Power regulation and control method and device based on dynamic electrical carbon factor, terminal equipment and storage medium

Abstract

The invention discloses an electric power regulation and control method and device based on a dynamic electric carbon factor, terminal equipment and a storage medium. The method comprises the following steps: acquiring operation data of an electric power system; constructing an electric carbon factor model of each node of the power system based on the operation data; under a preset constraint condition, constructing an electric carbon emission model based on each electric carbon factor model; and solving the electric power carbon emission model through a vector weighted average algorithm to obtain a carbon emission degree capacity instruction of the electric power system, and further regulating and controlling the electric power system based on the carbon emission degree capacity instruction. According to the method, the power carbon factor model is constructed based on the operation data of the power system, the power carbon factor model is used as the input of the power carbon emission model, the power carbon emission model is solved through the vector weighted average algorithm, regulation and control of the power grid are optimized based on the solved result, the accuracy of power regulation and control is greatly improved, and the power regulation and control efficiency is improved. And the carbon emission of the power system can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of power regulation technology, and in particular to a power regulation control method, device, terminal equipment and storage medium based on a dynamic electric carbon factor. Background Art

[0002] In recent years, the methods and strategies for reducing carbon emissions from electricity users include the following methods: time-of-use pricing, which customizes electricity prices at different times according to current demand and supply conditions, encouraging electricity consumers to shift consumption to non-peak hours with lower carbon intensity; critical peak pricing, which provides higher rates during peak demand or high carbon intensity periods to encourage reduced use; coordinating multiple users through demand response aggregators to aggregate demand response capabilities and optimize economic and carbon efficiency; providing real-time data on energy consumption and carbon intensity through smart meters and advanced metering infrastructure; on-site power generation, installing solar panels or small wind turbines at home or business; smart grid, implementing smart grid technology to achieve real-time monitoring and management of energy use, etc. However, existing technologies do not take into account the impact of grid-related equipment (generators, etc.) on power regulation, resulting in excessive carbon emissions in the power system.

[0003] Therefore, there is an urgent need for a power regulation and control strategy to solve the problem of excessive carbon emissions in the power system. Summary of the invention

[0004] The embodiments of the present invention provide a power regulation and control method, device, terminal equipment and storage medium based on a dynamic electric carbon factor to solve the problem of excessive carbon emissions in the power system.

[0005] In order to solve the above problems, an embodiment of the present invention provides a power regulation and control method based on a dynamic electric carbon factor, comprising:

[0006] Obtaining operation data of power system;

[0007] Based on the operating data, construct an electric carbon factor model for each node of the power system;

[0008] Under preset constraints, constructing an electricity carbon emission model based on each of the electricity carbon factor models;

[0009] The electric power carbon emission model is dynamically solved by a vector weighted average algorithm to obtain a carbon emission capacity instruction for the electric power system, and then the electric power system is regulated based on the carbon emission capacity instruction.

[0010] As an improvement of the above scheme, the operating data includes: total power generation, total number of generators, injected power of generators, total amount of electric energy of the generating end connected to the node, number of generating ends of the power system, active power of loads connected to the node, total number of loads connected to the node, node power flow injection connected to the current node, total number of nodes connected to the current node; based on the operating data, constructing an electric carbon factor model for each node of the power system includes:

[0011] According to the total power generation, the total number of generators and the injected power of the generators, the random load calculation formula is substituted to obtain the random load data of each node of the power system; wherein the random load calculation formula satisfies the following conditions:

[0012]

[0013] Where D i is the random load data, G max is the total power generation, g max is the total number of generators, is the injected power of the jth generator to the ith node, b is the proportional parameter, a i is a random parameter, a j is the proportion of the jth load to the total load, and d is the number of nodes;

[0014] Taking each of the random load data as input, combined with the total amount of electric energy of the power generation end connected to the node, the number of power generation ends of the power system, the active power of the load connected to the node, the total number of loads connected to the node, the node power flow injection connected to the current node, and the total number of nodes connected to the current node, an electric carbon factor model of each node in the power system is constructed; wherein the electric carbon factor model satisfies the following conditions:

[0015]

[0016] In the formula, g i and They represent the total amount of electric energy produced by the kth power generation terminal connected to the i-th node in the power system, the total number of power generation ports included in the power system, and the corresponding electric carbon factor; and d i They represent the active power of the load connected to the i-th node and the total number of loads connected to the i-th node respectively; and n i They represent the power flow injected from the jth node to the ith node and the total number of nodes connected to the ith node; is the diagonal component in the matrix, representing the total power flowing out of the i-th node; P i jis the non-diagonal component, which is the power flow injected from the j-th node to the i-th node.

[0017] As an improvement to the above solution, the constraint condition satisfies the following conditions:

[0018]

[0019] In the formula, f(X i ) represents the actual target calculated value of the i-th group of influencing factors; F(X i ) represents the calculated practicality value of the i-th group; is the minimum power constraint of the system, indicating the minimum power output allowed; ΔP C (k) is the power change at the kth data update, indicating the dynamic power load change of the system at the current moment; is the maximum power constraint of the system, indicating the maximum power output allowed; X i (j) represents the contribution value of the jth influencing factor in the i-th group to the power, which represents the power distribution of each influencing factor in the system.

[0020] As an improvement of the above solution, the electricity carbon emission model meets the following conditions:

[0021]

[0022] In the formula, The carbon emission capacity instruction representing the carbon emission level of the power data obtained when the kth data is updated; is the carbon emission capacity instruction obtained when the carbon emission level of the power data is updated for the k+1th time; p i is an n-dimensional stable vector, representing the proportion of the i-th influencing factor in the entire electricity carbon emissions; represents the dynamic electric carbon factor of the i-th node at the k-th update; and are the minimum and maximum allowed powers of the i+1th node, respectively, limiting the power output range of the system; and are the minimum and maximum power output constraints of the jth node respectively; and They are the minimum and maximum power output constraints of the i+2-nth nodes, respectively, and are used to calculate the contribution of different nodes in power allocation.

[0023] As an improvement of the above scheme, the method of solving the electric power carbon emission model by a vector weighted average algorithm to obtain the carbon emission capacity instruction of the electric power system includes:

[0024] Cyclic execution of the electricity carbon emission model operation, and outputting a carbon emission level capacity instruction when the number of cycles is a cycle threshold;

[0025] The specific operation of the electricity carbon emission model is as follows:

[0026] Obtain a new vector; wherein the new vector satisfies the following conditions:

[0027]

[0028] In the formula, is the current position of the solution, and is the position virtual random number, ρ is the position judgment random number, r1 and r2 are weighted means;

[0029] Based on a preset exploration function, a weighted average factor and a scaling factor are calculated; wherein the exploration function satisfies the following conditions:

[0030] δ=2r1×rr

[0031] σ=2r2×rr

[0032]

[0033] Where r1 and r2 represent random numbers between 0 and 1; l is the latest iteration in the optimization process, r, r1 and r2 represent weighted means, and k max is the total number of iterations; δ is the weighted average factor, σ is the scaling factor;

[0034] The active power of the load connected to the node in the electric carbon factor model and the node power flow connected to the current node are injected as the initial vector, and merged with the newly created vector and the random number to obtain the merged vector;

[0035] Based on the weighted average factor, the scaling factor and the combined vector, the electric power carbon emission model is calculated to obtain the carbon emission degree capacity instruction;

[0036] Determine whether the current carbon emission capacity instruction meets the preset instruction conditions;

[0037] If not, a new newly created vector is obtained again as the input of the next electricity carbon emission model operation, and the electricity carbon emission model operation is re-executed;

[0038] If so, the current newly created vector is updated, and the updated newly created vector is verified based on the preset verification conditions. The verified newly created vector is used as the input of the next electricity carbon emission model operation, and the electricity carbon emission model operation is re-executed.

[0039] As an improvement of the above solution, the updating of the current newly created vector includes:

[0040] According to a preset vector update formula, the current newly created vector is updated to obtain an updated newly created vector; wherein the vector update formula is specifically:

[0041]

[0042] Where d1, d2, dn, rand, and φ are random numbers between 0 and 1; d1 is used to determine which update strategy to choose; X best is the position of the best individual in the current population, usually corresponding to the optimal solution of the current carbon emission optimization target; dn is used to adjust the step size and affect the vector update amplitude; is the adjustment matrix of the ith individual in the lth iteration, which is used to control the degree of migration of the individual to the optimal solution and to further refine the selection of the update strategy; X w is the calculated central position, usually a weighted average of multiple individual positions, used to balance exploration and development; v1 and v2 are auxiliary variables that control the selection results in some conditional branches; rand is a random number; X a , X b , X c Three different individual positions are used to calculate the population center or for random search to increase the diversity of the search.

[0043] As an improvement to the above solution, the verification condition satisfies the following conditions:

[0044]

[0045] In the formula, and is the minimum and maximum allowable power, limiting the power output range of the system; j is the jth individual, representing the i-th node; n is the total number of individuals, i.e. the total number of nodes; is the position of the i-th individual after the l+1th iteration; is the position of the i-th individual after the l-th iteration; is the calculated value representing the vector position of the i-th group after the l-th iteration; represents the calculated practicality value of the i-th group after the l-th iteration.

[0046] Accordingly, an embodiment of the present invention further provides a power regulation and control device based on a dynamic electric carbon factor, comprising: a data acquisition module, a first model building module, a second model building module and a result generation module;

[0047] The data acquisition module is used to acquire the operation data of the power system;

[0048] The first model building module is used to build an electric carbon factor model for each node of the power system based on the operating data;

[0049] The second model building module is used to build an electric power carbon emission model based on each of the electric carbon factor models under preset constraints;

[0050] The result generation module is used to dynamically solve the power carbon emission model through a vector weighted average algorithm to obtain the carbon emission level capacity instruction of the power system, and then regulate the power system based on the carbon emission level capacity instruction.

[0051] Correspondingly, an embodiment of the present invention further provides a computer terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a power regulation and control method based on a dynamic electric-carbon factor as described in the present invention.

[0052] Correspondingly, an embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a power regulation and control method based on a dynamic electric carbon factor as described in the present invention.

[0053] As can be seen from the above, the present invention has the following beneficial effects:

[0054] The present invention provides a power regulation and control method based on a dynamic electric carbon factor, which obtains the operating data of the power system; constructs an electric carbon factor model of each node of the power system based on the operating data; constructs an electric carbon emission model based on each electric carbon factor model under preset constraints; solves the electric carbon emission model by a vector weighted average algorithm to obtain a carbon emission capacity instruction of the power system, and then regulates the power system based on the carbon emission capacity instruction. The present invention constructs an electric carbon factor model based on the operating data of the power system, and uses the electric carbon factor model as the input of the electric carbon emission model, which can track the carbon emissions related to power generation, and solves the electric carbon emission model based on a vector weighted average algorithm, and then optimizes the regulation of the power grid based on the solution result, taking into account the influence of the power system on power regulation, greatly improving the accuracy of power regulation, and thus reducing the carbon emissions of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a power regulation and control method based on a dynamic electric carbon factor provided by an embodiment of the present invention;

[0056] Figure 2 It is a structural schematic diagram of a power regulation and control device based on a dynamic electric carbon factor provided by an embodiment of the present invention;

[0057] Figure 3 It is a schematic diagram of the structure of a terminal device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] 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 only 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.

[0059] Embodiment 1

[0060] See also Figure 1 , Figure 1 is a flow chart of a power regulation and control method based on a dynamic electric carbon factor provided by an embodiment of the present invention, such as Figure 1 As shown, this embodiment includes steps 101 to 104, and each step is specifically as follows:

[0061] Step 101: Acquire operation data of the power system.

[0062] In this embodiment,

[0063] Step 102: Based on the operating data, construct an electric carbon factor model for each node in the power system.

[0064] In this embodiment, the operating data includes: total power generation, total number of generators, injected power of generators, total amount of electric energy of the generating end connected to the node, number of generating ends of the power system, active power of loads connected to the node, total number of loads connected to the node, node power flow injection connected to the current node, total number of nodes connected to the current node; based on the operating data, constructing an electric carbon factor model for each node of the power system includes:

[0065] According to the total power generation, the total number of generators and the injected power of the generators, the random load calculation formula is substituted to obtain the random load data of each node of the power system; wherein the random load calculation formula satisfies the following conditions:

[0066]

[0067] Where D i is the random load data, G max is the total power generation, g max is the total number of generators, is the injected power of the jth generator to the ith node, b is the proportional parameter, a i is a random parameter, a j is the proportion of the jth load to the total load, and d is the number of nodes;

[0068] Taking each of the random load data as input, combined with the total amount of electric energy of the power generation end connected to the node, the number of power generation ends of the power system, the active power of the load connected to the node, the total number of loads connected to the node, the node power flow injection connected to the current node, and the total number of nodes connected to the current node, an electric carbon factor model of each node in the power system is constructed; wherein the electric carbon factor model satisfies the following conditions:

[0069]

[0070] In the formula, g i and They represent the total amount of electric energy produced by the kth power generation terminal connected to the i-th node in the power system, the total number of power generation ports included in the power system, and the corresponding electric carbon factor; and d i They represent the active power of the load connected to the i-th node and the total number of loads connected to the i-th node respectively; and n i They represent the power flow injected from the jth node to the ith node and the total number of nodes connected to the ith node; is the diagonal component in the matrix, representing the total power flowing out of the i-th node; P i j is the non-diagonal component, which is the power flow injected from the j-th node to the i-th node.

[0071] In a specific embodiment, the carbon emission factor can be obtained by calculating the active power flow between each network node. The current input from the power generation end and the corresponding carbon emission intensity directly determine the carbon emission factor of the node.

[0072] It should be noted that the dynamic electricity carbon factor is based on the calculation of carbon emission flow. The process combines the data concentration of the data source area and the regional power supply carbon emission factor to accurately quantify and analyze the real-time carbon emissions of the power system network, and provide users with targeted carbon emission reduction electricity technology guidance.

[0073] It is understandable that based on the above-mentioned electric carbon factor calculation model, the carbon emissions affecting each node are analyzed. Because the power flow changes of each node are difficult to control, the load of each node is selected As input variables, the calculation results of the electric carbon factor of each node As output, it is transformed into a d-dimensional load input and n-dimensional electric carbon factor output model.

[0074] According to the maximum power generation of the system, the maximum range of total load is set. To ensure that the input load data is random data, n+1 random numbers from 0 to 1 are set, of which n random parameters are the proportion of load to total load a i (i=1,2...d), the remaining random parameter is the ratio parameter b(0 <b<1)。

[0075] Step 103: Under preset constraints, construct an electricity carbon emission model based on each of the electricity carbon factor models.

[0076] In this embodiment, the constraints satisfy the following conditions:

[0077]

[0078] In the formula, f(X i ) represents the actual target calculated value of the i-th group of influencing factors; F(X i ) represents the calculated practicality value of the i-th group; is the minimum power constraint of the system, indicating the minimum power output allowed; ΔP C (k) is the power change at the kth data update, indicating the dynamic power load change of the system at the current moment; is the maximum power constraint of the system, indicating the maximum power output allowed; X i (j) represents the contribution value of the jth influencing factor in the i-th group to the power, which represents the power distribution of each influencing factor in the system.

[0079] In this embodiment, the electricity carbon emission model meets the following conditions:

[0080]

[0081] In the formula, The carbon emission capacity instruction representing the carbon emission level of the power data obtained when the kth data is updated; is the carbon emission capacity instruction obtained when the carbon emission level of the power data is updated for the k+1th time; p i is an n-dimensional stable vector, representing the proportion of the i-th influencing factor in the entire electricity carbon emissions; represents the dynamic electric carbon factor of the i-th node at the k-th update; and are the minimum and maximum allowed powers of the i+1th node, respectively, limiting the power output range of the system; and are the minimum and maximum power output constraints of the jth node respectively; and They are the minimum and maximum power output constraints of the i+2-nth nodes, respectively, and are used to calculate the contribution of different nodes in power allocation.

[0082] Step 104: dynamically solve the power carbon emission model through a vector weighted average algorithm to obtain a carbon emission capacity instruction of the power system, and then regulate the power system based on the carbon emission capacity instruction.

[0083] In a specific embodiment, the vector weighted average algorithm is a method of obtaining a weighted average of a group of vectors by updating the position of each generation of vectors in the iteration process by searching different operators of the group in the space, and there are four stages. Before the algorithm starts, the data is preprocessed, the total number of nodes in the power network is initialized as the population size, set to N, the maximum number of optimization iterations is set to kmax, and the original solution set is X0∈R N×2(n-1) The minimum total carbon emissions of the power grid are evaluated as Initialize the two most important factors that affect the search ability: weighted average factor δ and scaling factor σ. These two parameters change dynamically according to the generation situation. The initial solution is set to X0∈R N×2(n-1) , the initial adaptation rate includes the random initialization of the regulation source power, set to

[0084] It should be noted that the vector weighted average algorithm consists of four stages:

[0085] Phase 1: Update rule phase, using mean-based rules to update vector positions. First, extract from the weighted mean of a set of random vectors. At the same time, convergence acceleration is added as a rule for the update operator to improve the global search capability. The two main initialization random parameters are the weighted average factor δ and the scaling factor σ, as well as an exploration function to increase the amount of spatial search.

[0086] The second stage: the vector merging stage, the vector merging is the previous vector that will be used to refresh the resolution and The newly created vector and the random number ρ used in the merging process.

[0087] The third stage: regional exploration update, in order to prevent the selected influencing factors from falling into the local optimal solution, the combination vector will be updated according to the given operation.

[0088] Phase 4: Vector Refresh,According to the above process, after considering the best rule, the worst rule, and the weighted average rule, a new vector will be created to update the vector.

[0089] In this embodiment, solving the electric power carbon emission model by a vector weighted average algorithm to obtain the carbon emission capacity instruction of the electric power system includes:

[0090] Cyclic execution of the electricity carbon emission model operation, and outputting a carbon emission level capacity instruction when the number of cycles is a cycle threshold;

[0091] The specific operation of the electricity carbon emission model is as follows:

[0092] Obtain a new vector; wherein the new vector satisfies the following conditions:

[0093]

[0094] In the formula, is the current position of the solution, and is the position virtual random number, ρ is the position judgment random number, r1 and r2 are weighted means;

[0095] Based on a preset exploration function, a weighted average factor and a scaling factor are calculated; wherein the exploration function satisfies the following conditions:

[0096] δ=2r1×rr

[0097] σ=2r2×rr

[0098]

[0099] Where r1 and r2 represent random numbers between 0 and 1; l is the latest iteration in the optimization process, r, r1 and r2 represent weighted means, and k max is the total number of iterations; δ is the weighted average factor, σ is the scaling factor;

[0100] The active power of the load connected to the node in the electric carbon factor model and the node power flow connected to the current node are injected as the initial vector, and merged with the newly created vector and the random number to obtain the merged vector;

[0101] Based on the weighted average factor, the scaling factor and the combined vector, the electric power carbon emission model is calculated to obtain the carbon emission degree capacity instruction;

[0102] Determine whether the current carbon emission capacity instruction meets the preset instruction conditions;

[0103] If not, a new newly created vector is obtained again as the input of the next electricity carbon emission model operation, and the electricity carbon emission model operation is re-executed;

[0104] If so, the current newly created vector is updated, and the updated newly created vector is verified based on the preset verification conditions. The verified newly created vector is used as the input of the next electricity carbon emission model operation, and the electricity carbon emission model operation is re-executed.

[0105] In this embodiment, updating the current newly created vector includes:

[0106] According to a preset vector update formula, the current newly created vector is updated to obtain an updated newly created vector; wherein the vector update formula is specifically:

[0107]

[0108] Where d1, d2, dn, rand, and φ are random numbers between 0 and 1; d1 is used to determine which update strategy to choose; X best is the position of the best individual in the current population, usually corresponding to the optimal solution of the current carbon emission optimization target; dn is used to adjust the step size and affect the vector update amplitude; is the adjustment matrix of the ith individual in the lth iteration, which is used to control the degree of migration of the individual to the optimal solution and to further refine the selection of the update strategy; X w is the calculated central position, usually a weighted average of multiple individual positions, used to balance exploration and development; v1 and v2 are auxiliary variables that control the selection results in some conditional branches; rand is a random number; X a , X b , X c Three different individual positions are used to calculate the population center or for random search to increase the diversity of the search.

[0109] In this embodiment, the verification condition satisfies the following conditions:

[0110]

[0111] Where P i min and P i max is the minimum and maximum allowable power, limiting the power output range of the system; j is the jth individual, representing the i-th node; n is the total number of individuals, i.e. the total number of nodes; is the position of the i-th individual after the l+1th iteration; is the position of the i-th individual after the l-th iteration; is the calculated value representing the vector position of the i-th group after the l-th iteration; represents the calculated practicality value of the i-th group after the l-th iteration.

[0112] It is understandable that after each iteration, the vector that meets the requirements will be updated so that the vector optimization update can be performed in the subsequent iteration. If the current number of iterations exceeds the maximum number of iterations kmax, the optimization process will end. According to the recommended optimization results, the factors that have the most obvious impact on carbon emissions in the power data will be obtained, which will provide technical guidance for the corresponding regulation of power users' needs.

[0113] See also Figure 2 , Figure 2 It is a structural schematic diagram of a power regulation and control device based on a dynamic electric carbon factor provided by an embodiment of the present invention, comprising: a data acquisition module 201, a first model construction module 202, a second model construction module 203 and a result generation module 204;

[0114] The data acquisition module is used to acquire the operation data of the power system;

[0115] The first model building module is used to build an electric carbon factor model for each node of the power system based on the operating data;

[0116] The second model building module is used to build an electric power carbon emission model based on each of the electric carbon factor models under preset constraints;

[0117] The result generation module is used to dynamically solve the power carbon emission model through a vector weighted average algorithm to obtain the carbon emission level capacity instruction of the power system, and then regulate the power system based on the carbon emission level capacity instruction.

[0118] It can be understood that the above-mentioned system item embodiment corresponds to the method item embodiment of the present invention, which can implement the power regulation and control method based on the dynamic electric carbon factor provided by any one of the above-mentioned method item embodiments of the present invention.

[0119] This embodiment obtains the operating data of the power system; constructs an electric carbon factor model for each node of the power system based on the operating data; constructs an electric carbon emission model based on each of the electric carbon factor models under preset constraints; solves the electric carbon emission model through a vector weighted average algorithm to obtain a carbon emission capacity instruction for the power system, and then regulates the power system based on the carbon emission capacity instruction. The present invention constructs an electric carbon factor model based on the operating data of the power system, and uses the electric carbon factor model as the input of the electric carbon emission model, which can track carbon emissions related to power generation, and solves the electric carbon emission model based on a vector weighted average algorithm, and then optimizes the regulation of the power grid based on the solution results, taking into account the impact of the power system on power regulation, greatly improving the accuracy of power regulation, and thus reducing the carbon emissions of the power system.

[0120] Embodiment 2

[0121] See also Figure 3 , Figure 3 It is a schematic diagram of the structure of a terminal device provided in one embodiment of the present invention.

[0122] A terminal device of this embodiment includes: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, the steps of the above-mentioned power regulation and control method based on the dynamic electric carbon factor in the embodiment are implemented, for example Figure 1 All steps of the power regulation and control method based on the dynamic electric carbon factor shown. Alternatively, when the processor executes the computer program, the functions of each module in the above-mentioned system embodiments are realized, for example: Figure 2 All modules of the power regulation control device based on dynamic electric carbon factor are shown.

[0123] In addition, an embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the power regulation and control method based on the dynamic electric carbon factor as described in any of the above embodiments.

[0124] Those skilled in the art will understand that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0125] The processor 301 may be a central processing unit (CPU), or 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. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 301 is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0126] The memory 302 can be used to store the computer program and / or module. The processor 301 implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0127] Wherein, if the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or system capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0128] It should be noted that the system embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the system embodiment provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0129] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A power regulation and control method based on dynamic electric carbon factor, characterized in that: include: Obtaining operation data of power system; Based on the operating data, construct an electric carbon factor model for each node of the power system; Under preset constraints, constructing an electricity carbon emission model based on each of the electricity carbon factor models; The electric power carbon emission model is dynamically solved by a vector weighted average algorithm to obtain a carbon emission capacity instruction for the electric power system, and then the electric power system is regulated based on the carbon emission capacity instruction.

2. The power regulation and control method based on dynamic electric carbon factor according to claim 1 is characterized in that: The operation data includes: total power generation, total number of generators, injected power of generators, total amount of electric energy of the generating end connected to the node, number of generating ends of the power system, active power of loads connected to the node, total number of loads connected to the node, node power flow injection connected to the current node, total number of nodes connected to the current node; based on the operation data, constructing an electric carbon factor model for each node of the power system includes: According to the total power generation, the total number of generators and the injected power of the generators, the random load calculation formula is substituted to obtain the random load data of each node of the power system; wherein the random load calculation formula satisfies the following conditions: Where D i is the random load data, G max is the total power generation, g max is the total number of generators, is the injected power of the jth generator to the ith node, b is the proportional parameter, a i is a random parameter, a j is the proportion of the jth load to the total load, and d is the number of nodes; Taking each of the random load data as input, combined with the total amount of electric energy of the power generation end connected to the node, the number of power generation ends of the power system, the active power of the load connected to the node, the total number of loads connected to the node, the node power flow injection connected to the current node, and the total number of nodes connected to the current node, an electric carbon factor model of each node in the power system is constructed; wherein the electric carbon factor model satisfies the following conditions: In the formula, g i and They represent the total amount of electric energy produced by the kth power generation terminal connected to the i-th node in the power system, the total number of power generation ports included in the power system, and the corresponding electric carbon factor; and d i They represent the active power of the load connected to the i-th node and the total number of loads connected to the i-th node respectively; and n i They represent the power flow injected from the jth node to the ith node and the total number of nodes connected to the ith node; is the diagonal component in the matrix, representing the total power flowing out of the i-th node; is the non-diagonal component, which is the power flow injected from the j-th node to the i-th node.

3. The power regulation and control method based on dynamic electric carbon factor according to claim 1 is characterized in that: The constraints satisfy the following conditions: In the formula, f(X i ) represents the actual target calculated value of the i-th group of influencing factors; F(X i ) represents the calculated practicality value of the i-th group; is the minimum power constraint of the system, indicating the minimum power output allowed; ΔP C (k) is the power change at the kth data update, indicating the dynamic power load change of the system at the current moment; is the maximum power constraint of the system, indicating the maximum power output allowed; X i (j) represents the contribution value of the jth influencing factor in the i-th group to the power, which represents the power distribution of each influencing factor in the system.

4. The power regulation and control method based on dynamic electric carbon factor according to claim 3 is characterized in that: The electricity carbon emission model meets the following conditions: In the formula, The carbon emission capacity instruction representing the carbon emission level of the power data obtained when the kth data is updated; is the carbon emission capacity instruction obtained when the carbon emission level of the power data is updated for the k+1th time; p i is an n-dimensional stable vector, representing the proportion of the i-th influencing factor in the entire electricity carbon emissions; represents the dynamic electric carbon factor of the i-th node at the k-th update; and are the minimum and maximum allowed powers of the i+1th node, respectively, limiting the power output range of the system; and are the minimum and maximum power output constraints of the jth node respectively; and They are the minimum and maximum power output constraints of the i+2-nth nodes, respectively, and are used to calculate the contribution of different nodes in power allocation.

5. The power regulation and control method based on dynamic electric carbon factor according to claim 1 is characterized in that: The method of solving the electric power carbon emission model by using a vector weighted average algorithm to obtain the carbon emission capacity instruction of the electric power system includes: Cyclic execution of the electricity carbon emission model operation, and outputting a carbon emission level capacity instruction when the number of cycles is a cycle threshold; The specific operation of the electricity carbon emission model is as follows: Obtain a new vector; wherein the new vector satisfies the following conditions: In the formula, is the current position of the solution, and is the position virtual random number, ρ is the position judgment random number, r1 and r2 are weighted means; Based on a preset exploration function, a weighted average factor and a scaling factor are calculated; wherein the exploration function satisfies the following conditions: δ=2r1×rr σ=2r2×rr Where r1 and r2 represent random numbers between 0 and 1; l is the latest iteration in the optimization process, r, r1 and r2 represent weighted means, and k max is the total number of iterations; δ is the weighted average factor, σ is the scaling factor; The active power of the load connected to the node in the electric carbon factor model and the node power flow connected to the current node are injected as the initial vector, and merged with the newly created vector and the random number to obtain the merged vector; Based on the weighted average factor, the scaling factor and the combined vector, the electric power carbon emission model is calculated to obtain the carbon emission degree capacity instruction; Determine whether the current carbon emission capacity instruction meets the preset instruction conditions; If not, a new newly created vector is obtained again as the input of the next electricity carbon emission model operation, and the electricity carbon emission model operation is re-executed; If so, the current newly created vector is updated, and the updated newly created vector is verified based on the preset verification conditions. The verified newly created vector is used as the input of the next electricity carbon emission model operation, and the electricity carbon emission model operation is re-executed.

6. The power regulation and control method based on dynamic electric carbon factor according to claim 5 is characterized in that: The updating of the current newly created vector includes: According to a preset vector update formula, the current newly created vector is updated to obtain an updated newly created vector; wherein the vector update formula is specifically: Where d1, d2, dn, rand, and φ are random numbers between 0 and 1; d1 is used to determine which update strategy to choose; X best is the position of the best individual in the current population, usually corresponding to the optimal solution of the current carbon emission optimization target; dn is used to adjust the step size and affect the vector update amplitude; is the adjustment matrix of the ith individual in the lth iteration, which is used to control the degree of migration of the individual to the optimal solution and to further refine the selection of the update strategy; X w is the calculated central position, usually a weighted average of multiple individual positions, used to balance exploration and development; v1 and v2 are auxiliary variables that control the selection results in some conditional branches; rand is a random number; X a , X b , X c Three different individual positions are used to calculate the population center or for random search to increase the diversity of the search.

7. The power regulation and control method based on dynamic electric carbon factor according to claim 6 is characterized in that: The verification conditions meet the following conditions: In the formula, and is the minimum and maximum allowable power, limiting the power output range of the system; j is the jth individual, representing the i-th node; n is the total number of individuals, i.e. the total number of nodes; is the position of the i-th individual after the l+1th iteration; is the position of the i-th individual after the l-th iteration; is the calculated value representing the vector position of the i-th group after the l-th iteration; represents the calculated practicality value of the i-th group after the l-th iteration.

8. A power regulation and control device based on dynamic electric carbon factor, characterized in that: include: A data acquisition module, a first model building module, a second model building module and a result generation module; The data acquisition module is used to acquire the operation data of the power system; The first model building module is used to build an electric carbon factor model for each node of the power system based on the operating data; The second model building module is used to build an electric power carbon emission model based on each of the electric carbon factor models under preset constraints; The result generation module is used to dynamically solve the power carbon emission model through a vector weighted average algorithm to obtain the carbon emission level capacity instruction of the power system, and then regulate the power system based on the carbon emission level capacity instruction.

9. A computer terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements an electric power regulation and control method based on a dynamic electric carbon factor as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute an electric power regulation and control method based on a dynamic electric carbon factor as described in any one of claims 1 to 7.

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