CHP low-carbon scheduling method and system considering carbon emission-fuel-power coupling

Abstract

The invention discloses a CHP low-carbon scheduling method and system considering carbon emission-fuel-power coupling, and belongs to the technical field of optimization control of a combined heat and power generation system. The method comprises the steps that carbon emission-fuel-power coupling is considered, and a CHP operation domain model is established; fitting a plurality of planes of the CHP operation domain by adopting an IRANSAC (Irregular Random Sample Consensus) algorithm; performing convex enveloping on the plurality of planes fitted by the IRANSAC algorithm, and then performing triangular segmentation to obtain a CHP operation domain convex reconstruction model; and building a CHP low-carbon scheduling model by considering the start-stop and climbing constraints of the CHP to perform CHP scheduling. According to the method, the real operation condition of the CHP is simulated and represented, meanwhile, an iterative RANSAC algorithm is provided, mixed integer linear form reconstruction of the carbon emission-fuel-power coupling relation is achieved in combination with the triangular segmentation theory, convex representation is conducted on the trade-off relation between low carbon and economy, and the method can be conveniently integrated into the low-carbon scheduling problem.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of cogeneration system optimization control, and particularly relates to a CHP low-carbon scheduling method and system considering carbon emission-fuel-power coupling. BACKGROUND

[0002] As an efficient energy conversion device, combined heat and power (CHP) unit can realize power generation and heat supply at the same time, and becomes an indispensable existence in the energy system. Generally speaking, CHP unit can work in two modes: extraction condensing and back pressure mode. In the extraction condensing mode, the heat-to-power ratio of the CHP unit is adjustable, while in the back pressure mode, the CHP unit often works in the state of "heat determines power". Therefore, the CHP unit has higher flexibility in the extraction condensing mode, and many scholars have carried out extensive research on the modeling of CHP unit, and proposed a two-dimensional operating region model of CHP unit, visualizing the coupling relationship between power generation power and heat supply power; considering the limitation of the operating region of CHP unit, a heat storage tank and a heat boiler are introduced to run in parallel with the combined heat and power unit to expand the overall heat and power production area, and an expanded two-dimensional operating region model is visualized; the steam flow of the CHP unit is re-modeled by improving the slope and reserve, and the influence of the proportion of steam entering the heat supply network on the power generation is considered, and an improved CHP unit climbing and standby capacity model is proposed to better adapt to the integration of renewable energy. The above research focuses on the modeling of the operating condition of CHP unit, but does not focus on the influence of different operating conditions on economy and carbon emission.

[0003] Regarding the scheduling of CHP unit, the most common method is to regard the operation cost of CHP unit as a quadratic coupling function of power generation power and heat supply power, and to fit the coefficients of the function by empirical formula, to establish a higher level of comprehensive energy system, and to optimize scheduling and pricing. However, this method has obvious limitations, the coupling product of power generation power and heat supply power will make the objective function have quadratic term and bilinear term, significantly increasing the difficulty of solving the scheduling problem, and easily falling into local suboptimal solution. In addition, some studies focus on the carbon emission accounting in the operation process of CHP unit, but they either construct the relationship between carbon emission and power generation, heat supply power as a quadratic coupling function similar to operation cost, or directly use a fixed constant as the unit carbon emission factor. The above two treatments either significantly increase the difficulty of solving the model, or ignore the carbon emission difference of CHP unit under different operating conditions, so as to realize accurate accounting of carbon emission.

[0004] In general, the current research is not clear about the coupling relationship between the operating condition of CHP unit and cost, carbon emission, and the commonly used model has problems of nonlinearity, difficulty in solving, inaccuracy, etc.

[0005] As Chinese patent application CN202510696534.0 proposes a cogeneration system control strategy based on AME-TD3 algorithm, the strategy aims at the insufficient exploration of the existing TD3 algorithm, and adds a dynamically self-adjusting noise generation function to improve the convergence speed and enhance the adaptive adjustment ability of the system to load fluctuations and environmental condition changes. However, the network of the AME-TD3 reinforcement learning algorithm established is too complex and theoretical, and cannot be applied in practice; For example, Chinese patent application CN202411957171.3 discloses a zero-carbon scheduling method for a virtual power plant containing a photovoltaic thermal power plant considering multi-resource game, which constructs a master-slave game model with one master and multiple followers, with power balance as the constraint condition, the leader aiming to realize zero-carbon operation and maximize total revenue, and the follower aiming to maximize revenue. However, the analysis directly uses a fixed constant as the unit carbon emission factor, which cannot realize accurate accounting of carbon emissions; For example, Chinese patent application CN202410352939.8 provides a multi-time scale regulation method and device for a comprehensive energy system, which constructs a heat flow model according to the heat flow method, obtains a system day-ahead operation plan of the comprehensive energy system, and performs actual regulation to obtain the actual output of the unit of the heating system throughout the day and performs intraday operation optimization. However, the objective function contains quadratic terms and bilinear terms, which significantly increases the difficulty of solving the scheduling problem.

[0006] Therefore, a CHP low-carbon scheduling method considering carbon emission-fuel-power coupling is proposed. SUMMARY

[0007] In view of the deficiencies of the prior art, the purpose of the present application is to provide a CHP low-carbon scheduling method and system considering carbon emission-fuel-power coupling, which solves the problems in the prior art.

[0008] The purpose of the present application can be achieved by the following technical solutions: The CHP low-carbon scheduling method considering carbon emission-fuel-power coupling comprises the following steps: Considering carbon emission-fuel-power coupling, a CHP operation domain model is established; Based on the CHP operation domain model, an IRANSAC algorithm is used to fit multiple planes of the CHP operation domain; After fitting the multiple planes by the IRANSAC algorithm, a convex envelope is performed, and then triangular division is performed to obtain a CHP operation domain convex reconstruction model; Based on the CHP operation domain convex reconstruction model, a CHP low-carbon scheduling model is established considering the start-stop and climbing constraints of the CHP to perform CHP scheduling.

[0009] Further, the CHP operation domain model is: In the formula, and CHP at time Operating costs and carbon emissions, and These are the unit purchase cost of fuel and the unit carbon emissions, respectively. It is a moment Fuel consumption; It is the total calorific value of the fuel. For the set of scheduling moments, It is CHP at time Heat loss rate; It is the power supply. and heating power The vector formed It is a symmetric matrix composed of the coefficients of the quadratic terms. It is a vector consisting of the coefficients of the linear terms, where c is a constant. , , Denotes the coefficient of the quadratic term. and This represents the coefficient of the linear term.

[0010] Furthermore, the steps for fitting multiple planes of the CHP operating domain using the IRANSAC algorithm include: Step 1, Initialize the plane index Plane parameter set Set of points in a plane and the set of points outside the plane , Input point cloud dataset; Step 2, when Steps 21 to 23 are executed in a loop, where, The threshold value is the outer point threshold. Step 21, Initialize the optimal interior point ratio Number of dynamic iterations , among which, Maximum number of iterations; for the th In the next loop, Random sampling There are 10 points, of which 10 are points. For the first Fitting plane in the next iteration The set of external points; Step 22: Use SVD decomposition to calculate plane parameters and obtain the parameters at the 3rd 4th 5th 6th 7th 8th 9th 10th 11th 12th 13th 14th 15th 16th 17th 18th 19 ... Fitting plane in the next iteration center point and normal vector ; Step 23, calculate the distance between all points and the fitted plane: , ; when When calculating the interior point ratio ,in For the first Fitting plane in the next iteration The set of interior points; when Update , , , , ; Step 3, Update , Output the fitted plane results and parameter information.

[0011] Furthermore, the CHP runtime domain convex reconstruction model is as follows: In the formula, Indicates whether the run point falls on the th Within each triangle, , , These are the convex combination coefficients of the triangle vertices. The number of triangles. , , The first The three-dimensional coordinate vectors of the three vertices of a triangle. Let be the three-dimensional coordinate vector of the operating point of CHP, representing electrical power, thermal power, and coal consumption, respectively.

[0012] Furthermore, the start / stop and ramp constraints of the CHP are as follows: In the formula, , and These represent the time intervals of CHP. Whether it is online, running, or stopped, when , and A value of 1 indicates that the system is in online, running, or stopped status, while a value of 0 indicates the opposite. and These are the minimum startup and shutdown times for CHP, respectively. The slope rate of CHP. For the installed capacity of CHP, The index is accumulated over time.

[0013] Furthermore, the operational boundary conditions for CHP are: In the formula, For the three-dimensional bounded operating domain surface of CHP It is a three-dimensional set of real numbers.

[0014] A CHP low-carbon dispatch system considering carbon emission-fuel-power coupling includes: Operating domain construction module: Considering carbon emission-fuel-power coupling, a CHP operating domain model is established; Fitting module: Based on the CHP operating domain model, the IRANSAC algorithm is used to fit multiple planes of the CHP operating domain; Triangulation module: After convex hulling of multiple planes fitted by the IRANSAC algorithm, triangulation is then performed to obtain the CHP running domain convex reconstruction model. Scheduling Module: Based on the CHP runtime domain convex reconstruction model, taking into account the start-up, shutdown, and ramp-up constraints of CHP, a low-carbon scheduling model for CHP is established to perform CHP scheduling.

[0015] A computer storage medium storing a readable program that, when executed, instructs a computing device to perform the CHP low-carbon scheduling method as described above, which takes into account carbon emissions-fuel-power coupling.

[0016] An electronic device includes: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the CHP low-carbon scheduling method that takes into account carbon emission-fuel-power coupling as described above.

[0017] A computer program product includes computer instructions that instruct a computing device to perform operations corresponding to the CHP low-carbon scheduling method that considers carbon emissions-fuel-power coupling as described above.

[0018] The beneficial effects of this invention are: 1. This invention proposes a CHP operation model that considers the coupling relationship between carbon emissions, fuel, and power. It effectively characterizes the coupling relationship between economic carbon emissions and CHP operation, and fully considers the ramp-up and start-up / shutdown characteristics of CHP operation, effectively simulating and characterizing the real operating conditions of CHP. At the same time, it proposes an iterative RANSAC algorithm and combines it with triangular partitioning theory to realize the mixed integer linear form reconstruction of the carbon emissions-fuel-power coupling relationship, and convexizes the trade-off between low carbon and economy, which can be easily integrated into the low carbon scheduling problem.

[0019] 2. This invention proposes a three-dimensional operating domain model of CHP with carbon emission-fuel-power coupling, which realizes the visualization of carbon emission, cost and equipment operating conditions, while accurately characterizing the carbon emission and coal consumption during the operation of CHP, effectively avoiding the coarse characterization of carbon emission and coal consumption during the operation of CHP by traditional methods.

[0020] 3. This invention proposes a multi-plane fitting method for the CHP three-dimensional running domain using the IRANSAC algorithm. This method can achieve adaptive multi-plane fitting without manually specifying the number of fitting planes. Compared with the manually specified method, it can significantly improve the accuracy of the fitting planes while ensuring the scalability of the algorithm.

[0021] 4. This invention proposes a convex reconstruction method for the CHP operating domain based on triangular segmentation theory. The method uses triangular segmentation theory to partition the fitted multi-plane, dividing the irregular plane into a set of regular triangles. By selecting different triangles to describe different operating conditions of CHP, the convex reconstruction of the three-dimensional operating domain model of CHP is realized, which can be easily integrated into conventional low-carbon scheduling models.

[0022] 5. This invention proposes a start-stop and ramp-up model for CHP, introducing three binary variables to simulate whether CHP is in online, offline, or start-stop states, and using ramp-up rate to characterize the increase or decrease of CHP's output power between two time points, effectively representing the real operation of CHP.

[0023] 6. This invention proposes a three-dimensional bounded operating domain model for CHP, and uses a visual method to explicitly express the boundary constraints during the operation of CHP, providing a direct reference for relevant schedulers to schedule and control CHP. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of the CHP low-carbon scheduling method of the present invention; Figure 2 This is a schematic diagram of the operating domain of the CHP unit carbon emissions-fuel-power according to the present invention; Figure 3 This is a schematic diagram of the fitting plane of the operating domain of the CHP unit according to the present invention; Figure 4 This is a triangular segmentation diagram of the operating domain of the CHP unit according to the present invention; Figure 5 This is a schematic diagram showing the distribution of operating points of the CHP unit of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Example 1 like Figure 1 As shown, the CHP low-carbon dispatch method considering carbon emission-fuel-power coupling includes the following steps: S1. Considering carbon emission-fuel-power coupling, a CHP operating domain model is established. The fuel heat rate curve model for CHP is as follows: In the formula, It is CHP at time Heat rate, It is the power supply. and heating power The vector formed It is a symmetric matrix composed of the coefficients of the quadratic terms. It is a vector consisting of the coefficients of the linear terms, where c is a constant. , , Denotes the coefficient of the quadratic term. , Denotes the coefficient of the linear term; It is a moment fuel consumption, It is the total calorific value (GHV) of the fuel. It is the set of scheduling moments.

[0028] like Figure 2 As shown, the carbon emission-fuel-power coupling model of CHP is as follows: In the formula, and CHP at time Operating costs and carbon emissions, and These are the unit purchase cost of fuel and the unit carbon emissions, respectively.

[0029] S2, Based on the CHP operating domain model, the Iterative Random Sample Consensus (IRANSAC) algorithm is used to fit multiple planes of the CHP operating domain; The steps for fitting multiple planes in the CHP operating domain using the IRANSAC algorithm include: S21, Parameter Initialization: Initialize Plane Index Plane parameter set Set of points in a plane and the set of points outside the plane , The input is a point cloud dataset.

[0030] S22, Multiplane Fitting: When The loop executes S221~S223, where, The threshold value is the outer point threshold. S221, Single-plane fitting: Initializing the optimal in-plane ratio Number of dynamic iterations , among which, Maximum number of iterations; for the th In the next loop, Random sampling There are 10 points, of which 10 are points. For the first Fitting plane in the next iteration The set of external points; S222, Model Estimation: Singular Value Decomposition (SVD) is used to calculate plane parameters, obtaining the parameters at the 3rd plane. Fitting plane in the next iteration center point and normal vector .

[0031] S223, Interior Point Detection: Calculate the distance between all points and the fitted plane. in ; when When calculating the interior point ratio ,in For the first Fitting plane in the next iteration The set of interior points; when Update , , , , ; S23, Parameter Update: Update , Output the fitted plane results and parameter information.

[0032] S3. After applying convex hulls to multiple planes fitted by the IRANSAC algorithm, triangulation is then performed to obtain the CHP running domain convex reconstruction model. The process of convex hull formation is as follows: First, the boundaries on each plane fitted by the IRANSAC algorithm are extracted. Then, appropriate boundary points are selected based on the curvature of the boundary lines and the required accuracy. The selection criterion for boundary points is: the greater the curvature, the more boundary points are selected. Finally, the selected boundary points on each plane are connected sequentially to realize the convex hull of each plane.

[0033] The process of triangulation is as follows: Based on the convex hull results of each plane, three boundary points are arbitrarily selected in each plane and connected sequentially to construct a triangle; the above operation is then repeated for the remaining boundary points to construct multiple triangles until all boundary points have their own triangles, thus achieving triangulation of all planes.

[0034] After triangulation, the resulting convex reconstruction model of the CHP runtime domain is as follows: In the formula, Indicates whether the run point falls on the th Within each triangle, , , These are the convex combination coefficients of the triangle vertices. The number of triangles. , , The first The three-dimensional coordinate vectors of the three vertices of a triangle. Let be the three-dimensional coordinate vector of the operating point of CHP, representing electrical power, thermal power, and coal consumption, respectively.

[0035] S4. Based on the convex reconstruction model of the CHP operating domain, taking into account the start-up, shutdown and ramp-up constraints of CHP, a low-carbon scheduling model for CHP is established to perform CHP scheduling. The CHP low-carbon scheduling model includes: the convex reconstruction model in S3, the operating boundary conditions of CHP, and the start-stop and ramp-up constraints of CHP.

[0036] The boundary conditions for CHP operation are: In the formula, For the three-dimensional bounded operating domain surface of CHP It is a three-dimensional set of real numbers.

[0037] The start / stop and ramp constraints for CHP are: In the formula, , and These represent the time intervals of CHP. Whether it is in an online, startup, or shutdown state. A value of 1 indicates that it is in an online, startup, or shutdown state, and a value of 0 indicates the opposite. and These are the minimum startup and shutdown times for CHP, respectively. The slope rate of CHP. For the installed capacity of CHP, The index is accumulated over time.

[0038] Specifically, the CHP low-carbon scheduling model is as follows: The objective function is to minimize the running cost of CHP. The constraints include the following: Carbon emission constraints: In the formula, This is the upper limit for carbon emissions.

[0039] Runtime boundary constraints: Convexation Reconstruction Constraints: Climbing and start-stop constraints: The process of using the CHP low-carbon scheduling model for low-carbon scheduling is as follows: The objective function of the above CHP low-carbon scheduling model is only the operating cost of CHP itself, which can be easily extended to other low-carbon scheduling scenarios, such as coordinating the scheduling of conventional thermal power units and new energy units. Only the operating costs of thermal power units and new energy units need to be added to the objective function. In this invention, the operating constraints of CHP have been convexized and reconstructed into the form of mixed integer linear programming (MILP), which can be easily integrated into low-carbon scheduling scenarios that include other equipment, ensuring the accuracy of the scheduling strategy and effectively reducing the computational burden of strategy generation.

[0040] Based on a similar inventive concept, embodiments of the present invention also provide a computer storage medium storing a readable program that, when run by a processor, can execute the aforementioned CHP low-carbon scheduling method.

[0041] Based on a similar inventive concept, this invention provides an electronic device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the CHP low-carbon scheduling method described above.

[0042] Based on a similar inventive concept, embodiments of the present invention also provide a computer program product, including computer instructions, which instruct a computing device to perform the operations corresponding to the above-described CHP low-carbon scheduling method.

[0043] Example 2 In this embodiment, a specific CHP unit operation example is given to demonstrate the superiority of the CHP low-carbon scheduling method proposed in Embodiment 1. Among them, the operating domain of CHP unit carbon emissions-fuel-power is as follows: Figure 2 As shown, the maximum power generation capacity of CHP is 247MW, the minimum power generation capacity is 81MW, the maximum heating capacity is 180MW, the minimum heating capacity is 0MW, and the minimum start-up and shutdown time is 8 hours; other relevant parameters of the operating domain are shown in Table 1. Table 1. Statistics of Operating Domain Related Parameters The fitting plane of the CHP unit operating domain is as follows: Figure 3 As shown, 9 boundaries are selected in fitting plane 1 to form a convex hull, 10 boundaries are selected in fitting plane 2 to form a convex hull, and 9 boundaries are selected in fitting plane 3 to form a convex hull.

[0044] The triangular division of the CHP unit operating domain is as follows: Figure 4 As shown, fitting plane 1 is divided into 7 adjacent triangles, fitting plane 2 is divided into 6 adjacent triangles, and fitting plane 3 is divided into 3 adjacent triangles.

[0045] Distribution of operating points of CHP units under low-carbon dispatching is as follows Figure 5 As shown, it can be seen that in the example, the operating points of CHP at each time point belong to the operating domain, indicating that the present invention intuitively and accurately represents the mapping relationship between CHP unit cost, carbon emissions and operating conditions, thereby simulating the real operating state of CHP.

[0046] Example 3 A CHP low-carbon dispatch system considering carbon emission-fuel-power coupling includes: Operating domain construction module: Considering carbon emission-fuel-power coupling, a CHP operating domain model is established; Fitting module: Based on the CHP operating domain model, the IRANSAC algorithm is used to fit multiple planes of the CHP operating domain; Triangulation module: After convex hulling of multiple planes fitted by the IRANSAC algorithm, triangulation is then performed to obtain the CHP running domain convex reconstruction model. Scheduling Module: Based on the CHP runtime domain convex reconstruction model, taking into account the start-up, shutdown, and ramp-up constraints of CHP, a low-carbon scheduling model for CHP is established to perform CHP scheduling.

[0047] The methods of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses the code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for performing the methods shown herein.

[0048] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A CHP low-carbon dispatching method considering carbon emission-fuel-power coupling, characterized in that, Includes the following steps: Considering carbon emission-fuel-power coupling, a CHP operating domain model is established; Based on the CHP runtime model, the IRANSAC algorithm is used to fit multiple planes of the CHP runtime. After applying convex hulls to multiple planes fitted by the IRANSAC algorithm, triangulation is then performed to obtain the convex reconstruction model of the CHP running domain. Based on the convex reconstruction model of the CHP operating domain, and taking into account the start-up, shutdown, and ramp-up constraints of CHP, a low-carbon scheduling model for CHP is established for CHP scheduling.

2. The CHP low-carbon scheduling method considering carbon emission-fuel-power coupling according to claim 1, characterized in that, The CHP runtime domain model is as follows: In the formula, and CHP at time Operating costs and carbon emissions, and These are the unit purchase cost of fuel and the unit carbon emissions, respectively. It is a moment Fuel consumption; It is the total calorific value of the fuel. For the set of scheduling moments, It is CHP at the moment Heat loss rate; It is the power supply. and heating power The vector formed It is a symmetric matrix composed of the coefficients of the quadratic terms. It is a vector consisting of the coefficients of the linear terms, where c is a constant. , , Denotes the coefficient of the quadratic term. and This represents the coefficient of the linear term.

3. The CHP low-carbon dispatching method considering carbon emission-fuel-power coupling according to claim 1, characterized in that, The steps for fitting multiple planes in the CHP operating domain using the IRANSAC algorithm include: Step 1, Initialize the plane index Plane parameter set Set of points in a plane and the set of points outside the plane , Input point cloud dataset; Step 2, when Steps 21 to 23 are executed in a loop, where, This is the threshold for the outer point; Step 21, Initialize the optimal interior point ratio Number of dynamic iterations , among which, Maximum number of iterations; for the th In the next loop, Random sampling There are 10 points, of which 10 are points. For the first Fitting plane in the next iteration The set of external points; Step 22: Use SVD decomposition to calculate plane parameters and obtain the parameters at the 3rd 4th 5th 6th 7th 8th 9th 10th 11th 12th 13th 14th 15th 16th 17th 18th 19 ... Fitting plane in the next iteration center point and normal vector ; Step 23, calculate the distance between all points and the fitted plane: , ； when When calculating the interior point ratio ,in For the first Fitting plane in the next iteration The set of interior points; when Update , , , , ; Step 3, Update , Output the fitted plane results and parameter information.

4. The CHP low-carbon scheduling method considering carbon emission-fuel-power coupling according to claim 1, characterized in that, The CHP runtime domain convex reconstruction model is as follows: In the formula, Indicates whether the run point falls on the th Within each triangle, , , These are the convex combination coefficients of the triangle vertices. The number of triangles. , , The first The three-dimensional coordinate vectors of the three vertices of a triangle. Let be the three-dimensional coordinate vector of the operating point of CHP, representing electrical power, thermal power, and coal consumption, respectively.

5. The CHP low-carbon dispatching method considering carbon emission-fuel-power coupling according to claim 2, characterized in that, The start / stop and ramp constraints for the CHP are as follows: In the formula, , and These represent the time intervals of CHP. Whether it is online, running, or stopped, when , and A value of 1 indicates that the system is in online, running, or stopped status, while a value of 0 indicates the opposite. and These are the minimum startup and shutdown times for CHP, respectively. The slope rate of CHP. For the installed capacity of CHP, The index is accumulated over time.

6. The CHP low-carbon dispatching method considering carbon emission-fuel-power coupling according to claim 5, characterized in that, The boundary conditions for CHP operation are: In the formula, For the three-dimensional bounded operational domain surface of CHP It is a three-dimensional set of real numbers.

7. A CHP low-carbon dispatching system considering carbon emission-fuel-power coupling, characterized in that, include: Operating domain construction module: Considering carbon emission-fuel-power coupling, a CHP operating domain model is established; Fitting module: Based on the CHP operating domain model, the IRANSAC algorithm is used to fit multiple planes of the CHP operating domain; Triangulation module: After convex hulling of multiple planes fitted by the IRANSAC algorithm, triangulation is then performed to obtain the CHP running domain convex reconstruction model. Scheduling Module: Based on the CHP runtime domain convex reconstruction model, taking into account the start-up, shutdown, and ramp-up constraints of CHP, a low-carbon scheduling model for CHP is established to perform CHP scheduling.

8. A computer storage medium storing a readable program, characterized in that, When the program is running, the program can instruct the computing device to execute the CHP low-carbon scheduling method that takes into account carbon emission-fuel-power coupling as described in any one of claims 1-6.

9. An electronic device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the CHP low-carbon scheduling method that takes into account carbon emission-fuel-power coupling as described in any one of claims 1-6.

10. A computer program product comprising computer instructions, characterized in that, The computer instructions instruct the computing device to perform the operations corresponding to the CHP low-carbon scheduling method that takes into account carbon emission-fuel-power coupling as described in any one of claims 1-6.

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