and related components for allocating carbon emissions from an integrated energy system

Abstract

The present invention discloses a method, device and related components for allocating carbon emissions in an integrated energy system. The method includes obtaining data of various uncertain factors, allocating different scenario deviation coefficients to the data of each of the uncertain factors, obtaining updated data of the uncertain factors and combining them to form different combined scenarios; using a genetic algorithm to calculate the value of the objective function corresponding to each task under each of the combined scenarios to obtain the optimal target planning; based on a pre-constructed carbon emission flow model and the optimal target planning, the carbon emissions are allocated. The method first uses a variety of uncertain factors to construct multiple change scenarios, thereby ensuring the robustness of the model and making the planning more universal and instructive, and then uses a genetic algorithm to calculate the planning results under each scenario, and uses the carbon emission flow model to allocate the carbon emissions in the system.

Description

Technical Field

[0001] The present invention relates to the field of new energy optimization scheduling, and in particular to a carbon emission allocation method, device and related components for an integrated energy system. Background Art

[0002] At present, due to the high energy utilization rate of the integrated energy system, the research technology related to the integrated energy system planning has developed rapidly. The development of the integrated energy system promotes the development of multiple energy sources. The integrated energy system can couple multiple energy sources together, such as wind and solar energy. However, carbon dioxide will be produced when these energy sources are consumed. However, when multiple energy sources are coupled together, that is, multiple energy sources are converted into each other in the integrated energy system, researchers are unable to observe, analyze, and calculate the carbon emissions generated by energy consumption. Therefore, reasonable carbon emission observation methods are very important. On the one hand, it can reasonably calculate the generation of carbon emissions; on the other hand, it can reasonably allocate carbon emissions to various parts of the integrated energy system.

[0003] The planning of integrated energy systems involves a variety of different energy sources, so it is necessary to consider the impact of various uncertain factors on the system. For example, wind power and photovoltaic power are affected by weather and environment, which will cause uncertainty to the system. Therefore, if you simply plan the integrated energy system without considering the uncertain factors in the system, it is difficult to ensure that the system planning can be carried out under energy shortages and environmental degradation. Summary of the invention

[0004] The purpose of the present invention is to provide a carbon emission allocation method, device and related components for an integrated energy system, aiming to solve the problem that the existing integrated energy system planning does not deal with the uncertain factors in the system, resulting in unreasonable planning of the existing integrated energy system.

[0005] In order to solve the above technical problems, the purpose of the present invention is achieved through the following technical solutions: providing a method for allocating carbon emissions from an integrated energy system, which comprises:

[0006] Acquire data of each uncertain factor, assign different scenario deviation coefficients to the data of each uncertain factor, obtain updated data of the uncertain factor and combine them to form different combined scenarios, wherein the uncertain factors include wind speed, light intensity, cooling load level, heating load level, electricity load level, and hydrogen load level;

[0007] Using a genetic algorithm to calculate the value of the objective function corresponding to each task in each of the combined scenarios, to obtain the optimal target planning;

[0008] Carbon emissions are allocated based on a pre-built carbon emission flow model and optimal target planning.

[0009] In addition, the technical problem to be solved by the present invention is to provide a carbon emission distribution device for an integrated energy system, which comprises:

[0010] A combination unit, used to obtain data of each uncertain factor, assign different scenario deviation coefficients to the data of each uncertain factor, obtain updated data of the uncertain factor and combine them to form different combination scenarios, wherein the uncertain factors include wind speed, light intensity, cooling load level, heating load level, electric load level, and hydrogen load level;

[0011] A calculation unit, used to calculate the value of the objective function corresponding to each task under each of the combined scenarios by using a genetic algorithm to obtain an optimal target plan;

[0012] The allocation unit is used to allocate carbon emissions based on a pre-built carbon emission flow model and optimal target planning.

[0013] In addition, an embodiment of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the carbon emission allocation method for the integrated energy system described in the first aspect above is implemented.

[0014] In addition, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the integrated energy system carbon emission allocation method described in the first aspect above.

[0015] The embodiment of the present invention discloses a method, device and related components for allocating carbon emissions in an integrated energy system, wherein the method includes: obtaining data of various uncertain factors, allocating different scenario deviation coefficients to the data of each of the uncertain factors, obtaining updated data of the uncertain factors and combining them to form different combined scenarios, wherein the uncertain factors include wind speed, light intensity, cold load level, heat load level, electric load level, and hydrogen load level; using a genetic algorithm to calculate the value of the objective function corresponding to each task under each of the combined scenarios to obtain the optimal target planning; based on a pre-constructed carbon emission flow model and the optimal target planning, carbon emissions are allocated. The method first uses a variety of uncertain factors to construct multiple change scenarios, thereby ensuring the robustness of the model and making the planning more universal and instructive, and then calculates the planning results under each scenario through a genetic algorithm, and allocates the carbon emissions in the system through a carbon emission flow model. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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 will be briefly introduced below. Obviously, the accompanying drawings described below are 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 any creative work.

[0017] Figure 1 A schematic diagram of a flow chart of a carbon emission allocation method for an integrated energy system provided in an embodiment of the present invention;

[0018] Figure 2 A first structural schematic diagram of a carbon emission flow model of a carbon emission allocation method for an integrated energy system provided in an embodiment of the present invention;

[0019] Figure 3 A second structural schematic diagram of a carbon emission flow model of a carbon emission allocation method for an integrated energy system provided in an embodiment of the present invention;

[0020] Figure 4 A schematic diagram of a structure of a combined scenario of a method for allocating carbon emissions from an integrated energy system provided by an embodiment of the present invention;

[0021] Figure 5 A schematic diagram of the structure of a genetic algorithm in a carbon emission allocation method for an integrated energy system provided by an embodiment of the present invention;

[0022] Figure 6 A schematic block diagram of a carbon emission distribution device for an integrated energy system provided by an embodiment of the present invention;

[0023] Figure 7 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] 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.

[0025] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0026] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0027] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0028] See also Figure 1 , Figure 1 A schematic diagram of a flow chart of a carbon emission allocation method for an integrated energy system provided in an embodiment of the present invention;

[0029] like Figure 1 As shown, the method includes steps S101 to S103.

[0030] S101, obtaining data of each uncertain factor, assigning different scenario deviation coefficients to the data of each uncertain factor, obtaining updated data of the uncertain factors and combining them to form different combined scenarios, wherein the uncertain factors include wind speed, light intensity, cooling load level, heating load level, electric load level, and hydrogen load level;

[0031] S102, using a genetic algorithm to calculate the value of the objective function corresponding to each task in each combined scenario to obtain an optimal target planning;

[0032] S103. Allocate carbon emissions based on a pre-built carbon emission flow model and optimal target planning.

[0033] In this embodiment, on the one hand, the present application aims at the fact that the uncertainty factors in the integrated energy system in the prior art will have a negative impact on the system, and therefore constructs a variety of combined scenarios to analyze the uncertainty factors in the integrated energy system, thereby ensuring the robustness of the model and making the planning more universal and instructive; on the second hand, the distribution problem of carbon emissions in the integrated energy system is considered, and the carbon emissions are simulated into a virtual carbon emission flow. By tracking and calculating the carbon emission flow, the carbon emissions in the integrated energy system can be quantified; on the third hand, the computational information in the integrated energy system planning is utilized through a genetic algorithm, so that the planning scheme of the integrated energy system can be calculated more efficiently and accurately.

[0034] Specifically, first obtain the data of wind speed, light intensity, cooling load level, heating load level, electric load level, and hydrogen load level, then assign different or the same scene deviation coefficients to the data of wind speed, light intensity, cooling load level, heating load level, electric load level, and hydrogen load level, respectively, to obtain the latest data of wind speed, light intensity, cooling load level, heating load level, electric load level, and hydrogen load level, and then combine the new data of wind speed, light intensity, cooling load level, heating load level, electric load level, and hydrogen load level to obtain different combination scenes, such as wind speed a, light intensity b, cooling load level c, heating load level d, and hydrogen load level d. The data of electric load level d, electric load level e, and hydrogen load level f are respectively assigned scenario deviation coefficients A and scenario deviation coefficients B, and the combinations obtained are [aA, bA, cA, dA, eA, fA], [aB, bA, cA, dA, eA, fA], [aA, bB, cA, dA, eA, fA], [aA, bA, cB, dA, eA, fA], and so on, to obtain all combined scenarios, and then use genetic algorithm to calculate each combined scenario to obtain the optimal target planning. Finally, based on the pre-built carbon emission flow model and the optimal target planning, the carbon emissions are allocated.

[0035] It should be noted that the tasks in the constituent scenarios of this application include but are not limited to operating costs, grid costs, construction costs, and carbon emission costs.

[0036] This application also provides a carbon emission flow model, such as Figure 2 As shown, the specific structure is as follows:

[0037] It includes wind turbines, thermal power generators, hydrogen gas sources, natural gas sources, and multiple energy consumption nodes. Among them, wind turbines are connected to thermal turbines through the power network, thermal turbines, hydrogen gas sources, and natural gas sources are all connected to various energy consumption nodes through the power network, and hydrogen gas sources are connected to natural gas sources through the power network. It should be noted that each power network has network losses. Figure 2 The dashed arrows shown indicate the direction of carbon emission flow.

[0038] In order to more clearly explain the carbon emission flow model, this application uses Figure 3 The model is explained again. The sunlight supplies energy to the heat collector and photovoltaic power generation equipment. The power grid and natural gas directly provide energy. The corresponding loads such as electric load, cooling load, heat load and hydrogen load are output through each supply line. It should be noted that CCHP represents the combined heat and power system, CC represents the compression refrigerator; EB represents the electric boiler; Tank represents the container for storing hydrogen; ED represents the electrolysis device; AB represents the auxiliary boiler; SMR represents the natural gas reforming hydrogen production device; in addition, Figure 3The dashed arrows shown indicate the direction and amount of carbon emission flow, in kgCO2 / h.

[0039] In one embodiment, the step S101 includes the following steps:

[0040] S10, obtaining historical data of all uncertain factors within a specified period;

[0041] S11. Sort all the corresponding historical data to obtain a corresponding historical data set, and multiply the preset scene deviation coefficient corresponding to different scenes by the historical data set to form a plurality of different combined scenes.

[0042] Combination Figure 4 In this embodiment, a combined scenario includes 6 uncertain factors, and the uncertain factors are arranged in the following order: data of wind speed, light intensity, cooling load level, heating load level, electric load level, and hydrogen load level. The data of wind speed and light intensity are respectively assigned 0.9×expected value (i.e., historical data), 1×expected value, and 1.1×expected value. The data of cooling load level, heating load level, electric load level, and hydrogen load level are respectively assigned 0.8×expected value, expected value, and 1.2×expected value. The following is obtained: Figure 4 There are 18 combined scenarios shown, each scenario description represents a vector. Taking wind speed data as an example, the historical data of wind speed for 24 hours a day is as follows: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24] m / s, then the wind speed in scenario S1 is 0.9×[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24] m / s.

[0043] In the above manner, the uncertain factors under all combined scenarios are updated to obtain the latest combined scenario.

[0044] In one embodiment, step S102 includes the following steps:

[0045] S20, obtaining the objective function of the genetic algorithm;

[0046] S21, for a combination scenario, obtaining decision variables of each task in the combination scenario, and performing binary encoding on each obtained decision variable to obtain a population formed by n individual combinations generated after encoding, wherein each of the individuals respectively contains chromosome information;

[0047] S22, performing chromosome operation, crossover operation, and mutation operation on all individuals of the population to obtain the optimal individuals corresponding to each of the tasks;

[0048] S23. Decode the optimal individuals corresponding to each of the tasks to convert them into corresponding optimal decision variables, and calculate the value of the objective function based on each of the optimal decision variables.

[0049] In this embodiment, combined with Figure 5 , multiple individuals form a population, each population corresponds to a task, and multiple tasks form a combined scenario, wherein the combined scenario refers to some data in the running process of the task, and each task needs to use the data in the combined scenario; this embodiment obtains the value (true value) of the objective function of the current task by performing chromosome operations, crossover operations, and mutation operations on the individuals in the current task, and judges the degree of proximity between the calculated true value and the target value. The closer the true value is to the target value, the better the optimal individual for the current task is found. After obtaining the optimal individuals in all tasks, these optimal individuals are used to calculate the value of the objective function to obtain the optimal target planning.

[0050] This embodiment calculates the planning result in each combination scenario through a genetic algorithm, and because various uncertain factors are taken into consideration, the robustness of the model is guaranteed, making the planning more universal and instructive.

[0051] It should be noted that the genetic algorithm in the prior art has the characteristics of accuracy and ease of operation. The genetic algorithm can seek an ideal integrated energy system planning scheme through multiple iterations. However, the genetic algorithm takes a lot of time while undergoing multiple iterations. The present application improves the genetic algorithm to promote the convergence speed of the genetic algorithm and find a more optimized planning scheme, as follows:

[0052] In one embodiment, the step S22 includes the following steps:

[0053] S30, based on the chromosome information of each individual, calculating the fitness value of the chromosome of each individual, and after obtaining all the fitness values, selecting the individual corresponding to the fitness value with the largest numerical value as the elite individual;

[0054] S31, obtaining the probability of information exchange between different tasks, and constructing a corresponding learning factor matrix and obtaining the value of the learning factor matrix, and based on the value of the learning factor matrix, cross-exchanging the chromosome information of each elite individual;

[0055] S32, based on a preset probability change rule, updating the elite individuals after each crossover operation, and recording the number of updates;

[0056] S33, judging whether the update times reaches the update threshold, if not, performing chromosome operation, crossover operation and mutation operation on the updated elite individuals until the update times reaches the update threshold and outputs the optimal individual, if reached, directly outputs the optimal individual.

[0057] like Figure 5 As shown, in this embodiment, the chromosome information, that is, the binary 0 / 1 information, is optimized by sequentially performing chromosome operations, crossover operations, and mutation operations on each individual in the current task, that is, by continuously iteratively optimizing the chromosomes of individuals in each task and transmitting information between different tasks, the optimal individuals in each task are obtained, and finally converted into decision variables through a decoding process to obtain the value of the objective function.

[0058] In step S32, the optimal solution is searched from different directions so that the genetic algorithm will not fall into the situation of a local optimal solution.

[0059] In one embodiment, the step S31 of obtaining the probability of information exchange between different tasks, constructing a corresponding learning factor matrix, and obtaining the value of the learning factor matrix includes the following steps:

[0060] S40. Calculate the crossover degree rmp between the kth task and the jth task according to the following formula k，j :

[0061]

[0062] Among them, g k (p k , d) indicates that task k is in population p k The probability distribution model of the dth individual in g j (p j , d) indicates that task j is in population p j The probability distribution model of the dth individual in, K represents the total number of tasks, N k represents the total number of individuals in a population, and i represents the i-th individual in population p;

[0063] The learning factor RMP is calculated as follows:

[0064]

[0065] In this embodiment, an online learning factor RMP (random exchange probability) is proposed in the genetic algorithm to calculate the probability of information exchange between multiple tasks, that is, to use the calculation information in the integrated energy system planning to assist the operation of the genetic algorithm, thereby improving the iteration speed of the genetic algorithm.

[0066] It should be noted that step S40 is the maximum value in the calculation formula, which indicates that the genetic algorithm is solving the objective function of the task, but the energy flow is running during the solution process.

[0067] In one embodiment, the step S31 cross-exchanges the chromosome information of each elite individual based on the value of the learning factor matrix, including the following steps:

[0068] S50, obtaining the value of the learning factor, using an algorithm to randomly generate a random value of 0 to 1, and determining whether the value of the learning factor is less than the value of the learning factor. If so, executing step S51; if so, directly entering step S32;

[0069] S51, cross-exchange the chromosome information between the two tasks, and enter the step S32;

[0070] In this embodiment, for example, the value of the learning factor is 0.5, and the algorithm randomly generates a value between 0 and 1. If the randomly generated value is smaller than the value of the learning factor, the information between the two tasks will be cross-exchanged.

[0071] In one embodiment, the step S103 includes the following steps:

[0072] S60, constructing a carbon emission flow model based on the optimal target planning;

[0073] S61. Based on the carbon emission flow model, calculate the energy flow in each carbon emission network, and calculate the active power flowing into the line in the corresponding carbon emission network, the active power flowing out of the line, the active power of the generator, the active power consumed by the energy node, and the carbon emission intensity of the generator based on the energy flow;

[0074] S62. Calculate the amount of carbon emissions according to the following formula:

[0075] R G =P G *E G

[0076] Among them, P G Indicates the active power of the generator, E G Indicates the carbon emission intensity of the generator;

[0077] S63. Calculate the carbon emission intensity of energy consumption nodes in the carbon emission network according to the following formula:

[0078]

[0079] Among them, P N is the active power consumed by the energy-consuming node, represents the transpose of the active power flowing out of the line;

[0080] S64. Calculate the amount of carbon emissions allocated to each energy consumption node according to the following formula:

[0081] R N =P N *E N .

[0082] In this embodiment, in step S60, for example, from 18 combination scenarios, the optimal combination scenario S1 is finally obtained, and then a carbon emission flow model is constructed based on the optimal combination scenario, and then the amount of carbon emissions allocated to all energy consumption nodes is calculated, and carbon emissions are allocated according to the calculation results.

[0083] It should be noted that the carbon emission network in this embodiment includes the power network, the natural gas network, and the hydrogen energy network, and the energy flow also correspondingly includes the power flow, the natural gas flow, and the hydrogen energy flow.

[0084] The embodiment of the present invention further provides a device for allocating carbon emissions from an integrated energy system, which is used to execute any embodiment of the aforementioned method for allocating carbon emissions from an integrated energy system. Figure 6 , Figure 6 It is a schematic block diagram of a carbon emission allocation device for an integrated energy system provided in an embodiment of the present invention.

[0085] like Figure 6 As shown, the integrated energy system carbon emission allocation device 500 includes:

[0086] The combination unit 501 is used to obtain data of each uncertain factor, assign different scenario deviation coefficients to the data of each uncertain factor, obtain updated data of the uncertain factor and combine them to form different combination scenarios, wherein the uncertain factors include wind speed, light intensity, cooling load level, heating load level, electric load level, and hydrogen load level;

[0087] A calculation unit 502 is used to calculate the value of the objective function corresponding to each task under each of the combined scenarios by using a genetic algorithm to obtain an optimal target plan;

[0088] The allocation unit 503 is used to allocate the carbon emissions based on the pre-built carbon emission flow model and the optimal target planning.

[0089] On the one hand, the device aims at the negative impact of uncertainty factors in the integrated energy system in the existing technology on the system, and therefore constructs a variety of combined scenarios to analyze the uncertainty factors in the integrated energy system, thereby ensuring the robustness of the model and making the planning more universal and instructive; on the second hand, the distribution problem of carbon emissions in the integrated energy system is considered, and the carbon emissions are simulated into a virtual carbon emission flow. By tracking and calculating the carbon emission flow, the carbon emissions in the integrated energy system can be quantified; on the third hand, the computational information in the integrated energy system planning is utilized through genetic algorithms, so that the planning scheme of the integrated energy system can be calculated more efficiently and accurately.

[0090] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0091] The above-mentioned integrated energy system carbon emission allocation device can be implemented in the form of a computer program, which can be used in Figure 7 Runs on the computer device shown.

[0092] See also Figure 7 , Figure 7 11 is a schematic block diagram of a computer device provided by an embodiment of the present invention. The computer device 1100 is a server, which may be an independent server or a server cluster composed of multiple servers.

[0093] See also Figure 7 The computer device 1100 includes a processor 1102 , a memory, and a network interface 1105 connected via a system bus 1101 , wherein the memory may include a non-volatile storage medium 1103 and an internal memory 1104 .

[0094] The non-volatile storage medium 1103 can store an operating system 11031 and a computer program 11032. When the computer program 11032 is executed, the processor 1102 can execute the integrated energy system carbon emission allocation method.

[0095] The processor 1102 is used to provide computing and control capabilities to support the operation of the entire computer device 1100 .

[0096] The internal memory 1104 provides an environment for the operation of the computer program 11032 in the non-volatile storage medium 1103. When the computer program 11032 is executed by the processor 1102, the processor 1102 can execute the carbon emission allocation method for the integrated energy system.

[0097] The network interface 1105 is used for network communication, such as providing data information transmission, etc. Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device 1100 to which the solution of the present invention is applied. The specific computer device 1100 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0098] Those skilled in the art will understand that Figure 7 The embodiments of the computer device shown in the figure do not constitute a limitation on the specific composition of the computer device. In other embodiments, the computer device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, in some embodiments, the computer device may only include a memory and a processor. In such embodiments, the structure and function of the memory and the processor are the same as those of the embodiment of the present invention. Figure 7 The embodiments shown are consistent and will not be described again here.

[0099] It should be understood that in the embodiment of the present invention, the processor 1102 may be a central processing unit (CPU), and the processor 1102 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0100] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium may be a non-volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the integrated energy system carbon emission allocation method of the embodiment of the present invention is implemented.

[0101] The storage medium is a physical, non-transient storage medium, for example, it can be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk, etc., which can store program codes.

[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0103] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for allocating carbon emissions from an integrated energy system, characterized in that: include: Acquire data of each uncertain factor, assign different scenario deviation coefficients to the data of each uncertain factor, obtain updated data of the uncertain factor and combine them to form different combined scenarios, wherein the uncertain factors include wind speed, light intensity, cooling load level, heating load level, electricity load level, and hydrogen load level; Using a genetic algorithm to calculate the value of the objective function corresponding to each task in each of the combined scenarios, to obtain the optimal target planning; Allocate carbon emissions based on a pre-built carbon emission flow model and optimal target planning; The step of obtaining data of each uncertainty factor, assigning different scenario deviation coefficients to the data of each uncertainty factor, obtaining updated data of the uncertainty factor and combining them to form different combined scenarios includes: obtaining historical data of all uncertainty factors within a specified period; sorting all corresponding historical data to obtain corresponding historical data sets, and multiplying preset scenario deviation coefficients corresponding to different scenarios by the historical data sets to form multiple different combined scenarios; The method of using a genetic algorithm to calculate the value of the objective function corresponding to each task under each of the combined scenarios to obtain the optimal target planning includes: obtaining the objective function of the genetic algorithm; for a combined scenario, obtaining the decision variables of each task under the combined scenario, and binary encoding each of the obtained decision variables to obtain a population formed by a combination of n individuals generated after encoding, wherein each of the individuals respectively contains chromosome information; performing chromosome operations, crossover operations, and mutation operations on all individuals of the population to obtain the optimal individuals corresponding to each of the tasks; decoding the optimal individuals corresponding to each of the tasks to convert them into corresponding optimal decision variables, and calculating the value of the objective function based on each of the optimal decision variables; The steps of performing chromosome operation, crossover operation and mutation operation on all individuals of the population to obtain the optimal individuals corresponding to each task include: calculating the fitness value of the chromosome of each individual based on the chromosome information of each individual, and after obtaining all the fitness values, selecting the individual corresponding to the fitness value with the largest value as the elite individual; obtaining the probability of information exchange between different tasks, constructing a corresponding learning factor matrix and obtaining the value of the learning factor matrix, and performing crossover exchange on the chromosome information of each elite individual based on the value of the learning factor matrix; updating the elite individuals after each crossover operation based on a preset probability change rule, and recording the number of updates; judging whether the number of updates reaches an update threshold, and if not, performing chromosome operation, crossover operation and mutation operation on the updated elite individuals until the number of updates reaches the update threshold and the optimal individual is output, and if so, directly outputting the optimal individual; The obtaining of the probability of information exchange between different tasks, constructing a corresponding learning factor matrix, and obtaining the value of the learning factor matrix includes: Calculate the degree of overlap between the kth task and the jth task rmp as follows: k，j : Among them, g k (p k , d) indicates that task k is in population p k The probability distribution model of the dth individual in g j (p j , d) indicates that task j is in population p j The probability distribution model of the dth individual in, K represents the total number of tasks, N k represents the total number of individuals in a population, and i represents the i-th individual in population p; The learning factor RMP is calculated as follows: The carbon emission amount is allocated based on the pre-constructed carbon emission flow model and the optimal target planning, including: constructing a carbon emission flow model based on the optimal target planning; calculating the energy flow in each carbon emission network based on the carbon emission flow model, and calculating the active power flowing into the line in the corresponding carbon emission network, the active power flowing out of the line, the active power of the generator, the active power consumed by the energy node, and the carbon emission intensity of the generator based on the energy flow; The amount of carbon emissions is calculated as follows: R G =P G *E G Among them, P G Indicates the active power of the generator, E G Indicates the carbon emission intensity of the generator; The carbon emission intensity of energy consumption nodes in the carbon emission network is calculated as follows: Among them, P N is the active power consumed by the energy-consuming node, represents the transpose of the active power flowing out of the line; The amount of carbon emissions allocated to each energy consumption node is calculated as follows: R N =P N *E N 。 2. The integrated energy system carbon emission allocation method according to claim 1, characterized in that: The step of cross-exchanging the chromosome information of each elite individual based on the value of the learning factor matrix includes: Get the value of the learning factor, use the algorithm to randomly generate a random value of 0 to 1, and determine whether the value of the learning factor is less than the value of the learning factor. If it is less than, cross-exchange the chromosome information between the two tasks and incorporate it into the next mutation operation. If it is greater, directly enter the next mutation operation.

3. A carbon emission distribution device for an integrated energy system, characterized in that: include: A combination unit is used to obtain data of each uncertain factor, assign different scene deviation coefficients to the data of each uncertain factor, obtain updated data of the uncertain factor and combine them to form different combination scenes, wherein the uncertain factors include wind speed, light intensity, cooling load level, heating load level, electric load level, and hydrogen load level; the data of each uncertain factor is obtained, different scene deviation coefficients are assigned to the data of each uncertain factor, and updated data of the uncertain factor is obtained and combined to form different combination scenes, including: obtaining historical data of all uncertain factors within a specified period; sorting all corresponding historical data to obtain corresponding historical data sets, and multiplying preset scene deviation coefficients corresponding to different scenes by the historical data sets to form multiple different combination scenes; A calculation unit is used to use a genetic algorithm to calculate the value of the objective function corresponding to each task under each of the combined scenarios to obtain an optimal target plan; the use of a genetic algorithm to calculate the value of the objective function corresponding to each task under each of the combined scenarios to obtain an optimal target plan includes: obtaining the objective function of the genetic algorithm; for a combined scenario, obtaining the decision variables of each task under the combined scenario, and binary encoding the obtained decision variables to obtain a population formed by a combination of n individuals generated after encoding, wherein each of the individuals respectively contains chromosome information; performing chromosome operations, crossover operations, and mutation operations on all individuals of the population to obtain the optimal individuals corresponding to each of the tasks; decoding the optimal individuals corresponding to each of the tasks to convert them into corresponding optimal decision variables, and calculating the value of the objective function based on each of the optimal decision variables; performing chromosome operations, crossover operations, and mutation operations on all individuals of the population to obtain the optimal individuals corresponding to each of the tasks The corresponding optimal individual includes: based on the chromosome information of each individual, calculating the fitness value of the chromosome of each individual, and after obtaining all the fitness values, selecting the individual corresponding to the fitness value with the largest value as the elite individual; obtaining the probability of information exchange between different tasks, and constructing a corresponding learning factor matrix and obtaining the value of the learning factor matrix, and based on the value of the learning factor matrix, cross-exchanging the chromosome information of each elite individual; based on the preset probability change rule, updating the elite individual after each cross-exchange operation, and recording the number of updates; judging whether the number of updates reaches the update threshold, if not, performing chromosome operation, crossover operation, and mutation operation on the updated elite individual until the number of updates reaches the update threshold and outputs the optimal individual, if reached, directly outputting the optimal individual; the obtaining the probability of information exchange between different tasks, and constructing the corresponding learning factor matrix and obtaining the value of the learning factor matrix, includes: Calculate the degree of overlap between the kth task and the jth task rmp as follows: k，j : Among them, g k (p k , d) indicates that task k is in population p k The probability distribution model of the dth individual in g j (p j , d) indicates that task j is in population p j The probability distribution model of the dth individual in, K represents the total number of tasks, N k represents the total number of individuals in a population, and i represents the i-th individual in population p; The learning factor RMP is calculated as follows: An allocation unit, for allocating carbon emissions based on a pre-constructed carbon emission flow model and an optimal target plan; the allocation of carbon emissions based on the pre-constructed carbon emission flow model and the optimal target plan includes: constructing a carbon emission flow model based on the optimal target plan; calculating the energy flow in each carbon emission network based on the carbon emission flow model, and calculating the active power flowing into the line in the corresponding carbon emission network, the active power flowing out of the line, the active power of the generator, the active power consumed by the energy-consuming node, and the carbon emission intensity of the generator based on the energy flow; The amount of carbon emissions is calculated as follows: R G =P G *E G Among them, P G Indicates the active power of the generator, E G Indicates the carbon emission intensity of the generator; The carbon emission intensity of energy consumption nodes in the carbon emission network is calculated as follows: Among them, P N is the active power consumed by the energy-consuming node, represents the transpose of the active power flowing out of the line; The amount of carbon emissions allocated to each energy consumption node is calculated as follows: R N =P N *E N 。 4. 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 integrated energy system carbon emission allocation method according to any one of claims 1 to 2 is implemented.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to execute the integrated energy system carbon emission allocation method as described in any one of claims 1 to 2.

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